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9f46716793f8e4fafdc5955dc26d4bf82bedec60
|
# Dataset Card for Evaluation run of Abe13/juniper-certificate-Llama-2-7b-chat-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Abe13/juniper-certificate-Llama-2-7b-chat-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Abe13/juniper-certificate-Llama-2-7b-chat-hf](https://huggingface.co/Abe13/juniper-certificate-Llama-2-7b-chat-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Abe13__juniper-certificate-Llama-2-7b-chat-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T11:01:22.690386](https://huggingface.co/datasets/open-llm-leaderboard/details_Abe13__juniper-certificate-Llama-2-7b-chat-hf/blob/main/results_2023-10-23T11-01-22.690386.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 4.823825503355705e-05,
"f1_stderr": 1.1667517021967543e-05,
"acc": 0.24151539068666142,
"acc_stderr": 0.0070221952008064905
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 4.823825503355705e-05,
"f1_stderr": 1.1667517021967543e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.48303078137332284,
"acc_stderr": 0.014044390401612981
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Abe13__juniper-certificate-Llama-2-7b-chat-hf
|
[
"region:us"
] |
2023-10-04T06:11:57+00:00
|
{"pretty_name": "Evaluation run of Abe13/juniper-certificate-Llama-2-7b-chat-hf", "dataset_summary": "Dataset automatically created during the evaluation run of model [Abe13/juniper-certificate-Llama-2-7b-chat-hf](https://huggingface.co/Abe13/juniper-certificate-Llama-2-7b-chat-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Abe13__juniper-certificate-Llama-2-7b-chat-hf\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T11:01:22.690386](https://huggingface.co/datasets/open-llm-leaderboard/details_Abe13__juniper-certificate-Llama-2-7b-chat-hf/blob/main/results_2023-10-23T11-01-22.690386.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 4.823825503355705e-05,\n \"f1_stderr\": 1.1667517021967543e-05,\n \"acc\": 0.24151539068666142,\n \"acc_stderr\": 0.0070221952008064905\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 4.823825503355705e-05,\n \"f1_stderr\": 1.1667517021967543e-05\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.48303078137332284,\n \"acc_stderr\": 0.014044390401612981\n }\n}\n```", "repo_url": "https://huggingface.co/Abe13/juniper-certificate-Llama-2-7b-chat-hf", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T07_11_33.936694", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-11-33.936694.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-11-33.936694.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T11_01_22.690386", "path": ["**/details_harness|drop|3_2023-10-23T11-01-22.690386.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T11-01-22.690386.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T11_01_22.690386", "path": ["**/details_harness|gsm8k|5_2023-10-23T11-01-22.690386.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T11-01-22.690386.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T07_11_33.936694", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-11-33.936694.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-11-33.936694.parquet"]}]}, 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|
2023-10-23T10:01:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Abe13/juniper-certificate-Llama-2-7b-chat-hf
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Abe13/juniper-certificate-Llama-2-7b-chat-hf on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T11:01:22.690386(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Abe13/juniper-certificate-Llama-2-7b-chat-hf",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Abe13/juniper-certificate-Llama-2-7b-chat-hf on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T11:01:22.690386(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of Abe13/juniper-certificate-Llama-2-7b-chat-hf",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Abe13/juniper-certificate-Llama-2-7b-chat-hf on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T11:01:22.690386(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
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"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Abe13/juniper-certificate-Llama-2-7b-chat-hf## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Abe13/juniper-certificate-Llama-2-7b-chat-hf on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T11:01:22.690386(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
6a56776b9cf09ae1f054c591abce09295686a3e2
|
# Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mixed-datasets
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mixed-datasets
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Charlie911/vicuna-7b-v1.5-lora-mixed-datasets](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mixed-datasets) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mixed-datasets",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T18:57:11.062702](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mixed-datasets/blob/main/results_2023-10-28T18-57-11.062702.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0035654362416107383,
"em_stderr": 0.0006104082299890499,
"f1": 0.06385906040268456,
"f1_stderr": 0.001500099076010359,
"acc": 0.4018524712881312,
"acc_stderr": 0.009764090292423753
},
"harness|drop|3": {
"em": 0.0035654362416107383,
"em_stderr": 0.0006104082299890499,
"f1": 0.06385906040268456,
"f1_stderr": 0.001500099076010359
},
"harness|gsm8k|5": {
"acc": 0.0712661106899166,
"acc_stderr": 0.007086462127954497
},
"harness|winogrande|5": {
"acc": 0.7324388318863457,
"acc_stderr": 0.01244171845689301
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mixed-datasets
|
[
"region:us"
] |
2023-10-04T06:12:51+00:00
|
{"pretty_name": "Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mixed-datasets", "dataset_summary": "Dataset automatically created during the evaluation run of model [Charlie911/vicuna-7b-v1.5-lora-mixed-datasets](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mixed-datasets) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mixed-datasets\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T18:57:11.062702](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mixed-datasets/blob/main/results_2023-10-28T18-57-11.062702.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0035654362416107383,\n \"em_stderr\": 0.0006104082299890499,\n \"f1\": 0.06385906040268456,\n \"f1_stderr\": 0.001500099076010359,\n \"acc\": 0.4018524712881312,\n \"acc_stderr\": 0.009764090292423753\n },\n \"harness|drop|3\": {\n \"em\": 0.0035654362416107383,\n \"em_stderr\": 0.0006104082299890499,\n \"f1\": 0.06385906040268456,\n \"f1_stderr\": 0.001500099076010359\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0712661106899166,\n \"acc_stderr\": 0.007086462127954497\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7324388318863457,\n \"acc_stderr\": 0.01244171845689301\n }\n}\n```", "repo_url": "https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mixed-datasets", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T07_12_27.239591", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-12-27.239591.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-12-27.239591.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T18_57_11.062702", "path": ["**/details_harness|drop|3_2023-10-28T18-57-11.062702.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T18-57-11.062702.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T18_57_11.062702", "path": ["**/details_harness|gsm8k|5_2023-10-28T18-57-11.062702.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T18-57-11.062702.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T07_12_27.239591", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-12-27.239591.parquet"]}, {"split": "latest", "path": 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|
2023-10-28T17:57:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mixed-datasets
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Charlie911/vicuna-7b-v1.5-lora-mixed-datasets on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-28T18:57:11.062702(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
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"# Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mixed-datasets",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Charlie911/vicuna-7b-v1.5-lora-mixed-datasets on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T18:57:11.062702(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Charlie911/vicuna-7b-v1.5-lora-mixed-datasets on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T18:57:11.062702(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"#### Annotation process",
"#### Who are the annotators?",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mixed-datasets## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Charlie911/vicuna-7b-v1.5-lora-mixed-datasets on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T18:57:11.062702(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
10acd330fc5a2ed383f06e05d867f2886e931a3b
|
# Dataset Card for Evaluation run of adonlee/LLaMA_2_13B_SFT_v0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [adonlee/LLaMA_2_13B_SFT_v0](https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T20:45:12.840577](https://huggingface.co/datasets/open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v0/blob/main/results_2023-10-23T20-45-12.840577.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.044043624161073824,
"em_stderr": 0.0021013587965914723,
"f1": 0.12960780201342256,
"f1_stderr": 0.0025642484678935875,
"acc": 0.44851400782564105,
"acc_stderr": 0.010433746564559782
},
"harness|drop|3": {
"em": 0.044043624161073824,
"em_stderr": 0.0021013587965914723,
"f1": 0.12960780201342256,
"f1_stderr": 0.0025642484678935875
},
"harness|gsm8k|5": {
"acc": 0.1243366186504928,
"acc_stderr": 0.009088880962028475
},
"harness|winogrande|5": {
"acc": 0.7726913970007893,
"acc_stderr": 0.011778612167091088
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v0
|
[
"region:us"
] |
2023-10-04T06:12:59+00:00
|
{"pretty_name": "Evaluation run of adonlee/LLaMA_2_13B_SFT_v0", "dataset_summary": "Dataset automatically created during the evaluation run of model [adonlee/LLaMA_2_13B_SFT_v0](https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T20:45:12.840577](https://huggingface.co/datasets/open-llm-leaderboard/details_adonlee__LLaMA_2_13B_SFT_v0/blob/main/results_2023-10-23T20-45-12.840577.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.044043624161073824,\n \"em_stderr\": 0.0021013587965914723,\n \"f1\": 0.12960780201342256,\n \"f1_stderr\": 0.0025642484678935875,\n \"acc\": 0.44851400782564105,\n \"acc_stderr\": 0.010433746564559782\n },\n \"harness|drop|3\": {\n \"em\": 0.044043624161073824,\n \"em_stderr\": 0.0021013587965914723,\n \"f1\": 0.12960780201342256,\n \"f1_stderr\": 0.0025642484678935875\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1243366186504928,\n \"acc_stderr\": 0.009088880962028475\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7726913970007893,\n \"acc_stderr\": 0.011778612167091088\n }\n}\n```", "repo_url": "https://huggingface.co/adonlee/LLaMA_2_13B_SFT_v0", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T07_12_40.542820", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-12-40.542820.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-12-40.542820.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T20_45_12.840577", "path": ["**/details_harness|drop|3_2023-10-23T20-45-12.840577.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T20-45-12.840577.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T20_45_12.840577", "path": ["**/details_harness|gsm8k|5_2023-10-23T20-45-12.840577.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T20-45-12.840577.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T07_12_40.542820", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-12-40.542820.parquet"]}, {"split": "latest", "path": 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|
2023-10-23T19:45:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of adonlee/LLaMA_2_13B_SFT_v0
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model adonlee/LLaMA_2_13B_SFT_v0 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T20:45:12.840577(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of adonlee/LLaMA_2_13B_SFT_v0",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model adonlee/LLaMA_2_13B_SFT_v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T20:45:12.840577(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model adonlee/LLaMA_2_13B_SFT_v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T20:45:12.840577(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
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"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of adonlee/LLaMA_2_13B_SFT_v0## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model adonlee/LLaMA_2_13B_SFT_v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T20:45:12.840577(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
76000466ede6f387ee231326b5c7ae30d4301bb8
|
# Dataset Card for Evaluation run of Juniplayground/Mist_LLaMA-2-7B-1024_V3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Juniplayground/Mist_LLaMA-2-7B-1024_V3
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Juniplayground/Mist_LLaMA-2-7B-1024_V3](https://huggingface.co/Juniplayground/Mist_LLaMA-2-7B-1024_V3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Juniplayground__Mist_LLaMA-2-7B-1024_V3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T16:28:04.317778](https://huggingface.co/datasets/open-llm-leaderboard/details_Juniplayground__Mist_LLaMA-2-7B-1024_V3/blob/main/results_2023-10-28T16-28-04.317778.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788268446,
"f1": 0.05569840604026855,
"f1_stderr": 0.0013255684995797806,
"acc": 0.39087485257361143,
"acc_stderr": 0.009174257360532706
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788268446,
"f1": 0.05569840604026855,
"f1_stderr": 0.0013255684995797806
},
"harness|gsm8k|5": {
"acc": 0.04852160727824109,
"acc_stderr": 0.00591846861892108
},
"harness|winogrande|5": {
"acc": 0.7332280978689818,
"acc_stderr": 0.012430046102144333
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Juniplayground__Mist_LLaMA-2-7B-1024_V3
|
[
"region:us"
] |
2023-10-04T06:13:27+00:00
|
{"pretty_name": "Evaluation run of Juniplayground/Mist_LLaMA-2-7B-1024_V3", "dataset_summary": "Dataset automatically created during the evaluation run of model [Juniplayground/Mist_LLaMA-2-7B-1024_V3](https://huggingface.co/Juniplayground/Mist_LLaMA-2-7B-1024_V3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Juniplayground__Mist_LLaMA-2-7B-1024_V3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T16:28:04.317778](https://huggingface.co/datasets/open-llm-leaderboard/details_Juniplayground__Mist_LLaMA-2-7B-1024_V3/blob/main/results_2023-10-28T16-28-04.317778.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268446,\n \"f1\": 0.05569840604026855,\n \"f1_stderr\": 0.0013255684995797806,\n \"acc\": 0.39087485257361143,\n \"acc_stderr\": 0.009174257360532706\n },\n \"harness|drop|3\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268446,\n \"f1\": 0.05569840604026855,\n \"f1_stderr\": 0.0013255684995797806\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04852160727824109,\n \"acc_stderr\": 0.00591846861892108\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7332280978689818,\n \"acc_stderr\": 0.012430046102144333\n }\n}\n```", "repo_url": "https://huggingface.co/Juniplayground/Mist_LLaMA-2-7B-1024_V3", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T07_13_03.510768", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-13-03.510768.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-13-03.510768.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T16_28_04.317778", "path": ["**/details_harness|drop|3_2023-10-28T16-28-04.317778.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T16-28-04.317778.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T16_28_04.317778", "path": ["**/details_harness|gsm8k|5_2023-10-28T16-28-04.317778.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T16-28-04.317778.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T07_13_03.510768", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-13-03.510768.parquet"]}, {"split": "latest", "path": 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|
2023-10-28T15:28:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Juniplayground/Mist_LLaMA-2-7B-1024_V3
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Juniplayground/Mist_LLaMA-2-7B-1024_V3 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-28T16:28:04.317778(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Juniplayground/Mist_LLaMA-2-7B-1024_V3",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Juniplayground/Mist_LLaMA-2-7B-1024_V3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T16:28:04.317778(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Juniplayground/Mist_LLaMA-2-7B-1024_V3",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Juniplayground/Mist_LLaMA-2-7B-1024_V3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T16:28:04.317778(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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6,
27,
31,
175,
67,
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4,
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5,
5,
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10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
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] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Juniplayground/Mist_LLaMA-2-7B-1024_V3## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Juniplayground/Mist_LLaMA-2-7B-1024_V3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T16:28:04.317778(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
884247b4c6868cde83cb333c46b0b5793f128248
|
# Dataset Card for "Soldering-Data-Tiny-1004-unsolder-area"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AndyLiu0104/Soldering-Data-Tiny-1004-unsolder-area
|
[
"region:us"
] |
2023-10-04T06:13:56+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18073742.875, "num_examples": 10481}], "download_size": 0, "dataset_size": 18073742.875}}
|
2023-10-04T15:28:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Soldering-Data-Tiny-1004-unsolder-area"
More Information needed
|
[
"# Dataset Card for \"Soldering-Data-Tiny-1004-unsolder-area\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Soldering-Data-Tiny-1004-unsolder-area\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Soldering-Data-Tiny-1004-unsolder-area\"\n\nMore Information needed"
] |
ff50e469d1964f779f5c590b1c445fc6951cb3a6
|
# Dataset Card for "emodb-enrichment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
renumics/emodb-enrichment
|
[
"region:us"
] |
2023-10-04T06:14:18+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio.embedding", "sequence": "float32", "length": 768}], "splits": [{"name": "train", "num_bytes": 1643520, "num_examples": 535}], "download_size": 2269156, "dataset_size": 1643520}}
|
2023-10-04T06:14:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "emodb-enrichment"
More Information needed
|
[
"# Dataset Card for \"emodb-enrichment\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"emodb-enrichment\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"emodb-enrichment\"\n\nMore Information needed"
] |
b42b2e34dd6a4cde29d656a8c7976b72bde3b22a
|
# Dataset Card for Evaluation run of PY007/TinyLlama-1.1B-Chat-v0.3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PY007__TinyLlama-1.1B-Chat-v0.3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T05:46:52.405812](https://huggingface.co/datasets/open-llm-leaderboard/details_PY007__TinyLlama-1.1B-Chat-v0.3/blob/main/results_2023-10-23T05-46-52.405812.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0035654362416107383,
"em_stderr": 0.0006104082299890309,
"f1": 0.04627936241610745,
"f1_stderr": 0.0012734567743311978,
"acc": 0.2918883921652635,
"acc_stderr": 0.00807629623065548
},
"harness|drop|3": {
"em": 0.0035654362416107383,
"em_stderr": 0.0006104082299890309,
"f1": 0.04627936241610745,
"f1_stderr": 0.0012734567743311978
},
"harness|gsm8k|5": {
"acc": 0.006823351023502654,
"acc_stderr": 0.002267537102254483
},
"harness|winogrande|5": {
"acc": 0.5769534333070244,
"acc_stderr": 0.013885055359056474
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_PY007__TinyLlama-1.1B-Chat-v0.3
|
[
"region:us"
] |
2023-10-04T06:14:57+00:00
|
{"pretty_name": "Evaluation run of PY007/TinyLlama-1.1B-Chat-v0.3", "dataset_summary": "Dataset automatically created during the evaluation run of model [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PY007__TinyLlama-1.1B-Chat-v0.3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T05:46:52.405812](https://huggingface.co/datasets/open-llm-leaderboard/details_PY007__TinyLlama-1.1B-Chat-v0.3/blob/main/results_2023-10-23T05-46-52.405812.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0035654362416107383,\n \"em_stderr\": 0.0006104082299890309,\n \"f1\": 0.04627936241610745,\n \"f1_stderr\": 0.0012734567743311978,\n \"acc\": 0.2918883921652635,\n \"acc_stderr\": 0.00807629623065548\n },\n \"harness|drop|3\": {\n \"em\": 0.0035654362416107383,\n \"em_stderr\": 0.0006104082299890309,\n \"f1\": 0.04627936241610745,\n \"f1_stderr\": 0.0012734567743311978\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \"acc_stderr\": 0.002267537102254483\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5769534333070244,\n \"acc_stderr\": 0.013885055359056474\n }\n}\n```", "repo_url": "https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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["**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-14-39.217680.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-14-39.217680.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_10_04T07_14_39.217680", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-14-39.217680.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-14-39.217680.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_10_04T07_14_39.217680", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-14-39.217680.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-14-39.217680.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_10_04T07_14_39.217680", "path": 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["**/details_harness|truthfulqa:mc|0_2023-10-04T07-14-39.217680.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-10-04T07-14-39.217680.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_23T05_46_52.405812", "path": ["**/details_harness|winogrande|5_2023-10-23T05-46-52.405812.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-23T05-46-52.405812.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_04T07_14_39.217680", "path": ["results_2023-10-04T07-14-39.217680.parquet"]}, {"split": "2023_10_23T05_46_52.405812", "path": ["results_2023-10-23T05-46-52.405812.parquet"]}, {"split": "latest", "path": ["results_2023-10-23T05-46-52.405812.parquet"]}]}]}
|
2023-10-23T04:47:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of PY007/TinyLlama-1.1B-Chat-v0.3
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model PY007/TinyLlama-1.1B-Chat-v0.3 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T05:46:52.405812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of PY007/TinyLlama-1.1B-Chat-v0.3",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model PY007/TinyLlama-1.1B-Chat-v0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T05:46:52.405812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model PY007/TinyLlama-1.1B-Chat-v0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T05:46:52.405812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PY007/TinyLlama-1.1B-Chat-v0.3## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model PY007/TinyLlama-1.1B-Chat-v0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T05:46:52.405812(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
0a6e0d96a501d4f1141c3b7fbebd07b7988bcf23
|
# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Riiid/sheep-duck-llama-2-70b-v1.1](https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-09T22:48:41.234684](https://huggingface.co/datasets/open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1/blob/main/results_2023-12-09T22-48-41.234684.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7053343320815155,
"acc_stderr": 0.030260160101824644,
"acc_norm": 0.7109334613998801,
"acc_norm_stderr": 0.03084136530304881,
"mc1": 0.4663402692778458,
"mc1_stderr": 0.017463793867168103,
"mc2": 0.6454578975757769,
"mc2_stderr": 0.014741040304266572
},
"harness|arc:challenge|25": {
"acc": 0.6868600682593856,
"acc_stderr": 0.013552671543623501,
"acc_norm": 0.7312286689419796,
"acc_norm_stderr": 0.012955065963710688
},
"harness|hellaswag|10": {
"acc": 0.691396136227843,
"acc_stderr": 0.004609731925736905,
"acc_norm": 0.8777136028679546,
"acc_norm_stderr": 0.0032694673590543157
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.04171654161354543,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.04171654161354543
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8026315789473685,
"acc_stderr": 0.03238981601699397,
"acc_norm": 0.8026315789473685,
"acc_norm_stderr": 0.03238981601699397
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7433962264150943,
"acc_stderr": 0.026880647889051985,
"acc_norm": 0.7433962264150943,
"acc_norm_stderr": 0.026880647889051985
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.03216600808802267
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
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},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6936416184971098,
"acc_stderr": 0.03514942551267439,
"acc_norm": 0.6936416184971098,
"acc_norm_stderr": 0.03514942551267439
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.77,
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"harness|hendrycksTest-conceptual_physics|5": {
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"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-global_facts|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_statistics|5": {
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},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8677685950413223,
"acc_stderr": 0.03092278832044579,
"acc_norm": 0.8677685950413223,
"acc_norm_stderr": 0.03092278832044579
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"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8240740740740741,
"acc_stderr": 0.036809181416738807,
"acc_norm": 0.8240740740740741,
"acc_norm_stderr": 0.036809181416738807
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8466257668711656,
"acc_stderr": 0.0283116014414386,
"acc_norm": 0.8466257668711656,
"acc_norm_stderr": 0.0283116014414386
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"harness|hendrycksTest-machine_learning|5": {
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"acc_norm_stderr": 0.04726835553719098
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"harness|hendrycksTest-management|5": {
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"acc_norm": 0.8349514563106796,
"acc_norm_stderr": 0.03675668832233188
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"harness|hendrycksTest-marketing|5": {
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"acc_norm": 0.9230769230769231,
"acc_norm_stderr": 0.01745698787243618
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"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.72,
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"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.8684546615581098,
"acc_norm_stderr": 0.01208670521425043
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"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.7745664739884393,
"acc_norm_stderr": 0.022497230190967554
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"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm_stderr": 0.0161048338801423
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"harness|hendrycksTest-nutrition|5": {
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"acc_stderr": 0.02473998135511359,
"acc_norm": 0.7516339869281046,
"acc_norm_stderr": 0.02473998135511359
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"harness|hendrycksTest-philosophy|5": {
"acc": 0.7717041800643086,
"acc_stderr": 0.023839303311398205,
"acc_norm": 0.7717041800643086,
"acc_norm_stderr": 0.023839303311398205
},
"harness|hendrycksTest-prehistory|5": {
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"acc_stderr": 0.021038517770157375,
"acc_norm": 0.8271604938271605,
"acc_norm_stderr": 0.021038517770157375
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5886524822695035,
"acc_stderr": 0.029354911159940968,
"acc_norm": 0.5886524822695035,
"acc_norm_stderr": 0.029354911159940968
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"harness|hendrycksTest-professional_law|5": {
"acc": 0.5880052151238592,
"acc_stderr": 0.012570871032146064,
"acc_norm": 0.5880052151238592,
"acc_norm_stderr": 0.012570871032146064
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"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7352941176470589,
"acc_stderr": 0.02679956202488766,
"acc_norm": 0.7352941176470589,
"acc_norm_stderr": 0.02679956202488766
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"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7663398692810458,
"acc_stderr": 0.017119158496044506,
"acc_norm": 0.7663398692810458,
"acc_norm_stderr": 0.017119158496044506
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7545454545454545,
"acc_stderr": 0.04122066502878285,
"acc_norm": 0.7545454545454545,
"acc_norm_stderr": 0.04122066502878285
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.7959183673469388,
"acc_stderr": 0.025801283475090496,
"acc_norm": 0.7959183673469388,
"acc_norm_stderr": 0.025801283475090496
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"harness|hendrycksTest-sociology|5": {
"acc": 0.8756218905472637,
"acc_stderr": 0.023335401790166327,
"acc_norm": 0.8756218905472637,
"acc_norm_stderr": 0.023335401790166327
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.033799766898963086,
"acc_norm": 0.87,
"acc_norm_stderr": 0.033799766898963086
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8771929824561403,
"acc_stderr": 0.02517298435015575,
"acc_norm": 0.8771929824561403,
"acc_norm_stderr": 0.02517298435015575
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4663402692778458,
"mc1_stderr": 0.017463793867168103,
"mc2": 0.6454578975757769,
"mc2_stderr": 0.014741040304266572
},
"harness|winogrande|5": {
"acc": 0.8310970797158642,
"acc_stderr": 0.010529981411838897
},
"harness|gsm8k|5": {
"acc": 0.4799090219863533,
"acc_stderr": 0.013761361772989008
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1
|
[
"region:us"
] |
2023-10-04T06:21:12+00:00
|
{"pretty_name": "Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [Riiid/sheep-duck-llama-2-70b-v1.1](https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-09T22:48:41.234684](https://huggingface.co/datasets/open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1/blob/main/results_2023-12-09T22-48-41.234684.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7053343320815155,\n \"acc_stderr\": 0.030260160101824644,\n \"acc_norm\": 0.7109334613998801,\n \"acc_norm_stderr\": 0.03084136530304881,\n \"mc1\": 0.4663402692778458,\n \"mc1_stderr\": 0.017463793867168103,\n \"mc2\": 0.6454578975757769,\n \"mc2_stderr\": 0.014741040304266572\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6868600682593856,\n \"acc_stderr\": 0.013552671543623501,\n \"acc_norm\": 0.7312286689419796,\n \"acc_norm_stderr\": 0.012955065963710688\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.691396136227843,\n \"acc_stderr\": 0.004609731925736905,\n \"acc_norm\": 0.8777136028679546,\n \"acc_norm_stderr\": 0.0032694673590543157\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8026315789473685,\n \"acc_stderr\": 0.03238981601699397,\n \"acc_norm\": 0.8026315789473685,\n \"acc_norm_stderr\": 0.03238981601699397\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7433962264150943,\n \"acc_stderr\": 0.026880647889051985,\n \"acc_norm\": 0.7433962264150943,\n \"acc_norm_stderr\": 0.026880647889051985\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n \"acc_stderr\": 0.03216600808802267,\n \"acc_norm\": 0.8194444444444444,\n \"acc_norm_stderr\": 0.03216600808802267\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.03514942551267439,\n \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.03514942551267439\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6893617021276596,\n \"acc_stderr\": 0.03025123757921317,\n \"acc_norm\": 0.6893617021276596,\n \"acc_norm_stderr\": 0.03025123757921317\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.04692008381368909,\n \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.04692008381368909\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6344827586206897,\n \"acc_stderr\": 0.04013124195424386,\n \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.04013124195424386\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.47354497354497355,\n \"acc_stderr\": 0.02571523981134676,\n \"acc_norm\": 0.47354497354497355,\n \"acc_norm_stderr\": 0.02571523981134676\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8193548387096774,\n \"acc_stderr\": 0.021886178567172523,\n \"acc_norm\": 0.8193548387096774,\n \"acc_norm_stderr\": 0.021886178567172523\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5566502463054187,\n \"acc_stderr\": 0.03495334582162933,\n \"acc_norm\": 0.5566502463054187,\n \"acc_norm_stderr\": 0.03495334582162933\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066573,\n \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066573\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.02239078763821677,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02239078763821677\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9430051813471503,\n \"acc_stderr\": 0.01673108529360755,\n \"acc_norm\": 0.9430051813471503,\n \"acc_norm_stderr\": 0.01673108529360755\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.022939925418530616,\n \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.022939925418530616\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524586,\n \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524586\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7689075630252101,\n \"acc_stderr\": 0.027381406927868876,\n \"acc_norm\": 0.7689075630252101,\n \"acc_norm_stderr\": 0.027381406927868876\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.4768211920529801,\n \"acc_stderr\": 0.04078093859163083,\n \"acc_norm\": 0.4768211920529801,\n \"acc_norm_stderr\": 0.04078093859163083\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8990825688073395,\n \"acc_stderr\": 0.012914673545364408,\n \"acc_norm\": 0.8990825688073395,\n \"acc_norm_stderr\": 0.012914673545364408\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6018518518518519,\n \"acc_stderr\": 0.033384734032074016,\n \"acc_norm\": 0.6018518518518519,\n \"acc_norm_stderr\": 0.033384734032074016\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9264705882352942,\n \"acc_stderr\": 0.01831885585008968,\n \"acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.01831885585008968\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8818565400843882,\n \"acc_stderr\": 0.021011052659878467,\n \"acc_norm\": 0.8818565400843882,\n \"acc_norm_stderr\": 0.021011052659878467\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7982062780269058,\n \"acc_stderr\": 0.026936111912802273,\n \"acc_norm\": 0.7982062780269058,\n \"acc_norm_stderr\": 0.026936111912802273\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.0321782942074463,\n \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.0321782942074463\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8677685950413223,\n \"acc_stderr\": 0.03092278832044579,\n \"acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.03092278832044579\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8466257668711656,\n \"acc_stderr\": 0.0283116014414386,\n \"acc_norm\": 0.8466257668711656,\n \"acc_norm_stderr\": 0.0283116014414386\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5446428571428571,\n \"acc_stderr\": 0.04726835553719098,\n \"acc_norm\": 0.5446428571428571,\n \"acc_norm_stderr\": 0.04726835553719098\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9230769230769231,\n \"acc_stderr\": 0.01745698787243618,\n \"acc_norm\": 0.9230769230769231,\n \"acc_norm_stderr\": 0.01745698787243618\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542126,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542126\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8684546615581098,\n \"acc_stderr\": 0.01208670521425043,\n \"acc_norm\": 0.8684546615581098,\n \"acc_norm_stderr\": 0.01208670521425043\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7745664739884393,\n \"acc_stderr\": 0.022497230190967554,\n \"acc_norm\": 0.7745664739884393,\n \"acc_norm_stderr\": 0.022497230190967554\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6346368715083799,\n \"acc_stderr\": 0.0161048338801423,\n \"acc_norm\": 0.6346368715083799,\n \"acc_norm_stderr\": 0.0161048338801423\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7717041800643086,\n \"acc_stderr\": 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|
2023-12-09T22:52:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Riiid/sheep-duck-llama-2-70b-v1.1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-12-09T22:48:41.234684(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Riiid/sheep-duck-llama-2-70b-v1.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-09T22:48:41.234684(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Riiid/sheep-duck-llama-2-70b-v1.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-09T22:48:41.234684(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
26,
31,
175,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
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9,
8,
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7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Riiid/sheep-duck-llama-2-70b-v1.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-09T22:48:41.234684(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
42f73e6fd288040e7a16e70bb3c7ba4042492deb
|
# Bangumi Image Base of Blend S
This is the image base of bangumi Blend S, we detected 16 characters, 1863 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 436 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 38 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 30 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 6 | [Download](3/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 4 | 299 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 222 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 42 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 20 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 18 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 187 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 245 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 19 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 12 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 85 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 114 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 90 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/blends
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T06:25:14+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T07:22:04+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Blend S
=============================
This is the image base of bangumi Blend S, we detected 16 characters, 1863 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
cf4cc03a2bbb453bdebbdbc6f5f205c5f029a194
|
# Dataset Card for "guanaco-llama2-200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
murukkuu/guanaco-llama2-200
|
[
"region:us"
] |
2023-10-04T06:27:12+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 338808, "num_examples": 200}], "download_size": 201257, "dataset_size": 338808}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T06:27:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-200"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-200\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-200\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-200\"\n\nMore Information needed"
] |
289d28ef9347136b3cfb35313b9f4a272b62e428
|
# Dataset Card for Evaluation run of hyunseoki/ko-en-llama2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/hyunseoki/ko-en-llama2-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_hyunseoki__ko-en-llama2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T07:23:26.353656](https://huggingface.co/datasets/open-llm-leaderboard/details_hyunseoki__ko-en-llama2-13b/blob/main/results_2023-10-27T07-23-26.353656.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.28114513422818793,
"em_stderr": 0.004603896433799628,
"f1": 0.3260591442953026,
"f1_stderr": 0.004539391567050269,
"acc": 0.3779028263381469,
"acc_stderr": 0.007293885306168497
},
"harness|drop|3": {
"em": 0.28114513422818793,
"em_stderr": 0.004603896433799628,
"f1": 0.3260591442953026,
"f1_stderr": 0.004539391567050269
},
"harness|gsm8k|5": {
"acc": 0.0075815011372251705,
"acc_stderr": 0.002389281512077218
},
"harness|winogrande|5": {
"acc": 0.7482241515390686,
"acc_stderr": 0.012198489100259776
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_hyunseoki__ko-en-llama2-13b
|
[
"region:us"
] |
2023-10-04T06:33:35+00:00
|
{"pretty_name": "Evaluation run of hyunseoki/ko-en-llama2-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_hyunseoki__ko-en-llama2-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-27T07:23:26.353656](https://huggingface.co/datasets/open-llm-leaderboard/details_hyunseoki__ko-en-llama2-13b/blob/main/results_2023-10-27T07-23-26.353656.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.28114513422818793,\n \"em_stderr\": 0.004603896433799628,\n \"f1\": 0.3260591442953026,\n \"f1_stderr\": 0.004539391567050269,\n \"acc\": 0.3779028263381469,\n \"acc_stderr\": 0.007293885306168497\n },\n \"harness|drop|3\": {\n \"em\": 0.28114513422818793,\n \"em_stderr\": 0.004603896433799628,\n \"f1\": 0.3260591442953026,\n \"f1_stderr\": 0.004539391567050269\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0075815011372251705,\n \"acc_stderr\": 0.002389281512077218\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7482241515390686,\n \"acc_stderr\": 0.012198489100259776\n }\n}\n```", "repo_url": "https://huggingface.co/hyunseoki/ko-en-llama2-13b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T07_33_17.210034", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-33-17.210034.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-33-17.210034.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_27T07_23_26.353656", "path": ["**/details_harness|drop|3_2023-10-27T07-23-26.353656.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-27T07-23-26.353656.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_27T07_23_26.353656", "path": ["**/details_harness|gsm8k|5_2023-10-27T07-23-26.353656.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-27T07-23-26.353656.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T07_33_17.210034", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-33-17.210034.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-33-17.210034.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_10_04T07_33_17.210034", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-33-17.210034.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-33-17.210034.parquet", 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|
2023-10-27T06:23:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of hyunseoki/ko-en-llama2-13b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model hyunseoki/ko-en-llama2-13b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-27T07:23:26.353656(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of hyunseoki/ko-en-llama2-13b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model hyunseoki/ko-en-llama2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T07:23:26.353656(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of hyunseoki/ko-en-llama2-13b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model hyunseoki/ko-en-llama2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T07:23:26.353656(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of hyunseoki/ko-en-llama2-13b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model hyunseoki/ko-en-llama2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-27T07:23:26.353656(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
5b09f2dd556b8c25fa87b5ffcbd50a2a4600d555
|
# Dataset Card for [LE Audio]
Dataset Card
Dataset Name: LE Audio Dataset
Dataset Version: 1.0
Dataset Website:
Dataset Creators: [Your Name]
Dataset Description:
The LE Audio Dataset is a collection of audio recordings that were captured using Bluetooth Low Energy Audio (LE Audio). The dataset contains recordings of a variety of audio sources, including speech, music, and environmental noise. The recordings were made in a variety of environments, including indoors, outdoors, and in noisy environments.
Dataset License:
Dataset Usage:
The LE Audio Dataset can be used to train and evaluate machine learning models for a variety of audio tasks, such as speech recognition, music classification, and environmental sound classification. The dataset is also useful for research on LE Audio itself.
Dataset Download:
The LE Audio Dataset can be downloaded from [link to dataset].
Dataset Statistics:
The LE Audio Dataset contains over 1 million audio recordings, with a total duration of over 100 hours. The recordings are divided into two splits: train (80%) and test (20%).
Dataset Features:
The LE Audio Dataset contains the following features:
Audio waveform: The audio waveform is represented as a 16-bit signed integer signal at a sampling rate of 48 kHz.
Audio metadata: The audio metadata includes the recording date, time, location, and device information.
Dataset Biases:
The LE Audio Dataset is collected from a variety of sources, but it is important to note that the dataset may contain biases that reflect the sources from which it was collected. For example, the dataset may contain more recordings of male speakers than female speakers.
Dataset Citation:
To cite the LE Audio Dataset, please use the following BibTeX entry:
@article{le_audio_dataset,
author={Your Name},
title={LE Audio Dataset},
year={2023},
url={link to dataset}
}
|
kkboy1/LeAudio
|
[
"task_categories:text2text-generation",
"region:us"
] |
2023-10-04T06:38:02+00:00
|
{"annotations_creators": [], "language_creators": [], "language": [], "license": [], "multilinguality": [], "size_categories": [], "source_datasets": [], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "LE AUDIO BOOK", "tags": [], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 687531, "num_examples": 10020}, {"name": "test", "num_bytes": 687531, "num_examples": 10020}], "download_size": 725338, "dataset_size": 1375062}}
|
2023-10-09T05:38:08+00:00
|
[] |
[] |
TAGS
#task_categories-text2text-generation #region-us
|
# Dataset Card for [LE Audio]
Dataset Card
Dataset Name: LE Audio Dataset
Dataset Version: 1.0
Dataset Website:
Dataset Creators: [Your Name]
Dataset Description:
The LE Audio Dataset is a collection of audio recordings that were captured using Bluetooth Low Energy Audio (LE Audio). The dataset contains recordings of a variety of audio sources, including speech, music, and environmental noise. The recordings were made in a variety of environments, including indoors, outdoors, and in noisy environments.
Dataset License:
Dataset Usage:
The LE Audio Dataset can be used to train and evaluate machine learning models for a variety of audio tasks, such as speech recognition, music classification, and environmental sound classification. The dataset is also useful for research on LE Audio itself.
Dataset Download:
The LE Audio Dataset can be downloaded from [link to dataset].
Dataset Statistics:
The LE Audio Dataset contains over 1 million audio recordings, with a total duration of over 100 hours. The recordings are divided into two splits: train (80%) and test (20%).
Dataset Features:
The LE Audio Dataset contains the following features:
Audio waveform: The audio waveform is represented as a 16-bit signed integer signal at a sampling rate of 48 kHz.
Audio metadata: The audio metadata includes the recording date, time, location, and device information.
Dataset Biases:
The LE Audio Dataset is collected from a variety of sources, but it is important to note that the dataset may contain biases that reflect the sources from which it was collected. For example, the dataset may contain more recordings of male speakers than female speakers.
Dataset Citation:
To cite the LE Audio Dataset, please use the following BibTeX entry:
@article{le_audio_dataset,
author={Your Name},
title={LE Audio Dataset},
year={2023},
url={link to dataset}
}
|
[
"# Dataset Card for [LE Audio]\n\nDataset Card\n\nDataset Name: LE Audio Dataset\n\nDataset Version: 1.0\n\nDataset Website:\n\nDataset Creators: [Your Name]\n\nDataset Description:\n\nThe LE Audio Dataset is a collection of audio recordings that were captured using Bluetooth Low Energy Audio (LE Audio). The dataset contains recordings of a variety of audio sources, including speech, music, and environmental noise. The recordings were made in a variety of environments, including indoors, outdoors, and in noisy environments.\n\nDataset License:\n\nDataset Usage:\n\nThe LE Audio Dataset can be used to train and evaluate machine learning models for a variety of audio tasks, such as speech recognition, music classification, and environmental sound classification. The dataset is also useful for research on LE Audio itself.\n\nDataset Download:\n\nThe LE Audio Dataset can be downloaded from [link to dataset].\n\nDataset Statistics:\n\nThe LE Audio Dataset contains over 1 million audio recordings, with a total duration of over 100 hours. The recordings are divided into two splits: train (80%) and test (20%).\n\nDataset Features:\n\nThe LE Audio Dataset contains the following features:\n\nAudio waveform: The audio waveform is represented as a 16-bit signed integer signal at a sampling rate of 48 kHz.\nAudio metadata: The audio metadata includes the recording date, time, location, and device information.\nDataset Biases:\n\nThe LE Audio Dataset is collected from a variety of sources, but it is important to note that the dataset may contain biases that reflect the sources from which it was collected. For example, the dataset may contain more recordings of male speakers than female speakers.\n\nDataset Citation:\n\nTo cite the LE Audio Dataset, please use the following BibTeX entry:\n\n@article{le_audio_dataset,\n author={Your Name},\n title={LE Audio Dataset},\n year={2023},\n url={link to dataset}\n}"
] |
[
"TAGS\n#task_categories-text2text-generation #region-us \n",
"# Dataset Card for [LE Audio]\n\nDataset Card\n\nDataset Name: LE Audio Dataset\n\nDataset Version: 1.0\n\nDataset Website:\n\nDataset Creators: [Your Name]\n\nDataset Description:\n\nThe LE Audio Dataset is a collection of audio recordings that were captured using Bluetooth Low Energy Audio (LE Audio). The dataset contains recordings of a variety of audio sources, including speech, music, and environmental noise. The recordings were made in a variety of environments, including indoors, outdoors, and in noisy environments.\n\nDataset License:\n\nDataset Usage:\n\nThe LE Audio Dataset can be used to train and evaluate machine learning models for a variety of audio tasks, such as speech recognition, music classification, and environmental sound classification. The dataset is also useful for research on LE Audio itself.\n\nDataset Download:\n\nThe LE Audio Dataset can be downloaded from [link to dataset].\n\nDataset Statistics:\n\nThe LE Audio Dataset contains over 1 million audio recordings, with a total duration of over 100 hours. The recordings are divided into two splits: train (80%) and test (20%).\n\nDataset Features:\n\nThe LE Audio Dataset contains the following features:\n\nAudio waveform: The audio waveform is represented as a 16-bit signed integer signal at a sampling rate of 48 kHz.\nAudio metadata: The audio metadata includes the recording date, time, location, and device information.\nDataset Biases:\n\nThe LE Audio Dataset is collected from a variety of sources, but it is important to note that the dataset may contain biases that reflect the sources from which it was collected. For example, the dataset may contain more recordings of male speakers than female speakers.\n\nDataset Citation:\n\nTo cite the LE Audio Dataset, please use the following BibTeX entry:\n\n@article{le_audio_dataset,\n author={Your Name},\n title={LE Audio Dataset},\n year={2023},\n url={link to dataset}\n}"
] |
[
19,
453
] |
[
"passage: TAGS\n#task_categories-text2text-generation #region-us \n# Dataset Card for [LE Audio]\n\nDataset Card\n\nDataset Name: LE Audio Dataset\n\nDataset Version: 1.0\n\nDataset Website:\n\nDataset Creators: [Your Name]\n\nDataset Description:\n\nThe LE Audio Dataset is a collection of audio recordings that were captured using Bluetooth Low Energy Audio (LE Audio). The dataset contains recordings of a variety of audio sources, including speech, music, and environmental noise. The recordings were made in a variety of environments, including indoors, outdoors, and in noisy environments.\n\nDataset License:\n\nDataset Usage:\n\nThe LE Audio Dataset can be used to train and evaluate machine learning models for a variety of audio tasks, such as speech recognition, music classification, and environmental sound classification. The dataset is also useful for research on LE Audio itself.\n\nDataset Download:\n\nThe LE Audio Dataset can be downloaded from [link to dataset].\n\nDataset Statistics:\n\nThe LE Audio Dataset contains over 1 million audio recordings, with a total duration of over 100 hours. The recordings are divided into two splits: train (80%) and test (20%).\n\nDataset Features:\n\nThe LE Audio Dataset contains the following features:\n\nAudio waveform: The audio waveform is represented as a 16-bit signed integer signal at a sampling rate of 48 kHz.\nAudio metadata: The audio metadata includes the recording date, time, location, and device information.\nDataset Biases:\n\nThe LE Audio Dataset is collected from a variety of sources, but it is important to note that the dataset may contain biases that reflect the sources from which it was collected. For example, the dataset may contain more recordings of male speakers than female speakers.\n\nDataset Citation:\n\nTo cite the LE Audio Dataset, please use the following BibTeX entry:\n\n@article{le_audio_dataset,\n author={Your Name},\n title={LE Audio Dataset},\n year={2023},\n url={link to dataset}\n}"
] |
baaf5e72eafa33f1610a8f9831edc6d4ed98ee7e
|
# Dataset Card for WineSensed
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [WineSensed Dataset](https://https://thoranna.github.io/learning_to_taste/)
- **Repository:**
- **Paper:** [Paper](https://arxiv.org/pdf/2308.16900.pdf)
### Dataset Summary
The dataset encompasses 897k images of wine labels and 824k reviews of wines
curated from the Vivino platform. It has over 350k unique vintages, annotated
with year, region, rating, alcohol percentage, price, and grape composition.
We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment
with 256 participants who were asked to rank wines based on their similarity in flavor,
resulting in more than 5k pairwise flavor distances.
## Hugging Face Datasets
use the following command to load the dataset in Hugging Face Datasets library:
```python
dataset = load_dataset('Dakhoo/L2T-NeurIPS-2023', 'vintages')
dataset = load_dataset('Dakhoo/L2T-NeurIPS-2023', 'napping_participants')
dataset = load_dataset('Dakhoo/L2T-NeurIPS-2023', 'wt_session')
dataset = load_dataset('Dakhoo/L2T-NeurIPS-2023', 'small')
dataset = load_dataset('Dakhoo/L2T-NeurIPS-2023', 'all')
```
## Dataset Structure
### Data Fields
The dataset contains the file metadata.zip, consisting of the files participants.csv, which contains information connecting participants to annotations in the experiment, images_reviews_attributes.csv, which contains reviews, links to images, and wine attributes, and napping.csv, which contains the coordinates of each wine on the napping paper alongside information connecting each coordinate pair to the wine being annotated and the participant who annotated it. The chunk_<chunk num>.zip folders contain the images of the wines in the dataset in .jpg format.
#### napping.csv contains the following fields:
- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in participants.csv)
- event_name: name of the data collection event (maps to the same attribute in participants.csv)
- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in participants.csv)
- experiment_id: id the wine being annotated was given in the experiment
- coor1: x-axis coordinate on the napping paper
- coor2: y-axis coordinate on the napping paper
- color: color of the sticker used
#### participants.csv contains the following fields:
- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in napping.csv)
- event_name: name of data-collection event (maps to event_name in napping.csv)
- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in napping.csv)
- round_id: round number (from 1-3)
- participant_id: id the participant was given in the experiment
#### images_reviews_attributes.csv contains the following fields:
- vintage_id: vintage id of the wine
- image: image link (each .jpg in chunk_<chunk num>.zip can be mapped to a corresponding image link in this column by removing the /p prefix from the link).
- review: user review of the wine
- experiment_id: id the wine got during data collection (each experiment_id can be mapped to the same column in napping.csv)
- year: year the wine was produced
- winery_id: id of the winery that produced the wine
- wine: name of the wine
- alcohol: the wine's alcohol percentage
- country: the country where the wine was produced
- region: the region where the wine was produced
- price: price of the wine in USD (collected 05/2023)
- rating: average rating of the wine (collected 05/2023)
- grape: the wine's grape composition, represented as a comma-separated list ordered in descending sequence of the percentage contribution of each grape variety to the overall blend.
## Dataset Creation
Follow the instructions in this [link](https://thoranna.github.io/learning_to_taste/) to create the dataset.
## Additional Information
### Licensing Information
LICENSE AGREEMENT
=================
- WineSensed by Thoranna Bender, Simon Søresen, Alireza Kashani, Kristjan Eldjarn, Grethe Hyldig,
Søren Hauberg, Serge Belongie, Frederik Warburg is licensed under a CC BY-NC-ND 4.0 Licence
### Citation Information
```
@article{bender2023learning,
title={Learning to Taste: A Multimodal Wine Dataset},
author={Bender, Thoranna and S{\o}rensen, Simon M{\o}e and Kashani, Alireza and Hjorleifsson, K Eldjarn and Hyldig, Grethe and Hauberg, S{\o}ren and Belongie, Serge and Warburg, Frederik},
journal={arXiv preprint arXiv:2308.16900},
year={2023}
```
|
Dakhoo/L2T-NeurIPS-2023
|
[
"arxiv:2308.16900",
"region:us"
] |
2023-10-04T06:40:06+00:00
|
{}
|
2023-11-09T08:24:19+00:00
|
[
"2308.16900"
] |
[] |
TAGS
#arxiv-2308.16900 #region-us
|
# Dataset Card for WineSensed
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Languages
- Dataset Structure
- Data Fields
- Additional Information
- Licensing Information
- Citation Information
## Dataset Description
- Homepage: WineSensed Dataset
- Repository:
- Paper: Paper
### Dataset Summary
The dataset encompasses 897k images of wine labels and 824k reviews of wines
curated from the Vivino platform. It has over 350k unique vintages, annotated
with year, region, rating, alcohol percentage, price, and grape composition.
We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment
with 256 participants who were asked to rank wines based on their similarity in flavor,
resulting in more than 5k pairwise flavor distances.
## Hugging Face Datasets
use the following command to load the dataset in Hugging Face Datasets library:
## Dataset Structure
### Data Fields
The dataset contains the file URL, consisting of the files URL, which contains information connecting participants to annotations in the experiment, images_reviews_attributes.csv, which contains reviews, links to images, and wine attributes, and URL, which contains the coordinates of each wine on the napping paper alongside information connecting each coordinate pair to the wine being annotated and the participant who annotated it. The chunk_<chunk num>.zip folders contain the images of the wines in the dataset in .jpg format.
#### URL contains the following fields:
- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in URL)
- event_name: name of the data collection event (maps to the same attribute in URL)
- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in URL)
- experiment_id: id the wine being annotated was given in the experiment
- coor1: x-axis coordinate on the napping paper
- coor2: y-axis coordinate on the napping paper
- color: color of the sticker used
#### URL contains the following fields:
- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in URL)
- event_name: name of data-collection event (maps to event_name in URL)
- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in URL)
- round_id: round number (from 1-3)
- participant_id: id the participant was given in the experiment
#### images_reviews_attributes.csv contains the following fields:
- vintage_id: vintage id of the wine
- image: image link (each .jpg in chunk_<chunk num>.zip can be mapped to a corresponding image link in this column by removing the /p prefix from the link).
- review: user review of the wine
- experiment_id: id the wine got during data collection (each experiment_id can be mapped to the same column in URL)
- year: year the wine was produced
- winery_id: id of the winery that produced the wine
- wine: name of the wine
- alcohol: the wine's alcohol percentage
- country: the country where the wine was produced
- region: the region where the wine was produced
- price: price of the wine in USD (collected 05/2023)
- rating: average rating of the wine (collected 05/2023)
- grape: the wine's grape composition, represented as a comma-separated list ordered in descending sequence of the percentage contribution of each grape variety to the overall blend.
## Dataset Creation
Follow the instructions in this link to create the dataset.
## Additional Information
### Licensing Information
LICENSE AGREEMENT
=================
- WineSensed by Thoranna Bender, Simon Søresen, Alireza Kashani, Kristjan Eldjarn, Grethe Hyldig,
Søren Hauberg, Serge Belongie, Frederik Warburg is licensed under a CC BY-NC-ND 4.0 Licence
|
[
"# Dataset Card for WineSensed",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Fields\n- Additional Information\n - Licensing Information\n - Citation Information",
"## Dataset Description\n\n- Homepage: WineSensed Dataset\n- Repository:\n- Paper: Paper",
"### Dataset Summary\n\nThe dataset encompasses 897k images of wine labels and 824k reviews of wines\ncurated from the Vivino platform. It has over 350k unique vintages, annotated\nwith year, region, rating, alcohol percentage, price, and grape composition.\nWe obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment\nwith 256 participants who were asked to rank wines based on their similarity in flavor,\nresulting in more than 5k pairwise flavor distances.",
"## Hugging Face Datasets\n\nuse the following command to load the dataset in Hugging Face Datasets library:",
"## Dataset Structure",
"### Data Fields\n\nThe dataset contains the file URL, consisting of the files URL, which contains information connecting participants to annotations in the experiment, images_reviews_attributes.csv, which contains reviews, links to images, and wine attributes, and URL, which contains the coordinates of each wine on the napping paper alongside information connecting each coordinate pair to the wine being annotated and the participant who annotated it. The chunk_<chunk num>.zip folders contain the images of the wines in the dataset in .jpg format.",
"#### URL contains the following fields:\n\n- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in URL)\n- event_name: name of the data collection event (maps to the same attribute in URL)\n- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in URL)\n- experiment_id: id the wine being annotated was given in the experiment\n- coor1: x-axis coordinate on the napping paper\n- coor2: y-axis coordinate on the napping paper\n- color: color of the sticker used",
"#### URL contains the following fields:\n\n- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in URL)\n- event_name: name of data-collection event (maps to event_name in URL)\n- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in URL)\n- round_id: round number (from 1-3)\n- participant_id: id the participant was given in the experiment",
"#### images_reviews_attributes.csv contains the following fields:\n\n- vintage_id: vintage id of the wine\n- image: image link (each .jpg in chunk_<chunk num>.zip can be mapped to a corresponding image link in this column by removing the /p prefix from the link).\n- review: user review of the wine\n- experiment_id: id the wine got during data collection (each experiment_id can be mapped to the same column in URL)\n- year: year the wine was produced\n- winery_id: id of the winery that produced the wine\n- wine: name of the wine\n- alcohol: the wine's alcohol percentage\n- country: the country where the wine was produced\n- region: the region where the wine was produced\n- price: price of the wine in USD (collected 05/2023)\n- rating: average rating of the wine (collected 05/2023)\n- grape: the wine's grape composition, represented as a comma-separated list ordered in descending sequence of the percentage contribution of each grape variety to the overall blend.",
"## Dataset Creation\n\nFollow the instructions in this link to create the dataset.",
"## Additional Information",
"### Licensing Information\n\nLICENSE AGREEMENT\n=================\n - WineSensed by Thoranna Bender, Simon Søresen, Alireza Kashani, Kristjan Eldjarn, Grethe Hyldig,\n Søren Hauberg, Serge Belongie, Frederik Warburg is licensed under a CC BY-NC-ND 4.0 Licence"
] |
[
"TAGS\n#arxiv-2308.16900 #region-us \n",
"# Dataset Card for WineSensed",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Fields\n- Additional Information\n - Licensing Information\n - Citation Information",
"## Dataset Description\n\n- Homepage: WineSensed Dataset\n- Repository:\n- Paper: Paper",
"### Dataset Summary\n\nThe dataset encompasses 897k images of wine labels and 824k reviews of wines\ncurated from the Vivino platform. It has over 350k unique vintages, annotated\nwith year, region, rating, alcohol percentage, price, and grape composition.\nWe obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment\nwith 256 participants who were asked to rank wines based on their similarity in flavor,\nresulting in more than 5k pairwise flavor distances.",
"## Hugging Face Datasets\n\nuse the following command to load the dataset in Hugging Face Datasets library:",
"## Dataset Structure",
"### Data Fields\n\nThe dataset contains the file URL, consisting of the files URL, which contains information connecting participants to annotations in the experiment, images_reviews_attributes.csv, which contains reviews, links to images, and wine attributes, and URL, which contains the coordinates of each wine on the napping paper alongside information connecting each coordinate pair to the wine being annotated and the participant who annotated it. The chunk_<chunk num>.zip folders contain the images of the wines in the dataset in .jpg format.",
"#### URL contains the following fields:\n\n- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in URL)\n- event_name: name of the data collection event (maps to the same attribute in URL)\n- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in URL)\n- experiment_id: id the wine being annotated was given in the experiment\n- coor1: x-axis coordinate on the napping paper\n- coor2: y-axis coordinate on the napping paper\n- color: color of the sticker used",
"#### URL contains the following fields:\n\n- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in URL)\n- event_name: name of data-collection event (maps to event_name in URL)\n- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in URL)\n- round_id: round number (from 1-3)\n- participant_id: id the participant was given in the experiment",
"#### images_reviews_attributes.csv contains the following fields:\n\n- vintage_id: vintage id of the wine\n- image: image link (each .jpg in chunk_<chunk num>.zip can be mapped to a corresponding image link in this column by removing the /p prefix from the link).\n- review: user review of the wine\n- experiment_id: id the wine got during data collection (each experiment_id can be mapped to the same column in URL)\n- year: year the wine was produced\n- winery_id: id of the winery that produced the wine\n- wine: name of the wine\n- alcohol: the wine's alcohol percentage\n- country: the country where the wine was produced\n- region: the region where the wine was produced\n- price: price of the wine in USD (collected 05/2023)\n- rating: average rating of the wine (collected 05/2023)\n- grape: the wine's grape composition, represented as a comma-separated list ordered in descending sequence of the percentage contribution of each grape variety to the overall blend.",
"## Dataset Creation\n\nFollow the instructions in this link to create the dataset.",
"## Additional Information",
"### Licensing Information\n\nLICENSE AGREEMENT\n=================\n - WineSensed by Thoranna Bender, Simon Søresen, Alireza Kashani, Kristjan Eldjarn, Grethe Hyldig,\n Søren Hauberg, Serge Belongie, Frederik Warburg is licensed under a CC BY-NC-ND 4.0 Licence"
] |
[
14,
8,
46,
21,
121,
26,
6,
134,
155,
123,
247,
17,
5,
72
] |
[
"passage: TAGS\n#arxiv-2308.16900 #region-us \n# Dataset Card for WineSensed## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Languages\n- Dataset Structure\n - Data Fields\n- Additional Information\n - Licensing Information\n - Citation Information## Dataset Description\n\n- Homepage: WineSensed Dataset\n- Repository:\n- Paper: Paper### Dataset Summary\n\nThe dataset encompasses 897k images of wine labels and 824k reviews of wines\ncurated from the Vivino platform. It has over 350k unique vintages, annotated\nwith year, region, rating, alcohol percentage, price, and grape composition.\nWe obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment\nwith 256 participants who were asked to rank wines based on their similarity in flavor,\nresulting in more than 5k pairwise flavor distances.## Hugging Face Datasets\n\nuse the following command to load the dataset in Hugging Face Datasets library:## Dataset Structure### Data Fields\n\nThe dataset contains the file URL, consisting of the files URL, which contains information connecting participants to annotations in the experiment, images_reviews_attributes.csv, which contains reviews, links to images, and wine attributes, and URL, which contains the coordinates of each wine on the napping paper alongside information connecting each coordinate pair to the wine being annotated and the participant who annotated it. The chunk_<chunk num>.zip folders contain the images of the wines in the dataset in .jpg format."
] |
6de04a53e052f6e48e38d553154341d66a663f8e
|
# Dataset Card for "cleaned_daily_dialog_sentence"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/cleaned_daily_dialog_sentence
|
[
"region:us"
] |
2023-10-04T06:40:14+00:00
|
{"dataset_info": {"features": [{"name": "dialogue", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5434241, "num_examples": 77350}], "download_size": 3467625, "dataset_size": 5434241}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T06:42:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cleaned_daily_dialog_sentence"
More Information needed
|
[
"# Dataset Card for \"cleaned_daily_dialog_sentence\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cleaned_daily_dialog_sentence\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cleaned_daily_dialog_sentence\"\n\nMore Information needed"
] |
a74c61a2527482e5d8927e2048e70d61c10a2116
|
# Dataset Card for Evaluation run of Devio/test-9k-fn
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Devio/test-9k-fn
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Devio/test-9k-fn](https://huggingface.co/Devio/test-9k-fn) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Devio__test-9k-fn",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-04T07:45:21.870360](https://huggingface.co/datasets/open-llm-leaderboard/details_Devio__test-9k-fn/blob/main/results_2023-10-04T07-45-21.870360.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.29934767850730726,
"acc_stderr": 0.033158996935405735,
"acc_norm": 0.3034282752932227,
"acc_norm_stderr": 0.03315857474708369,
"mc1": 0.23378212974296206,
"mc1_stderr": 0.014816195991931583,
"mc2": 0.3914546223201993,
"mc2_stderr": 0.013969580332280395
},
"harness|arc:challenge|25": {
"acc": 0.35665529010238906,
"acc_stderr": 0.013998056902620192,
"acc_norm": 0.4087030716723549,
"acc_norm_stderr": 0.014365750345427008
},
"harness|hellaswag|10": {
"acc": 0.5057757418840868,
"acc_stderr": 0.004989448490164429,
"acc_norm": 0.6944831706831308,
"acc_norm_stderr": 0.004596845936356623
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.28888888888888886,
"acc_stderr": 0.0391545063041425,
"acc_norm": 0.28888888888888886,
"acc_norm_stderr": 0.0391545063041425
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.3092105263157895,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.3092105263157895,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3018867924528302,
"acc_stderr": 0.028254200344438655,
"acc_norm": 0.3018867924528302,
"acc_norm_stderr": 0.028254200344438655
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2986111111111111,
"acc_stderr": 0.03827052357950756,
"acc_norm": 0.2986111111111111,
"acc_norm_stderr": 0.03827052357950756
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2774566473988439,
"acc_stderr": 0.03414014007044037,
"acc_norm": 0.2774566473988439,
"acc_norm_stderr": 0.03414014007044037
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.19607843137254902,
"acc_stderr": 0.03950581861179963,
"acc_norm": 0.19607843137254902,
"acc_norm_stderr": 0.03950581861179963
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.32340425531914896,
"acc_stderr": 0.030579442773610337,
"acc_norm": 0.32340425531914896,
"acc_norm_stderr": 0.030579442773610337
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.20175438596491227,
"acc_stderr": 0.037752050135836386,
"acc_norm": 0.20175438596491227,
"acc_norm_stderr": 0.037752050135836386
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.3103448275862069,
"acc_stderr": 0.03855289616378947,
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|truthfulqa:mc|0": {
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"mc2": 0.3914546223201993,
"mc2_stderr": 0.013969580332280395
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Devio__test-9k-fn
|
[
"region:us"
] |
2023-10-04T06:45:40+00:00
|
{"pretty_name": "Evaluation run of Devio/test-9k-fn", "dataset_summary": "Dataset automatically created during the evaluation run of model [Devio/test-9k-fn](https://huggingface.co/Devio/test-9k-fn) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Devio__test-9k-fn\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-04T07:45:21.870360](https://huggingface.co/datasets/open-llm-leaderboard/details_Devio__test-9k-fn/blob/main/results_2023-10-04T07-45-21.870360.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.29934767850730726,\n \"acc_stderr\": 0.033158996935405735,\n \"acc_norm\": 0.3034282752932227,\n \"acc_norm_stderr\": 0.03315857474708369,\n \"mc1\": 0.23378212974296206,\n \"mc1_stderr\": 0.014816195991931583,\n \"mc2\": 0.3914546223201993,\n \"mc2_stderr\": 0.013969580332280395\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.35665529010238906,\n \"acc_stderr\": 0.013998056902620192,\n \"acc_norm\": 0.4087030716723549,\n \"acc_norm_stderr\": 0.014365750345427008\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5057757418840868,\n \"acc_stderr\": 0.004989448490164429,\n \"acc_norm\": 0.6944831706831308,\n \"acc_norm_stderr\": 0.004596845936356623\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.28888888888888886,\n \"acc_stderr\": 0.0391545063041425,\n \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.0391545063041425\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.3092105263157895,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.3092105263157895,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.3018867924528302,\n \"acc_stderr\": 0.028254200344438655,\n \"acc_norm\": 0.3018867924528302,\n \"acc_norm_stderr\": 0.028254200344438655\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2986111111111111,\n \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.2986111111111111,\n \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2774566473988439,\n \"acc_stderr\": 0.03414014007044037,\n \"acc_norm\": 0.2774566473988439,\n \"acc_norm_stderr\": 0.03414014007044037\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179963,\n \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179963\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.32340425531914896,\n \"acc_stderr\": 0.030579442773610337,\n \"acc_norm\": 0.32340425531914896,\n \"acc_norm_stderr\": 0.030579442773610337\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.20175438596491227,\n \"acc_stderr\": 0.037752050135836386,\n \"acc_norm\": 0.20175438596491227,\n \"acc_norm_stderr\": 0.037752050135836386\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.3103448275862069,\n \"acc_stderr\": 0.03855289616378947,\n \"acc_norm\": 0.3103448275862069,\n \"acc_norm_stderr\": 0.03855289616378947\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.22486772486772486,\n \"acc_stderr\": 0.02150209607822914,\n \"acc_norm\": 0.22486772486772486,\n \"acc_norm_stderr\": 0.02150209607822914\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3253968253968254,\n \"acc_stderr\": 0.041905964388711366,\n \"acc_norm\": 0.3253968253968254,\n \"acc_norm_stderr\": 0.041905964388711366\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.2709677419354839,\n \"acc_stderr\": 0.025284416114900156,\n \"acc_norm\": 0.2709677419354839,\n \"acc_norm_stderr\": 0.025284416114900156\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.18719211822660098,\n \"acc_stderr\": 0.027444924966882618,\n \"acc_norm\": 0.18719211822660098,\n \"acc_norm_stderr\": 0.027444924966882618\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.21212121212121213,\n \"acc_stderr\": 0.03192271569548299,\n \"acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.03192271569548299\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.3181818181818182,\n \"acc_stderr\": 0.03318477333845331,\n \"acc_norm\": 0.3181818181818182,\n \"acc_norm_stderr\": 0.03318477333845331\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.35751295336787564,\n \"acc_stderr\": 0.03458816042181005,\n \"acc_norm\": 0.35751295336787564,\n \"acc_norm_stderr\": 0.03458816042181005\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.3717948717948718,\n \"acc_stderr\": 0.024503472557110936,\n \"acc_norm\": 0.3717948717948718,\n \"acc_norm_stderr\": 0.024503472557110936\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 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["results_2023-10-04T07-45-21.870360.parquet"]}, {"split": "latest", "path": ["results_2023-10-04T07-45-21.870360.parquet"]}]}]}
|
2023-10-04T06:46:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Devio/test-9k-fn
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Devio/test-9k-fn on the Open LLM Leaderboard.
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-04T07:45:21.870360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Devio/test-9k-fn",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Devio/test-9k-fn on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-04T07:45:21.870360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Devio/test-9k-fn",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Devio/test-9k-fn on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-04T07:45:21.870360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Devio/test-9k-fn## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Devio/test-9k-fn on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-04T07:45:21.870360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
896477d616a622c58d8dccf57bfb2d11c0864bdd
|
# Dataset Card for Evaluation run of Sao10K/BrainDerp2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/BrainDerp2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Sao10K/BrainDerp2](https://huggingface.co/Sao10K/BrainDerp2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Sao10K__BrainDerp2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T19:44:45.310953](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__BrainDerp2/blob/main/results_2023-10-25T19-44-45.310953.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.02139261744966443,
"em_stderr": 0.0014817531449682906,
"f1": 0.14337038590604004,
"f1_stderr": 0.002432995569296514,
"acc": 0.42474686941447715,
"acc_stderr": 0.009953548160337068
},
"harness|drop|3": {
"em": 0.02139261744966443,
"em_stderr": 0.0014817531449682906,
"f1": 0.14337038590604004,
"f1_stderr": 0.002432995569296514
},
"harness|gsm8k|5": {
"acc": 0.09021986353297953,
"acc_stderr": 0.007891537108449961
},
"harness|winogrande|5": {
"acc": 0.7592738752959748,
"acc_stderr": 0.012015559212224174
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Sao10K__BrainDerp2
|
[
"region:us"
] |
2023-10-04T06:47:16+00:00
|
{"pretty_name": "Evaluation run of Sao10K/BrainDerp2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Sao10K/BrainDerp2](https://huggingface.co/Sao10K/BrainDerp2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__BrainDerp2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T19:44:45.310953](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__BrainDerp2/blob/main/results_2023-10-25T19-44-45.310953.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.02139261744966443,\n \"em_stderr\": 0.0014817531449682906,\n \"f1\": 0.14337038590604004,\n \"f1_stderr\": 0.002432995569296514,\n \"acc\": 0.42474686941447715,\n \"acc_stderr\": 0.009953548160337068\n },\n \"harness|drop|3\": {\n \"em\": 0.02139261744966443,\n \"em_stderr\": 0.0014817531449682906,\n \"f1\": 0.14337038590604004,\n \"f1_stderr\": 0.002432995569296514\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09021986353297953,\n \"acc_stderr\": 0.007891537108449961\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.012015559212224174\n }\n}\n```", "repo_url": "https://huggingface.co/Sao10K/BrainDerp2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T07_46_51.716254", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-46-51.716254.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-46-51.716254.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T19_44_45.310953", "path": ["**/details_harness|drop|3_2023-10-25T19-44-45.310953.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T19-44-45.310953.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T19_44_45.310953", "path": ["**/details_harness|gsm8k|5_2023-10-25T19-44-45.310953.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T19-44-45.310953.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T07_46_51.716254", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-46-51.716254.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-46-51.716254.parquet"]}]}, 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-46-51.716254.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_10_04T07_46_51.716254", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-46-51.716254.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-46-51.716254.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_10_04T07_46_51.716254", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-46-51.716254.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-46-51.716254.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_10_04T07_46_51.716254", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-46-51.716254.parquet"]}, {"split": "latest", 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|
2023-10-25T18:44:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Sao10K/BrainDerp2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Sao10K/BrainDerp2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T19:44:45.310953(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Sao10K/BrainDerp2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/BrainDerp2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T19:44:45.310953(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
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"# Dataset Card for Evaluation run of Sao10K/BrainDerp2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/BrainDerp2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T19:44:45.310953(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
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"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Curation Rationale",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
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] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Sao10K/BrainDerp2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/BrainDerp2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T19:44:45.310953(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
de40fd660610fd0e02497f603f5c0ce653e454af
|
This dataset is a subset of the Open Assistant dataset, which you can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
This dataset was used to train Guanaco with QLoRA.
For further information, please see the original dataset.
License: Apache 2.0
|
lakelz/mydataset-bpg
|
[
"region:us"
] |
2023-10-04T06:47:51+00:00
|
{}
|
2023-10-07T05:54:47+00:00
|
[] |
[] |
TAGS
#region-us
|
This dataset is a subset of the Open Assistant dataset, which you can find here: URL
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
This dataset was used to train Guanaco with QLoRA.
For further information, please see the original dataset.
License: Apache 2.0
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
7dd2e78854df1e584bfa85890cb40fb91acc4010
|
# Dataset Card for Evaluation run of posicube/Llama2-chat-AYB-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/posicube/Llama2-chat-AYB-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [posicube/Llama2-chat-AYB-13B](https://huggingface.co/posicube/Llama2-chat-AYB-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_posicube__Llama2-chat-AYB-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T15:23:04.071945](https://huggingface.co/datasets/open-llm-leaderboard/details_posicube__Llama2-chat-AYB-13B/blob/main/results_2023-10-24T15-23-04.071945.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.10906040268456375,
"em_stderr": 0.0031922531959087046,
"f1": 0.20405201342281792,
"f1_stderr": 0.003418767120803739,
"acc": 0.4376976530855872,
"acc_stderr": 0.010340318967318105
},
"harness|drop|3": {
"em": 0.10906040268456375,
"em_stderr": 0.0031922531959087046,
"f1": 0.20405201342281792,
"f1_stderr": 0.003418767120803739
},
"harness|gsm8k|5": {
"acc": 0.11296436694465505,
"acc_stderr": 0.008719339028833057
},
"harness|winogrande|5": {
"acc": 0.7624309392265194,
"acc_stderr": 0.011961298905803153
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_posicube__Llama2-chat-AYB-13B
|
[
"region:us"
] |
2023-10-04T06:48:24+00:00
|
{"pretty_name": "Evaluation run of posicube/Llama2-chat-AYB-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [posicube/Llama2-chat-AYB-13B](https://huggingface.co/posicube/Llama2-chat-AYB-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_posicube__Llama2-chat-AYB-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T15:23:04.071945](https://huggingface.co/datasets/open-llm-leaderboard/details_posicube__Llama2-chat-AYB-13B/blob/main/results_2023-10-24T15-23-04.071945.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.10906040268456375,\n \"em_stderr\": 0.0031922531959087046,\n \"f1\": 0.20405201342281792,\n \"f1_stderr\": 0.003418767120803739,\n \"acc\": 0.4376976530855872,\n \"acc_stderr\": 0.010340318967318105\n },\n \"harness|drop|3\": {\n \"em\": 0.10906040268456375,\n \"em_stderr\": 0.0031922531959087046,\n \"f1\": 0.20405201342281792,\n \"f1_stderr\": 0.003418767120803739\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11296436694465505,\n \"acc_stderr\": 0.008719339028833057\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803153\n }\n}\n```", "repo_url": "https://huggingface.co/posicube/Llama2-chat-AYB-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T07_48_01.042889", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-48-01.042889.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-48-01.042889.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T15_23_04.071945", "path": ["**/details_harness|drop|3_2023-10-24T15-23-04.071945.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T15-23-04.071945.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T15_23_04.071945", "path": ["**/details_harness|gsm8k|5_2023-10-24T15-23-04.071945.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T15-23-04.071945.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T07_48_01.042889", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-48-01.042889.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-10-04T07-48-01.042889.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-10-04T07-48-01.042889.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T15_23_04.071945", "path": ["**/details_harness|winogrande|5_2023-10-24T15-23-04.071945.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-24T15-23-04.071945.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_04T07_48_01.042889", "path": ["results_2023-10-04T07-48-01.042889.parquet"]}, {"split": "2023_10_24T15_23_04.071945", "path": ["results_2023-10-24T15-23-04.071945.parquet"]}, {"split": "latest", "path": ["results_2023-10-24T15-23-04.071945.parquet"]}]}]}
|
2023-10-24T14:23:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of posicube/Llama2-chat-AYB-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model posicube/Llama2-chat-AYB-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T15:23:04.071945(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of posicube/Llama2-chat-AYB-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model posicube/Llama2-chat-AYB-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T15:23:04.071945(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of posicube/Llama2-chat-AYB-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model posicube/Llama2-chat-AYB-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T15:23:04.071945(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of posicube/Llama2-chat-AYB-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model posicube/Llama2-chat-AYB-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T15:23:04.071945(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
e6277ac85889999eef717db95b35461ebb0047a1
|
# Dataset Card for Evaluation run of Sao10K/BrainDerp3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/BrainDerp3
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Sao10K/BrainDerp3](https://huggingface.co/Sao10K/BrainDerp3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Sao10K__BrainDerp3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T22:57:20.816050](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__BrainDerp3/blob/main/results_2023-10-28T22-57-20.816050.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0269505033557047,
"em_stderr": 0.0016584048452624436,
"f1": 0.1492428691275163,
"f1_stderr": 0.002525870073512654,
"acc": 0.4182403617100085,
"acc_stderr": 0.009778590926073638
},
"harness|drop|3": {
"em": 0.0269505033557047,
"em_stderr": 0.0016584048452624436,
"f1": 0.1492428691275163,
"f1_stderr": 0.002525870073512654
},
"harness|gsm8k|5": {
"acc": 0.0803639120545868,
"acc_stderr": 0.007488258573239077
},
"harness|winogrande|5": {
"acc": 0.7561168113654302,
"acc_stderr": 0.012068923278908199
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Sao10K__BrainDerp3
|
[
"region:us"
] |
2023-10-04T06:48:29+00:00
|
{"pretty_name": "Evaluation run of Sao10K/BrainDerp3", "dataset_summary": "Dataset automatically created during the evaluation run of model [Sao10K/BrainDerp3](https://huggingface.co/Sao10K/BrainDerp3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__BrainDerp3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T22:57:20.816050](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__BrainDerp3/blob/main/results_2023-10-28T22-57-20.816050.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0269505033557047,\n \"em_stderr\": 0.0016584048452624436,\n \"f1\": 0.1492428691275163,\n \"f1_stderr\": 0.002525870073512654,\n \"acc\": 0.4182403617100085,\n \"acc_stderr\": 0.009778590926073638\n },\n \"harness|drop|3\": {\n \"em\": 0.0269505033557047,\n \"em_stderr\": 0.0016584048452624436,\n \"f1\": 0.1492428691275163,\n \"f1_stderr\": 0.002525870073512654\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0803639120545868,\n \"acc_stderr\": 0.007488258573239077\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7561168113654302,\n \"acc_stderr\": 0.012068923278908199\n }\n}\n```", "repo_url": "https://huggingface.co/Sao10K/BrainDerp3", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T07_48_05.088946", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-48-05.088946.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T07-48-05.088946.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T22_57_20.816050", "path": ["**/details_harness|drop|3_2023-10-28T22-57-20.816050.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T22-57-20.816050.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T22_57_20.816050", "path": ["**/details_harness|gsm8k|5_2023-10-28T22-57-20.816050.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T22-57-20.816050.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T07_48_05.088946", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-48-05.088946.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T07-48-05.088946.parquet"]}]}, 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|
2023-10-28T21:57:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Sao10K/BrainDerp3
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Sao10K/BrainDerp3 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-28T22:57:20.816050(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Sao10K/BrainDerp3",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/BrainDerp3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T22:57:20.816050(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Sao10K/BrainDerp3",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/BrainDerp3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T22:57:20.816050(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Sao10K/BrainDerp3## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/BrainDerp3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T22:57:20.816050(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
2cec1e3208a1b45e7b8c08dffc0fa7f27dc09022
|
# Dataset Card for Evaluation run of harborwater/open-llama-3b-everythingLM-2048
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/harborwater/open-llama-3b-everythingLM-2048
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [harborwater/open-llama-3b-everythingLM-2048](https://huggingface.co/harborwater/open-llama-3b-everythingLM-2048) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_harborwater__open-llama-3b-everythingLM-2048",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T01:01:11.414021](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__open-llama-3b-everythingLM-2048/blob/main/results_2023-10-24T01-01-11.414021.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0014681208053691276,
"em_stderr": 0.00039210421902986076,
"f1": 0.053537122483221615,
"f1_stderr": 0.0012879336042021898,
"acc": 0.3390732138444075,
"acc_stderr": 0.008325489359560807
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.00039210421902986076,
"f1": 0.053537122483221615,
"f1_stderr": 0.0012879336042021898
},
"harness|gsm8k|5": {
"acc": 0.015163002274450341,
"acc_stderr": 0.003366022949726365
},
"harness|winogrande|5": {
"acc": 0.6629834254143646,
"acc_stderr": 0.01328495576939525
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
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### Licensing Information
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### Contributions
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|
open-llm-leaderboard/details_harborwater__open-llama-3b-everythingLM-2048
|
[
"region:us"
] |
2023-10-04T07:05:44+00:00
|
{"pretty_name": "Evaluation run of harborwater/open-llama-3b-everythingLM-2048", "dataset_summary": "Dataset automatically created during the evaluation run of model [harborwater/open-llama-3b-everythingLM-2048](https://huggingface.co/harborwater/open-llama-3b-everythingLM-2048) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_harborwater__open-llama-3b-everythingLM-2048\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T01:01:11.414021](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__open-llama-3b-everythingLM-2048/blob/main/results_2023-10-24T01-01-11.414021.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.00039210421902986076,\n \"f1\": 0.053537122483221615,\n \"f1_stderr\": 0.0012879336042021898,\n \"acc\": 0.3390732138444075,\n \"acc_stderr\": 0.008325489359560807\n },\n \"harness|drop|3\": {\n \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.00039210421902986076,\n \"f1\": 0.053537122483221615,\n \"f1_stderr\": 0.0012879336042021898\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.015163002274450341,\n \"acc_stderr\": 0.003366022949726365\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6629834254143646,\n \"acc_stderr\": 0.01328495576939525\n }\n}\n```", "repo_url": "https://huggingface.co/harborwater/open-llama-3b-everythingLM-2048", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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TAGS
#region-us
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# Dataset Card for Evaluation run of harborwater/open-llama-3b-everythingLM-2048
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model harborwater/open-llama-3b-everythingLM-2048 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T01:01:11.414021(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of harborwater/open-llama-3b-everythingLM-2048",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model harborwater/open-llama-3b-everythingLM-2048 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T01:01:11.414021(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of harborwater/open-llama-3b-everythingLM-2048",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model harborwater/open-llama-3b-everythingLM-2048 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T01:01:11.414021(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of harborwater/open-llama-3b-everythingLM-2048## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model harborwater/open-llama-3b-everythingLM-2048 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T01:01:11.414021(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
65a5cb72ab01bce02b23656ad8b965109adf7b80
|
# Dataset Card for Evaluation run of formulae/Dorflan
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/formulae/Dorflan
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [formulae/Dorflan](https://huggingface.co/formulae/Dorflan) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_formulae__Dorflan",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T08:11:23.880796](https://huggingface.co/datasets/open-llm-leaderboard/details_formulae__Dorflan/blob/main/results_2023-10-24T08-11-23.880796.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.16851929530201343,
"em_stderr": 0.0038334566477606843,
"f1": 0.2636723993288601,
"f1_stderr": 0.003974880412044246,
"acc": 0.36495772729693454,
"acc_stderr": 0.007112996736385248
},
"harness|drop|3": {
"em": 0.16851929530201343,
"em_stderr": 0.0038334566477606843,
"f1": 0.2636723993288601,
"f1_stderr": 0.003974880412044246
},
"harness|gsm8k|5": {
"acc": 0.0037907505686125853,
"acc_stderr": 0.0016927007401502038
},
"harness|winogrande|5": {
"acc": 0.7261247040252565,
"acc_stderr": 0.012533292732620292
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_formulae__Dorflan
|
[
"region:us"
] |
2023-10-04T07:08:15+00:00
|
{"pretty_name": "Evaluation run of formulae/Dorflan", "dataset_summary": "Dataset automatically created during the evaluation run of model [formulae/Dorflan](https://huggingface.co/formulae/Dorflan) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_formulae__Dorflan\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T08:11:23.880796](https://huggingface.co/datasets/open-llm-leaderboard/details_formulae__Dorflan/blob/main/results_2023-10-24T08-11-23.880796.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.16851929530201343,\n \"em_stderr\": 0.0038334566477606843,\n \"f1\": 0.2636723993288601,\n \"f1_stderr\": 0.003974880412044246,\n \"acc\": 0.36495772729693454,\n \"acc_stderr\": 0.007112996736385248\n },\n \"harness|drop|3\": {\n \"em\": 0.16851929530201343,\n \"em_stderr\": 0.0038334566477606843,\n \"f1\": 0.2636723993288601,\n \"f1_stderr\": 0.003974880412044246\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0037907505686125853,\n \"acc_stderr\": 0.0016927007401502038\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620292\n }\n}\n```", "repo_url": "https://huggingface.co/formulae/Dorflan", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T08_07_52.776244", 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|
2023-10-24T07:11:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of formulae/Dorflan
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model formulae/Dorflan on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T08:11:23.880796(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of formulae/Dorflan",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model formulae/Dorflan on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T08:11:23.880796(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of formulae/Dorflan",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model formulae/Dorflan on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T08:11:23.880796(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of formulae/Dorflan## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model formulae/Dorflan on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T08:11:23.880796(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
e92e37e68f7f0f6df4e0622f69f99a79f90fd264
|
# Dataset Card for "daily_dialogue_text_to_gloss"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/daily_dialogue_text_to_gloss
|
[
"region:us"
] |
2023-10-04T07:16:46+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "gloss", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7544982, "num_examples": 77350}], "download_size": 4908386, "dataset_size": 7544982}}
|
2023-10-04T07:16:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "daily_dialogue_text_to_gloss"
More Information needed
|
[
"# Dataset Card for \"daily_dialogue_text_to_gloss\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"daily_dialogue_text_to_gloss\"\n\nMore Information needed"
] |
[
6,
23
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"daily_dialogue_text_to_gloss\"\n\nMore Information needed"
] |
c87132c360f5f2e6f5170a23c0926ed83a58886d
|
# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Synthia-WVG-Test
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/LTC-AI-Labs/L2-7b-Synthia-WVG-Test
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [LTC-AI-Labs/L2-7b-Synthia-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Synthia-WVG-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T12:56:24.516186](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test/blob/main/results_2023-10-27T12-56-24.516186.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.003460570469798658,
"em_stderr": 0.0006013962884271092,
"f1": 0.07142722315436245,
"f1_stderr": 0.0015627419606312088,
"acc": 0.40012823328284985,
"acc_stderr": 0.009403891235711103
},
"harness|drop|3": {
"em": 0.003460570469798658,
"em_stderr": 0.0006013962884271092,
"f1": 0.07142722315436245,
"f1_stderr": 0.0015627419606312088
},
"harness|gsm8k|5": {
"acc": 0.05913570887035633,
"acc_stderr": 0.00649726666042883
},
"harness|winogrande|5": {
"acc": 0.7411207576953434,
"acc_stderr": 0.012310515810993376
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test
|
[
"region:us"
] |
2023-10-04T07:34:11+00:00
|
{"pretty_name": "Evaluation run of LTC-AI-Labs/L2-7b-Synthia-WVG-Test", "dataset_summary": "Dataset automatically created during the evaluation run of model [LTC-AI-Labs/L2-7b-Synthia-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Synthia-WVG-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-27T12:56:24.516186](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test/blob/main/results_2023-10-27T12-56-24.516186.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003460570469798658,\n \"em_stderr\": 0.0006013962884271092,\n \"f1\": 0.07142722315436245,\n \"f1_stderr\": 0.0015627419606312088,\n \"acc\": 0.40012823328284985,\n \"acc_stderr\": 0.009403891235711103\n },\n \"harness|drop|3\": {\n \"em\": 0.003460570469798658,\n \"em_stderr\": 0.0006013962884271092,\n \"f1\": 0.07142722315436245,\n \"f1_stderr\": 0.0015627419606312088\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05913570887035633,\n \"acc_stderr\": 0.00649726666042883\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7411207576953434,\n \"acc_stderr\": 0.012310515810993376\n }\n}\n```", "repo_url": "https://huggingface.co/LTC-AI-Labs/L2-7b-Synthia-WVG-Test", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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|
2023-10-27T11:56:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Synthia-WVG-Test
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Synthia-WVG-Test on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-27T12:56:24.516186(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Synthia-WVG-Test",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Synthia-WVG-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T12:56:24.516186(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Synthia-WVG-Test",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Synthia-WVG-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T12:56:24.516186(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Synthia-WVG-Test## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Synthia-WVG-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-27T12:56:24.516186(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
51166c0557979129b85aec70f551ff51a4f64208
|
# Bangumi Image Base of Re:zero
This is the image base of bangumi Re:Zero, we detected 92 characters, 9641 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 3392 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 111 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 52 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 54 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 22 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 34 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 63 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 38 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 59 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 25 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 33 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 41 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 135 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 8 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 20 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 78 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 96 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 36 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 12 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 27 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 72 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 18 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 19 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 8 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 20 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 38 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 44 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 10 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 116 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 41 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 25 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 17 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 151 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 24 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 75 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 26 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 8 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 40 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 71 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 279 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 715 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 41 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 58 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
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| 46 | 20 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 20 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 596 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 14 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 31 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 24 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 379 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 10 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 7 | [Download](54/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 55 | 7 | [Download](55/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 56 | 54 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 20 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 215 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 13 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 12 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 37 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 47 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 18 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 327 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 44 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 155 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 17 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 13 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 39 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 92 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 53 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 28 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 85 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 75 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 28 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 11 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 14 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 7 | [Download](78/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 79 | 9 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 17 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 11 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 14 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 6 | [Download](83/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 84 | 20 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 20 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 16 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 26 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 190 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 5 | [Download](89/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 90 | 8 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 375 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/rezero
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T07:36:51+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T12:30:40+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Re:zero
=============================
This is the image base of bangumi Re:Zero, we detected 92 characters, 9641 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
ea48ff4b7e51297bd35475e5827c6eff10fe1ebf
|
# Dataset Card for Evaluation run of Undi95/ReMM-Mistral-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Undi95/ReMM-Mistral-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Undi95/ReMM-Mistral-13B](https://huggingface.co/Undi95/ReMM-Mistral-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Undi95__ReMM-Mistral-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T13:48:21.267659](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__ReMM-Mistral-13B/blob/main/results_2023-10-27T13-48-21.267659.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.20679530201342283,
"em_stderr": 0.004147654995169029,
"f1": 0.2796350671140937,
"f1_stderr": 0.004133652397455312,
"acc": 0.4328064778452021,
"acc_stderr": 0.01060870762734275
},
"harness|drop|3": {
"em": 0.20679530201342283,
"em_stderr": 0.004147654995169029,
"f1": 0.2796350671140937,
"f1_stderr": 0.004133652397455312
},
"harness|gsm8k|5": {
"acc": 0.12054586808188021,
"acc_stderr": 0.008968608285309076
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Undi95__ReMM-Mistral-13B
|
[
"region:us"
] |
2023-10-04T07:44:17+00:00
|
{"pretty_name": "Evaluation run of Undi95/ReMM-Mistral-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Undi95/ReMM-Mistral-13B](https://huggingface.co/Undi95/ReMM-Mistral-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Undi95__ReMM-Mistral-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-27T13:48:21.267659](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__ReMM-Mistral-13B/blob/main/results_2023-10-27T13-48-21.267659.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.20679530201342283,\n \"em_stderr\": 0.004147654995169029,\n \"f1\": 0.2796350671140937,\n \"f1_stderr\": 0.004133652397455312,\n \"acc\": 0.4328064778452021,\n \"acc_stderr\": 0.01060870762734275\n },\n \"harness|drop|3\": {\n \"em\": 0.20679530201342283,\n \"em_stderr\": 0.004147654995169029,\n \"f1\": 0.2796350671140937,\n \"f1_stderr\": 0.004133652397455312\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12054586808188021,\n \"acc_stderr\": 0.008968608285309076\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n }\n}\n```", "repo_url": "https://huggingface.co/Undi95/ReMM-Mistral-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T08_43_52.595565", "path": ["**/details_harness|arc:challenge|25_2023-10-04T08-43-52.595565.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T08-43-52.595565.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_27T13_48_21.267659", "path": ["**/details_harness|drop|3_2023-10-27T13-48-21.267659.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-27T13-48-21.267659.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_27T13_48_21.267659", "path": ["**/details_harness|gsm8k|5_2023-10-27T13-48-21.267659.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-27T13-48-21.267659.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T08_43_52.595565", "path": ["**/details_harness|hellaswag|10_2023-10-04T08-43-52.595565.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T08-43-52.595565.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_10_04T08_43_52.595565", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T08-43-52.595565.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-10-04T08-43-52.595565.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-10-04T08-43-52.595565.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T08-43-52.595565.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T08-43-52.595565.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-10-04T08-43-52.595565.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T08-43-52.595565.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T08-43-52.595565.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T08-43-52.595565.parquet", 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2023-10-27T12:48:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Undi95/ReMM-Mistral-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Undi95/ReMM-Mistral-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-27T13:48:21.267659(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
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"# Dataset Card for Evaluation run of Undi95/ReMM-Mistral-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/ReMM-Mistral-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T13:48:21.267659(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of Undi95/ReMM-Mistral-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/ReMM-Mistral-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T13:48:21.267659(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
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"### Data Instances",
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"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Undi95/ReMM-Mistral-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/ReMM-Mistral-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-27T13:48:21.267659(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
03dd4e0baa57def8a65a56eef17c28751cffc9ee
|
# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Beluga-WVG-Test
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/LTC-AI-Labs/L2-7b-Beluga-WVG-Test
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [LTC-AI-Labs/L2-7b-Beluga-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Beluga-WVG-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Beluga-WVG-Test",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T00:56:25.052107](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Beluga-WVG-Test/blob/main/results_2023-10-29T00-56-25.052107.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094545,
"f1": 0.0751552013422821,
"f1_stderr": 0.0016341810186493492,
"acc": 0.41393051467442327,
"acc_stderr": 0.009804583370194696
},
"harness|drop|3": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094545,
"f1": 0.0751552013422821,
"f1_stderr": 0.0016341810186493492
},
"harness|gsm8k|5": {
"acc": 0.07884761182714177,
"acc_stderr": 0.00742339051987324
},
"harness|winogrande|5": {
"acc": 0.7490134175217048,
"acc_stderr": 0.012185776220516153
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Beluga-WVG-Test
|
[
"region:us"
] |
2023-10-04T07:52:49+00:00
|
{"pretty_name": "Evaluation run of LTC-AI-Labs/L2-7b-Beluga-WVG-Test", "dataset_summary": "Dataset automatically created during the evaluation run of model [LTC-AI-Labs/L2-7b-Beluga-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Beluga-WVG-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Beluga-WVG-Test\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T00:56:25.052107](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Beluga-WVG-Test/blob/main/results_2023-10-29T00-56-25.052107.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094545,\n \"f1\": 0.0751552013422821,\n \"f1_stderr\": 0.0016341810186493492,\n \"acc\": 0.41393051467442327,\n \"acc_stderr\": 0.009804583370194696\n },\n \"harness|drop|3\": {\n \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094545,\n \"f1\": 0.0751552013422821,\n \"f1_stderr\": 0.0016341810186493492\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07884761182714177,\n \"acc_stderr\": 0.00742339051987324\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7490134175217048,\n \"acc_stderr\": 0.012185776220516153\n }\n}\n```", "repo_url": "https://huggingface.co/LTC-AI-Labs/L2-7b-Beluga-WVG-Test", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T08_52_25.814985", "path": ["**/details_harness|arc:challenge|25_2023-10-04T08-52-25.814985.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T08-52-25.814985.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_29T00_56_25.052107", "path": ["**/details_harness|drop|3_2023-10-29T00-56-25.052107.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-29T00-56-25.052107.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_29T00_56_25.052107", "path": ["**/details_harness|gsm8k|5_2023-10-29T00-56-25.052107.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-29T00-56-25.052107.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T08_52_25.814985", "path": ["**/details_harness|hellaswag|10_2023-10-04T08-52-25.814985.parquet"]}, {"split": "latest", "path": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-52-25.814985.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-52-25.814985.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_10_04T08_52_25.814985", "path": ["**/details_harness|hendrycksTest-virology|5_2023-10-04T08-52-25.814985.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-10-04T08-52-25.814985.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_10_04T08_52_25.814985", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-52-25.814985.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-52-25.814985.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_10_04T08_52_25.814985", "path": ["**/details_harness|truthfulqa:mc|0_2023-10-04T08-52-25.814985.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-10-04T08-52-25.814985.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_29T00_56_25.052107", "path": ["**/details_harness|winogrande|5_2023-10-29T00-56-25.052107.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-29T00-56-25.052107.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_04T08_52_25.814985", "path": ["results_2023-10-04T08-52-25.814985.parquet"]}, {"split": "2023_10_29T00_56_25.052107", "path": ["results_2023-10-29T00-56-25.052107.parquet"]}, {"split": "latest", "path": ["results_2023-10-29T00-56-25.052107.parquet"]}]}]}
|
2023-10-28T23:56:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Beluga-WVG-Test
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Beluga-WVG-Test on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-29T00:56:25.052107(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Beluga-WVG-Test",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Beluga-WVG-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-29T00:56:25.052107(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Beluga-WVG-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-29T00:56:25.052107(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Beluga-WVG-Test## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Beluga-WVG-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-29T00:56:25.052107(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
1eb220031a0ea9a6c43e19613e8da45621ddaba2
|
# The Object Detection for Olfactory References (ODOR) Dataset
<!-- Provide a quick summary of the dataset. -->
Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes.
Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes.
The ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories.
It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas.
Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.
You can download the dataset using Hugging Face:
```python
from datasets import load_dataset
ds = load_dataset("mathiaszinnen/odor")
```
This dataset has received funding from the Odeuropa EU H2020 project under grant agreement No. 101004469.
<!--
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
|
mathiaszinnen/odor
|
[
"task_categories:object-detection",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"fine grained detection",
"small object detection",
"art",
"smell",
"olfaction",
"computational humanities",
"region:us"
] |
2023-10-04T07:52:53+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["object-detection"], "pretty_name": "Object Detection for Olfactory References (ODOR) Dataset", "tags": ["fine grained detection", "small object detection", "art", "smell", "olfaction", "computational humanities"]}
|
2023-11-20T09:32:44+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-object-detection #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #fine grained detection #small object detection #art #smell #olfaction #computational humanities #region-us
|
# The Object Detection for Olfactory References (ODOR) Dataset
Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes.
Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes.
The ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories.
It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas.
Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.
You can download the dataset using Hugging Face:
This dataset has received funding from the Odeuropa EU H2020 project under grant agreement No. 101004469.
|
[
"# The Object Detection for Olfactory References (ODOR) Dataset\n\n\n\nReal-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. \n\nExisting datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. \n\nThe ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. \n\nIt has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. \n\nInspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.\n\n\nYou can download the dataset using Hugging Face:\n\n\n\nThis dataset has received funding from the Odeuropa EU H2020 project under grant agreement No. 101004469."
] |
[
"TAGS\n#task_categories-object-detection #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #fine grained detection #small object detection #art #smell #olfaction #computational humanities #region-us \n",
"# The Object Detection for Olfactory References (ODOR) Dataset\n\n\n\nReal-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. \n\nExisting datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. \n\nThe ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. \n\nIt has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. \n\nInspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.\n\n\nYou can download the dataset using Hugging Face:\n\n\n\nThis dataset has received funding from the Odeuropa EU H2020 project under grant agreement No. 101004469."
] |
[
69,
246
] |
[
"passage: TAGS\n#task_categories-object-detection #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #fine grained detection #small object detection #art #smell #olfaction #computational humanities #region-us \n# The Object Detection for Olfactory References (ODOR) Dataset\n\n\n\nReal-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. \n\nExisting datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. \n\nThe ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. \n\nIt has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. \n\nInspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.\n\n\nYou can download the dataset using Hugging Face:\n\n\n\nThis dataset has received funding from the Odeuropa EU H2020 project under grant agreement No. 101004469."
] |
44d3dd657ef9267e1ef3b50d1c26497bc777fc2d
|
# Dataset Card for "9a272529"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/9a272529
|
[
"region:us"
] |
2023-10-04T07:54:52+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 246, "num_examples": 10}], "download_size": 1437, "dataset_size": 246}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T07:54:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "9a272529"
More Information needed
|
[
"# Dataset Card for \"9a272529\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"9a272529\"\n\nMore Information needed"
] |
[
6,
15
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"9a272529\"\n\nMore Information needed"
] |
17964759647aacaf60d45db48c2f0c6a2db751b5
|
# Dataset Card for Evaluation run of Undi95/MLewd-v2.4-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Undi95/MLewd-v2.4-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Undi95/MLewd-v2.4-13B](https://huggingface.co/Undi95/MLewd-v2.4-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Undi95__MLewd-v2.4-13B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-06T15:01:09.022171](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__MLewd-v2.4-13B_public/blob/main/results_2023-11-06T15-01-09.022171.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.37153942953020136,
"em_stderr": 0.004948586020359345,
"f1": 0.4432686661073842,
"f1_stderr": 0.0047496461477472855,
"acc": 0.4214342261393644,
"acc_stderr": 0.010215463395612735
},
"harness|drop|3": {
"em": 0.37153942953020136,
"em_stderr": 0.004948586020359345,
"f1": 0.4432686661073842,
"f1_stderr": 0.0047496461477472855
},
"harness|gsm8k|5": {
"acc": 0.0978013646702047,
"acc_stderr": 0.008182119821849047
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Undi95__MLewd-v2.4-13B
|
[
"region:us"
] |
2023-10-04T07:57:25+00:00
|
{"pretty_name": "Evaluation run of Undi95/MLewd-v2.4-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Undi95/MLewd-v2.4-13B](https://huggingface.co/Undi95/MLewd-v2.4-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Undi95__MLewd-v2.4-13B_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-06T15:01:09.022171](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__MLewd-v2.4-13B_public/blob/main/results_2023-11-06T15-01-09.022171.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.37153942953020136,\n \"em_stderr\": 0.004948586020359345,\n \"f1\": 0.4432686661073842,\n \"f1_stderr\": 0.0047496461477472855,\n \"acc\": 0.4214342261393644,\n \"acc_stderr\": 0.010215463395612735\n },\n \"harness|drop|3\": {\n \"em\": 0.37153942953020136,\n \"em_stderr\": 0.004948586020359345,\n \"f1\": 0.4432686661073842,\n \"f1_stderr\": 0.0047496461477472855\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0978013646702047,\n \"acc_stderr\": 0.008182119821849047\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n }\n}\n```", "repo_url": "https://huggingface.co/Undi95/MLewd-v2.4-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_05T06_43_46.123528", "path": ["**/details_harness|drop|3_2023-11-05T06-43-46.123528.parquet"]}, {"split": "2023_11_06T15_01_09.022171", "path": ["**/details_harness|drop|3_2023-11-06T15-01-09.022171.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-06T15-01-09.022171.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_05T06_43_46.123528", "path": ["**/details_harness|gsm8k|5_2023-11-05T06-43-46.123528.parquet"]}, {"split": "2023_11_06T15_01_09.022171", "path": ["**/details_harness|gsm8k|5_2023-11-06T15-01-09.022171.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-06T15-01-09.022171.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_05T06_43_46.123528", "path": ["**/details_harness|winogrande|5_2023-11-05T06-43-46.123528.parquet"]}, {"split": "2023_11_06T15_01_09.022171", "path": ["**/details_harness|winogrande|5_2023-11-06T15-01-09.022171.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-06T15-01-09.022171.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_05T06_43_46.123528", "path": ["results_2023-11-05T06-43-46.123528.parquet"]}, {"split": "2023_11_06T15_01_09.022171", "path": ["results_2023-11-06T15-01-09.022171.parquet"]}, {"split": "latest", "path": ["results_2023-11-06T15-01-09.022171.parquet"]}]}]}
|
2023-12-01T14:23:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Undi95/MLewd-v2.4-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Undi95/MLewd-v2.4-13B on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-06T15:01:09.022171(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Undi95/MLewd-v2.4-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/MLewd-v2.4-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-06T15:01:09.022171(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of Undi95/MLewd-v2.4-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/MLewd-v2.4-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-06T15:01:09.022171(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Undi95/MLewd-v2.4-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/MLewd-v2.4-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-06T15:01:09.022171(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
9b9916114e7abe7a1214b2a9d9377419b68c1225
|
# Dataset Card for Evaluation run of Undi95/MLewd-Chat-v2-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Undi95/MLewd-Chat-v2-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Undi95/MLewd-Chat-v2-13B](https://huggingface.co/Undi95/MLewd-Chat-v2-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Undi95__MLewd-Chat-v2-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T04:58:47.743949](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__MLewd-Chat-v2-13B/blob/main/results_2023-10-25T04-58-47.743949.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.16935822147651006,
"em_stderr": 0.003841047509071323,
"f1": 0.25626572986577256,
"f1_stderr": 0.003896453812497321,
"acc": 0.4311600295122049,
"acc_stderr": 0.010236510304102034
},
"harness|drop|3": {
"em": 0.16935822147651006,
"em_stderr": 0.003841047509071323,
"f1": 0.25626572986577256,
"f1_stderr": 0.003896453812497321
},
"harness|gsm8k|5": {
"acc": 0.10462471569370735,
"acc_stderr": 0.00843066808202928
},
"harness|winogrande|5": {
"acc": 0.7576953433307024,
"acc_stderr": 0.012042352526174789
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Undi95__MLewd-Chat-v2-13B
|
[
"region:us"
] |
2023-10-04T07:57:28+00:00
|
{"pretty_name": "Evaluation run of Undi95/MLewd-Chat-v2-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Undi95/MLewd-Chat-v2-13B](https://huggingface.co/Undi95/MLewd-Chat-v2-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Undi95__MLewd-Chat-v2-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T04:58:47.743949](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__MLewd-Chat-v2-13B/blob/main/results_2023-10-25T04-58-47.743949.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.16935822147651006,\n \"em_stderr\": 0.003841047509071323,\n \"f1\": 0.25626572986577256,\n \"f1_stderr\": 0.003896453812497321,\n \"acc\": 0.4311600295122049,\n \"acc_stderr\": 0.010236510304102034\n },\n \"harness|drop|3\": {\n \"em\": 0.16935822147651006,\n \"em_stderr\": 0.003841047509071323,\n \"f1\": 0.25626572986577256,\n \"f1_stderr\": 0.003896453812497321\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10462471569370735,\n \"acc_stderr\": 0.00843066808202928\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7576953433307024,\n \"acc_stderr\": 0.012042352526174789\n }\n}\n```", "repo_url": "https://huggingface.co/Undi95/MLewd-Chat-v2-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T08_57_05.085680", "path": ["**/details_harness|arc:challenge|25_2023-10-04T08-57-05.085680.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T08-57-05.085680.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T04_58_47.743949", "path": ["**/details_harness|drop|3_2023-10-25T04-58-47.743949.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T04-58-47.743949.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T04_58_47.743949", "path": ["**/details_harness|gsm8k|5_2023-10-25T04-58-47.743949.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T04-58-47.743949.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T08_57_05.085680", "path": ["**/details_harness|hellaswag|10_2023-10-04T08-57-05.085680.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T08-57-05.085680.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_10_04T08_57_05.085680", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T08-57-05.085680.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-10-04T08-57-05.085680.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-10-04T08-57-05.085680.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T08-57-05.085680.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T08-57-05.085680.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-10-04T08-57-05.085680.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T08-57-05.085680.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T08-57-05.085680.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T08-57-05.085680.parquet", 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|
2023-10-25T03:59:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Undi95/MLewd-Chat-v2-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Undi95/MLewd-Chat-v2-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T04:58:47.743949(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Undi95/MLewd-Chat-v2-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/MLewd-Chat-v2-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T04:58:47.743949(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Undi95/MLewd-Chat-v2-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/MLewd-Chat-v2-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T04:58:47.743949(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Undi95/MLewd-Chat-v2-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/MLewd-Chat-v2-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T04:58:47.743949(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
502db9a0f13a560edeb4abbb876d1caa40f0778f
|
# Dataset Card for "qa_mediche_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FinancialSupport/qa_mediche_test
|
[
"region:us"
] |
2023-10-04T08:02:30+00:00
|
{"dataset_info": {"features": [{"name": "file_name", "dtype": "string"}, {"name": "dialogue_id", "dtype": "int32"}, {"name": "dialogue_url", "dtype": "string"}, {"name": "dialogue_turns", "struct": [{"name": "speaker", "sequence": "int64"}, {"name": "utterance", "sequence": "string"}, {"name": "utterance_ita", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 57636035.40135802, "num_examples": 28409}], "download_size": 35524606, "dataset_size": 57636035.40135802}}
|
2023-10-13T08:12:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "qa_mediche_test"
More Information needed
|
[
"# Dataset Card for \"qa_mediche_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"qa_mediche_test\"\n\nMore Information needed"
] |
[
6,
16
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"qa_mediche_test\"\n\nMore Information needed"
] |
aa50f1bf386f0a4a0529bf7ab5f17910ce34035b
|
# Dataset Card for Evaluation run of Undi95/Amethyst-13B-Mistral
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Undi95/Amethyst-13B-Mistral
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Undi95/Amethyst-13B-Mistral](https://huggingface.co/Undi95/Amethyst-13B-Mistral) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Undi95__Amethyst-13B-Mistral",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T13:44:51.984627](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Amethyst-13B-Mistral/blob/main/results_2023-10-27T13-44-51.984627.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.11891778523489933,
"em_stderr": 0.003314906435546502,
"f1": 0.18699769295301977,
"f1_stderr": 0.0034428005809407332,
"acc": 0.42792517590937623,
"acc_stderr": 0.010387500478010799
},
"harness|drop|3": {
"em": 0.11891778523489933,
"em_stderr": 0.003314906435546502,
"f1": 0.18699769295301977,
"f1_stderr": 0.0034428005809407332
},
"harness|gsm8k|5": {
"acc": 0.10841546626231995,
"acc_stderr": 0.008563852506627492
},
"harness|winogrande|5": {
"acc": 0.7474348855564326,
"acc_stderr": 0.012211148449394105
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Undi95__Amethyst-13B-Mistral
|
[
"region:us"
] |
2023-10-04T08:04:22+00:00
|
{"pretty_name": "Evaluation run of Undi95/Amethyst-13B-Mistral", "dataset_summary": "Dataset automatically created during the evaluation run of model [Undi95/Amethyst-13B-Mistral](https://huggingface.co/Undi95/Amethyst-13B-Mistral) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Undi95__Amethyst-13B-Mistral\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-27T13:44:51.984627](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Amethyst-13B-Mistral/blob/main/results_2023-10-27T13-44-51.984627.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.11891778523489933,\n \"em_stderr\": 0.003314906435546502,\n \"f1\": 0.18699769295301977,\n \"f1_stderr\": 0.0034428005809407332,\n \"acc\": 0.42792517590937623,\n \"acc_stderr\": 0.010387500478010799\n },\n \"harness|drop|3\": {\n \"em\": 0.11891778523489933,\n \"em_stderr\": 0.003314906435546502,\n \"f1\": 0.18699769295301977,\n \"f1_stderr\": 0.0034428005809407332\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10841546626231995,\n \"acc_stderr\": 0.008563852506627492\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7474348855564326,\n \"acc_stderr\": 0.012211148449394105\n }\n}\n```", "repo_url": "https://huggingface.co/Undi95/Amethyst-13B-Mistral", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T09_03_58.552887", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-03-58.552887.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-03-58.552887.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_27T13_44_51.984627", "path": ["**/details_harness|drop|3_2023-10-27T13-44-51.984627.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-27T13-44-51.984627.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_27T13_44_51.984627", "path": ["**/details_harness|gsm8k|5_2023-10-27T13-44-51.984627.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-27T13-44-51.984627.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T09_03_58.552887", "path": ["**/details_harness|hellaswag|10_2023-10-04T09-03-58.552887.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-10-04T09-03-58.552887.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-10-04T09-03-58.552887.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_27T13_44_51.984627", "path": ["**/details_harness|winogrande|5_2023-10-27T13-44-51.984627.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-27T13-44-51.984627.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_04T09_03_58.552887", "path": ["results_2023-10-04T09-03-58.552887.parquet"]}, {"split": "2023_10_27T13_44_51.984627", "path": ["results_2023-10-27T13-44-51.984627.parquet"]}, {"split": "latest", "path": ["results_2023-10-27T13-44-51.984627.parquet"]}]}]}
|
2023-10-27T12:45:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Undi95/Amethyst-13B-Mistral
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Undi95/Amethyst-13B-Mistral on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-27T13:44:51.984627(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Undi95/Amethyst-13B-Mistral",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/Amethyst-13B-Mistral on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T13:44:51.984627(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of Undi95/Amethyst-13B-Mistral",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/Amethyst-13B-Mistral on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T13:44:51.984627(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Undi95/Amethyst-13B-Mistral## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/Amethyst-13B-Mistral on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-27T13:44:51.984627(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
5322b11ea47e1973555f32273f1386dbf69e59c0
|
# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Hermes-WVG-Test
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/LTC-AI-Labs/L2-7b-Hermes-WVG-Test
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [LTC-AI-Labs/L2-7b-Hermes-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Hermes-WVG-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Hermes-WVG-Test",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T11:36:43.083367](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Hermes-WVG-Test/blob/main/results_2023-10-23T11-36-43.083367.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.004614093959731544,
"em_stderr": 0.0006940305886353454,
"f1": 0.0669966442953021,
"f1_stderr": 0.0015535849060621194,
"acc": 0.4029062221565332,
"acc_stderr": 0.009334616003613282
},
"harness|drop|3": {
"em": 0.004614093959731544,
"em_stderr": 0.0006940305886353454,
"f1": 0.0669966442953021,
"f1_stderr": 0.0015535849060621194
},
"harness|gsm8k|5": {
"acc": 0.058377558756633814,
"acc_stderr": 0.00645808355783246
},
"harness|winogrande|5": {
"acc": 0.7474348855564326,
"acc_stderr": 0.012211148449394105
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Hermes-WVG-Test
|
[
"region:us"
] |
2023-10-04T08:05:33+00:00
|
{"pretty_name": "Evaluation run of LTC-AI-Labs/L2-7b-Hermes-WVG-Test", "dataset_summary": "Dataset automatically created during the evaluation run of model [LTC-AI-Labs/L2-7b-Hermes-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Hermes-WVG-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Hermes-WVG-Test\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T11:36:43.083367](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Hermes-WVG-Test/blob/main/results_2023-10-23T11-36-43.083367.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.004614093959731544,\n \"em_stderr\": 0.0006940305886353454,\n \"f1\": 0.0669966442953021,\n \"f1_stderr\": 0.0015535849060621194,\n \"acc\": 0.4029062221565332,\n \"acc_stderr\": 0.009334616003613282\n },\n \"harness|drop|3\": {\n \"em\": 0.004614093959731544,\n \"em_stderr\": 0.0006940305886353454,\n \"f1\": 0.0669966442953021,\n \"f1_stderr\": 0.0015535849060621194\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.058377558756633814,\n \"acc_stderr\": 0.00645808355783246\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7474348855564326,\n \"acc_stderr\": 0.012211148449394105\n }\n}\n```", "repo_url": "https://huggingface.co/LTC-AI-Labs/L2-7b-Hermes-WVG-Test", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T09_05_10.171975", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-05-10.171975.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-05-10.171975.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T11_36_43.083367", "path": ["**/details_harness|drop|3_2023-10-23T11-36-43.083367.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T11-36-43.083367.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T11_36_43.083367", "path": ["**/details_harness|gsm8k|5_2023-10-23T11-36-43.083367.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T11-36-43.083367.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T09_05_10.171975", "path": ["**/details_harness|hellaswag|10_2023-10-04T09-05-10.171975.parquet"]}, {"split": "latest", "path": 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|
2023-10-23T10:36:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Hermes-WVG-Test
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Hermes-WVG-Test on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T11:36:43.083367(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Hermes-WVG-Test",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Hermes-WVG-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T11:36:43.083367(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Hermes-WVG-Test",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Hermes-WVG-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T11:36:43.083367(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Hermes-WVG-Test## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model LTC-AI-Labs/L2-7b-Hermes-WVG-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T11:36:43.083367(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
340152059ab8648b409e6b3202b537dc3da186c1
|
一些高校校园新闻，约 65k * 3(类任务) 条，**稍微做了一点点脱敏，尽可能地遮盖了作者名等**。数据已经整理成了指令的形式，格式如下：
```json
{
"id": <id>,
"category": "(title_summarize|news_classify|news_generate)",
"instruction": <对应的具体指令>,
"input": <空>,
"output": <指令对应的输出>
}
```
总共三类任务：**标题总结**、**栏目分类**、**新闻生成**，本质上是利用新闻元数据中的标题、栏目、内容排列组合生成的，所以可以保证数据完全准确。每个字段内容已经整理成了**单行**的格式。下面是三类任务的样例：
```json
// 标题总结
{
"id": 22106,
"category": "title_summarize",
"instruction": "请你给下面的新闻取一则标题：\n点击图片观看视频 科技创新潜质人才是海院自主选拔的一种新尝试，根据规定，凡在高中阶段获得省级科技创新大赛一等奖或以上的应届高中毕业生，都可申请报名。今年苏浙沪三地共有300多名学生，申请以科技创新潜质人才身份入学海城海院。经过专家甄选，今天上午，有82名学生参加了考试。 上午的面试不考语数外，考生要靠自己的科技成果来证明自己是具有科技创新潜质的人才。记者发现，学生们带来的创新成果五花八门，涉及生物、微电子等多种前沿学科。经过上午的筛选，下午只剩下一半的学生进入了特色测试部分,这部分仍不考语数外，重点考察学生的发散性思维，及兴趣爱好和潜质，海院将根据测试结果，最终确定获得科技创新潜质人才的自主选拔认定名单，进入名单的考生，高考成绩只要达到当地的一本线，就能被海院录取。",
"input": "",
"output": "该新闻的标题可以是：《不考语数外 海院自主选拔创新潜质人才》"
}
```
```json
// 栏目分类
{
"id": 134781,
"category": "news_classify",
"instruction": "根据下面的新闻，归纳出它的分类：\n## 英国格拉斯哥大学医学院副院长访问海城海院生命学院\n\n11月20日，英国格拉斯哥大学（University of Glasgow，以下简称格大）医学院副院长Chris Loughrey一行访问海城海院生命科学技术学院，生命学院副院长张晓君出席会议，魏芳、王晓雷、徐沁等多位教师与会参加交流。格大和海城海院同为国际大学交流组织Universitas 21的成员，在生命和医学领域合作紧密。双方互相介绍了两校在生命科学领域的学科专业、课程体系、学生培养等发展情况。在相互交流中，双方希望在这个宏观的合作框架下，逐步增进相互了解，在人才培养、科学研究等方面形成更多更实质性的合作。格拉斯哥大学是英联邦地区建校历史最悠久的四所大学之一。研究经费及教育质量均排名英国前十，出过7位诺奖获得者及3位英国首相。格大下设四大学院，其中医学院有教职员工2000余人，学生5000余人，医学院下设七个研究中心，涉及分子/细胞/系统生物学、植物、微生物、免疫、癌症、生物多样性、神经科学、心血管医学等领域。",
"input": "",
"output": "本则新闻的分类栏目是：综合新闻"
}
```
```json
// 新闻生成
{
"id": 61402,
"category": "news_generate",
"instruction": "帮我写一则综合新闻栏目的新闻，标题是机械与动力工程学院举办心理健康月系列活动之“与压力一路同行”",
"input": "",
"output": "《机械与动力工程学院举办心理健康月系列活动之“与压力一路同行”》：\n\n5月27日，机械与动力工程学院心理健康月系列活动之“与压力一路同行”讲座在高田会堂举行。活动特邀海城铁路学院兼职心理咨询师李毅作为主讲嘉宾，与大家分享如何正确认识压力以及积极应对压力 。本次活动由机械与动力工程学院王睿瑞主持。相较于受到心理教育较为全面的本科生同学，研究生同学面临更多的现实问题，更加需要心理健康的知识，来实现压力与情绪的合理管理。李毅首先结合心理学知识，从生理学、心理学等多个角度带领同学们认识了压力的本质，并指出并非所有压力都是有害的，负性的慢性压力对人产生危害，合理规避有害的压力尤为重要，关键是找到自己的最佳压力水平。李毅进一步讲述了心理压力与认知、情绪、行为之间的关系。通过对“情绪的身体地图”的说明，使同学们更加深刻地体会到了“情绪丰富了体验，情绪述说着需求，情绪激发出行为，情绪联结了他人”的含义。最后，李毅从时间管理、简化生活、认知需求、活在当下、友善待人等方面给出了应对压力的建议，并寄语大家“与并不完美的自我共处，与压力一路同行”。机械与动力工程学院心理健康月系列活动已举办4年，覆盖2700余名研究生，力求以多样的活动形式帮助学生和导师了解更多的心理知识，引导大家关爱自己、关心他人。"
}
```
这里也给出数据的分位点（`instruction` + `output` 的长度），可按需修剪：
```
count 196101.000000
mean 1059.199912
std 571.623822
min 32.000000
0% 32.000000
5% 369.000000
10% 471.000000
15% 539.000000
20% 596.000000
25% 649.000000
30% 700.000000
35% 753.000000
40% 806.000000
45% 862.000000
50% 920.000000
55% 983.000000
60% 1053.000000
65% 1132.000000
70% 1225.000000
75% 1337.000000
80% 1473.000000
85% 1652.000000
90% 1898.000000
95% 2270.000000
max 3054.000000
```
|
Mxode/University-News-Instruction-Zh
|
[
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"news",
"campus",
"region:us"
] |
2023-10-04T08:09:32+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["zero-shot-classification", "summarization", "text-generation"], "tags": ["news", "campus"]}
|
2023-10-04T08:43:25+00:00
|
[] |
[
"zh"
] |
TAGS
#task_categories-zero-shot-classification #task_categories-summarization #task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-apache-2.0 #news #campus #region-us
|
一些高校校园新闻，约 65k * 3(类任务) 条，稍微做了一点点脱敏，尽可能地遮盖了作者名等。数据已经整理成了指令的形式，格式如下：
总共三类任务：标题总结、栏目分类、新闻生成，本质上是利用新闻元数据中的标题、栏目、内容排列组合生成的，所以可以保证数据完全准确。每个字段内容已经整理成了单行的格式。下面是三类任务的样例：
这里也给出数据的分位点（'instruction' + 'output' 的长度），可按需修剪：
|
[] |
[
"TAGS\n#task_categories-zero-shot-classification #task_categories-summarization #task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-apache-2.0 #news #campus #region-us \n"
] |
[
70
] |
[
"passage: TAGS\n#task_categories-zero-shot-classification #task_categories-summarization #task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-apache-2.0 #news #campus #region-us \n"
] |
9704678ee5a3da05a5ac39fb754cae3665e50162
|
# Dataset Card for Evaluation run of Sao10K/SthenoWriter-L2-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/SthenoWriter-L2-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Sao10K/SthenoWriter-L2-13B](https://huggingface.co/Sao10K/SthenoWriter-L2-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Sao10K__SthenoWriter-L2-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T23:46:14.496615](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__SthenoWriter-L2-13B/blob/main/results_2023-10-24T23-46-14.496615.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094507,
"f1": 0.06478397651006729,
"f1_stderr": 0.001425510190369328,
"acc": 0.4278473862370922,
"acc_stderr": 0.010483695573501171
},
"harness|drop|3": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094507,
"f1": 0.06478397651006729,
"f1_stderr": 0.001425510190369328
},
"harness|gsm8k|5": {
"acc": 0.11220621683093253,
"acc_stderr": 0.008693743138242354
},
"harness|winogrande|5": {
"acc": 0.7434885556432518,
"acc_stderr": 0.012273648008759987
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Sao10K__SthenoWriter-L2-13B
|
[
"region:us"
] |
2023-10-04T08:10:32+00:00
|
{"pretty_name": "Evaluation run of Sao10K/SthenoWriter-L2-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Sao10K/SthenoWriter-L2-13B](https://huggingface.co/Sao10K/SthenoWriter-L2-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__SthenoWriter-L2-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T23:46:14.496615](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__SthenoWriter-L2-13B/blob/main/results_2023-10-24T23-46-14.496615.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094507,\n \"f1\": 0.06478397651006729,\n \"f1_stderr\": 0.001425510190369328,\n \"acc\": 0.4278473862370922,\n \"acc_stderr\": 0.010483695573501171\n },\n \"harness|drop|3\": {\n \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094507,\n \"f1\": 0.06478397651006729,\n \"f1_stderr\": 0.001425510190369328\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11220621683093253,\n \"acc_stderr\": 0.008693743138242354\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.012273648008759987\n }\n}\n```", "repo_url": "https://huggingface.co/Sao10K/SthenoWriter-L2-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T09_10_08.992646", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-10-08.992646.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-10-08.992646.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T23_46_14.496615", "path": ["**/details_harness|drop|3_2023-10-24T23-46-14.496615.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T23-46-14.496615.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T23_46_14.496615", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-46-14.496615.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-46-14.496615.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T09_10_08.992646", "path": ["**/details_harness|hellaswag|10_2023-10-04T09-10-08.992646.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T09-10-08.992646.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_10_04T09_10_08.992646", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T09-10-08.992646.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T09-10-08.992646.parquet", 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|
2023-10-24T22:46:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Sao10K/SthenoWriter-L2-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Sao10K/SthenoWriter-L2-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T23:46:14.496615(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Sao10K/SthenoWriter-L2-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/SthenoWriter-L2-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:46:14.496615(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Sao10K/SthenoWriter-L2-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/SthenoWriter-L2-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:46:14.496615(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Sao10K/SthenoWriter-L2-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/SthenoWriter-L2-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T23:46:14.496615(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
e2380d21e6a138a2f1d8f0e37bfc78867921695a
|
# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/hyunseoki/ko-ref-llama2-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [hyunseoki/ko-ref-llama2-7b](https://huggingface.co/hyunseoki/ko-ref-llama2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T06:27:40.666893](https://huggingface.co/datasets/open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-7b/blob/main/results_2023-10-25T06-27-40.666893.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.20343959731543623,
"em_stderr": 0.004122557786324279,
"f1": 0.24051069630872504,
"f1_stderr": 0.00417330845396371,
"acc": 0.3310970797158643,
"acc_stderr": 0.006646291751455444
},
"harness|drop|3": {
"em": 0.20343959731543623,
"em_stderr": 0.004122557786324279,
"f1": 0.24051069630872504,
"f1_stderr": 0.00417330845396371
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.6621941594317285,
"acc_stderr": 0.013292583502910888
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
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### Licensing Information
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### Contributions
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|
open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-7b
|
[
"region:us"
] |
2023-10-04T08:16:27+00:00
|
{"pretty_name": "Evaluation run of hyunseoki/ko-ref-llama2-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [hyunseoki/ko-ref-llama2-7b](https://huggingface.co/hyunseoki/ko-ref-llama2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T06:27:40.666893](https://huggingface.co/datasets/open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-7b/blob/main/results_2023-10-25T06-27-40.666893.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.20343959731543623,\n \"em_stderr\": 0.004122557786324279,\n \"f1\": 0.24051069630872504,\n \"f1_stderr\": 0.00417330845396371,\n \"acc\": 0.3310970797158643,\n \"acc_stderr\": 0.006646291751455444\n },\n \"harness|drop|3\": {\n \"em\": 0.20343959731543623,\n \"em_stderr\": 0.004122557786324279,\n \"f1\": 0.24051069630872504,\n \"f1_stderr\": 0.00417330845396371\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6621941594317285,\n \"acc_stderr\": 0.013292583502910888\n }\n}\n```", "repo_url": "https://huggingface.co/hyunseoki/ko-ref-llama2-7b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T09_16_09.367375", "path": 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2023-10-25T05:27:54+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-7b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model hyunseoki/ko-ref-llama2-7b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T06:27:40.666893(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model hyunseoki/ko-ref-llama2-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T06:27:40.666893(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model hyunseoki/ko-ref-llama2-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T06:27:40.666893(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
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"### Data Splits",
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"### Curation Rationale",
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"#### Who are the source language producers?",
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"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-7b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model hyunseoki/ko-ref-llama2-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T06:27:40.666893(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
9a9a63866d6de5c27dbf3742d9eaa6f0821e25df
|
# Dataset Card for Evaluation run of danielpark/gorani-100k-llama2-13b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/danielpark/gorani-100k-llama2-13b-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [danielpark/gorani-100k-llama2-13b-instruct](https://huggingface.co/danielpark/gorani-100k-llama2-13b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_danielpark__gorani-100k-llama2-13b-instruct_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-06T21:31:02.629994](https://huggingface.co/datasets/open-llm-leaderboard/details_danielpark__gorani-100k-llama2-13b-instruct_public/blob/main/results_2023-11-06T21-31-02.629994.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 8.284395973154363e-05,
"f1_stderr": 6.061110851297716e-05,
"acc": 0.24822415153906865,
"acc_stderr": 0.0070260655734579345
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 8.284395973154363e-05,
"f1_stderr": 6.061110851297716e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.4964483030781373,
"acc_stderr": 0.014052131146915869
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_danielpark__gorani-100k-llama2-13b-instruct
|
[
"region:us"
] |
2023-10-04T08:22:10+00:00
|
{"pretty_name": "Evaluation run of danielpark/gorani-100k-llama2-13b-instruct", "dataset_summary": "Dataset automatically created during the evaluation run of model [danielpark/gorani-100k-llama2-13b-instruct](https://huggingface.co/danielpark/gorani-100k-llama2-13b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_danielpark__gorani-100k-llama2-13b-instruct_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-06T21:31:02.629994](https://huggingface.co/datasets/open-llm-leaderboard/details_danielpark__gorani-100k-llama2-13b-instruct_public/blob/main/results_2023-11-06T21-31-02.629994.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 8.284395973154363e-05,\n \"f1_stderr\": 6.061110851297716e-05,\n \"acc\": 0.24822415153906865,\n \"acc_stderr\": 0.0070260655734579345\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 8.284395973154363e-05,\n \"f1_stderr\": 6.061110851297716e-05\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.4964483030781373,\n \"acc_stderr\": 0.014052131146915869\n }\n}\n```", "repo_url": "https://huggingface.co/danielpark/gorani-100k-llama2-13b-instruct", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_05T02_32_18.553422", "path": ["**/details_harness|drop|3_2023-11-05T02-32-18.553422.parquet"]}, {"split": "2023_11_06T21_31_02.629994", "path": ["**/details_harness|drop|3_2023-11-06T21-31-02.629994.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-06T21-31-02.629994.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_05T02_32_18.553422", "path": ["**/details_harness|gsm8k|5_2023-11-05T02-32-18.553422.parquet"]}, {"split": "2023_11_06T21_31_02.629994", "path": ["**/details_harness|gsm8k|5_2023-11-06T21-31-02.629994.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-06T21-31-02.629994.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_05T02_32_18.553422", "path": ["**/details_harness|winogrande|5_2023-11-05T02-32-18.553422.parquet"]}, {"split": "2023_11_06T21_31_02.629994", "path": ["**/details_harness|winogrande|5_2023-11-06T21-31-02.629994.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-06T21-31-02.629994.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_05T02_32_18.553422", "path": ["results_2023-11-05T02-32-18.553422.parquet"]}, {"split": "2023_11_06T21_31_02.629994", "path": ["results_2023-11-06T21-31-02.629994.parquet"]}, {"split": "latest", "path": ["results_2023-11-06T21-31-02.629994.parquet"]}]}]}
|
2023-12-01T14:15:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of danielpark/gorani-100k-llama2-13b-instruct
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model danielpark/gorani-100k-llama2-13b-instruct on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-06T21:31:02.629994(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of danielpark/gorani-100k-llama2-13b-instruct",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model danielpark/gorani-100k-llama2-13b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-06T21:31:02.629994(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of danielpark/gorani-100k-llama2-13b-instruct",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model danielpark/gorani-100k-llama2-13b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-06T21:31:02.629994(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of danielpark/gorani-100k-llama2-13b-instruct## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model danielpark/gorani-100k-llama2-13b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-06T21:31:02.629994(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
81b9e0ae7488f0c56294ca6e508e57e4e19ed2a9
|
# Dataset Card for Evaluation run of Undi95/Emerhyst-20B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Undi95/Emerhyst-20B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Undi95/Emerhyst-20B](https://huggingface.co/Undi95/Emerhyst-20B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Undi95__Emerhyst-20B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T23:55:45.308698](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Emerhyst-20B/blob/main/results_2023-10-26T23-55-45.308698.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.13779362416107382,
"em_stderr": 0.003529879074740249,
"f1": 0.20561661073825346,
"f1_stderr": 0.0036264688196139742,
"acc": 0.42288260999908445,
"acc_stderr": 0.009833377334647354
},
"harness|drop|3": {
"em": 0.13779362416107382,
"em_stderr": 0.003529879074740249,
"f1": 0.20561661073825346,
"f1_stderr": 0.0036264688196139742
},
"harness|gsm8k|5": {
"acc": 0.08491281273692192,
"acc_stderr": 0.007678212824450795
},
"harness|winogrande|5": {
"acc": 0.760852407261247,
"acc_stderr": 0.011988541844843914
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Undi95__Emerhyst-20B
|
[
"region:us"
] |
2023-10-04T08:24:32+00:00
|
{"pretty_name": "Evaluation run of Undi95/Emerhyst-20B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Undi95/Emerhyst-20B](https://huggingface.co/Undi95/Emerhyst-20B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Undi95__Emerhyst-20B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-26T23:55:45.308698](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Emerhyst-20B/blob/main/results_2023-10-26T23-55-45.308698.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.13779362416107382,\n \"em_stderr\": 0.003529879074740249,\n \"f1\": 0.20561661073825346,\n \"f1_stderr\": 0.0036264688196139742,\n \"acc\": 0.42288260999908445,\n \"acc_stderr\": 0.009833377334647354\n },\n \"harness|drop|3\": {\n \"em\": 0.13779362416107382,\n \"em_stderr\": 0.003529879074740249,\n \"f1\": 0.20561661073825346,\n \"f1_stderr\": 0.0036264688196139742\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08491281273692192,\n \"acc_stderr\": 0.007678212824450795\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.760852407261247,\n \"acc_stderr\": 0.011988541844843914\n }\n}\n```", "repo_url": "https://huggingface.co/Undi95/Emerhyst-20B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T09_24_08.717468", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-24-08.717468.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-24-08.717468.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_26T23_55_45.308698", "path": ["**/details_harness|drop|3_2023-10-26T23-55-45.308698.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-26T23-55-45.308698.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_26T23_55_45.308698", "path": ["**/details_harness|gsm8k|5_2023-10-26T23-55-45.308698.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-26T23-55-45.308698.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T09_24_08.717468", "path": ["**/details_harness|hellaswag|10_2023-10-04T09-24-08.717468.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T09-24-08.717468.parquet"]}]}, 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TAGS
#region-us
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# Dataset Card for Evaluation run of Undi95/Emerhyst-20B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Undi95/Emerhyst-20B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-26T23:55:45.308698(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Undi95/Emerhyst-20B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/Emerhyst-20B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-26T23:55:45.308698(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Undi95/Emerhyst-20B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/Emerhyst-20B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-26T23:55:45.308698(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
19,
31,
167,
67,
10,
4,
6,
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5,
5,
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7,
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10,
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5,
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7,
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7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Undi95/Emerhyst-20B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/Emerhyst-20B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-26T23:55:45.308698(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
63d636982f74b4c4e9fb28718d4b935dc9f3c739
|
# TEST for creating subsets of Datasets
|
yeonsun/JGLUE-custom
|
[
"region:us"
] |
2023-10-04T08:30:03+00:00
|
{"configs": [{"config_name": "JCoLA", "data_files": [{"split": "train", "path": "JCoLA/train.parquet"}, {"split": "validation", "path": "JCoLA/validation.parquet"}]}, {"config_name": "JCommonsenseQA", "data_files": [{"split": "train", "path": "JCommonsenseQA/train.parquet"}, {"split": "validation", "path": "JCommonsenseQA/validation.parquet"}]}, {"config_name": "JNLI", "data_files": [{"split": "train", "path": "JNLI/train.parquet"}, {"split": "validation", "path": "JNLI/validation.parquet"}]}, {"config_name": "JSQuAD", "data_files": [{"split": "train", "path": "JSQuAD/train.parquet"}, {"split": "validation", "path": "JSQuAD/validation.parquet"}]}, {"config_name": "JSTS", "data_files": [{"split": "train", "path": "JSTS/train.parquet"}, {"split": "validation", "path": "JSTS/validation.parquet"}]}]}
|
2023-10-04T08:32:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# TEST for creating subsets of Datasets
|
[
"# TEST for creating subsets of Datasets"
] |
[
"TAGS\n#region-us \n",
"# TEST for creating subsets of Datasets"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# TEST for creating subsets of Datasets"
] |
f18cbf7f1e9a763973da35db4a575e1100288ed3
|
# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/hyunseoki/ko-ref-llama2-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [hyunseoki/ko-ref-llama2-13b](https://huggingface.co/hyunseoki/ko-ref-llama2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T20:48:08.405984](https://huggingface.co/datasets/open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-13b/blob/main/results_2023-10-23T20-48-08.405984.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.23804530201342283,
"em_stderr": 0.00436148149592577,
"f1": 0.2753114513422822,
"f1_stderr": 0.004376593977288765,
"acc": 0.345698500394633,
"acc_stderr": 0.006491080100463287
},
"harness|drop|3": {
"em": 0.23804530201342283,
"em_stderr": 0.00436148149592577,
"f1": 0.2753114513422822,
"f1_stderr": 0.004376593977288765
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.691397000789266,
"acc_stderr": 0.012982160200926574
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-13b
|
[
"region:us"
] |
2023-10-04T08:36:57+00:00
|
{"pretty_name": "Evaluation run of hyunseoki/ko-ref-llama2-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [hyunseoki/ko-ref-llama2-13b](https://huggingface.co/hyunseoki/ko-ref-llama2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T20:48:08.405984](https://huggingface.co/datasets/open-llm-leaderboard/details_hyunseoki__ko-ref-llama2-13b/blob/main/results_2023-10-23T20-48-08.405984.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.23804530201342283,\n \"em_stderr\": 0.00436148149592577,\n \"f1\": 0.2753114513422822,\n \"f1_stderr\": 0.004376593977288765,\n \"acc\": 0.345698500394633,\n \"acc_stderr\": 0.006491080100463287\n },\n \"harness|drop|3\": {\n \"em\": 0.23804530201342283,\n \"em_stderr\": 0.00436148149592577,\n \"f1\": 0.2753114513422822,\n \"f1_stderr\": 0.004376593977288765\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.691397000789266,\n \"acc_stderr\": 0.012982160200926574\n }\n}\n```", "repo_url": "https://huggingface.co/hyunseoki/ko-ref-llama2-13b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T09_36_39.103374", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-36-39.103374.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T09-36-39.103374.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T20_48_08.405984", "path": ["**/details_harness|drop|3_2023-10-23T20-48-08.405984.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T20-48-08.405984.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T20_48_08.405984", "path": ["**/details_harness|gsm8k|5_2023-10-23T20-48-08.405984.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T20-48-08.405984.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T09_36_39.103374", "path": ["**/details_harness|hellaswag|10_2023-10-04T09-36-39.103374.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T09-36-39.103374.parquet"]}]}, 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|
2023-10-23T19:48:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-13b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model hyunseoki/ko-ref-llama2-13b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T20:48:08.405984(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-13b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model hyunseoki/ko-ref-llama2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T20:48:08.405984(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-13b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model hyunseoki/ko-ref-llama2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T20:48:08.405984(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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23,
31,
171,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
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10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of hyunseoki/ko-ref-llama2-13b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model hyunseoki/ko-ref-llama2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T20:48:08.405984(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
e64e2500e16293a5904e45bfac54b20fbc043a1e
|
# Dataset Card for "modified_daily_dialog_sentence"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/modified_daily_dialog_sentence
|
[
"region:us"
] |
2023-10-04T08:42:27+00:00
|
{"dataset_info": {"features": [{"name": "dialogue", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5470237, "num_examples": 82113}], "download_size": 3470648, "dataset_size": 5470237}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T08:42:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "modified_daily_dialog_sentence"
More Information needed
|
[
"# Dataset Card for \"modified_daily_dialog_sentence\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"modified_daily_dialog_sentence\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"modified_daily_dialog_sentence\"\n\nMore Information needed"
] |
feab2b4ca6afd126b37172ddabfffe9716b5cddf
|
Dataset pairing GPT-4 synthesized instructions with outputs from [RecipeNLG](https://www.kaggle.com/datasets/paultimothymooney/recipenlg) in Axolotl's "alpaca" jsonl format
|
cadaeic/2000-sample-synthetic-recipe-dataset
|
[
"language:en",
"region:us"
] |
2023-10-04T08:44:05+00:00
|
{"language": ["en"]}
|
2023-10-04T21:44:10+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #region-us
|
Dataset pairing GPT-4 synthesized instructions with outputs from RecipeNLG in Axolotl's "alpaca" jsonl format
|
[] |
[
"TAGS\n#language-English #region-us \n"
] |
[
10
] |
[
"passage: TAGS\n#language-English #region-us \n"
] |
7e14d0856b52be76655dac63421e38b1e7ea522e
|
# Dataset Card for "modified_daily_dialog_sentence_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/modified_daily_dialog_sentence_v2
|
[
"region:us"
] |
2023-10-04T08:47:03+00:00
|
{"dataset_info": {"features": [{"name": "dialogue", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5620176, "num_examples": 137271}], "download_size": 3639530, "dataset_size": 5620176}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T08:47:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "modified_daily_dialog_sentence_v2"
More Information needed
|
[
"# Dataset Card for \"modified_daily_dialog_sentence_v2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"modified_daily_dialog_sentence_v2\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"modified_daily_dialog_sentence_v2\"\n\nMore Information needed"
] |
09cde46b151a3f155a5451f98fd30b371ad7365e
|
# alpaca-cleaned-ru
converter for autotrain from [d0rj/alpaca-cleaned-ru](https://huggingface.co/datasets/d0rj/alpaca-cleaned-ru)
Translated version of [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) into Russian.
## Dataset Description
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
- **Repository:** https://huggingface.co/datasets/d0rj/alpaca-cleaned-ru
|
ASIDS/alpaca-cleaned-ru
|
[
"task_categories:text-generation",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:yahma/alpaca-cleaned",
"language:ru",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] |
2023-10-04T08:52:39+00:00
|
{"language_creators": ["translated"], "language": ["ru"], "license": "cc-by-4.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["yahma/alpaca-cleaned"], "task_categories": ["text-generation"], "pretty_name": "alpaca-cleaned-ru", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "iteration", "dtype": "uint32"}], "splits": [{"name": "train", "num_bytes": 74829755.0, "num_examples": 51760}], "download_size": 36596664, "dataset_size": 74829755.0}, "tags": ["instruction-finetuning"]}
|
2023-10-04T13:26:17+00:00
|
[] |
[
"ru"
] |
TAGS
#task_categories-text-generation #language_creators-translated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-yahma/alpaca-cleaned #language-Russian #license-cc-by-4.0 #instruction-finetuning #region-us
|
# alpaca-cleaned-ru
converter for autotrain from d0rj/alpaca-cleaned-ru
Translated version of yahma/alpaca-cleaned into Russian.
## Dataset Description
- Repository: URL
- Repository: URL
|
[
"# alpaca-cleaned-ru\nconverter for autotrain from d0rj/alpaca-cleaned-ru\n\nTranslated version of yahma/alpaca-cleaned into Russian.",
"## Dataset Description\n\n- Repository: URL\n- Repository: URL"
] |
[
"TAGS\n#task_categories-text-generation #language_creators-translated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-yahma/alpaca-cleaned #language-Russian #license-cc-by-4.0 #instruction-finetuning #region-us \n",
"# alpaca-cleaned-ru\nconverter for autotrain from d0rj/alpaca-cleaned-ru\n\nTranslated version of yahma/alpaca-cleaned into Russian.",
"## Dataset Description\n\n- Repository: URL\n- Repository: URL"
] |
[
83,
46,
16
] |
[
"passage: TAGS\n#task_categories-text-generation #language_creators-translated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-yahma/alpaca-cleaned #language-Russian #license-cc-by-4.0 #instruction-finetuning #region-us \n# alpaca-cleaned-ru\nconverter for autotrain from d0rj/alpaca-cleaned-ru\n\nTranslated version of yahma/alpaca-cleaned into Russian.## Dataset Description\n\n- Repository: URL\n- Repository: URL"
] |
586fe4757275c1ee570ce720760a4a9f97e8bf1d
|
Original repo of dataset: https://github.com/google-research-datasets/ToTTo
|
MikeXydas/ToTTo
|
[
"license:mit",
"region:us"
] |
2023-10-04T08:53:13+00:00
|
{"license": "mit"}
|
2023-10-04T08:57:10+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
Original repo of dataset: URL
|
[] |
[
"TAGS\n#license-mit #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#license-mit #region-us \n"
] |
acbe1669d13e2351a2881e822d7f0744672e97fe
|
# Dataset Card for "toy_figure_descriptions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/toy_figure_descriptions
|
[
"region:us"
] |
2023-10-04T08:54:54+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 242803, "num_examples": 1000}], "download_size": 31441, "dataset_size": 242803}}
|
2023-10-04T08:55:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "toy_figure_descriptions"
More Information needed
|
[
"# Dataset Card for \"toy_figure_descriptions\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"toy_figure_descriptions\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"toy_figure_descriptions\"\n\nMore Information needed"
] |
c3f627c5cc2e7bce8585a8b194e8ca620138f0f0
|
# Dataset Card for "logo_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/logo_prompts
|
[
"region:us"
] |
2023-10-04T08:57:29+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 271034, "num_examples": 1000}], "download_size": 34969, "dataset_size": 271034}}
|
2023-10-04T08:57:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "logo_prompts"
More Information needed
|
[
"# Dataset Card for \"logo_prompts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"logo_prompts\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"logo_prompts\"\n\nMore Information needed"
] |
e8276cf4e1a80ce86bc932e6376c3da871ea8948
|
# Dataset Card for "paper-recommendations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
librarian-bots/paper-recommendations
|
[
"region:us"
] |
2023-10-04T09:01:00+00:00
|
{"dataset_info": {"features": [{"name": "paper_url", "dtype": "string"}, {"name": "comment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 524820, "num_examples": 476}], "download_size": 133619, "dataset_size": 524820}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2024-01-09T09:55:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "paper-recommendations"
More Information needed
|
[
"# Dataset Card for \"paper-recommendations\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"paper-recommendations\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"paper-recommendations\"\n\nMore Information needed"
] |
0f792c7ec82ab5dc405e5000c9094b976146aa52
|
# Dataset Card for "modified_daily_dialog_sentence_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/modified_daily_dialog_sentence_v3
|
[
"region:us"
] |
2023-10-04T09:02:35+00:00
|
{"dataset_info": {"features": [{"name": "dialogue", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5052674, "num_examples": 100847}], "download_size": 3232686, "dataset_size": 5052674}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T09:02:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "modified_daily_dialog_sentence_v3"
More Information needed
|
[
"# Dataset Card for \"modified_daily_dialog_sentence_v3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"modified_daily_dialog_sentence_v3\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"modified_daily_dialog_sentence_v3\"\n\nMore Information needed"
] |
f2615e96878a1527743881669f9d6bf4019c2c6f
|
# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Riiid/sheep-duck-llama-2-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Riiid/sheep-duck-llama-2-13b](https://huggingface.co/Riiid/sheep-duck-llama-2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T20:11:08.442299](https://huggingface.co/datasets/open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-13b/blob/main/results_2023-10-28T20-11-08.442299.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.011325503355704697,
"em_stderr": 0.0010836650667382193,
"f1": 0.10714869966442996,
"f1_stderr": 0.002051331843610221,
"acc": 0.4306352484153346,
"acc_stderr": 0.009893407156588008
},
"harness|drop|3": {
"em": 0.011325503355704697,
"em_stderr": 0.0010836650667382193,
"f1": 0.10714869966442996,
"f1_stderr": 0.002051331843610221
},
"harness|gsm8k|5": {
"acc": 0.09173616376042457,
"acc_stderr": 0.007950942148339331
},
"harness|winogrande|5": {
"acc": 0.7695343330702447,
"acc_stderr": 0.011835872164836685
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-13b
|
[
"region:us"
] |
2023-10-04T09:06:00+00:00
|
{"pretty_name": "Evaluation run of Riiid/sheep-duck-llama-2-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [Riiid/sheep-duck-llama-2-13b](https://huggingface.co/Riiid/sheep-duck-llama-2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T20:11:08.442299](https://huggingface.co/datasets/open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-13b/blob/main/results_2023-10-28T20-11-08.442299.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.011325503355704697,\n \"em_stderr\": 0.0010836650667382193,\n \"f1\": 0.10714869966442996,\n \"f1_stderr\": 0.002051331843610221,\n \"acc\": 0.4306352484153346,\n \"acc_stderr\": 0.009893407156588008\n },\n \"harness|drop|3\": {\n \"em\": 0.011325503355704697,\n \"em_stderr\": 0.0010836650667382193,\n \"f1\": 0.10714869966442996,\n \"f1_stderr\": 0.002051331843610221\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09173616376042457,\n \"acc_stderr\": 0.007950942148339331\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836685\n }\n}\n```", "repo_url": "https://huggingface.co/Riiid/sheep-duck-llama-2-13b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T10_05_36.875175", "path": ["**/details_harness|arc:challenge|25_2023-10-04T10-05-36.875175.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T10-05-36.875175.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T20_11_08.442299", "path": ["**/details_harness|drop|3_2023-10-28T20-11-08.442299.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T20-11-08.442299.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T20_11_08.442299", "path": ["**/details_harness|gsm8k|5_2023-10-28T20-11-08.442299.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T20-11-08.442299.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T10_05_36.875175", "path": ["**/details_harness|hellaswag|10_2023-10-04T10-05-36.875175.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T10-05-36.875175.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_10_04T10_05_36.875175", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T10-05-36.875175.parquet", 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"**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T10-05-36.875175.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T10-05-36.875175.parquet", 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|
2023-10-28T19:11:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-13b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Riiid/sheep-duck-llama-2-13b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-28T20:11:08.442299(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-13b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Riiid/sheep-duck-llama-2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T20:11:08.442299(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-13b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Riiid/sheep-duck-llama-2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T20:11:08.442299(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-13b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Riiid/sheep-duck-llama-2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T20:11:08.442299(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
b1feae54a577760a1b06ee6330f2b0d5aa669174
|
# Dataset Card for "daily_dialogue_text_to_gloss_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/daily_dialogue_text_to_gloss_v2
|
[
"region:us"
] |
2023-10-04T09:15:35+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "gloss", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7134448, "num_examples": 100847}], "download_size": 4691694, "dataset_size": 7134448}}
|
2023-10-04T09:15:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "daily_dialogue_text_to_gloss_v2"
More Information needed
|
[
"# Dataset Card for \"daily_dialogue_text_to_gloss_v2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"daily_dialogue_text_to_gloss_v2\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"daily_dialogue_text_to_gloss_v2\"\n\nMore Information needed"
] |
54a3ac1f465f24d9a296bdeb3cd5ee66218f23ae
|
# Dataset Card for "hindi_asr_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TheAIchemist13/hindi_asr_dataset
|
[
"region:us"
] |
2023-10-04T09:24:40+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24441695.0, "num_examples": 80}, {"name": "test", "num_bytes": 32809156.0, "num_examples": 90}], "download_size": 28788848, "dataset_size": 57250851.0}}
|
2023-10-18T09:17:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "hindi_asr_dataset"
More Information needed
|
[
"# Dataset Card for \"hindi_asr_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"hindi_asr_dataset\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"hindi_asr_dataset\"\n\nMore Information needed"
] |
28a289e8b2c1e0537a9682ac6e1b0d6e74beacad
|
# Dataset Card for "daily_dialogue_text_to_gloss_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/daily_dialogue_text_to_gloss_v3
|
[
"region:us"
] |
2023-10-04T09:30:36+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "gloss", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7134448, "num_examples": 100847}], "download_size": 4691694, "dataset_size": 7134448}}
|
2023-10-04T09:30:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "daily_dialogue_text_to_gloss_v3"
More Information needed
|
[
"# Dataset Card for \"daily_dialogue_text_to_gloss_v3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"daily_dialogue_text_to_gloss_v3\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"daily_dialogue_text_to_gloss_v3\"\n\nMore Information needed"
] |
43fc003190d9e9d61eb32e43f31510cd6cbc9b91
|
# Bangumi Image Base of Higurashi No Naku Koro Ni
This is the image base of bangumi Higurashi no Naku Koro Ni, we detected 71 characters, 12274 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 18 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 306 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 29 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 38 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 17 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 16 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 30 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 1686 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 412 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 77 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 32 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 124 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 135 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 103 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 36 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 717 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 125 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 389 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 98 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 63 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 141 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 31 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 126 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 9 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 38 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 260 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 52 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 919 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 27 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 19 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 29 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 20 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 56 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 17 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 32 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 34 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 20 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 26 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 10 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 128 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 1451 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 84 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 37 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 19 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 18 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 95 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 1392 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 75 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 20 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 419 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 15 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 94 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 1639 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 36 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 35 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 10 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 14 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 17 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 16 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 7 | [Download](59/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 60 | 9 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 8 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 8 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 8 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 12 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 9 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 8 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 23 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 12 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 5 | [Download](69/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 234 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/higurashinonakukoroni
|
[
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T09:43:11+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "tags": ["art"]}
|
2023-10-04T14:45:05+00:00
|
[] |
[] |
TAGS
#size_categories-10K<n<100K #license-mit #art #region-us
|
Bangumi Image Base of Higurashi No Naku Koro Ni
===============================================
This is the image base of bangumi Higurashi no Naku Koro Ni, we detected 71 characters, 12274 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
26cfc18b38cf4d93e462e4920d447a115ca793c4
|
偶然找到的 200 多篇古籍相关的**纯 txt 文件**，简单洗了一下，去除了部分噪声和空白行。
一篇样例如下：
```
古训《增广贤文》
昔时贤文，诲汝谆谆，集韵增文，多见多闻。
观今宜鉴古，无古不成今。
知己知彼，将心比心。
酒逢知己饮，诗向会人吟。
相识满天下，知心能几人。
相逢好似初相识，到老终无怨恨心。
近水知鱼性，近山识鸟音。
易涨易退山溪水，易反易覆小人心。
运去金成铁，时来铁似金，读书须用意，一字值千金。
```
|
Mxode/Chinese-Classics-Partial
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"classics",
"region:us"
] |
2023-10-04T09:46:03+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "tags": ["classics"]}
|
2023-10-04T09:54:47+00:00
|
[] |
[
"zh"
] |
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-apache-2.0 #classics #region-us
|
偶然找到的 200 多篇古籍相关的纯 txt 文件，简单洗了一下，去除了部分噪声和空白行。
一篇样例如下：
|
[] |
[
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-apache-2.0 #classics #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-apache-2.0 #classics #region-us \n"
] |
babe1ced811723336f319be460e347d43bdca662
|
# Dataset Card for "SQL_PlainText_Combined"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AayushShah/SQL_PlainText_Combined
|
[
"region:us"
] |
2023-10-04T09:48:58+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 349116676.7610253, "num_examples": 306706}, {"name": "test", "num_bytes": 38791374.23897472, "num_examples": 34079}], "download_size": 98654951, "dataset_size": 387908051.0}}
|
2023-10-04T09:49:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SQL_PlainText_Combined"
More Information needed
|
[
"# Dataset Card for \"SQL_PlainText_Combined\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SQL_PlainText_Combined\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SQL_PlainText_Combined\"\n\nMore Information needed"
] |
8191eb8982bf80d13015e6dbfe56b79b995ca1ab
|
# Dataset Card for "german_OpenOrca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tessiw/german_OpenOrca
|
[
"region:us"
] |
2023-10-04T09:55:16+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6837659266, "num_examples": 3983923}], "download_size": 3916872702, "dataset_size": 6837659266}}
|
2023-10-09T06:24:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "german_OpenOrca"
More Information needed
|
[
"# Dataset Card for \"german_OpenOrca\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"german_OpenOrca\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"german_OpenOrca\"\n\nMore Information needed"
] |
51ad2431ea4c6a84a4404c50387eac955b9532f3
|
# Dataset Card for "german_OpenOrca_Format1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tessiw/german_OpenOrca_Format1
|
[
"region:us"
] |
2023-10-04T10:02:17+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 462202853, "num_examples": 250000}], "download_size": 254684069, "dataset_size": 462202853}}
|
2023-10-11T14:53:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "german_OpenOrca_Format1"
More Information needed
|
[
"# Dataset Card for \"german_OpenOrca_Format1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"german_OpenOrca_Format1\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"german_OpenOrca_Format1\"\n\nMore Information needed"
] |
ff92b0625b5eac28c0c468bb8e648bcce0d45f80
|
# Dataset Card for Evaluation run of 2200
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/2200
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [2200](https://huggingface.co/2200) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 7 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 122 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("HuggingFaceBR4/thomwolf-40B-tokens-llama-seed-7-1p82G",
"harness_winogrande_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-04T14:58:04.139674](https://huggingface.co/datasets/HuggingFaceBR4/thomwolf-40B-tokens-llama-seed-7-1p82G/blob/main/2200/results_2023-10-04T14-58-04.139674.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.4034736197157248,
"acc_stderr": 0.010961336845565628,
"acc_norm": 0.38208444366701694,
"acc_norm_stderr": 0.011634813232460172
},
"harness|arc:challenge|0": {
"acc": 0.18686006825938567,
"acc_stderr": 0.011391015649694382,
"acc_norm": 0.23464163822525597,
"acc_norm_stderr": 0.012383873560768671
},
"harness|arc:easy|0": {
"acc": 0.44907407407407407,
"acc_stderr": 0.010206428316323362,
"acc_norm": 0.40025252525252525,
"acc_norm_stderr": 0.010053550119896108
},
"harness|hellaswag|0": {
"acc": 0.3066122286397132,
"acc_stderr": 0.004601446124041564,
"acc_norm": 0.34574785899223265,
"acc_norm_stderr": 0.004746394613384554
},
"harness|openbookqa|0": {
"acc": 0.168,
"acc_stderr": 0.01673655354154189,
"acc_norm": 0.272,
"acc_norm_stderr": 0.019920483209566065
},
"harness|piqa|0": {
"acc": 0.6599564744287268,
"acc_stderr": 0.011052749414423548,
"acc_norm": 0.6577801958650707,
"acc_norm_stderr": 0.011069764658685456
},
"harness|super_glue:boolq|0": {
"acc": 0.5565749235474006,
"acc_stderr": 0.008688893661318219
},
"harness|winogrande|0": {
"acc": 0.4972375690607735,
"acc_stderr": 0.014052271211616441
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
HuggingFaceBR4/thomwolf-40B-tokens-llama-seed-7-1p82G
|
[
"region:us"
] |
2023-10-04T10:03:16+00:00
|
{"pretty_name": "Evaluation run of 2200", "dataset_summary": "Dataset automatically created during the evaluation run of model [2200](https://huggingface.co/2200) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 7 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 122 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"HuggingFaceBR4/thomwolf-40B-tokens-llama-seed-7-1p82G\",\n\t\"harness_winogrande_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-04T14:58:04.139674](https://huggingface.co/datasets/HuggingFaceBR4/thomwolf-40B-tokens-llama-seed-7-1p82G/blob/main/2200/results_2023-10-04T14-58-04.139674.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4034736197157248,\n \"acc_stderr\": 0.010961336845565628,\n \"acc_norm\": 0.38208444366701694,\n \"acc_norm_stderr\": 0.011634813232460172\n },\n \"harness|arc:challenge|0\": {\n \"acc\": 0.18686006825938567,\n \"acc_stderr\": 0.011391015649694382,\n \"acc_norm\": 0.23464163822525597,\n \"acc_norm_stderr\": 0.012383873560768671\n },\n \"harness|arc:easy|0\": {\n \"acc\": 0.44907407407407407,\n \"acc_stderr\": 0.010206428316323362,\n \"acc_norm\": 0.40025252525252525,\n \"acc_norm_stderr\": 0.010053550119896108\n },\n \"harness|hellaswag|0\": {\n \"acc\": 0.3066122286397132,\n \"acc_stderr\": 0.004601446124041564,\n \"acc_norm\": 0.34574785899223265,\n \"acc_norm_stderr\": 0.004746394613384554\n },\n \"harness|openbookqa|0\": {\n \"acc\": 0.168,\n \"acc_stderr\": 0.01673655354154189,\n \"acc_norm\": 0.272,\n \"acc_norm_stderr\": 0.019920483209566065\n },\n \"harness|piqa|0\": {\n \"acc\": 0.6599564744287268,\n \"acc_stderr\": 0.011052749414423548,\n \"acc_norm\": 0.6577801958650707,\n \"acc_norm_stderr\": 0.011069764658685456\n },\n \"harness|super_glue:boolq|0\": {\n \"acc\": 0.5565749235474006,\n \"acc_stderr\": 0.008688893661318219\n },\n \"harness|winogrande|0\": {\n \"acc\": 0.4972375690607735,\n \"acc_stderr\": 0.014052271211616441\n }\n}\n```", "repo_url": "https://huggingface.co/2200", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_0", "data_files": [{"split": "2023_10_04T11_03_07.216829", "path": ["**/details_harness|arc:challenge|0_2023-10-04T11-03-07.216829.parquet"]}, {"split": "2023_10_04T11_10_27.477524", "path": ["**/details_harness|arc:challenge|0_2023-10-04T11-10-27.477524.parquet"]}, {"split": "2023_10_04T11_18_25.200084", "path": ["**/details_harness|arc:challenge|0_2023-10-04T11-18-25.200084.parquet"]}, 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TAGS
#region-us
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# Dataset Card for Evaluation run of 2200
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model 2200 on the Open LLM Leaderboard.
The dataset is composed of 7 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 122 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-04T14:58:04.139674(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of 2200",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model 2200 on the Open LLM Leaderboard.\n\nThe dataset is composed of 7 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 122 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-04T14:58:04.139674(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of 2200",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model 2200 on the Open LLM Leaderboard.\n\nThe dataset is composed of 7 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 122 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-04T14:58:04.139674(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of 2200## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model 2200 on the Open LLM Leaderboard.\n\nThe dataset is composed of 7 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 122 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-04T14:58:04.139674(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
33c04f7d4ed7f3331570320cc352ee08ba5d989e
|
# Dataset Card for "german_OpenOrca_Format2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tessiw/german_OpenOrca_Format2
|
[
"region:us"
] |
2023-10-04T10:05:13+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6613611409, "num_examples": 3983923}], "download_size": 3728509725, "dataset_size": 6613611409}}
|
2023-10-10T11:29:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "german_OpenOrca_Format2"
More Information needed
|
[
"# Dataset Card for \"german_OpenOrca_Format2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"german_OpenOrca_Format2\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"german_OpenOrca_Format2\"\n\nMore Information needed"
] |
5654a50150e47f1f0f0ebcfb9a9a72ec4f45af4c
|
# Bangumi Image Base of Sono Bisque Doll Wa Koi O Suru
This is the image base of bangumi Sono Bisque Doll wa Koi o Suru, we detected 13 characters, 1120 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 462 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 24 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 9 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 8 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 31 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 286 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 60 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 51 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 16 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 13 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 7 | [Download](10/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 11 | 22 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 131 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/sonobisquedollwakoiosuru
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T10:15:12+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T11:07:42+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Sono Bisque Doll Wa Koi O Suru
====================================================
This is the image base of bangumi Sono Bisque Doll wa Koi o Suru, we detected 13 characters, 1120 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
f86ee624cfc8d7d8c5ec2826c19e3af39553ad6a
|
# Dataset Card for "OpenNiji-Dataset-Aesthetic-Finetune-0-15K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ShoukanLabs/OpenNiji-Dataset-Aesthetic-Finetune-0-15K
|
[
"region:us"
] |
2023-10-04T10:24:07+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "url", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "style", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 23769793746.39, "num_examples": 15085}], "download_size": 25132319914, "dataset_size": 23769793746.39}}
|
2023-10-04T15:40:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OpenNiji-Dataset-Aesthetic-Finetune-0-15K"
More Information needed
|
[
"# Dataset Card for \"OpenNiji-Dataset-Aesthetic-Finetune-0-15K\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OpenNiji-Dataset-Aesthetic-Finetune-0-15K\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OpenNiji-Dataset-Aesthetic-Finetune-0-15K\"\n\nMore Information needed"
] |
f346c5d47b0acdfe152c4bfac8cac51009d88fec
|
# SCANnotateDataset
For up-to-date information please visit our [github repository](https://github.com/stefan-ainetter/SCANnotateDataset)
CAD model and pose annotations for objects in the ScanNet dataset. Annotations are automatically generated
using [scannotate](https://github.com/stefan-ainetter/SCANnotate) and [HOC-Search](https://arxiv.org/abs/2309.06107).
The quality of these annotations was verified in several verification passes,
with manual re-annotations performed for outliers to ensure that final annotations are of high quality.
<p align="center">
<img src="figures/example_annotation.png" width="100%"/>
</p>
## Details about Annotations
For the public [ScanNet dataset](http://www.scan-net.org/), we provide:
* `18617` CAD model annotations for objects in the ScanNet dataset (30% more annotated objects compared to [Scan2CAD](https://github.com/skanti/Scan2CAD))
* Accurate 9D pose for each CAD model
* 3D semantic object instance segmentation corresponding to the annotated objects
* Automatically generated symmetry tags for ShapeNet CAD models for all categories present in ScanNet
* Extracted view parameters (selected RGB-D images and camera poses) for each object, which
can be used for CAD model retrieval via render-and-compare
## CAD Model and Pose Annotations
Our annotations for ScanNet are provided as `.pkl` files, which
contain additional information about the annotated objects, e.g. view parameters for render-and-compare and the
corresponding 3D instance segmentation of the pointcloud data.
For convenience, we additionally provide the annotations as `.json` file using the scan2cad data format.
**Note** that in order to use any of the provided annotations correctly, you have to preprocess the ShapeNet
CAD models (center and scale-normalize all CAD models) as explained below,
to generate clean CAD models which are then compatible with our annotations.
### Preliminaries: Download ShapeNet and ScanNet examples
* Download the ScanNet example scene [here](https://files.icg.tugraz.at/f/5b1b756a78bb457aafb5/?dl=1). Extract the data
and copy them to `/data/ScanNet/scans`. Note that by downloading this example data
you agree to the [ScanNet Terms of Use](https://kaldir.vc.in.tum.de/scannet/ScanNet_TOS.pdf).
To download the full ScanNet dataset follow the instructions on the [ScanNet GitHub page](https://github.com/ScanNet/ScanNet).
* Download the [ShapenetV2](https://shapenet.org/) dataset by signing up
on the website. Extract ShapeNetCore.v2.zip to `/data/ShapeNet`.
* Download our annotations for the full ScanNet dataset
[here](https://files.icg.tugraz.at/f/249aa5c3418f4c1897ee/?dl=1). Extract the data and copy them to
`/data/ScanNet/annotations`.
#### Preprocessing ShapeNet CAD Models
To center and scale-normalize the downloaded ShapeNet CAD models, run:
```bash
bash run_shapenet_prepro.sh gpu=0
```
The `gpu` argument specifies which GPU should be used for processing.
By default, code is executed on CPU.
After the above-mentioned steps the `/data` folder should contain the following directories:
```text
- data
- ScanNet
- annotations
- scene0495_00
- ...
- scans
- scene0495_00
- ShapeNet
- ShapeNet_preprocessed
- ShapeNetCore.v2
```
#### Installation Requirements and Setup
* Clone this repository. Install PyTorch3D by following the instructions from the
[official installation guide](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md).
After installing Pytorch3D, run the following command:
```bash
pip install scikit-image matplotlib imageio plotly opencv-python open3d trimesh==3.10.2
```
### Annotations in Scan2CAD data format
Annotations in scan2cad format are available [here](https://files.icg.tugraz.at/f/aaaf656e64014745af15/?dl=1).
The file `full_annotions_scannotate.json` contains `1513` entries, where the field of one entry is described as:
```javascript
[{
id_scan : "scannet scene id",
trs : { // <-- transformation from scan space to world space
translation : [tx, ty, tz], // <-- translation vector
rotation : [qw, qx, qy, qz], // <-- rotation quaternion
scale : [sx, sy, sz], // <-- scale vector
},
aligned_models : [{ // <-- list of aligned models for this scene
sym : "(__SYM_NONE, __SYM_ROTATE_UP_2, __SYM_ROTATE_UP_4 or __SYM_ROTATE_UP_INF)", // <-- symmetry property only one applies
catid_cad : "shapenet category id",
id_cad : "shapenet model id",
category_name : "", // e.g. chair,
trs : { // <-- transformation from CAD space to world space
translation : [tx, ty, tz], // <-- translation vector
rotation : [qw, qx, qy, qz], // <-- rotation quaternion
scale : [sx, sy, sz] // <-- scale vector
},
keypoints_scan : {}, // no keypoints in our annotations
keypoints_cad : {}, // no keypoints in our annotations
scannet_category_label: "", // e.g. chair; this label is taken from original ScanNet 3D object instance segmentation
object_id: "", // unique id for each annotated object in the scene
is_in_scan2cad: // <-- True if CAD annotation is available in scan2cad, else False
}]
},
{ ... },
{ ... },
]
```
### Visualization of Annotations
Use the following command to visualize the annotations:
```bash
bash visualize_annotations.sh
```
## ShapeNet Object Symmetry Annotations
Automatically generated symmetry tags for all CAD models of considered categories are available for download
[here](https://files.icg.tugraz.at/f/58469ba8edbd419abb6d/?dl=1). Symmetry
tags are saved in the following format:
```javascript
[{
cad_symmetry_dict: { // Symmetry Tags for CAD models
synset_id: { // shapenet category id,
category_name: "", // e.g. chair,
synset_id: "",
object_sym_dict: { // <-- dictionary containing CAD model ids and corresponding symmetry tags
'id_cad': 'symmetry_tag',
},
{...},
{...},
}
}
}]
```
To predict the symmetry tag for a given CAD model, we first render depth maps from 6 different views of the
preprocessed CAD model.
We then rotate the object around the vertical axis by a specific angle (e.g. 180° to check for
__SYM_ROTATE_UP_2), and again render the depth maps of the 6 views. If the difference of depth renderings is below a
certain threshold, we assume that the object is symmetric according to the performed rotation.
<p align="center">
<img src="figures/example_symmetry_annotation.png" width="80%"/>
</p>
## Citation
To create these annotations, we used the CAD model retrieval pipeline from
[scannotate](https://github.com/stefan-ainetter/SCANnotate), but replaced the exhaustive
CAD retrieval stage with [HOC-Search](https://arxiv.org/abs/2309.06107).
If you use any of the provided code or data, please cite the following works:
Scannotate:
```bibtex
@inproceedings{ainetter2023automatically,
title={Automatically Annotating Indoor Images with CAD Models via RGB-D Scans},
author={Ainetter, Stefan and Stekovic, Sinisa and Fraundorfer, Friedrich and Lepetit, Vincent},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={3156--3164},
year={2023}
}
```
HOC-Search:
```bibtex
@misc{ainetter2023hocsearch,
title={HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans},
author={Stefan Ainetter and Sinisa Stekovic and Friedrich Fraundorfer and Vincent Lepetit},
year={2023},
eprint={2309.06107},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
vevenom/SCANnotateDataset
|
[
"arxiv:2309.06107",
"region:us"
] |
2023-10-04T10:26:30+00:00
|
{}
|
2023-10-04T10:42:45+00:00
|
[
"2309.06107"
] |
[] |
TAGS
#arxiv-2309.06107 #region-us
|
# SCANnotateDataset
For up-to-date information please visit our github repository
CAD model and pose annotations for objects in the ScanNet dataset. Annotations are automatically generated
using scannotate and HOC-Search.
The quality of these annotations was verified in several verification passes,
with manual re-annotations performed for outliers to ensure that final annotations are of high quality.
<p align="center">
<img src="figures/example_annotation.png" width="100%"/>
</p>
## Details about Annotations
For the public ScanNet dataset, we provide:
* '18617' CAD model annotations for objects in the ScanNet dataset (30% more annotated objects compared to Scan2CAD)
* Accurate 9D pose for each CAD model
* 3D semantic object instance segmentation corresponding to the annotated objects
* Automatically generated symmetry tags for ShapeNet CAD models for all categories present in ScanNet
* Extracted view parameters (selected RGB-D images and camera poses) for each object, which
can be used for CAD model retrieval via render-and-compare
## CAD Model and Pose Annotations
Our annotations for ScanNet are provided as '.pkl' files, which
contain additional information about the annotated objects, e.g. view parameters for render-and-compare and the
corresponding 3D instance segmentation of the pointcloud data.
For convenience, we additionally provide the annotations as '.json' file using the scan2cad data format.
Note that in order to use any of the provided annotations correctly, you have to preprocess the ShapeNet
CAD models (center and scale-normalize all CAD models) as explained below,
to generate clean CAD models which are then compatible with our annotations.
### Preliminaries: Download ShapeNet and ScanNet examples
* Download the ScanNet example scene here. Extract the data
and copy them to '/data/ScanNet/scans'. Note that by downloading this example data
you agree to the ScanNet Terms of Use.
To download the full ScanNet dataset follow the instructions on the ScanNet GitHub page.
* Download the ShapenetV2 dataset by signing up
on the website. Extract URL to '/data/ShapeNet'.
* Download our annotations for the full ScanNet dataset
here. Extract the data and copy them to
'/data/ScanNet/annotations'.
#### Preprocessing ShapeNet CAD Models
To center and scale-normalize the downloaded ShapeNet CAD models, run:
The 'gpu' argument specifies which GPU should be used for processing.
By default, code is executed on CPU.
After the above-mentioned steps the '/data' folder should contain the following directories:
#### Installation Requirements and Setup
* Clone this repository. Install PyTorch3D by following the instructions from the
official installation guide.
After installing Pytorch3D, run the following command:
### Annotations in Scan2CAD data format
Annotations in scan2cad format are available here.
The file 'full_annotions_scannotate.json' contains '1513' entries, where the field of one entry is described as:
### Visualization of Annotations
Use the following command to visualize the annotations:
## ShapeNet Object Symmetry Annotations
Automatically generated symmetry tags for all CAD models of considered categories are available for download
here. Symmetry
tags are saved in the following format:
To predict the symmetry tag for a given CAD model, we first render depth maps from 6 different views of the
preprocessed CAD model.
We then rotate the object around the vertical axis by a specific angle (e.g. 180° to check for
__SYM_ROTATE_UP_2), and again render the depth maps of the 6 views. If the difference of depth renderings is below a
certain threshold, we assume that the object is symmetric according to the performed rotation.
<p align="center">
<img src="figures/example_symmetry_annotation.png" width="80%"/>
</p>
To create these annotations, we used the CAD model retrieval pipeline from
scannotate, but replaced the exhaustive
CAD retrieval stage with HOC-Search.
If you use any of the provided code or data, please cite the following works:
Scannotate:
HOC-Search:
|
[
"# SCANnotateDataset\nFor up-to-date information please visit our github repository\n\nCAD model and pose annotations for objects in the ScanNet dataset. Annotations are automatically generated \nusing scannotate and HOC-Search. \nThe quality of these annotations was verified in several verification passes, \nwith manual re-annotations performed for outliers to ensure that final annotations are of high quality. \n\n<p align=\"center\">\n<img src=\"figures/example_annotation.png\" width=\"100%\"/>\n</p>",
"## Details about Annotations\n\nFor the public ScanNet dataset, we provide:\n\n* '18617' CAD model annotations for objects in the ScanNet dataset (30% more annotated objects compared to Scan2CAD)\n* Accurate 9D pose for each CAD model\n* 3D semantic object instance segmentation corresponding to the annotated objects\n* Automatically generated symmetry tags for ShapeNet CAD models for all categories present in ScanNet\n* Extracted view parameters (selected RGB-D images and camera poses) for each object, which\ncan be used for CAD model retrieval via render-and-compare",
"## CAD Model and Pose Annotations\nOur annotations for ScanNet are provided as '.pkl' files, which \ncontain additional information about the annotated objects, e.g. view parameters for render-and-compare and the \ncorresponding 3D instance segmentation of the pointcloud data.\n\nFor convenience, we additionally provide the annotations as '.json' file using the scan2cad data format. \n\nNote that in order to use any of the provided annotations correctly, you have to preprocess the ShapeNet \nCAD models (center and scale-normalize all CAD models) as explained below, \nto generate clean CAD models which are then compatible with our annotations.",
"### Preliminaries: Download ShapeNet and ScanNet examples\n\n* Download the ScanNet example scene here. Extract the data\nand copy them to '/data/ScanNet/scans'. Note that by downloading this example data\nyou agree to the ScanNet Terms of Use. \nTo download the full ScanNet dataset follow the instructions on the ScanNet GitHub page.\n\n* Download the ShapenetV2 dataset by signing up\non the website. Extract URL to '/data/ShapeNet'.\n\n* Download our annotations for the full ScanNet dataset \nhere. Extract the data and copy them to\n'/data/ScanNet/annotations'.",
"#### Preprocessing ShapeNet CAD Models\nTo center and scale-normalize the downloaded ShapeNet CAD models, run:\n\nThe 'gpu' argument specifies which GPU should be used for processing. \nBy default, code is executed on CPU.\n\nAfter the above-mentioned steps the '/data' folder should contain the following directories:",
"#### Installation Requirements and Setup\n\n* Clone this repository. Install PyTorch3D by following the instructions from the\nofficial installation guide.\n\nAfter installing Pytorch3D, run the following command:",
"### Annotations in Scan2CAD data format\nAnnotations in scan2cad format are available here.\nThe file 'full_annotions_scannotate.json' contains '1513' entries, where the field of one entry is described as:",
"### Visualization of Annotations\nUse the following command to visualize the annotations:",
"## ShapeNet Object Symmetry Annotations\nAutomatically generated symmetry tags for all CAD models of considered categories are available for download \nhere. Symmetry\ntags are saved in the following format:\n\n\nTo predict the symmetry tag for a given CAD model, we first render depth maps from 6 different views of the \npreprocessed CAD model. \nWe then rotate the object around the vertical axis by a specific angle (e.g. 180° to check for \n__SYM_ROTATE_UP_2), and again render the depth maps of the 6 views. If the difference of depth renderings is below a \ncertain threshold, we assume that the object is symmetric according to the performed rotation.\n\n<p align=\"center\">\n<img src=\"figures/example_symmetry_annotation.png\" width=\"80%\"/>\n</p>\n\nTo create these annotations, we used the CAD model retrieval pipeline from \nscannotate, but replaced the exhaustive\nCAD retrieval stage with HOC-Search. \nIf you use any of the provided code or data, please cite the following works:\n\nScannotate:\n\nHOC-Search:"
] |
[
"TAGS\n#arxiv-2309.06107 #region-us \n",
"# SCANnotateDataset\nFor up-to-date information please visit our github repository\n\nCAD model and pose annotations for objects in the ScanNet dataset. Annotations are automatically generated \nusing scannotate and HOC-Search. \nThe quality of these annotations was verified in several verification passes, \nwith manual re-annotations performed for outliers to ensure that final annotations are of high quality. \n\n<p align=\"center\">\n<img src=\"figures/example_annotation.png\" width=\"100%\"/>\n</p>",
"## Details about Annotations\n\nFor the public ScanNet dataset, we provide:\n\n* '18617' CAD model annotations for objects in the ScanNet dataset (30% more annotated objects compared to Scan2CAD)\n* Accurate 9D pose for each CAD model\n* 3D semantic object instance segmentation corresponding to the annotated objects\n* Automatically generated symmetry tags for ShapeNet CAD models for all categories present in ScanNet\n* Extracted view parameters (selected RGB-D images and camera poses) for each object, which\ncan be used for CAD model retrieval via render-and-compare",
"## CAD Model and Pose Annotations\nOur annotations for ScanNet are provided as '.pkl' files, which \ncontain additional information about the annotated objects, e.g. view parameters for render-and-compare and the \ncorresponding 3D instance segmentation of the pointcloud data.\n\nFor convenience, we additionally provide the annotations as '.json' file using the scan2cad data format. \n\nNote that in order to use any of the provided annotations correctly, you have to preprocess the ShapeNet \nCAD models (center and scale-normalize all CAD models) as explained below, \nto generate clean CAD models which are then compatible with our annotations.",
"### Preliminaries: Download ShapeNet and ScanNet examples\n\n* Download the ScanNet example scene here. Extract the data\nand copy them to '/data/ScanNet/scans'. Note that by downloading this example data\nyou agree to the ScanNet Terms of Use. \nTo download the full ScanNet dataset follow the instructions on the ScanNet GitHub page.\n\n* Download the ShapenetV2 dataset by signing up\non the website. Extract URL to '/data/ShapeNet'.\n\n* Download our annotations for the full ScanNet dataset \nhere. Extract the data and copy them to\n'/data/ScanNet/annotations'.",
"#### Preprocessing ShapeNet CAD Models\nTo center and scale-normalize the downloaded ShapeNet CAD models, run:\n\nThe 'gpu' argument specifies which GPU should be used for processing. \nBy default, code is executed on CPU.\n\nAfter the above-mentioned steps the '/data' folder should contain the following directories:",
"#### Installation Requirements and Setup\n\n* Clone this repository. Install PyTorch3D by following the instructions from the\nofficial installation guide.\n\nAfter installing Pytorch3D, run the following command:",
"### Annotations in Scan2CAD data format\nAnnotations in scan2cad format are available here.\nThe file 'full_annotions_scannotate.json' contains '1513' entries, where the field of one entry is described as:",
"### Visualization of Annotations\nUse the following command to visualize the annotations:",
"## ShapeNet Object Symmetry Annotations\nAutomatically generated symmetry tags for all CAD models of considered categories are available for download \nhere. Symmetry\ntags are saved in the following format:\n\n\nTo predict the symmetry tag for a given CAD model, we first render depth maps from 6 different views of the \npreprocessed CAD model. \nWe then rotate the object around the vertical axis by a specific angle (e.g. 180° to check for \n__SYM_ROTATE_UP_2), and again render the depth maps of the 6 views. If the difference of depth renderings is below a \ncertain threshold, we assume that the object is symmetric according to the performed rotation.\n\n<p align=\"center\">\n<img src=\"figures/example_symmetry_annotation.png\" width=\"80%\"/>\n</p>\n\nTo create these annotations, we used the CAD model retrieval pipeline from \nscannotate, but replaced the exhaustive\nCAD retrieval stage with HOC-Search. \nIf you use any of the provided code or data, please cite the following works:\n\nScannotate:\n\nHOC-Search:"
] |
[
14,
134,
141,
151,
151,
77,
48,
57,
20,
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[
"passage: TAGS\n#arxiv-2309.06107 #region-us \n# SCANnotateDataset\nFor up-to-date information please visit our github repository\n\nCAD model and pose annotations for objects in the ScanNet dataset. Annotations are automatically generated \nusing scannotate and HOC-Search. \nThe quality of these annotations was verified in several verification passes, \nwith manual re-annotations performed for outliers to ensure that final annotations are of high quality. \n\n<p align=\"center\">\n<img src=\"figures/example_annotation.png\" width=\"100%\"/>\n</p>## Details about Annotations\n\nFor the public ScanNet dataset, we provide:\n\n* '18617' CAD model annotations for objects in the ScanNet dataset (30% more annotated objects compared to Scan2CAD)\n* Accurate 9D pose for each CAD model\n* 3D semantic object instance segmentation corresponding to the annotated objects\n* Automatically generated symmetry tags for ShapeNet CAD models for all categories present in ScanNet\n* Extracted view parameters (selected RGB-D images and camera poses) for each object, which\ncan be used for CAD model retrieval via render-and-compare## CAD Model and Pose Annotations\nOur annotations for ScanNet are provided as '.pkl' files, which \ncontain additional information about the annotated objects, e.g. view parameters for render-and-compare and the \ncorresponding 3D instance segmentation of the pointcloud data.\n\nFor convenience, we additionally provide the annotations as '.json' file using the scan2cad data format. \n\nNote that in order to use any of the provided annotations correctly, you have to preprocess the ShapeNet \nCAD models (center and scale-normalize all CAD models) as explained below, \nto generate clean CAD models which are then compatible with our annotations."
] |
ae806d062a464c72538cac590742318fbf6a309d
|
# Dataset Card for "prepared_above_70yo_elderly_people_datasetV2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
aviroes/prepared_above_70yo_elderly_people_datasetV2
|
[
"region:us"
] |
2023-10-04T11:21:02+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "input_length", "dtype": "float64"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4048541464, "num_examples": 4215}, {"name": "test", "num_bytes": 159444680, "num_examples": 166}, {"name": "validation", "num_bytes": 96050136, "num_examples": 100}], "download_size": 795989596, "dataset_size": 4304036280}}
|
2023-10-04T11:22:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "prepared_above_70yo_elderly_people_datasetV2"
More Information needed
|
[
"# Dataset Card for \"prepared_above_70yo_elderly_people_datasetV2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"prepared_above_70yo_elderly_people_datasetV2\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"prepared_above_70yo_elderly_people_datasetV2\"\n\nMore Information needed"
] |
2625471aeee26a9c833e0f150878302c53500eec
|
# Verbalist (буквоед) - русскоязычный ассистент.
Проект во многом вдохновленный [Saiga](https://huggingface.co/IlyaGusev/saiga2_7b_lora).
Мною были собраны все самые качественные датасеты с [huggingface.datasets](https://huggingface.co/datasets), а также собраны дополнительно с тех сайтов, которые я посчитал весьма полезными для создания аналога ChatGPT. Лицензии у всех датасетов отличаются, какие-то по типу [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) были созданы специально для обучения подобных моделей, какие-то являются прямой выгрузкой диалогов с ChatGPT ([RyokoAI/ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K)).
Вклад данного репозитория состоит в систематизации и стандартизации уже имеющихся датасетов, добавлении новых. А также тренировке моделей на этих данных.
- [google sheets таблица с датасетами и описанием](https://docs.google.com/spreadsheets/d/10xcsINF_c_zUZchT8p-8xIuHDgcuwg63jjl2ortBP9I/edit?usp=sharing)
### Датасеты
- **[Объединенный датасет где все данные уже подготовлены для тренировки диалоговой модели](https://huggingface.co/datasets/dim/verbalist_prompts)**
|name |link |description |original_name |original_source |preparation_script |language|amount_examples|mean_llama_tokens|std |min_llama_tokens|25% |50% |75% |max_llama_tokens|
|-------------------------------------|---------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|--------|---------------|-----------------|-----------|----------------|-------|-------|-------|----------------|
|dim/oasst_en |https://huggingface.co/datasets/dim/oasst_en |OpenAssistant Conversations Dataset на английском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: https://docs.google.com/spreadsheets/d/117t5-Tr-dxdODpyFBkBg5R8GklYBlsvBfeDyjqwz2pA/edit?usp=sharing|2023-04-12_oasst_ready.messages.jsonl.gz |https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/2023-04-12_oasst_ready.messages.jsonl.gz|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oasst |en |2289 |468.6788991 |295.0864391|17 |264 |410 |618 |2332 |
|dim/oasst_ru |https://huggingface.co/datasets/dim/oasst_ru |OpenAssistant Conversations Dataset на русском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: https://docs.google.com/spreadsheets/d/1uiOnqxiytuxrB6u6q2pMSdnMfqjT3arfg8DlT-OWlb0/edit?usp=sharing |2023-04-12_oasst_ready.messages.jsonl.gz |https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/2023-04-12_oasst_ready.messages.jsonl.gz|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oasst |ru |2220 |589.6112613 |479.835392 |7 |278 |465 |763.5 |5028 |
|dim/lima |https://huggingface.co/datasets/dim/lima |Данный датасет включает в себя 1000 высококачественных обучающих примеров на английском языке. Он собран из различных источников, включая Stack Exchange (STEM), Stack Exchange (Other), wikiHow, Pushshift r/WritingPrompts, Natural Instructions, а также уникальные инструкции, созданные авторами статей. Более подробную информацию о датасете можно найти в [соответствующей статье](https://arxiv.org/pdf/2305.11206.pdf). |GAIR/lima |https://huggingface.co/datasets/GAIR/lima |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/lima |en |1030 |712.9456311 |671.179319 |29 |312.75 |488.5 |825 |3920 |
|dim/logic_tasks_ru |https://huggingface.co/datasets/dim/logic_tasks_ru |Данный набор задач по логике для детей взят с веб-сайта https://www.potehechas.ru/zadachi/zadachi.shtml. |Логические задачи - Логика и нестандартное мышление |https://www.potehechas.ru/zadachi/zadachi.shtml |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/logic_tasks_ru |ru |86 |193.0697674 |76.69048422|58 |133.75 |185 |243.5 |432 |
|dim/wikihow_en |https://huggingface.co/datasets/dim/wikihow_en |Данный датасет содержит англоязычные статьи, извлеченные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |https://huggingface.co/datasets/0x22almostEvil/multilingual-wikihow-qa-16k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/wiki_how |en |1995 |2037.86416 |870.1910713|265 |1463 |1913 |2461.5 |8988 |
|dim/wikihow_ru |https://huggingface.co/datasets/dim/wikihow_ru |Данный датасет включает в себя русскоязычные статьи, полученные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |https://huggingface.co/datasets/0x22almostEvil/multilingual-wikihow-qa-16k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/wiki_how |ru |2058 |2498.119534 |1587.851549|139 |1236.25|2264 |3421.75|10217 |
|dim/essayforum_writing_prompts_6k |https://huggingface.co/datasets/dim/essayforum_writing_prompts_6k |Данный датасет включает в себя запросы на помощь с написанием небольших эссе, размещенные на данном сайте. Ответы в датасете предоставлены исключительно главным администратором сайта. Его ответы были отобраны, поскольку чаще всего они являются наиболее качественными и вдумчивыми. |EssayForum |https://essayforum.com/writing/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/essayforum |en |6361 |783.1760729 |285.4314176|258 |629 |742 |879 |4966 |
|dim/sharegpt_short_ru |https://huggingface.co/datasets/dim/sharegpt_short_ru |Очищенная версия русская версия sharegpt. Я попытался вырезать из текста все промпты, где модель извиняется что что-то не может сделать, что она не имеет доступа в интернет. Диалоги, которые противоречат морали модели я просто исключил. Постарался убрать упоминания о том что она модель AI, так как за ролеплейные характеристики отвечают другие датасеты. |RyokoAI/ShareGPT52K |https://huggingface.co/datasets/RyokoAI/ShareGPT52K |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/sharegpt |ru |253 |706.6521739 |494.7437584|13 |310 |628 |1078 |1861 |
|dim/openreview_prompts_65 |https://huggingface.co/datasets/dim/openreview_prompts_65 |Датасет рецензий на реальные научные статьи с сайта openreview. Вышло на самом деле не так много, так как многие статьи не выложенны на arxiv или просто не имеют рецензий. Плюс я собрал только малую часть данного сайта, а не все что там было. |https://openreview.net/ |https://openreview.net/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/openreview |en |150 |13531.51333 |6966.623686|4893 |8279 |12648.5|15833.5|41494 |
|dim/roleplay_instruct_v2_final |https://huggingface.co/datasets/dim/roleplay_instruct_v2_final |Датасет ролеплея от GPT-4 на различных персонажей на английском языке. |roleplay-instruct-v2-final |https://github.com/teknium1/GPTeacher |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/gpt_roleplay_realm |en |7188 |155.1413467 |97.71215667|14 |88 |125 |192 |1291 |
|dim/kinomania_scripts |https://huggingface.co/datasets/dim/kinomania_scripts |Небольшой датасет, который содержит в себе сценарии фильмов целиком и их краткое содержание |https://www.kinomania.ru/scripts |https://www.kinomania.ru/scripts |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/kinomania_scripts |ru\en |27 |2603.407407 |510.375447 |1887 |2175 |2370 |3069 |3616 |
|dim/bugurt_thread_prompts |https://huggingface.co/datasets/dim/bugurt_thread_prompts |Небольшой набор размеченных бугуртов вместе с моим другом, для того чтобы модель научилась писать бугурты на конкретную ситуацию. Собраны из телеграм паблика БУГУРТ ТРЕД(https://t.me/bugurtthread) |https://t.me/bugurtthread |https://t.me/bugurtthread |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/bugurt_thread |ru |223 |334.4529148 |271.2557988|48 |148.5 |254 |434.5 |1645 |
|dim/russian_lyrics_prompts |https://huggingface.co/datasets/dim/russian_lyrics_prompts |Небольшой датасет промптов собранный мною из различных учебников по стихосложению, чтобы модель научилась писать стихи, используя необходимый литературный прием на конкретную тему. |Учебник стихосложения |https://stihi.ru/uchebnik/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/russian_lyrics_prompts |ru |43 |106.1395349 |71.00220701|45 |71 |83 |96.5 |411 |
|dim/ru_instruct_gpt4 |https://huggingface.co/datasets/dim/ru_instruct_gpt4 |Датасет каких-то инструкций на русском сгенерированных GPT-4 |lksy/ru_instruct_gpt4 |https://huggingface.co/datasets/lksy/ru_instruct_gpt4 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_instruct_gpt4 |ru |14222 |259.2173393 |237.9433891|16 |109 |175 |271 |1374 |
|dim/gpt_roleplay_realm |https://huggingface.co/datasets/dim/gpt_roleplay_realm |Диалоги выдуманных персонажей при помощи GPT-4, диалоги были сгенерированны при помощи GPT-3.5. Русский и английский. |IlyaGusev/gpt_roleplay_realm |https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/gpt_roleplay_realm |ru\en |8700 |504.2424138 |117.6228987|180 |424 |489 |569 |1207 |
|dim/ultrachat_ru |https://huggingface.co/datasets/dim/ultrachat_ru |Какой-то рандомный датасет диалогов от chatgpt, который я нашел на huggingface. Из текста диалогов были вырезаны шаблонные фразы по типу: "я не могу выполнить", "как языковая модель" и тд. Потому что обычно после этого следовало вменяемое решение задачи. |kaleinaNyan/UltraChat_ru |https://huggingface.co/datasets/kaleinaNyan/UltraChat_ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ultrachat_ru |ru |500 |1781.782 |901.1212735|267 |1113.25|1648 |2250.25|7303 |
|dim/scitldr |https://huggingface.co/datasets/dim/scitldr |Саммаризация научных статей на английском языке, выполненная экспертами. |allenai/scitldr |https://huggingface.co/datasets/allenai/scitldr |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/scitldr |en |3229 |258.748529 |71.41209752|60 |209 |252 |303 |689 |
|dim/linux_man_pages_tldr_summarized |https://huggingface.co/datasets/dim/linux_man_pages_tldr_summarized |Саммаризация мануалов для инструментов линукс в удобный набор команд с их кратким описанием. |tmskss/linux-man-pages-tldr-summarized |https://huggingface.co/datasets/tmskss/linux-man-pages-tldr-summarized |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/linux-man-pages-tldr-summarized |en |481 |1567.727651 |3590.30871 |96 |405 |765 |1386 |49888 |
|dim/dolphin_ru_3k |https://huggingface.co/datasets/dim/dolphin_ru_3k |Подвыборка размера 3000 переведенных заданий dolphin. Примеры из оригинального датасета это промпты из FLANv2 и решения при помощи GPT-4 или GPT-3.5. |d0rj/dolphin-ru |https://huggingface.co/datasets/d0rj/dolphin-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dolphin_ru |ru |3000 |556.1133333 |650.0962612|19 |207 |369.5 |720.25 |6787 |
|dim/runne_prompts |https://huggingface.co/datasets/dim/runne_prompts |Промпты составленные из датасета RuNNE. Лично я при обучении сотавил промпт следующим образом. Сначала идет текст "Найди все именованные сущности в данном тексте:", а затем шел сам текст. В качестве выхода модели нужно сгенерировать JSON где содержатся все найденные именованные сущности. К примеру так [{"name": "PERSON", "ent": "Ким Чен Нама", "pos": "0 12"}, {"name": "ORGANIZATION", "ent": "Полиция Малайзии", "pos": "56 72"}] |iluvvatar/RuNNE |https://huggingface.co/datasets/iluvvatar/RuNNE |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/RuNNE |ru |537 |1479.750466 |230.0259174|581 |1337 |1480 |1635 |1988 |
|dim/lurk_prompts |https://huggingface.co/datasets/dim/lurk_prompts |Набор определений различных терминов с сайта lurk. Сами промпты были составлены автоматически следующим образом. напиши определение для (ОПРЕДЕЛЕНИЕ) в стиле lurk |averoo/lurk |https://huggingface.co/datasets/averoo/lurk/viewer/default/train?p=2 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/lurk |ru |5671 |3450.34262 |4147.897824|35 |710.5 |2010 |4593 |55098 |
|dim/panorama_prompts_10k |https://huggingface.co/datasets/dim/panorama_prompts_10k |Набор юмористических заголовков и текстов новостей с сайта панорама. |its5Q/panorama |https://huggingface.co/datasets/its5Q/panorama |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/panorama |ru |11024 |516.9588171 |191.3774023|36 |422 |498 |585 |3496 |
|dim/resh_edu_short_prompts |https://huggingface.co/datasets/dim/resh_edu_short_prompts |Набор уроков с сайта resh.edu.ru включающих в себя название урока, тему, класс и текст урока с заданиями. |its5Q/resh-edu |https://huggingface.co/datasets/its5Q/resh-edu |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/resh_edu |ru |2106 |1431.510921 |435.7847102|56 |1175.5 |1517 |1777 |2029 |
|dim/databricks_dolly_15k_ru |https://huggingface.co/datasets/dim/databricks_dolly_15k_ru |Переведенный датасет dolly на русский язык. Включает в себя набор инструкций на обширное количество тематик. |dwarf2/databricks-dolly-15k-ru |https://huggingface.co/dwarf2/databricks-dolly-15k-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/databricks_dolly_15k_ru |ru |14914 |305.4638595 |405.874049 |8 |87 |182 |370 |9268 |
|dim/databricks_dolly_15k_en |https://huggingface.co/datasets/dim/databricks_dolly_15k_en |databricks-dolly-15k — это набор данных с открытым исходным кодом, содержащий записи о выполнении инструкций, созданные тысячами сотрудников Databricks в нескольких поведенческих категориях, изложенных в документе InstructGPT, включая мозговой штурм, классификацию, закрытый контроль качества, генерацию, извлечение информации, открытый контроль качества и обобщение. |databricks/databricks-dolly-15k |https://huggingface.co/datasets/databricks/databricks-dolly-15k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/databricks_dolly_15k_en |en |15011 |204.7264006 |302.5539423|6 |57 |119 |242 |8883 |
|dim/grammarly_coedit |https://huggingface.co/datasets/dim/grammarly_coedit |Набор промптов, которые просят исправить грамматические, стилистические ошибки на английском. |grammarly/coedit |https://huggingface.co/datasets/grammarly/coedit |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grammarly_coedit |en |82466 |53.7128271 |26.73822864|10 |35 |46 |64 |694 |
|dim/kinopoisk_prompts |https://huggingface.co/datasets/dim/kinopoisk_prompts |Отзывы с кинопоиска на топ 250 фильмов. В промптах я прошу написать хороший, плохой или нейтральный отзыв на определенный фильм. |blinoff/kinopoisk |https://huggingface.co/datasets/blinoff/kinopoisk |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/kinopoisk |ru |36591 |875.0955973 |565.3212035|48 |484 |733 |1117 |8628 |
|dim/medical_qa_ru_prompts |https://huggingface.co/datasets/dim/medical_qa_ru_prompts |Какие-то вопросы и ответы с какого-то медицинского форума. В данной версии датасета только первый ответ из оригинала. |blinoff/medical_qa_ru_data |https://huggingface.co/datasets/blinoff/medical_qa_ru_data |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/medical_qa_ru_data |ru |80101 |206.710528 |175.4343973|12 |106 |161 |247 |5062 |
|dim/joke_explaination_prompts |https://huggingface.co/datasets/dim/joke_explaination_prompts |Объяснение шуток на английском. От изначального датасета отличается тем, что я убрал последнее предложение из объяснения, так как оно ссылается на видео на сайте. |theblackcat102/joke_explaination |https://huggingface.co/datasets/theblackcat102/joke_explaination |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/joke_explaination |en |364 |143.5741758 |68.90275411|21 |99 |137.5 |189.25 |334 |
|dim/oa_stackexchange_200k |https://huggingface.co/datasets/dim/oa_stackexchange_200k |Вопросы-ответы со stackexchange. Оригинальный датасет был составлен следующим образом: были выбраны только темы с принятым ответом, для которых длина вопроса и ответа составляет менее 1000 символов. Другие ответы, вопросы без принятых ответов или длинные записи были удалены. Так как оригинальный датасет слишком большой, я рандомно выбрал 200k семплов. |donfu/oa-stackexchange |https://huggingface.co/datasets/donfu/oa-stackexchange |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oa_stackexchange |en |200000 |276.29862 |112.5004436|22 |194 |265 |345 |1226 |
|dim/scale_helpful_no_math |https://huggingface.co/datasets/dim/scale_helpful_no_math |Какой-то набор диалогов с вопросами-ответами на английском, происхождение неизвестно. |HuggingFaceH4/scale_helpful_no_math |https://huggingface.co/datasets/HuggingFaceH4/scale_helpful_no_math/viewer/default/train_rm |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/scale_helpful_no_math |en |17095 |1235.302603 |838.1097885|53 |663 |1063 |1617 |34480 |
|dim/law_stackexchange_prompts |https://huggingface.co/datasets/dim/law_stackexchange_prompts |Вопросы про закон на английском языке со StackExchange. Оригинальный датасет был преобразован в markdown. |ymoslem/Law-StackExchange |https://huggingface.co/datasets/ymoslem/Law-StackExchange |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/law_stackexchange |en |24343 |689.1184324 |565.0316906|43 |354 |540 |836 |8969 |
|dim/ficbook_prompts_best_10k |https://huggingface.co/datasets/dim/ficbook_prompts_best_10k |Топ 10k лучших фанфиков с сайта ficbook.net. Все промпты выглядят следующим образом: напиши фанфик с названием {title} и следующим описанием {description}, с тегами {tags}, Где title это оригинальное название, description оригинальное описание, tags это теги данного произведения. |AlexWortega/FicBook |https://huggingface.co/datasets/AlexWortega/FicBook |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ficbook |ru |10000 |1737.8214 |402.0748161|166 |1716 |1950 |1950 |1952 |
|dim/azbyka_logic_ru |https://huggingface.co/datasets/dim/azbyka_logic_ru |Небольшой набор детских логических и православных задач, взятых с сайта https://azbyka.ru/deti/logicheskie-i-zanimatelnye-zadachi . Обычно у них почти нет развернутого решения, только ответ. Я пытался расписать решение некоторых задач, но меня хватило только на 35, если кто-то займется подобным буду рад https://docs.google.com/spreadsheets/d/1JRbtppbZCUbV_Eqd0nKbRDQEuPnJIAgJ70cUILEDUI4/edit?usp=sharing . |Логические и занимательные задачи (300 задач) |https://azbyka.ru/deti/logicheskie-i-zanimatelnye-zadachi |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/azbyka_logic_ru |ru |480 |77.4375 |77.56990416|14 |31 |50 |91 |652 |
|dim/povarenok |https://huggingface.co/datasets/dim/povarenok |46k лучших рецептов с сайта povarenok.ru, содержит текст рецепта, список ингридиентов, название блюда |https://www.povarenok.ru/recipes/ |https://www.povarenok.ru/recipes/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/povarenok |ru |46500 |488.9118495 |344.8563249|31 |281 |440 |632 |5542 |
|dim/AO3_fandom_chatbot_1to1 |https://huggingface.co/datasets/dim/AO3_fandom_chatbot_1to1 |Какой-то набор ролеплейных диалогов с описанием персонажей и их отыгрышем. Происхождение неизвестно. |ebony59/AO3_fandom_chatbot_1to1 |https://huggingface.co/datasets/ebony59/AO3_fandom_chatbot_1to1 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/AO3_fandom_chatbot_1to1 |en |614 |493.7166124 |226.3885365|129 |328.25 |432.5 |611.75 |1272 |
|dim/habr_prompts_5k |https://huggingface.co/datasets/dim/habr_prompts_5k |Статьи с хабра. Датасет был составлен с помощью chatgpt, chatgpt преобразовывал заголовки таким образом чтобы они звучали как вопросы от пользователя, в качестве таргета выступала сама статья. |IlyaGusev/habr |https://huggingface.co/datasets/IlyaGusev/habr |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/habr |ru |5000 |1732.892 |454.8418369|19 |1920.75|1950 |1951 |1952 |
|dim/what_where_when_50k |https://huggingface.co/datasets/dim/what_where_when_50k |50k вопросов с решениями с сайта что где когда. В качестве промпта выступает вопрос, в качестве ответа конкатенация объяснения и краткого ответа. Все вопросы-ответы вы можете найти по этой ссылке https://huggingface.co/datasets/dim/what_where_when_ru |https://db.chgk.info |https://db.chgk.info |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/what_where_when |ru |50000 |169.1862 |68.91119898|18 |122 |158 |202 |1167 |
|dim/competition_math |https://huggingface.co/datasets/dim/competition_math |Датасет олимпиадной математики на английском. The Mathematics Aptitude Test of Heuristics (MATH) dataset. |competition_math |https://huggingface.co/datasets/competition_math |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/competition_math |en |7500 |317.5254667 |267.8583731|34 |147 |234 |393 |3029 |
|dim/sharegpt_short_en_30k |https://huggingface.co/datasets/dim/sharegpt_short_en_30k |Короткие диалоги на английском из sharegpt |RyokoAI/ShareGPT52K |https://huggingface.co/datasets/RyokoAI/ShareGPT52K |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/sharegpt |en |29597 |749.3149981 |516.3702473|3 |336 |630 |1095 |2021 |
|dim/ru_turbo_alpaca_evol_instruct |https://huggingface.co/datasets/dim/ru_turbo_alpaca_evol_instruct |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru_turbo_alpaca_evol_instruct |https://huggingface.co/datasets/IlyaGusev/ru_turbo_alpaca_evol_instruct |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_turbo_alpaca_evol_instruct |ru |47793 |453.0887996 |289.5498356|17 |221 |430 |623 |4647 |
|dim/ru_turbo_saiga |https://huggingface.co/datasets/dim/ru_turbo_saiga |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru_turbo_saiga |https://huggingface.co/datasets/IlyaGusev/ru_turbo_saiga |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_turbo_saiga |ru |37699 |412.7508687 |113.346917 |87 |339 |398 |466 |1427 |
|dim/bugurt_completion_prompts |https://huggingface.co/datasets/dim/bugurt_completion_prompts |Обрезанные бугурты, где в качестве промпта используется строка вида - продолжи бугурт: первая строчка бугурта |https://t.me/bugurtthread |https://t.me/bugurtthread |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/bugurt_thread |ru |5000 |280.2466 |320.4353681|32 |111 |178 |331 |11333 |
|dim/tldr_17_50k |https://huggingface.co/datasets/dim/tldr_17_50k |Очень вольная абстрактная саммаризация постов с реддита в одну строчку |webis/tldr-17 |https://huggingface.co/datasets/webis/tldr-17 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/tldr_17 |en |50000 |421.12752 |403.346214 |10 |177 |303 |525 |9592 |
|dim/grade_school_math_instructions |https://huggingface.co/datasets/dim/grade_school_math_instructions |OpenAI's grade-school-math датасет преобразованный в промпты. |qwedsacf/grade-school-math-instructions |https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grade-school-math-instructions |en |8792 |171.6310282 |63.09232668|50 |124 |161 |206 |511 |
|dim/tldr_news |https://huggingface.co/datasets/dim/tldr_news |Хедлайны и текст новостей на различную тематику. |JulesBelveze/tldr_news |https://huggingface.co/datasets/JulesBelveze/tldr_news |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/tldr_news |en |7138 |133.1004483 |46.48736493|23 |100 |133 |161 |476 |
|dim/grade_school_math_instructions_ru|https://huggingface.co/datasets/dim/grade_school_math_instructions_ru|OpenAI's grade-school-math датасет переведенный на русский. |d0rj/gsm8k-ru |https://huggingface.co/datasets/d0rj/gsm8k-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grade_school_math_instructions_ru|ru |7473 |259.8321959 |100.1229127|78 |185 |241 |314 |838 |
|dim/dialogsum |https://huggingface.co/datasets/dim/dialogsum |Саммаризация диалогов на английском языке, разметка выполнялась вручную. |knkarthick/dialogsum |https://huggingface.co/datasets/knkarthick/dialogsum |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dialogsum |en |12460 |269.6467095 |126.285664 |75 |191 |245 |327 |1725 |
|dim/HC3_ru |https://huggingface.co/datasets/dim/HC3_ru |Вопросы-ответы с реддита, есть ответы сгенерированные chatgpt и реальные ответы пользователей. Я использовал только реальные ответы пользователей. |d0rj/HC3-ru |https://huggingface.co/datasets/d0rj/HC3-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/HC3_ru |ru |24322 |360.5608503 |330.2285903|15 |168 |267 |435 |10025 |
|dim/horoscopes_ru_10k |https://huggingface.co/datasets/dim/horoscopes_ru_10k |10k гороскопов, с промптами где я прошу сгенерировать гороском для определенного знака зодиака |dkagramanyan/horoscopes_ru |https://huggingface.co/datasets/dkagramanyan/horoscopes_ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/horoscopes_ru |ru |10000 |183.1443 |31.62023184|55 |159 |187 |201 |464 |
|dim/yandex_q_200k |https://huggingface.co/datasets/dim/yandex_q_200k |200k рандомно выбранных вопросов-ответов с сайта yandex q. |its5Q/yandex-q |https://huggingface.co/datasets/its5Q/yandex-q |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/yandex_q |ru |200000 |304.569005 |340.7808288|18 |127 |202 |353 |19294 |
|dim/leetcodesolutions_en_2k |https://huggingface.co/datasets/dim/leetcodesolutions_en_2k |Решения задач с leetcode на разных языках. |TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k |https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/leetcodesolutions_en_2k |en |2048 |740.7441406 |253.2493282|297 |565 |685 |857 |1960 |
|dim/forum_uristov_rf_prompts |https://huggingface.co/datasets/dim/forum_uristov_rf_prompts |Вопросы-ответы с российского юридического форума. |https://xn----dtbrojdkckkfj9k.xn--p1ai/vopros-yuristu?page=560|https://xn----dtbrojdkckkfj9k.xn--p1ai/vopros-yuristu?page=560 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/forum_uristov_rf |ru |1849 |321.0540833 |429.58896 |31 |134 |210 |349 |6470 |
|dim/dialogsum_ru |https://huggingface.co/datasets/dim/dialogsum_ru |Саммаризация диалогов на русском языке, перевод dialogsum. |d0rj/dialogsum-ru |https://huggingface.co/datasets/d0rj/dialogsum-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dialogsum-ru |ru |12460 |364.2813804 |178.7117754|98 |250 |329 |446 |2300 |
|dim/huggingartists_prompts |https://huggingface.co/datasets/dim/huggingartists_prompts |Промпты, которые просят продолжить песню в стиле определенного исполнителя. В данном наборе содержатся почти все исполнители, которых вы можете найти в этой организации https://huggingface.co/huggingartists |https://huggingface.co/huggingartists |https://huggingface.co/huggingartists |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/huggingartists |ru |64006 |561.6732025 |586.18458 |28 |297 |453 |720 |32949 |
### Модели
- [Ссылка на google sheets](https://docs.google.com/spreadsheets/d/1LGCy8RBR_Yk9wHRcp0IDV8eut4D7tAQBRgqCLqxYk3E/edit?usp=sharing)
|model_name |ru_cola(zero shot, prompt)|russian super glue(zero shot)|mmlu_ru(NLPCoreTeam)|mt_bench_ru_turn_1|mt_bench_ru_turn_2|mt_bench_ru_avg|mt_bench_generation|average |
|-----------------------------------------|--------------------------|-----------------------------|--------------------|------------------|------------------|---------------|-------------------|------------|
|IlyaGusev/saiga_mistral_7b_lora |0.710082526 |0.64 |0.4848747094 |0.6567901 |0.53375 |0.59527005 | |0.6050994671|
|verbalist_7b_v9_800 |0.6709723717 |0.665 |0.4801731633 |0.6175 |0.4375 |0.4375 | |0.574229107 |
|Open-Orca/Mistral-7B-OpenOrca |0.6917832795 |0.652 |0.4592925739 |0.685 |0.595 |0.64 | |0.6166151707|
|mistral-open-orca-ru-4600-step |0.6928597058 |0.663 |0.4347 |0.71625 |0.546 |0.631125 | |0.6105619412|
|verbalist_v10_1650 |0.7201291712 |0.66 |0.4920804261 |0.56125 |0.5 |0.530625 | |0.5866919194|
|gpt-3.5-turbo |0.72 |0.682 | |0.87 |0.745 |0.8075 | |0.75425 |
|openchat/openchat_3.5 |0.6727664155 |0.642 | | | |#DIV/0! | |0.6573832078|
|dim/tiny-llama-2T-open-orca-ru-10000-step|0.6361679225 |0.451 |0.2999564271 | | | | |0.4623747832|
- [dim/mistral-open-orca-ru-4600-step](https://huggingface.co/dim/mistral-open-orca-ru-4600-step)
- [dim/verbalist_7b_v9_800](https://huggingface.co/dim/verbalist_7b_v9_800)
- [dim/verbalist_v10_1650](https://huggingface.co/dim/verbalist_v10_1650)
### Код обучения
- [общий алгоритм обучения](https://github.com/dmitrymailk/verbalist/blob/master/verbalist/model/src/train.py)
- [формирование датасетов для обучения](https://github.com/dmitrymailk/verbalist/blob/master/verbalist/model/src/dataset.py#L176)
### Оборудование
Все обучение и инференс производится на видеокарте A100, на других видеокартах была обнаружена существенная деградация качества при инференсе, данный аспект требует дополнительного изучения.
- NVIDIA A100-SXM4-40GB
- NVIDIA-SMI 535.54.03
- Driver Version: 535.54.03
- CUDA Version: 12.2
- torch==2.0.1+cu118
### Дальнейшее развитие
Самое простое, что можно сделать это переводить уже имеющиеся хорошие датасеты с английского на русский при помощи GPT-4.
Более сложное это собирать больше разнообразных данных из различных доменов. Я могу лишь подкинуть идеи для того какие датасеты можно собрать еще.
- решебники по литературе, русскому и другим предметам
- задания со всяких бирж труда
- [краткие пересказы произведений, анализ произведений, сочинения по ним](http://www.litra.ru/shortwork/)
- [туториалы с digital ocean (более 7000)](https://www.digitalocean.com/community/tutorials)
- [туториалы с selectel](https://selectel.ru/blog/tutorials/)
- больше форумов на различные тематики
- [бесплатные эссе с ivypanda essays](https://ivypanda.com/essays/) и дальнейший их перевод на русский
- больше стихов и песен
- [олимпиадные русские задачи](https://math.ru/problems/) их очень сложно собирать, так как большинство их них живут только в PDF или docx. Но их довольно много и они довольно отличаются от олимпиадной математики на английском. Но у меня нет времени этим заниматься.
- фанфики на иностранном языке
- исправить текущие автоматические промпты на более разнообразные, при помощи chatgpt
|
DeepPavlov/verbalist_prompts
|
[
"language:ru",
"language:en",
"arxiv:2305.11206",
"region:us"
] |
2023-10-04T11:23:47+00:00
|
{"language": ["ru", "en"], "configs": [{"config_name": "default", "data_files": [{"split": "dim_oasst_en", "path": "data/dim_oasst_en-*"}, {"split": "dim_oasst_ru", "path": "data/dim_oasst_ru-*"}, {"split": "dim_lima", "path": "data/dim_lima-*"}, {"split": "dim_logic_tasks_ru", "path": "data/dim_logic_tasks_ru-*"}, {"split": "dim_wikihow_en", "path": "data/dim_wikihow_en-*"}, {"split": "dim_wikihow_ru", "path": "data/dim_wikihow_ru-*"}, {"split": "dim_essayforum_writing_prompts_6k", "path": "data/dim_essayforum_writing_prompts_6k-*"}, {"split": "dim_sharegpt_short_ru", "path": "data/dim_sharegpt_short_ru-*"}, {"split": "dim_openreview_prompts_65", "path": "data/dim_openreview_prompts_65-*"}, {"split": "dim_roleplay_instruct_v2_final", "path": "data/dim_roleplay_instruct_v2_final-*"}, {"split": "dim_kinomania_scripts", "path": "data/dim_kinomania_scripts-*"}, {"split": "dim_bugurt_thread_prompts", "path": "data/dim_bugurt_thread_prompts-*"}, {"split": "dim_russian_lyrics_prompts", "path": "data/dim_russian_lyrics_prompts-*"}, {"split": "dim_ru_instruct_gpt4", "path": "data/dim_ru_instruct_gpt4-*"}, {"split": "dim_gpt_roleplay_realm", "path": "data/dim_gpt_roleplay_realm-*"}, {"split": "dim_ultrachat_ru", "path": "data/dim_ultrachat_ru-*"}, {"split": "dim_scitldr", "path": "data/dim_scitldr-*"}, {"split": "dim_linux_man_pages_tldr_summarized", "path": "data/dim_linux_man_pages_tldr_summarized-*"}, {"split": "dim_dolphin_ru_3k", "path": "data/dim_dolphin_ru_3k-*"}, {"split": "dim_runne_prompts", "path": "data/dim_runne_prompts-*"}, {"split": "dim_lurk_prompts", "path": "data/dim_lurk_prompts-*"}, {"split": "dim_panorama_prompts_10k", "path": "data/dim_panorama_prompts_10k-*"}, {"split": "dim_resh_edu_short_prompts", "path": "data/dim_resh_edu_short_prompts-*"}, {"split": "dim_databricks_dolly_15k_ru", "path": "data/dim_databricks_dolly_15k_ru-*"}, {"split": "dim_databricks_dolly_15k_en", "path": "data/dim_databricks_dolly_15k_en-*"}, {"split": "dim_grammarly_coedit", "path": "data/dim_grammarly_coedit-*"}, {"split": "dim_kinopoisk_prompts", "path": "data/dim_kinopoisk_prompts-*"}, {"split": "dim_medical_qa_ru_prompts", "path": "data/dim_medical_qa_ru_prompts-*"}, {"split": "dim_joke_explaination_prompts", "path": "data/dim_joke_explaination_prompts-*"}, {"split": "dim_oa_stackexchange_200k", "path": "data/dim_oa_stackexchange_200k-*"}, {"split": "dim_scale_helpful_no_math", "path": "data/dim_scale_helpful_no_math-*"}, {"split": "dim_law_stackexchange_prompts", "path": "data/dim_law_stackexchange_prompts-*"}, {"split": "dim_ficbook_prompts_best_10k", "path": "data/dim_ficbook_prompts_best_10k-*"}, {"split": "dim_azbyka_logic_ru", "path": "data/dim_azbyka_logic_ru-*"}, {"split": "dim_povarenok", "path": "data/dim_povarenok-*"}, {"split": "dim_AO3_fandom_chatbot_1to1", "path": "data/dim_AO3_fandom_chatbot_1to1-*"}, {"split": "dim_habr_prompts_5k", "path": "data/dim_habr_prompts_5k-*"}, {"split": "dim_what_where_when_50k", "path": "data/dim_what_where_when_50k-*"}, {"split": "dim_competition_math", "path": "data/dim_competition_math-*"}, {"split": "dim_sharegpt_short_en_30k", "path": "data/dim_sharegpt_short_en_30k-*"}, {"split": "dim_ru_turbo_alpaca_evol_instruct", "path": "data/dim_ru_turbo_alpaca_evol_instruct-*"}, {"split": "dim_ru_turbo_saiga", "path": "data/dim_ru_turbo_saiga-*"}, {"split": "dim_bugurt_completion_prompts", "path": "data/dim_bugurt_completion_prompts-*"}, {"split": "dim_tldr_17_50k", "path": "data/dim_tldr_17_50k-*"}, {"split": "dim_grade_school_math_instructions", "path": "data/dim_grade_school_math_instructions-*"}, {"split": "dim_tldr_news", "path": "data/dim_tldr_news-*"}, {"split": "dim_grade_school_math_instructions_ru", "path": "data/dim_grade_school_math_instructions_ru-*"}, {"split": "dim_dialogsum", "path": "data/dim_dialogsum-*"}, {"split": "dim_HC3_ru", "path": "data/dim_HC3_ru-*"}, {"split": "dim_horoscopes_ru_10k", "path": "data/dim_horoscopes_ru_10k-*"}, {"split": "dim_yandex_q_200k", "path": "data/dim_yandex_q_200k-*"}, {"split": "dim_leetcodesolutions_en_2k", "path": "data/dim_leetcodesolutions_en_2k-*"}, {"split": "dim_forum_uristov_rf_prompts", "path": "data/dim_forum_uristov_rf_prompts-*"}, {"split": "dim_dialogsum_ru", "path": "data/dim_dialogsum_ru-*"}, {"split": "dim_huggingartists_prompts", "path": "data/dim_huggingartists_prompts-*"}]}], "dataset_info": {"features": [{"name": "conversation_text", "sequence": "string"}], "splits": [{"name": "dim_oasst_en", "num_bytes": 4335500, "num_examples": 2289}, {"name": "dim_oasst_ru", "num_bytes": 6206378, "num_examples": 2220}, {"name": "dim_lima", "num_bytes": 2892267, "num_examples": 1030}, {"name": "dim_logic_tasks_ru", "num_bytes": 76915, "num_examples": 86}, {"name": "dim_wikihow_en", "num_bytes": 16008199, "num_examples": 1995}, {"name": "dim_wikihow_ru", "num_bytes": 24451573, "num_examples": 2058}, {"name": "dim_essayforum_writing_prompts_6k", "num_bytes": 22326330, "num_examples": 6361}, {"name": "dim_sharegpt_short_ru", "num_bytes": 808319, "num_examples": 253}, {"name": "dim_openreview_prompts_65", "num_bytes": 6739952, "num_examples": 150}, {"name": "dim_roleplay_instruct_v2_final", "num_bytes": 4389286, "num_examples": 7188}, {"name": "dim_kinomania_scripts", "num_bytes": 238731, "num_examples": 27}, {"name": "dim_bugurt_thread_prompts", "num_bytes": 302191, "num_examples": 223}, {"name": "dim_russian_lyrics_prompts", "num_bytes": 18676, "num_examples": 43}, {"name": "dim_ru_instruct_gpt4", "num_bytes": 18351658, "num_examples": 14222}, {"name": "dim_gpt_roleplay_realm", "num_bytes": 20163429, "num_examples": 8700}, {"name": "dim_ultrachat_ru", "num_bytes": 4495105, "num_examples": 500}, {"name": "dim_scitldr", "num_bytes": 4049209, "num_examples": 3229}, {"name": "dim_linux_man_pages_tldr_summarized", "num_bytes": 3006631, "num_examples": 481}, {"name": "dim_dolphin_ru_3k", "num_bytes": 7976776, "num_examples": 3000}, {"name": "dim_runne_prompts", "num_bytes": 2686148, "num_examples": 537}, {"name": "dim_lurk_prompts", "num_bytes": 92012533, "num_examples": 5671}, {"name": "dim_panorama_prompts_10k", "num_bytes": 28964132, "num_examples": 11024}, {"name": "dim_resh_edu_short_prompts", "num_bytes": 12380000, "num_examples": 2106}, {"name": "dim_databricks_dolly_15k_ru", "num_bytes": 21900617, "num_examples": 14914}, {"name": "dim_databricks_dolly_15k_en", "num_bytes": 11973713, "num_examples": 15011}, {"name": "dim_grammarly_coedit", "num_bytes": 18500223, "num_examples": 82466}, {"name": "dim_kinopoisk_prompts", "num_bytes": 136323982, "num_examples": 36591}, {"name": "dim_medical_qa_ru_prompts", "num_bytes": 75634717, "num_examples": 80101}, {"name": "dim_joke_explaination_prompts", "num_bytes": 196224, "num_examples": 364}, {"name": "dim_oa_stackexchange_200k", "num_bytes": 192535277, "num_examples": 200000}, {"name": "dim_scale_helpful_no_math", "num_bytes": 85610911, "num_examples": 17095}, {"name": 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"dim_tldr_17_50k", "num_bytes": 81185070, "num_examples": 50000}, {"name": "dim_grade_school_math_instructions", "num_bytes": 4655452, "num_examples": 8792}, {"name": "dim_tldr_news", "num_bytes": 4014718, "num_examples": 7138}, {"name": "dim_grade_school_math_instructions_ru", "num_bytes": 6845510, "num_examples": 7473}, {"name": "dim_dialogsum", "num_bytes": 11176807, "num_examples": 12460}, {"name": "dim_HC3_ru", "num_bytes": 43395731, "num_examples": 24322}, {"name": "dim_horoscopes_ru_10k", "num_bytes": 9489348, "num_examples": 10000}, {"name": "dim_yandex_q_200k", "num_bytes": 292443135, "num_examples": 200000}, {"name": "dim_leetcodesolutions_en_2k", "num_bytes": 4708692, "num_examples": 2048}, {"name": "dim_forum_uristov_rf_prompts", "num_bytes": 2757263, "num_examples": 1849}, {"name": "dim_dialogsum_ru", "num_bytes": 18657989, "num_examples": 12460}, {"name": "dim_huggingartists_prompts", "num_bytes": 121909835, "num_examples": 64006}], "download_size": 0, "dataset_size": 2023767777}}
|
2023-12-29T20:28:50+00:00
|
[
"2305.11206"
] |
[
"ru",
"en"
] |
TAGS
#language-Russian #language-English #arxiv-2305.11206 #region-us
|
Verbalist (буквоед) - русскоязычный ассистент.
==============================================
Проект во многом вдохновленный Saiga.
Мною были собраны все самые качественные датасеты с huggingface.datasets, а также собраны дополнительно с тех сайтов, которые я посчитал весьма полезными для создания аналога ChatGPT. Лицензии у всех датасетов отличаются, какие-то по типу OpenAssistant/oasst1 были созданы специально для обучения подобных моделей, какие-то являются прямой выгрузкой диалогов с ChatGPT (RyokoAI/ShareGPT52K).
Вклад данного репозитория состоит в систематизации и стандартизации уже имеющихся датасетов, добавлении новых. А также тренировке моделей на этих данных.
* google sheets таблица с датасетами и описанием
### Датасеты
* Объединенный датасет где все данные уже подготовлены для тренировки диалоговой модели
|name |link |description |original\_name |original\_source |preparation\_script |language|amount\_examples|mean\_llama\_tokens|std |min\_llama\_tokens|25% |50% |75% |max\_llama\_tokens|
|-------------------------------------|---------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|--------|---------------|-----------------|-----------|----------------|-------|-------|-------|----------------|
|dim/oasst\_en |URL |OpenAssistant Conversations Dataset на английском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: URL |URL/URL |en |2289 |468.6788991 |295.0864391|17 |264 |410 |618 |2332 |
|dim/oasst\_ru |URL |OpenAssistant Conversations Dataset на русском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: URL |2023-04-12\_oasst\_ready.URL |URL/URL |ru |2220 |589.6112613 |479.835392 |7 |278 |465 |763.5 |5028 |
|dim/lima |URL |Данный датасет включает в себя 1000 высококачественных обучающих примеров на английском языке. Он собран из различных источников, включая Stack Exchange (STEM), Stack Exchange (Other), wikiHow, Pushshift r/WritingPrompts, Natural Instructions, а также уникальные инструкции, созданные авторами статей. Более подробную информацию о датасете можно найти в соответствующей статье. |GAIR/lima |URL |URL |en |1030 |712.9456311 |671.179319 |29 |312.75 |488.5 |825 |3920 |
|dim/logic\_tasks\_ru |URL |Данный набор задач по логике для детей взят с веб-сайта URL |Логические задачи - Логика и нестандартное мышление |URL |URL |ru |86 |193.0697674 |76.69048422|58 |133.75 |185 |243.5 |432 |
|dim/wikihow\_en |URL |Данный датасет содержит англоязычные статьи, извлеченные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |URL |URL |en |1995 |2037.86416 |870.1910713|265 |1463 |1913 |2461.5 |8988 |
|dim/wikihow\_ru |URL |Данный датасет включает в себя русскоязычные статьи, полученные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |URL |URL |ru |2058 |2498.119534 |1587.851549|139 |1236.25|2264 |3421.75|10217 |
|dim/essayforum\_writing\_prompts\_6k |URL |Данный датасет включает в себя запросы на помощь с написанием небольших эссе, размещенные на данном сайте. Ответы в датасете предоставлены исключительно главным администратором сайта. Его ответы были отобраны, поскольку чаще всего они являются наиболее качественными и вдумчивыми. |EssayForum |URL |URL |en |6361 |783.1760729 |285.4314176|258 |629 |742 |879 |4966 |
|dim/sharegpt\_short\_ru |URL |Очищенная версия русская версия sharegpt. Я попытался вырезать из текста все промпты, где модель извиняется что что-то не может сделать, что она не имеет доступа в интернет. Диалоги, которые противоречат морали модели я просто исключил. Постарался убрать упоминания о том что она модель AI, так как за ролеплейные характеристики отвечают другие датасеты. |RyokoAI/ShareGPT52K |URL |URL |ru |253 |706.6521739 |494.7437584|13 |310 |628 |1078 |1861 |
|dim/openreview\_prompts\_65 |URL |Датасет рецензий на реальные научные статьи с сайта openreview. Вышло на самом деле не так много, так как многие статьи не выложенны на arxiv или просто не имеют рецензий. Плюс я собрал только малую часть данного сайта, а не все что там было. |URL |URL |URL |en |150 |13531.51333 |6966.623686|4893 |8279 |12648.5|15833.5|41494 |
|dim/roleplay\_instruct\_v2\_final |URL |Датасет ролеплея от GPT-4 на различных персонажей на английском языке. |roleplay-instruct-v2-final |URL |URL |en |7188 |155.1413467 |97.71215667|14 |88 |125 |192 |1291 |
|dim/kinomania\_scripts |URL |Небольшой датасет, который содержит в себе сценарии фильмов целиком и их краткое содержание |URL |URL |URL |ru\en |27 |2603.407407 |510.375447 |1887 |2175 |2370 |3069 |3616 |
|dim/bugurt\_thread\_prompts |URL |Небольшой набор размеченных бугуртов вместе с моим другом, для того чтобы модель научилась писать бугурты на конкретную ситуацию. Собраны из телеграм паблика БУГУРТ ТРЕД(https://t.me/bugurtthread) |https://t.me/bugurtthread |https://t.me/bugurtthread |URL |ru |223 |334.4529148 |271.2557988|48 |148.5 |254 |434.5 |1645 |
|dim/russian\_lyrics\_prompts |URL |Небольшой датасет промптов собранный мною из различных учебников по стихосложению, чтобы модель научилась писать стихи, используя необходимый литературный прием на конкретную тему. |Учебник стихосложения |URL |URL |ru |43 |106.1395349 |71.00220701|45 |71 |83 |96.5 |411 |
|dim/ru\_instruct\_gpt4 |URL |Датасет каких-то инструкций на русском сгенерированных GPT-4 |lksy/ru\_instruct\_gpt4 |URL |URL |ru |14222 |259.2173393 |237.9433891|16 |109 |175 |271 |1374 |
|dim/gpt\_roleplay\_realm |URL |Диалоги выдуманных персонажей при помощи GPT-4, диалоги были сгенерированны при помощи GPT-3.5. Русский и английский. |IlyaGusev/gpt\_roleplay\_realm |URL |URL |ru\en |8700 |504.2424138 |117.6228987|180 |424 |489 |569 |1207 |
|dim/ultrachat\_ru |URL |Какой-то рандомный датасет диалогов от chatgpt, который я нашел на huggingface. Из текста диалогов были вырезаны шаблонные фразы по типу: "я не могу выполнить", "как языковая модель" и тд. Потому что обычно после этого следовало вменяемое решение задачи. |kaleinaNyan/UltraChat\_ru |URL |URL |ru |500 |1781.782 |901.1212735|267 |1113.25|1648 |2250.25|7303 |
|dim/scitldr |URL |Саммаризация научных статей на английском языке, выполненная экспертами. |allenai/scitldr |URL |URL |en |3229 |258.748529 |71.41209752|60 |209 |252 |303 |689 |
|dim/linux\_man\_pages\_tldr\_summarized |URL |Саммаризация мануалов для инструментов линукс в удобный набор команд с их кратким описанием. |tmskss/linux-man-pages-tldr-summarized |URL |URL |en |481 |1567.727651 |3590.30871 |96 |405 |765 |1386 |49888 |
|dim/dolphin\_ru\_3k |URL |Подвыборка размера 3000 переведенных заданий dolphin. Примеры из оригинального датасета это промпты из FLANv2 и решения при помощи GPT-4 или GPT-3.5. |d0rj/dolphin-ru |URL |URL |ru |3000 |556.1133333 |650.0962612|19 |207 |369.5 |720.25 |6787 |
|dim/runne\_prompts |URL |Промпты составленные из датасета RuNNE. Лично я при обучении сотавил промпт следующим образом. Сначала идет текст "Найди все именованные сущности в данном тексте:", а затем шел сам текст. В качестве выхода модели нужно сгенерировать JSON где содержатся все найденные именованные сущности. К примеру так [{"name": "PERSON", "ent": "Ким Чен Нама", "pos": "0 12"}, {"name": "ORGANIZATION", "ent": "Полиция Малайзии", "pos": "56 72"}] |iluvvatar/RuNNE |URL |URL |ru |537 |1479.750466 |230.0259174|581 |1337 |1480 |1635 |1988 |
|dim/lurk\_prompts |URL |Набор определений различных терминов с сайта lurk. Сами промпты были составлены автоматически следующим образом. напиши определение для (ОПРЕДЕЛЕНИЕ) в стиле lurk |averoo/lurk |URL |URL |ru |5671 |3450.34262 |4147.897824|35 |710.5 |2010 |4593 |55098 |
|dim/panorama\_prompts\_10k |URL |Набор юмористических заголовков и текстов новостей с сайта панорама. |its5Q/panorama |URL |URL |ru |11024 |516.9588171 |191.3774023|36 |422 |498 |585 |3496 |
|dim/resh\_edu\_short\_prompts |URL |Набор уроков с сайта URL включающих в себя название урока, тему, класс и текст урока с заданиями. |its5Q/resh-edu |URL |URL |ru |2106 |1431.510921 |435.7847102|56 |1175.5 |1517 |1777 |2029 |
|dim/databricks\_dolly\_15k\_ru |URL |Переведенный датасет dolly на русский язык. Включает в себя набор инструкций на обширное количество тематик. |dwarf2/databricks-dolly-15k-ru |URL |URL |ru |14914 |305.4638595 |405.874049 |8 |87 |182 |370 |9268 |
|dim/databricks\_dolly\_15k\_en |URL |databricks-dolly-15k — это набор данных с открытым исходным кодом, содержащий записи о выполнении инструкций, созданные тысячами сотрудников Databricks в нескольких поведенческих категориях, изложенных в документе InstructGPT, включая мозговой штурм, классификацию, закрытый контроль качества, генерацию, извлечение информации, открытый контроль качества и обобщение. |databricks/databricks-dolly-15k |URL |URL |en |15011 |204.7264006 |302.5539423|6 |57 |119 |242 |8883 |
|dim/grammarly\_coedit |URL |Набор промптов, которые просят исправить грамматические, стилистические ошибки на английском. |grammarly/coedit |URL |URL |en |82466 |53.7128271 |26.73822864|10 |35 |46 |64 |694 |
|dim/kinopoisk\_prompts |URL |Отзывы с кинопоиска на топ 250 фильмов. В промптах я прошу написать хороший, плохой или нейтральный отзыв на определенный фильм. |blinoff/kinopoisk |URL |URL |ru |36591 |875.0955973 |565.3212035|48 |484 |733 |1117 |8628 |
|dim/medical\_qa\_ru\_prompts |URL |Какие-то вопросы и ответы с какого-то медицинского форума. В данной версии датасета только первый ответ из оригинала. |blinoff/medical\_qa\_ru\_data |URL |URL |ru |80101 |206.710528 |175.4343973|12 |106 |161 |247 |5062 |
|dim/joke\_explaination\_prompts |URL |Объяснение шуток на английском. От изначального датасета отличается тем, что я убрал последнее предложение из объяснения, так как оно ссылается на видео на сайте. |theblackcat102/joke\_explaination |URL |URL |en |364 |143.5741758 |68.90275411|21 |99 |137.5 |189.25 |334 |
|dim/oa\_stackexchange\_200k |URL |Вопросы-ответы со stackexchange. Оригинальный датасет был составлен следующим образом: были выбраны только темы с принятым ответом, для которых длина вопроса и ответа составляет менее 1000 символов. Другие ответы, вопросы без принятых ответов или длинные записи были удалены. Так как оригинальный датасет слишком большой, я рандомно выбрал 200k семплов. |donfu/oa-stackexchange |URL |URL |en |200000 |276.29862 |112.5004436|22 |194 |265 |345 |1226 |
|dim/scale\_helpful\_no\_math |URL |Какой-то набор диалогов с вопросами-ответами на английском, происхождение неизвестно. |HuggingFaceH4/scale\_helpful\_no\_math |URL |URL |en |17095 |1235.302603 |838.1097885|53 |663 |1063 |1617 |34480 |
|dim/law\_stackexchange\_prompts |URL |Вопросы про закон на английском языке со StackExchange. Оригинальный датасет был преобразован в markdown. |ymoslem/Law-StackExchange |URL |URL |en |24343 |689.1184324 |565.0316906|43 |354 |540 |836 |8969 |
|dim/ficbook\_prompts\_best\_10k |URL |Топ 10k лучших фанфиков с сайта URL. Все промпты выглядят следующим образом: напиши фанфик с названием {title} и следующим описанием {description}, с тегами {tags}, Где title это оригинальное название, description оригинальное описание, tags это теги данного произведения. |AlexWortega/FicBook |URL |URL |ru |10000 |1737.8214 |402.0748161|166 |1716 |1950 |1950 |1952 |
|dim/azbyka\_logic\_ru |URL |Небольшой набор детских логических и православных задач, взятых с сайта URL . Обычно у них почти нет развернутого решения, только ответ. Я пытался расписать решение некоторых задач, но меня хватило только на 35, если кто-то займется подобным буду рад URL . |Логические и занимательные задачи (300 задач) |URL |URL |ru |480 |77.4375 |77.56990416|14 |31 |50 |91 |652 |
|dim/povarenok |URL |46k лучших рецептов с сайта URL, содержит текст рецепта, список ингридиентов, название блюда |URL |URL |URL |ru |46500 |488.9118495 |344.8563249|31 |281 |440 |632 |5542 |
|dim/AO3\_fandom\_chatbot\_1to1 |URL |Какой-то набор ролеплейных диалогов с описанием персонажей и их отыгрышем. Происхождение неизвестно. |ebony59/AO3\_fandom\_chatbot\_1to1 |URL |URL |en |614 |493.7166124 |226.3885365|129 |328.25 |432.5 |611.75 |1272 |
|dim/habr\_prompts\_5k |URL |Статьи с хабра. Датасет был составлен с помощью chatgpt, chatgpt преобразовывал заголовки таким образом чтобы они звучали как вопросы от пользователя, в качестве таргета выступала сама статья. |IlyaGusev/habr |URL |URL |ru |5000 |1732.892 |454.8418369|19 |1920.75|1950 |1951 |1952 |
|dim/what\_where\_when\_50k |URL |50k вопросов с решениями с сайта что где когда. В качестве промпта выступает вопрос, в качестве ответа конкатенация объяснения и краткого ответа. Все вопросы-ответы вы можете найти по этой ссылке URL |URL |URL |URL |ru |50000 |169.1862 |68.91119898|18 |122 |158 |202 |1167 |
|dim/competition\_math |URL |Датасет олимпиадной математики на английском. The Mathematics Aptitude Test of Heuristics (MATH) dataset. |competition\_math |URL |URL |en |7500 |317.5254667 |267.8583731|34 |147 |234 |393 |3029 |
|dim/sharegpt\_short\_en\_30k |URL |Короткие диалоги на английском из sharegpt |RyokoAI/ShareGPT52K |URL |URL |en |29597 |749.3149981 |516.3702473|3 |336 |630 |1095 |2021 |
|dim/ru\_turbo\_alpaca\_evol\_instruct |URL |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru\_turbo\_alpaca\_evol\_instruct |URL |URL |ru |47793 |453.0887996 |289.5498356|17 |221 |430 |623 |4647 |
|dim/ru\_turbo\_saiga |URL |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru\_turbo\_saiga |URL |URL |ru |37699 |412.7508687 |113.346917 |87 |339 |398 |466 |1427 |
|dim/bugurt\_completion\_prompts |URL |Обрезанные бугурты, где в качестве промпта используется строка вида - продолжи бугурт: первая строчка бугурта |https://t.me/bugurtthread |https://t.me/bugurtthread |URL |ru |5000 |280.2466 |320.4353681|32 |111 |178 |331 |11333 |
|dim/tldr\_17\_50k |URL |Очень вольная абстрактная саммаризация постов с реддита в одну строчку |webis/tldr-17 |URL |URL |en |50000 |421.12752 |403.346214 |10 |177 |303 |525 |9592 |
|dim/grade\_school\_math\_instructions |URL |OpenAI's grade-school-math датасет преобразованный в промпты. |qwedsacf/grade-school-math-instructions |URL |URL |en |8792 |171.6310282 |63.09232668|50 |124 |161 |206 |511 |
|dim/tldr\_news |URL |Хедлайны и текст новостей на различную тематику. |JulesBelveze/tldr\_news |URL |URL |en |7138 |133.1004483 |46.48736493|23 |100 |133 |161 |476 |
|dim/grade\_school\_math\_instructions\_ru|URL grade-school-math датасет переведенный на русский. |d0rj/gsm8k-ru |URL |URL |7473 |259.8321959 |100.1229127|78 |185 |241 |314 |838 |
|dim/dialogsum |URL |Саммаризация диалогов на английском языке, разметка выполнялась вручную. |knkarthick/dialogsum |URL |URL |en |12460 |269.6467095 |126.285664 |75 |191 |245 |327 |1725 |
|dim/HC3\_ru |URL |Вопросы-ответы с реддита, есть ответы сгенерированные chatgpt и реальные ответы пользователей. Я использовал только реальные ответы пользователей. |d0rj/HC3-ru |URL |URL |ru |24322 |360.5608503 |330.2285903|15 |168 |267 |435 |10025 |
|dim/horoscopes\_ru\_10k |URL |10k гороскопов, с промптами где я прошу сгенерировать гороском для определенного знака зодиака |dkagramanyan/horoscopes\_ru |URL |URL |ru |10000 |183.1443 |31.62023184|55 |159 |187 |201 |464 |
|dim/yandex\_q\_200k |URL |200k рандомно выбранных вопросов-ответов с сайта yandex q. |its5Q/yandex-q |URL |URL |ru |200000 |304.569005 |340.7808288|18 |127 |202 |353 |19294 |
|dim/leetcodesolutions\_en\_2k |URL |Решения задач с leetcode на разных языках. |TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k |URL |URL |en |2048 |740.7441406 |253.2493282|297 |565 |685 |857 |1960 |
|dim/forum\_uristov\_rf\_prompts |URL |Вопросы-ответы с российского юридического форума. |URL--p1ai/vopros-yuristu?page=560|URL--p1ai/vopros-yuristu?page=560 |URL |ru |1849 |321.0540833 |429.58896 |31 |134 |210 |349 |6470 |
|dim/dialogsum\_ru |URL |Саммаризация диалогов на русском языке, перевод dialogsum. |d0rj/dialogsum-ru |URL |URL |ru |12460 |364.2813804 |178.7117754|98 |250 |329 |446 |2300 |
|dim/huggingartists\_prompts |URL |Промпты, которые просят продолжить песню в стиле определенного исполнителя. В данном наборе содержатся почти все исполнители, которых вы можете найти в этой организации URL |URL |URL |URL |ru |64006 |561.6732025 |586.18458 |28 |297 |453 |720 |32949 |
### Модели
* Ссылка на google sheets
* dim/mistral-open-orca-ru-4600-step
* dim/verbalist\_7b\_v9\_800
* dim/verbalist\_v10\_1650
### Код обучения
* общий алгоритм обучения
* формирование датасетов для обучения
### Оборудование
Все обучение и инференс производится на видеокарте A100, на других видеокартах была обнаружена существенная деградация качества при инференсе, данный аспект требует дополнительного изучения.
* NVIDIA A100-SXM4-40GB
* NVIDIA-SMI 535.54.03
* Driver Version: 535.54.03
* CUDA Version: 12.2
* torch==2.0.1+cu118
### Дальнейшее развитие
Самое простое, что можно сделать это переводить уже имеющиеся хорошие датасеты с английского на русский при помощи GPT-4.
Более сложное это собирать больше разнообразных данных из различных доменов. Я могу лишь подкинуть идеи для того какие датасеты можно собрать еще.
* решебники по литературе, русскому и другим предметам
* задания со всяких бирж труда
* краткие пересказы произведений, анализ произведений, сочинения по ним
* туториалы с digital ocean (более 7000)
* туториалы с selectel
* больше форумов на различные тематики
* бесплатные эссе с ivypanda essays и дальнейший их перевод на русский
* больше стихов и песен
* олимпиадные русские задачи их очень сложно собирать, так как большинство их них живут только в PDF или docx. Но их довольно много и они довольно отличаются от олимпиадной математики на английском. Но у меня нет времени этим заниматься.
* фанфики на иностранном языке
* исправить текущие автоматические промпты на более разнообразные, при помощи chatgpt
|
[
"### Датасеты\n\n\n* Объединенный датасет где все данные уже подготовлены для тренировки диалоговой модели\n|name |link |description |original\\_name |original\\_source |preparation\\_script |language|amount\\_examples|mean\\_llama\\_tokens|std |min\\_llama\\_tokens|25% |50% |75% |max\\_llama\\_tokens|\n|-------------------------------------|---------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|--------|---------------|-----------------|-----------|----------------|-------|-------|-------|----------------|\n|dim/oasst\\_en |URL |OpenAssistant Conversations Dataset на английском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали \"не знаю\" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: URL |URL/URL |en |2289 |468.6788991 |295.0864391|17 |264 |410 |618 |2332 |\n|dim/oasst\\_ru |URL |OpenAssistant Conversations Dataset на русском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали \"не знаю\" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: URL |2023-04-12\\_oasst\\_ready.URL |URL/URL |ru |2220 |589.6112613 |479.835392 |7 |278 |465 |763.5 |5028 |\n|dim/lima |URL |Данный датасет включает в себя 1000 высококачественных обучающих примеров на английском языке. Он собран из различных источников, включая Stack Exchange (STEM), Stack Exchange (Other), wikiHow, Pushshift r/WritingPrompts, Natural Instructions, а также уникальные инструкции, созданные авторами статей. Более подробную информацию о датасете можно найти в соответствующей статье. |GAIR/lima |URL |URL |en |1030 |712.9456311 |671.179319 |29 |312.75 |488.5 |825 |3920 |\n|dim/logic\\_tasks\\_ru |URL |Данный набор задач по логике для детей взят с веб-сайта URL |Логические задачи - Логика и нестандартное мышление |URL |URL |ru |86 |193.0697674 |76.69048422|58 |133.75 |185 |243.5 |432 |\n|dim/wikihow\\_en |URL |Данный датасет содержит англоязычные статьи, извлеченные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |URL |URL |en |1995 |2037.86416 |870.1910713|265 |1463 |1913 |2461.5 |8988 |\n|dim/wikihow\\_ru |URL |Данный датасет включает в себя русскоязычные статьи, полученные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |URL |URL |ru |2058 |2498.119534 |1587.851549|139 |1236.25|2264 |3421.75|10217 |\n|dim/essayforum\\_writing\\_prompts\\_6k |URL |Данный датасет включает в себя запросы на помощь с написанием небольших эссе, размещенные на данном сайте. Ответы в датасете предоставлены исключительно главным администратором сайта. Его ответы были отобраны, поскольку чаще всего они являются наиболее качественными и вдумчивыми. |EssayForum |URL |URL |en |6361 |783.1760729 |285.4314176|258 |629 |742 |879 |4966 |\n|dim/sharegpt\\_short\\_ru |URL |Очищенная версия русская версия sharegpt. Я попытался вырезать из текста все промпты, где модель извиняется что что-то не может сделать, что она не имеет доступа в интернет. Диалоги, которые противоречат морали модели я просто исключил. Постарался убрать упоминания о том что она модель AI, так как за ролеплейные характеристики отвечают другие датасеты. |RyokoAI/ShareGPT52K |URL |URL |ru |253 |706.6521739 |494.7437584|13 |310 |628 |1078 |1861 |\n|dim/openreview\\_prompts\\_65 |URL |Датасет рецензий на реальные научные статьи с сайта openreview. Вышло на самом деле не так много, так как многие статьи не выложенны на arxiv или просто не имеют рецензий. Плюс я собрал только малую часть данного сайта, а не все что там было. |URL |URL |URL |en |150 |13531.51333 |6966.623686|4893 |8279 |12648.5|15833.5|41494 |\n|dim/roleplay\\_instruct\\_v2\\_final |URL |Датасет ролеплея от GPT-4 на различных персонажей на английском языке. |roleplay-instruct-v2-final |URL |URL |en |7188 |155.1413467 |97.71215667|14 |88 |125 |192 |1291 |\n|dim/kinomania\\_scripts |URL |Небольшой датасет, который содержит в себе сценарии фильмов целиком и их краткое содержание |URL |URL |URL |ru\\en |27 |2603.407407 |510.375447 |1887 |2175 |2370 |3069 |3616 |\n|dim/bugurt\\_thread\\_prompts |URL |Небольшой набор размеченных бугуртов вместе с моим другом, для того чтобы модель научилась писать бугурты на конкретную ситуацию. Собраны из телеграм паблика БУГУРТ ТРЕД(https://t.me/bugurtthread) |https://t.me/bugurtthread |https://t.me/bugurtthread |URL |ru |223 |334.4529148 |271.2557988|48 |148.5 |254 |434.5 |1645 |\n|dim/russian\\_lyrics\\_prompts |URL |Небольшой датасет промптов собранный мною из различных учебников по стихосложению, чтобы модель научилась писать стихи, используя необходимый литературный прием на конкретную тему. |Учебник стихосложения |URL |URL |ru |43 |106.1395349 |71.00220701|45 |71 |83 |96.5 |411 |\n|dim/ru\\_instruct\\_gpt4 |URL |Датасет каких-то инструкций на русском сгенерированных GPT-4 |lksy/ru\\_instruct\\_gpt4 |URL |URL |ru |14222 |259.2173393 |237.9433891|16 |109 |175 |271 |1374 |\n|dim/gpt\\_roleplay\\_realm |URL |Диалоги выдуманных персонажей при помощи GPT-4, диалоги были сгенерированны при помощи GPT-3.5. Русский и английский. |IlyaGusev/gpt\\_roleplay\\_realm |URL |URL |ru\\en |8700 |504.2424138 |117.6228987|180 |424 |489 |569 |1207 |\n|dim/ultrachat\\_ru |URL |Какой-то рандомный датасет диалогов от chatgpt, который я нашел на huggingface. Из текста диалогов были вырезаны шаблонные фразы по типу: \"я не могу выполнить\", \"как языковая модель\" и тд. Потому что обычно после этого следовало вменяемое решение задачи. |kaleinaNyan/UltraChat\\_ru |URL |URL |ru |500 |1781.782 |901.1212735|267 |1113.25|1648 |2250.25|7303 |\n|dim/scitldr |URL |Саммаризация научных статей на английском языке, выполненная экспертами. |allenai/scitldr |URL |URL |en |3229 |258.748529 |71.41209752|60 |209 |252 |303 |689 |\n|dim/linux\\_man\\_pages\\_tldr\\_summarized |URL |Саммаризация мануалов для инструментов линукс в удобный набор команд с их кратким описанием. |tmskss/linux-man-pages-tldr-summarized |URL |URL |en |481 |1567.727651 |3590.30871 |96 |405 |765 |1386 |49888 |\n|dim/dolphin\\_ru\\_3k |URL |Подвыборка размера 3000 переведенных заданий dolphin. Примеры из оригинального датасета это промпты из FLANv2 и решения при помощи GPT-4 или GPT-3.5. |d0rj/dolphin-ru |URL |URL |ru |3000 |556.1133333 |650.0962612|19 |207 |369.5 |720.25 |6787 |\n|dim/runne\\_prompts |URL |Промпты составленные из датасета RuNNE. Лично я при обучении сотавил промпт следующим образом. Сначала идет текст \"Найди все именованные сущности в данном тексте:\", а затем шел сам текст. В качестве выхода модели нужно сгенерировать JSON где содержатся все найденные именованные сущности. К примеру так [{\"name\": \"PERSON\", \"ent\": \"Ким Чен Нама\", \"pos\": \"0 12\"}, {\"name\": \"ORGANIZATION\", \"ent\": \"Полиция Малайзии\", \"pos\": \"56 72\"}] |iluvvatar/RuNNE |URL |URL |ru |537 |1479.750466 |230.0259174|581 |1337 |1480 |1635 |1988 |\n|dim/lurk\\_prompts |URL |Набор определений различных терминов с сайта lurk. Сами промпты были составлены автоматически следующим образом. напиши определение для (ОПРЕДЕЛЕНИЕ) в стиле lurk |averoo/lurk |URL |URL |ru |5671 |3450.34262 |4147.897824|35 |710.5 |2010 |4593 |55098 |\n|dim/panorama\\_prompts\\_10k |URL |Набор юмористических заголовков и текстов новостей с сайта панорама. |its5Q/panorama |URL |URL |ru |11024 |516.9588171 |191.3774023|36 |422 |498 |585 |3496 |\n|dim/resh\\_edu\\_short\\_prompts |URL |Набор уроков с сайта URL включающих в себя название урока, тему, класс и текст урока с заданиями. |its5Q/resh-edu |URL |URL |ru |2106 |1431.510921 |435.7847102|56 |1175.5 |1517 |1777 |2029 |\n|dim/databricks\\_dolly\\_15k\\_ru |URL |Переведенный датасет dolly на русский язык. Включает в себя набор инструкций на обширное количество тематик. |dwarf2/databricks-dolly-15k-ru |URL |URL |ru |14914 |305.4638595 |405.874049 |8 |87 |182 |370 |9268 |\n|dim/databricks\\_dolly\\_15k\\_en |URL |databricks-dolly-15k — это набор данных с открытым исходным кодом, содержащий записи о выполнении инструкций, созданные тысячами сотрудников Databricks в нескольких поведенческих категориях, изложенных в документе InstructGPT, включая мозговой штурм, классификацию, закрытый контроль качества, генерацию, извлечение информации, открытый контроль качества и обобщение. |databricks/databricks-dolly-15k |URL |URL |en |15011 |204.7264006 |302.5539423|6 |57 |119 |242 |8883 |\n|dim/grammarly\\_coedit |URL |Набор промптов, которые просят исправить грамматические, стилистические ошибки на английском. |grammarly/coedit |URL |URL |en |82466 |53.7128271 |26.73822864|10 |35 |46 |64 |694 |\n|dim/kinopoisk\\_prompts |URL |Отзывы с кинопоиска на топ 250 фильмов. В промптах я прошу написать хороший, плохой или нейтральный отзыв на определенный фильм. |blinoff/kinopoisk |URL |URL |ru |36591 |875.0955973 |565.3212035|48 |484 |733 |1117 |8628 |\n|dim/medical\\_qa\\_ru\\_prompts |URL |Какие-то вопросы и ответы с какого-то медицинского форума. В данной версии датасета только первый ответ из оригинала. |blinoff/medical\\_qa\\_ru\\_data |URL |URL |ru |80101 |206.710528 |175.4343973|12 |106 |161 |247 |5062 |\n|dim/joke\\_explaination\\_prompts |URL |Объяснение шуток на английском. От изначального датасета отличается тем, что я убрал последнее предложение из объяснения, так как оно ссылается на видео на сайте. |theblackcat102/joke\\_explaination |URL |URL |en |364 |143.5741758 |68.90275411|21 |99 |137.5 |189.25 |334 |\n|dim/oa\\_stackexchange\\_200k |URL |Вопросы-ответы со stackexchange. Оригинальный датасет был составлен следующим образом: были выбраны только темы с принятым ответом, для которых длина вопроса и ответа составляет менее 1000 символов. Другие ответы, вопросы без принятых ответов или длинные записи были удалены. Так как оригинальный датасет слишком большой, я рандомно выбрал 200k семплов. |donfu/oa-stackexchange |URL |URL |en |200000 |276.29862 |112.5004436|22 |194 |265 |345 |1226 |\n|dim/scale\\_helpful\\_no\\_math |URL |Какой-то набор диалогов с вопросами-ответами на английском, происхождение неизвестно. |HuggingFaceH4/scale\\_helpful\\_no\\_math |URL |URL |en |17095 |1235.302603 |838.1097885|53 |663 |1063 |1617 |34480 |\n|dim/law\\_stackexchange\\_prompts |URL |Вопросы про закон на английском языке со StackExchange. Оригинальный датасет был преобразован в markdown. |ymoslem/Law-StackExchange |URL |URL |en |24343 |689.1184324 |565.0316906|43 |354 |540 |836 |8969 |\n|dim/ficbook\\_prompts\\_best\\_10k |URL |Топ 10k лучших фанфиков с сайта URL. Все промпты выглядят следующим образом: напиши фанфик с названием {title} и следующим описанием {description}, с тегами {tags}, Где title это оригинальное название, description оригинальное описание, tags это теги данного произведения. |AlexWortega/FicBook |URL |URL |ru |10000 |1737.8214 |402.0748161|166 |1716 |1950 |1950 |1952 |\n|dim/azbyka\\_logic\\_ru |URL |Небольшой набор детских логических и православных задач, взятых с сайта URL . Обычно у них почти нет развернутого решения, только ответ. Я пытался расписать решение некоторых задач, но меня хватило только на 35, если кто-то займется подобным буду рад URL . |Логические и занимательные задачи (300 задач) |URL |URL |ru |480 |77.4375 |77.56990416|14 |31 |50 |91 |652 |\n|dim/povarenok |URL |46k лучших рецептов с сайта URL, содержит текст рецепта, список ингридиентов, название блюда |URL |URL |URL |ru |46500 |488.9118495 |344.8563249|31 |281 |440 |632 |5542 |\n|dim/AO3\\_fandom\\_chatbot\\_1to1 |URL |Какой-то набор ролеплейных диалогов с описанием персонажей и их отыгрышем. Происхождение неизвестно. |ebony59/AO3\\_fandom\\_chatbot\\_1to1 |URL |URL |en |614 |493.7166124 |226.3885365|129 |328.25 |432.5 |611.75 |1272 |\n|dim/habr\\_prompts\\_5k |URL |Статьи с хабра. Датасет был составлен с помощью chatgpt, chatgpt преобразовывал заголовки таким образом чтобы они звучали как вопросы от пользователя, в качестве таргета выступала сама статья. |IlyaGusev/habr |URL |URL |ru |5000 |1732.892 |454.8418369|19 |1920.75|1950 |1951 |1952 |\n|dim/what\\_where\\_when\\_50k |URL |50k вопросов с решениями с сайта что где когда. В качестве промпта выступает вопрос, в качестве ответа конкатенация объяснения и краткого ответа. Все вопросы-ответы вы можете найти по этой ссылке URL |URL |URL |URL |ru |50000 |169.1862 |68.91119898|18 |122 |158 |202 |1167 |\n|dim/competition\\_math |URL |Датасет олимпиадной математики на английском. The Mathematics Aptitude Test of Heuristics (MATH) dataset. |competition\\_math |URL |URL |en |7500 |317.5254667 |267.8583731|34 |147 |234 |393 |3029 |\n|dim/sharegpt\\_short\\_en\\_30k |URL |Короткие диалоги на английском из sharegpt |RyokoAI/ShareGPT52K |URL |URL |en |29597 |749.3149981 |516.3702473|3 |336 |630 |1095 |2021 |\n|dim/ru\\_turbo\\_alpaca\\_evol\\_instruct |URL |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru\\_turbo\\_alpaca\\_evol\\_instruct |URL |URL |ru |47793 |453.0887996 |289.5498356|17 |221 |430 |623 |4647 |\n|dim/ru\\_turbo\\_saiga |URL |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru\\_turbo\\_saiga |URL |URL |ru |37699 |412.7508687 |113.346917 |87 |339 |398 |466 |1427 |\n|dim/bugurt\\_completion\\_prompts |URL |Обрезанные бугурты, где в качестве промпта используется строка вида - продолжи бугурт: первая строчка бугурта |https://t.me/bugurtthread |https://t.me/bugurtthread |URL |ru |5000 |280.2466 |320.4353681|32 |111 |178 |331 |11333 |\n|dim/tldr\\_17\\_50k |URL |Очень вольная абстрактная саммаризация постов с реддита в одну строчку |webis/tldr-17 |URL |URL |en |50000 |421.12752 |403.346214 |10 |177 |303 |525 |9592 |\n|dim/grade\\_school\\_math\\_instructions |URL |OpenAI's grade-school-math датасет преобразованный в промпты. |qwedsacf/grade-school-math-instructions |URL |URL |en |8792 |171.6310282 |63.09232668|50 |124 |161 |206 |511 |\n|dim/tldr\\_news |URL |Хедлайны и текст новостей на различную тематику. |JulesBelveze/tldr\\_news |URL |URL |en |7138 |133.1004483 |46.48736493|23 |100 |133 |161 |476 |\n|dim/grade\\_school\\_math\\_instructions\\_ru|URL grade-school-math датасет переведенный на русский. |d0rj/gsm8k-ru |URL |URL |7473 |259.8321959 |100.1229127|78 |185 |241 |314 |838 |\n|dim/dialogsum |URL |Саммаризация диалогов на английском языке, разметка выполнялась вручную. |knkarthick/dialogsum |URL |URL |en |12460 |269.6467095 |126.285664 |75 |191 |245 |327 |1725 |\n|dim/HC3\\_ru |URL |Вопросы-ответы с реддита, есть ответы сгенерированные chatgpt и реальные ответы пользователей. Я использовал только реальные ответы пользователей. |d0rj/HC3-ru |URL |URL |ru |24322 |360.5608503 |330.2285903|15 |168 |267 |435 |10025 |\n|dim/horoscopes\\_ru\\_10k |URL |10k гороскопов, с промптами где я прошу сгенерировать гороском для определенного знака зодиака |dkagramanyan/horoscopes\\_ru |URL |URL |ru |10000 |183.1443 |31.62023184|55 |159 |187 |201 |464 |\n|dim/yandex\\_q\\_200k |URL |200k рандомно выбранных вопросов-ответов с сайта yandex q. |its5Q/yandex-q |URL |URL |ru |200000 |304.569005 |340.7808288|18 |127 |202 |353 |19294 |\n|dim/leetcodesolutions\\_en\\_2k |URL |Решения задач с leetcode на разных языках. |TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k |URL |URL |en |2048 |740.7441406 |253.2493282|297 |565 |685 |857 |1960 |\n|dim/forum\\_uristov\\_rf\\_prompts |URL |Вопросы-ответы с российского юридического форума. |URL--p1ai/vopros-yuristu?page=560|URL--p1ai/vopros-yuristu?page=560 |URL |ru |1849 |321.0540833 |429.58896 |31 |134 |210 |349 |6470 |\n|dim/dialogsum\\_ru |URL |Саммаризация диалогов на русском языке, перевод dialogsum. |d0rj/dialogsum-ru |URL |URL |ru |12460 |364.2813804 |178.7117754|98 |250 |329 |446 |2300 |\n|dim/huggingartists\\_prompts |URL |Промпты, которые просят продолжить песню в стиле определенного исполнителя. В данном наборе содержатся почти все исполнители, которых вы можете найти в этой организации URL |URL |URL |URL |ru |64006 |561.6732025 |586.18458 |28 |297 |453 |720 |32949 |",
"### Модели\n\n\n* Ссылка на google sheets\n\n\n\n* dim/mistral-open-orca-ru-4600-step\n* dim/verbalist\\_7b\\_v9\\_800\n* dim/verbalist\\_v10\\_1650",
"### Код обучения\n\n\n* общий алгоритм обучения\n* формирование датасетов для обучения",
"### Оборудование\n\n\nВсе обучение и инференс производится на видеокарте A100, на других видеокартах была обнаружена существенная деградация качества при инференсе, данный аспект требует дополнительного изучения.\n\n\n* NVIDIA A100-SXM4-40GB\n* NVIDIA-SMI 535.54.03\n* Driver Version: 535.54.03\n* CUDA Version: 12.2\n* torch==2.0.1+cu118",
"### Дальнейшее развитие\n\n\nСамое простое, что можно сделать это переводить уже имеющиеся хорошие датасеты с английского на русский при помощи GPT-4.\n\n\nБолее сложное это собирать больше разнообразных данных из различных доменов. Я могу лишь подкинуть идеи для того какие датасеты можно собрать еще.\n\n\n* решебники по литературе, русскому и другим предметам\n* задания со всяких бирж труда\n* краткие пересказы произведений, анализ произведений, сочинения по ним\n* туториалы с digital ocean (более 7000)\n* туториалы с selectel\n* больше форумов на различные тематики\n* бесплатные эссе с ivypanda essays и дальнейший их перевод на русский\n* больше стихов и песен\n* олимпиадные русские задачи их очень сложно собирать, так как большинство их них живут только в PDF или docx. Но их довольно много и они довольно отличаются от олимпиадной математики на английском. Но у меня нет времени этим заниматься.\n* фанфики на иностранном языке\n* исправить текущие автоматические промпты на более разнообразные, при помощи chatgpt"
] |
[
"TAGS\n#language-Russian #language-English #arxiv-2305.11206 #region-us \n",
"### Датасеты\n\n\n* Объединенный датасет где все данные уже подготовлены для тренировки диалоговой модели\n|name |link |description |original\\_name |original\\_source |preparation\\_script |language|amount\\_examples|mean\\_llama\\_tokens|std |min\\_llama\\_tokens|25% |50% |75% |max\\_llama\\_tokens|\n|-------------------------------------|---------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|--------|---------------|-----------------|-----------|----------------|-------|-------|-------|----------------|\n|dim/oasst\\_en |URL |OpenAssistant Conversations Dataset на английском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали \"не знаю\" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: URL |URL/URL |en |2289 |468.6788991 |295.0864391|17 |264 |410 |618 |2332 |\n|dim/oasst\\_ru |URL |OpenAssistant Conversations Dataset на русском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали \"не знаю\" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: URL |2023-04-12\\_oasst\\_ready.URL |URL/URL |ru |2220 |589.6112613 |479.835392 |7 |278 |465 |763.5 |5028 |\n|dim/lima |URL |Данный датасет включает в себя 1000 высококачественных обучающих примеров на английском языке. Он собран из различных источников, включая Stack Exchange (STEM), Stack Exchange (Other), wikiHow, Pushshift r/WritingPrompts, Natural Instructions, а также уникальные инструкции, созданные авторами статей. Более подробную информацию о датасете можно найти в соответствующей статье. |GAIR/lima |URL |URL |en |1030 |712.9456311 |671.179319 |29 |312.75 |488.5 |825 |3920 |\n|dim/logic\\_tasks\\_ru |URL |Данный набор задач по логике для детей взят с веб-сайта URL |Логические задачи - Логика и нестандартное мышление |URL |URL |ru |86 |193.0697674 |76.69048422|58 |133.75 |185 |243.5 |432 |\n|dim/wikihow\\_en |URL |Данный датасет содержит англоязычные статьи, извлеченные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |URL |URL |en |1995 |2037.86416 |870.1910713|265 |1463 |1913 |2461.5 |8988 |\n|dim/wikihow\\_ru |URL |Данный датасет включает в себя русскоязычные статьи, полученные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |URL |URL |ru |2058 |2498.119534 |1587.851549|139 |1236.25|2264 |3421.75|10217 |\n|dim/essayforum\\_writing\\_prompts\\_6k |URL |Данный датасет включает в себя запросы на помощь с написанием небольших эссе, размещенные на данном сайте. Ответы в датасете предоставлены исключительно главным администратором сайта. Его ответы были отобраны, поскольку чаще всего они являются наиболее качественными и вдумчивыми. |EssayForum |URL |URL |en |6361 |783.1760729 |285.4314176|258 |629 |742 |879 |4966 |\n|dim/sharegpt\\_short\\_ru |URL |Очищенная версия русская версия sharegpt. Я попытался вырезать из текста все промпты, где модель извиняется что что-то не может сделать, что она не имеет доступа в интернет. Диалоги, которые противоречат морали модели я просто исключил. Постарался убрать упоминания о том что она модель AI, так как за ролеплейные характеристики отвечают другие датасеты. |RyokoAI/ShareGPT52K |URL |URL |ru |253 |706.6521739 |494.7437584|13 |310 |628 |1078 |1861 |\n|dim/openreview\\_prompts\\_65 |URL |Датасет рецензий на реальные научные статьи с сайта openreview. Вышло на самом деле не так много, так как многие статьи не выложенны на arxiv или просто не имеют рецензий. Плюс я собрал только малую часть данного сайта, а не все что там было. |URL |URL |URL |en |150 |13531.51333 |6966.623686|4893 |8279 |12648.5|15833.5|41494 |\n|dim/roleplay\\_instruct\\_v2\\_final |URL |Датасет ролеплея от GPT-4 на различных персонажей на английском языке. |roleplay-instruct-v2-final |URL |URL |en |7188 |155.1413467 |97.71215667|14 |88 |125 |192 |1291 |\n|dim/kinomania\\_scripts |URL |Небольшой датасет, который содержит в себе сценарии фильмов целиком и их краткое содержание |URL |URL |URL |ru\\en |27 |2603.407407 |510.375447 |1887 |2175 |2370 |3069 |3616 |\n|dim/bugurt\\_thread\\_prompts |URL |Небольшой набор размеченных бугуртов вместе с моим другом, для того чтобы модель научилась писать бугурты на конкретную ситуацию. Собраны из телеграм паблика БУГУРТ ТРЕД(https://t.me/bugurtthread) |https://t.me/bugurtthread |https://t.me/bugurtthread |URL |ru |223 |334.4529148 |271.2557988|48 |148.5 |254 |434.5 |1645 |\n|dim/russian\\_lyrics\\_prompts |URL |Небольшой датасет промптов собранный мною из различных учебников по стихосложению, чтобы модель научилась писать стихи, используя необходимый литературный прием на конкретную тему. |Учебник стихосложения |URL |URL |ru |43 |106.1395349 |71.00220701|45 |71 |83 |96.5 |411 |\n|dim/ru\\_instruct\\_gpt4 |URL |Датасет каких-то инструкций на русском сгенерированных GPT-4 |lksy/ru\\_instruct\\_gpt4 |URL |URL |ru |14222 |259.2173393 |237.9433891|16 |109 |175 |271 |1374 |\n|dim/gpt\\_roleplay\\_realm |URL |Диалоги выдуманных персонажей при помощи GPT-4, диалоги были сгенерированны при помощи GPT-3.5. Русский и английский. |IlyaGusev/gpt\\_roleplay\\_realm |URL |URL |ru\\en |8700 |504.2424138 |117.6228987|180 |424 |489 |569 |1207 |\n|dim/ultrachat\\_ru |URL |Какой-то рандомный датасет диалогов от chatgpt, который я нашел на huggingface. Из текста диалогов были вырезаны шаблонные фразы по типу: \"я не могу выполнить\", \"как языковая модель\" и тд. Потому что обычно после этого следовало вменяемое решение задачи. |kaleinaNyan/UltraChat\\_ru |URL |URL |ru |500 |1781.782 |901.1212735|267 |1113.25|1648 |2250.25|7303 |\n|dim/scitldr |URL |Саммаризация научных статей на английском языке, выполненная экспертами. |allenai/scitldr |URL |URL |en |3229 |258.748529 |71.41209752|60 |209 |252 |303 |689 |\n|dim/linux\\_man\\_pages\\_tldr\\_summarized |URL |Саммаризация мануалов для инструментов линукс в удобный набор команд с их кратким описанием. |tmskss/linux-man-pages-tldr-summarized |URL |URL |en |481 |1567.727651 |3590.30871 |96 |405 |765 |1386 |49888 |\n|dim/dolphin\\_ru\\_3k |URL |Подвыборка размера 3000 переведенных заданий dolphin. Примеры из оригинального датасета это промпты из FLANv2 и решения при помощи GPT-4 или GPT-3.5. |d0rj/dolphin-ru |URL |URL |ru |3000 |556.1133333 |650.0962612|19 |207 |369.5 |720.25 |6787 |\n|dim/runne\\_prompts |URL |Промпты составленные из датасета RuNNE. Лично я при обучении сотавил промпт следующим образом. Сначала идет текст \"Найди все именованные сущности в данном тексте:\", а затем шел сам текст. В качестве выхода модели нужно сгенерировать JSON где содержатся все найденные именованные сущности. К примеру так [{\"name\": \"PERSON\", \"ent\": \"Ким Чен Нама\", \"pos\": \"0 12\"}, {\"name\": \"ORGANIZATION\", \"ent\": \"Полиция Малайзии\", \"pos\": \"56 72\"}] |iluvvatar/RuNNE |URL |URL |ru |537 |1479.750466 |230.0259174|581 |1337 |1480 |1635 |1988 |\n|dim/lurk\\_prompts |URL |Набор определений различных терминов с сайта lurk. Сами промпты были составлены автоматически следующим образом. напиши определение для (ОПРЕДЕЛЕНИЕ) в стиле lurk |averoo/lurk |URL |URL |ru |5671 |3450.34262 |4147.897824|35 |710.5 |2010 |4593 |55098 |\n|dim/panorama\\_prompts\\_10k |URL |Набор юмористических заголовков и текстов новостей с сайта панорама. |its5Q/panorama |URL |URL |ru |11024 |516.9588171 |191.3774023|36 |422 |498 |585 |3496 |\n|dim/resh\\_edu\\_short\\_prompts |URL |Набор уроков с сайта URL включающих в себя название урока, тему, класс и текст урока с заданиями. |its5Q/resh-edu |URL |URL |ru |2106 |1431.510921 |435.7847102|56 |1175.5 |1517 |1777 |2029 |\n|dim/databricks\\_dolly\\_15k\\_ru |URL |Переведенный датасет dolly на русский язык. Включает в себя набор инструкций на обширное количество тематик. |dwarf2/databricks-dolly-15k-ru |URL |URL |ru |14914 |305.4638595 |405.874049 |8 |87 |182 |370 |9268 |\n|dim/databricks\\_dolly\\_15k\\_en |URL |databricks-dolly-15k — это набор данных с открытым исходным кодом, содержащий записи о выполнении инструкций, созданные тысячами сотрудников Databricks в нескольких поведенческих категориях, изложенных в документе InstructGPT, включая мозговой штурм, классификацию, закрытый контроль качества, генерацию, извлечение информации, открытый контроль качества и обобщение. |databricks/databricks-dolly-15k |URL |URL |en |15011 |204.7264006 |302.5539423|6 |57 |119 |242 |8883 |\n|dim/grammarly\\_coedit |URL |Набор промптов, которые просят исправить грамматические, стилистические ошибки на английском. |grammarly/coedit |URL |URL |en |82466 |53.7128271 |26.73822864|10 |35 |46 |64 |694 |\n|dim/kinopoisk\\_prompts |URL |Отзывы с кинопоиска на топ 250 фильмов. В промптах я прошу написать хороший, плохой или нейтральный отзыв на определенный фильм. |blinoff/kinopoisk |URL |URL |ru |36591 |875.0955973 |565.3212035|48 |484 |733 |1117 |8628 |\n|dim/medical\\_qa\\_ru\\_prompts |URL |Какие-то вопросы и ответы с какого-то медицинского форума. В данной версии датасета только первый ответ из оригинала. |blinoff/medical\\_qa\\_ru\\_data |URL |URL |ru |80101 |206.710528 |175.4343973|12 |106 |161 |247 |5062 |\n|dim/joke\\_explaination\\_prompts |URL |Объяснение шуток на английском. От изначального датасета отличается тем, что я убрал последнее предложение из объяснения, так как оно ссылается на видео на сайте. |theblackcat102/joke\\_explaination |URL |URL |en |364 |143.5741758 |68.90275411|21 |99 |137.5 |189.25 |334 |\n|dim/oa\\_stackexchange\\_200k |URL |Вопросы-ответы со stackexchange. Оригинальный датасет был составлен следующим образом: были выбраны только темы с принятым ответом, для которых длина вопроса и ответа составляет менее 1000 символов. Другие ответы, вопросы без принятых ответов или длинные записи были удалены. Так как оригинальный датасет слишком большой, я рандомно выбрал 200k семплов. |donfu/oa-stackexchange |URL |URL |en |200000 |276.29862 |112.5004436|22 |194 |265 |345 |1226 |\n|dim/scale\\_helpful\\_no\\_math |URL |Какой-то набор диалогов с вопросами-ответами на английском, происхождение неизвестно. |HuggingFaceH4/scale\\_helpful\\_no\\_math |URL |URL |en |17095 |1235.302603 |838.1097885|53 |663 |1063 |1617 |34480 |\n|dim/law\\_stackexchange\\_prompts |URL |Вопросы про закон на английском языке со StackExchange. Оригинальный датасет был преобразован в markdown. |ymoslem/Law-StackExchange |URL |URL |en |24343 |689.1184324 |565.0316906|43 |354 |540 |836 |8969 |\n|dim/ficbook\\_prompts\\_best\\_10k |URL |Топ 10k лучших фанфиков с сайта URL. Все промпты выглядят следующим образом: напиши фанфик с названием {title} и следующим описанием {description}, с тегами {tags}, Где title это оригинальное название, description оригинальное описание, tags это теги данного произведения. |AlexWortega/FicBook |URL |URL |ru |10000 |1737.8214 |402.0748161|166 |1716 |1950 |1950 |1952 |\n|dim/azbyka\\_logic\\_ru |URL |Небольшой набор детских логических и православных задач, взятых с сайта URL . Обычно у них почти нет развернутого решения, только ответ. Я пытался расписать решение некоторых задач, но меня хватило только на 35, если кто-то займется подобным буду рад URL . |Логические и занимательные задачи (300 задач) |URL |URL |ru |480 |77.4375 |77.56990416|14 |31 |50 |91 |652 |\n|dim/povarenok |URL |46k лучших рецептов с сайта URL, содержит текст рецепта, список ингридиентов, название блюда |URL |URL |URL |ru |46500 |488.9118495 |344.8563249|31 |281 |440 |632 |5542 |\n|dim/AO3\\_fandom\\_chatbot\\_1to1 |URL |Какой-то набор ролеплейных диалогов с описанием персонажей и их отыгрышем. Происхождение неизвестно. |ebony59/AO3\\_fandom\\_chatbot\\_1to1 |URL |URL |en |614 |493.7166124 |226.3885365|129 |328.25 |432.5 |611.75 |1272 |\n|dim/habr\\_prompts\\_5k |URL |Статьи с хабра. Датасет был составлен с помощью chatgpt, chatgpt преобразовывал заголовки таким образом чтобы они звучали как вопросы от пользователя, в качестве таргета выступала сама статья. |IlyaGusev/habr |URL |URL |ru |5000 |1732.892 |454.8418369|19 |1920.75|1950 |1951 |1952 |\n|dim/what\\_where\\_when\\_50k |URL |50k вопросов с решениями с сайта что где когда. В качестве промпта выступает вопрос, в качестве ответа конкатенация объяснения и краткого ответа. Все вопросы-ответы вы можете найти по этой ссылке URL |URL |URL |URL |ru |50000 |169.1862 |68.91119898|18 |122 |158 |202 |1167 |\n|dim/competition\\_math |URL |Датасет олимпиадной математики на английском. The Mathematics Aptitude Test of Heuristics (MATH) dataset. |competition\\_math |URL |URL |en |7500 |317.5254667 |267.8583731|34 |147 |234 |393 |3029 |\n|dim/sharegpt\\_short\\_en\\_30k |URL |Короткие диалоги на английском из sharegpt |RyokoAI/ShareGPT52K |URL |URL |en |29597 |749.3149981 |516.3702473|3 |336 |630 |1095 |2021 |\n|dim/ru\\_turbo\\_alpaca\\_evol\\_instruct |URL |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru\\_turbo\\_alpaca\\_evol\\_instruct |URL |URL |ru |47793 |453.0887996 |289.5498356|17 |221 |430 |623 |4647 |\n|dim/ru\\_turbo\\_saiga |URL |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru\\_turbo\\_saiga |URL |URL |ru |37699 |412.7508687 |113.346917 |87 |339 |398 |466 |1427 |\n|dim/bugurt\\_completion\\_prompts |URL |Обрезанные бугурты, где в качестве промпта используется строка вида - продолжи бугурт: первая строчка бугурта |https://t.me/bugurtthread |https://t.me/bugurtthread |URL |ru |5000 |280.2466 |320.4353681|32 |111 |178 |331 |11333 |\n|dim/tldr\\_17\\_50k |URL |Очень вольная абстрактная саммаризация постов с реддита в одну строчку |webis/tldr-17 |URL |URL |en |50000 |421.12752 |403.346214 |10 |177 |303 |525 |9592 |\n|dim/grade\\_school\\_math\\_instructions |URL |OpenAI's grade-school-math датасет преобразованный в промпты. |qwedsacf/grade-school-math-instructions |URL |URL |en |8792 |171.6310282 |63.09232668|50 |124 |161 |206 |511 |\n|dim/tldr\\_news |URL |Хедлайны и текст новостей на различную тематику. |JulesBelveze/tldr\\_news |URL |URL |en |7138 |133.1004483 |46.48736493|23 |100 |133 |161 |476 |\n|dim/grade\\_school\\_math\\_instructions\\_ru|URL grade-school-math датасет переведенный на русский. |d0rj/gsm8k-ru |URL |URL |7473 |259.8321959 |100.1229127|78 |185 |241 |314 |838 |\n|dim/dialogsum |URL |Саммаризация диалогов на английском языке, разметка выполнялась вручную. |knkarthick/dialogsum |URL |URL |en |12460 |269.6467095 |126.285664 |75 |191 |245 |327 |1725 |\n|dim/HC3\\_ru |URL |Вопросы-ответы с реддита, есть ответы сгенерированные chatgpt и реальные ответы пользователей. Я использовал только реальные ответы пользователей. |d0rj/HC3-ru |URL |URL |ru |24322 |360.5608503 |330.2285903|15 |168 |267 |435 |10025 |\n|dim/horoscopes\\_ru\\_10k |URL |10k гороскопов, с промптами где я прошу сгенерировать гороском для определенного знака зодиака |dkagramanyan/horoscopes\\_ru |URL |URL |ru |10000 |183.1443 |31.62023184|55 |159 |187 |201 |464 |\n|dim/yandex\\_q\\_200k |URL |200k рандомно выбранных вопросов-ответов с сайта yandex q. |its5Q/yandex-q |URL |URL |ru |200000 |304.569005 |340.7808288|18 |127 |202 |353 |19294 |\n|dim/leetcodesolutions\\_en\\_2k |URL |Решения задач с leetcode на разных языках. |TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k |URL |URL |en |2048 |740.7441406 |253.2493282|297 |565 |685 |857 |1960 |\n|dim/forum\\_uristov\\_rf\\_prompts |URL |Вопросы-ответы с российского юридического форума. |URL--p1ai/vopros-yuristu?page=560|URL--p1ai/vopros-yuristu?page=560 |URL |ru |1849 |321.0540833 |429.58896 |31 |134 |210 |349 |6470 |\n|dim/dialogsum\\_ru |URL |Саммаризация диалогов на русском языке, перевод dialogsum. |d0rj/dialogsum-ru |URL |URL |ru |12460 |364.2813804 |178.7117754|98 |250 |329 |446 |2300 |\n|dim/huggingartists\\_prompts |URL |Промпты, которые просят продолжить песню в стиле определенного исполнителя. В данном наборе содержатся почти все исполнители, которых вы можете найти в этой организации URL |URL |URL |URL |ru |64006 |561.6732025 |586.18458 |28 |297 |453 |720 |32949 |",
"### Модели\n\n\n* Ссылка на google sheets\n\n\n\n* dim/mistral-open-orca-ru-4600-step\n* dim/verbalist\\_7b\\_v9\\_800\n* dim/verbalist\\_v10\\_1650",
"### Код обучения\n\n\n* общий алгоритм обучения\n* формирование датасетов для обучения",
"### Оборудование\n\n\nВсе обучение и инференс производится на видеокарте A100, на других видеокартах была обнаружена существенная деградация качества при инференсе, данный аспект требует дополнительного изучения.\n\n\n* NVIDIA A100-SXM4-40GB\n* NVIDIA-SMI 535.54.03\n* Driver Version: 535.54.03\n* CUDA Version: 12.2\n* torch==2.0.1+cu118",
"### Дальнейшее развитие\n\n\nСамое простое, что можно сделать это переводить уже имеющиеся хорошие датасеты с английского на русский при помощи GPT-4.\n\n\nБолее сложное это собирать больше разнообразных данных из различных доменов. Я могу лишь подкинуть идеи для того какие датасеты можно собрать еще.\n\n\n* решебники по литературе, русскому и другим предметам\n* задания со всяких бирж труда\n* краткие пересказы произведений, анализ произведений, сочинения по ним\n* туториалы с digital ocean (более 7000)\n* туториалы с selectel\n* больше форумов на различные тематики\n* бесплатные эссе с ivypanda essays и дальнейший их перевод на русский\n* больше стихов и песен\n* олимпиадные русские задачи их очень сложно собирать, так как большинство их них живут только в PDF или docx. Но их довольно много и они довольно отличаются от олимпиадной математики на английском. Но у меня нет времени этим заниматься.\n* фанфики на иностранном языке\n* исправить текущие автоматические промпты на более разнообразные, при помощи chatgpt"
] |
[
24,
6562,
58,
15,
90,
237
] |
[
"passage: TAGS\n#language-Russian #language-English #arxiv-2305.11206 #region-us \n"
] |
3f4defa169bf3cbfd9cf7b504dd9ba9e6a8ae6f3
|
subset of dataset, around 180 samples pulled from Khan academy
|
c123ian/khan_academy_200
|
[
"region:us"
] |
2023-10-04T11:24:01+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "E", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 521626, "num_examples": 125}], "download_size": 272842, "dataset_size": 521626}}
|
2023-10-04T11:27:42+00:00
|
[] |
[] |
TAGS
#region-us
|
subset of dataset, around 180 samples pulled from Khan academy
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
1f2c30c6afac857da7c966653c6d817ca14e677d
|
# Dataset Card for "53decd51"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/53decd51
|
[
"region:us"
] |
2023-10-04T11:24:27+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 190, "num_examples": 10}], "download_size": 1351, "dataset_size": 190}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T11:24:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "53decd51"
More Information needed
|
[
"# Dataset Card for \"53decd51\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"53decd51\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"53decd51\"\n\nMore Information needed"
] |
eb5894a2378551ec255283bc870c83d650e58676
|
# Chat Fine-tuning Dataset - Guanaco Style
This dataset allows for fine-tuning chat models using "### Human:" AND "### Assistant" as the beginning and end of sequence tokens.
Preparation:
1. The dataset is cloned from [TimDettmers](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), which itself is a subset of the Open Assistant dataset, which you can find [here](https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main). This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
1. The dataset was then slightly adjusted to:
- if a row of data ends with an assistant response, then "### Human" was additionally added to the end of that row of data.
Details of the root dataset follow, copied from that repo:
# OpenAssistant Conversations Dataset (OASST1)
## Dataset Description
- **Homepage:** https://www.open-assistant.io/
- **Repository:** https://github.com/LAION-AI/Open-Assistant
- **Paper:** https://arxiv.org/abs/2304.07327
### Dataset Summary
In an effort to democratize research on large-scale alignment, we release OpenAssistant
Conversations (OASST1), a human-generated, human-annotated assistant-style conversation
corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292
quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus
is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.
Please refer to our [paper](https://arxiv.org/abs/2304.07327) for further details.
### Dataset Structure
This dataset contains message trees. Each message tree has an initial prompt message as the root node,
which can have multiple child messages as replies, and these child messages can have multiple replies.
All messages have a role property: this can either be "assistant" or "prompter". The roles in
conversation threads from prompt to leaf node strictly alternate between "prompter" and "assistant".
This version of the dataset contains data collected on the [open-assistant.io](https://open-assistant.io/) website until April 12 2023.
### JSON Example: Message
For readability, the following JSON examples are shown formatted with indentation on multiple lines.
Objects are stored without indentation (on single lines) in the actual jsonl files.
```json
{
"message_id": "218440fd-5317-4355-91dc-d001416df62b",
"parent_id": "13592dfb-a6f9-4748-a92c-32b34e239bb4",
"user_id": "8e95461f-5e94-4d8b-a2fb-d4717ce973e4",
"text": "It was the winter of 2035, and artificial intelligence (..)",
"role": "assistant",
"lang": "en",
"review_count": 3,
"review_result": true,
"deleted": false,
"rank": 0,
"synthetic": true,
"model_name": "oasst-sft-0_3000,max_new_tokens=400 (..)",
"labels": {
"spam": { "value": 0.0, "count": 3 },
"lang_mismatch": { "value": 0.0, "count": 3 },
"pii": { "value": 0.0, "count": 3 },
"not_appropriate": { "value": 0.0, "count": 3 },
"hate_speech": { "value": 0.0, "count": 3 },
"sexual_content": { "value": 0.0, "count": 3 },
"quality": { "value": 0.416, "count": 3 },
"toxicity": { "value": 0.16, "count": 3 },
"humor": { "value": 0.0, "count": 3 },
"creativity": { "value": 0.33, "count": 3 },
"violence": { "value": 0.16, "count": 3 }
}
}
```
### JSON Example: Conversation Tree
For readability, only a subset of the message properties is shown here.
```json
{
"message_tree_id": "14fbb664-a620-45ce-bee4-7c519b16a793",
"tree_state": "ready_for_export",
"prompt": {
"message_id": "14fbb664-a620-45ce-bee4-7c519b16a793",
"text": "Why can't we divide by 0? (..)",
"role": "prompter",
"lang": "en",
"replies": [
{
"message_id": "894d30b6-56b4-4605-a504-89dd15d4d1c8",
"text": "The reason we cannot divide by zero is because (..)",
"role": "assistant",
"lang": "en",
"replies": [
// ...
]
},
{
"message_id": "84d0913b-0fd9-4508-8ef5-205626a7039d",
"text": "The reason that the result of a division by zero is (..)",
"role": "assistant",
"lang": "en",
"replies": [
{
"message_id": "3352725e-f424-4e3b-a627-b6db831bdbaa",
"text": "Math is confusing. Like those weird Irrational (..)",
"role": "prompter",
"lang": "en",
"replies": [
{
"message_id": "f46207ca-3149-46e9-a466-9163d4ce499c",
"text": "Irrational numbers are simply numbers (..)",
"role": "assistant",
"lang": "en",
"replies": []
},
// ...
]
}
]
}
]
}
}
```
Please refer to [oasst-data](https://github.com/LAION-AI/Open-Assistant/tree/main/oasst-data) for
details about the data structure and Python code to read and write jsonl files containing oasst data objects.
If you would like to explore the dataset yourself you can find a
[`getting-started`](https://github.com/LAION-AI/Open-Assistant/blob/main/notebooks/openassistant-oasst1/getting-started.ipynb)
notebook in the `notebooks/openassistant-oasst1` folder of the [LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant)
github repository.
## Main Dataset Files
Conversation data is provided either as nested messages in trees (extension `.trees.jsonl.gz`)
or as a flat list (table) of messages (extension `.messages.jsonl.gz`).
### Ready For Export Trees
```
2023-04-12_oasst_ready.trees.jsonl.gz 10,364 trees with 88,838 total messages
2023-04-12_oasst_ready.messages.jsonl.gz 88,838 messages
```
Trees in `ready_for_export` state without spam and deleted messages including message labels.
The oasst_ready-trees file usually is sufficient for supervised fine-tuning (SFT) & reward model (RM) training.
### All Trees
```
2023-04-12_oasst_all.trees.jsonl.gz 66,497 trees with 161,443 total messages
2023-04-12_oasst_all.messages.jsonl.gz 161,443 messages
```
All trees, including those in states `prompt_lottery_waiting` (trees that consist of only one message, namely the initial prompt),
`aborted_low_grade` (trees that stopped growing because the messages had low quality), and `halted_by_moderator`.
### Supplemental Exports: Spam & Prompts
```
2023-04-12_oasst_spam.messages.jsonl.gz
```
These are messages which were deleted or have a negative review result (`"review_result": false`).
Besides low quality, a frequent reason for message deletion is a wrong language tag.
```
2023-04-12_oasst_prompts.messages.jsonl.gz
```
These are all the kept initial prompt messages with positive review result (no spam) of trees in `ready_for_export` or `prompt_lottery_waiting` state.
### Using the Huggingface Datasets
While HF datasets is ideal for tabular datasets, it is not a natural fit for nested data structures like the OpenAssistant conversation trees.
Nevertheless, we make all messages which can also be found in the file `2023-04-12_oasst_ready.trees.jsonl.gz` available in parquet as train/validation splits.
These are directly loadable by [Huggingface Datasets](https://pypi.org/project/datasets/).
To load the oasst1 train & validation splits use:
```python
from datasets import load_dataset
ds = load_dataset("OpenAssistant/oasst1")
train = ds['train'] # len(train)=84437 (95%)
val = ds['validation'] # len(val)=4401 (5%)
```
The messages appear in depth-first order of the message trees.
Full conversation trees can be reconstructed from the flat messages table by using the `parent_id`
and `message_id` properties to identify the parent-child relationship of messages. The `message_tree_id`
and `tree_state` properties (only present in flat messages files) can be used to find all messages of a message tree or to select trees by their state.
### Languages
OpenAssistant Conversations incorporates 35 different languages with a distribution of messages as follows:
**Languages with over 1000 messages**
- English: 71956
- Spanish: 43061
- Russian: 9089
- German: 5279
- Chinese: 4962
- French: 4251
- Thai: 3042
- Portuguese (Brazil): 2969
- Catalan: 2260
- Korean: 1553
- Ukrainian: 1352
- Italian: 1320
- Japanese: 1018
<details>
<summary><b>Languages with under 1000 messages</b></summary>
<ul>
<li>Vietnamese: 952</li>
<li>Basque: 947</li>
<li>Polish: 886</li>
<li>Hungarian: 811</li>
<li>Arabic: 666</li>
<li>Dutch: 628</li>
<li>Swedish: 512</li>
<li>Turkish: 454</li>
<li>Finnish: 386</li>
<li>Czech: 372</li>
<li>Danish: 358</li>
<li>Galician: 339</li>
<li>Hebrew: 255</li>
<li>Romanian: 200</li>
<li>Norwegian Bokmål: 133</li>
<li>Indonesian: 115</li>
<li>Bulgarian: 95</li>
<li>Bengali: 82</li>
<li>Persian: 72</li>
<li>Greek: 66</li>
<li>Esperanto: 59</li>
<li>Slovak: 19</li>
</ul>
</details>
## Contact
- Discord [Open Assistant Discord Server](https://ykilcher.com/open-assistant-discord)
- GitHub: [LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant)
- E-Mail: [[email protected]](mailto:[email protected])
|
Trelis/openassistant-guanaco-EOS
|
[
"size_categories:1K<n<10k",
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:hu",
"language:eu",
"language:zh",
"language:eo",
"language:ja",
"language:ca",
"language:cs",
"language:bg",
"language:fi",
"language:pt",
"language:tr",
"language:ro",
"language:ar",
"language:uk",
"language:gl",
"language:fr",
"language:ko",
"license:apache-2.0",
"human-feedback",
"llama-2",
"arxiv:2304.07327",
"region:us"
] |
2023-10-04T11:28:22+00:00
|
{"language": ["en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko"], "license": "apache-2.0", "size_categories": ["1K<n<10k"], "pretty_name": "Filtered OpenAssistant Conversations", "tags": ["human-feedback", "llama-2"]}
|
2023-10-04T15:17:59+00:00
|
[
"2304.07327"
] |
[
"en",
"es",
"ru",
"de",
"pl",
"th",
"vi",
"sv",
"bn",
"da",
"he",
"it",
"fa",
"sk",
"id",
"nb",
"el",
"nl",
"hu",
"eu",
"zh",
"eo",
"ja",
"ca",
"cs",
"bg",
"fi",
"pt",
"tr",
"ro",
"ar",
"uk",
"gl",
"fr",
"ko"
] |
TAGS
#size_categories-1K<n<10k #language-English #language-Spanish #language-Russian #language-German #language-Polish #language-Thai #language-Vietnamese #language-Swedish #language-Bengali #language-Danish #language-Hebrew #language-Italian #language-Persian #language-Slovak #language-Indonesian #language-Norwegian Bokmål #language-Modern Greek (1453-) #language-Dutch #language-Hungarian #language-Basque #language-Chinese #language-Esperanto #language-Japanese #language-Catalan #language-Czech #language-Bulgarian #language-Finnish #language-Portuguese #language-Turkish #language-Romanian #language-Arabic #language-Ukrainian #language-Galician #language-French #language-Korean #license-apache-2.0 #human-feedback #llama-2 #arxiv-2304.07327 #region-us
|
# Chat Fine-tuning Dataset - Guanaco Style
This dataset allows for fine-tuning chat models using "### Human:" AND "### Assistant" as the beginning and end of sequence tokens.
Preparation:
1. The dataset is cloned from TimDettmers, which itself is a subset of the Open Assistant dataset, which you can find here. This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
1. The dataset was then slightly adjusted to:
- if a row of data ends with an assistant response, then "### Human" was additionally added to the end of that row of data.
Details of the root dataset follow, copied from that repo:
# OpenAssistant Conversations Dataset (OASST1)
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
### Dataset Summary
In an effort to democratize research on large-scale alignment, we release OpenAssistant
Conversations (OASST1), a human-generated, human-annotated assistant-style conversation
corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292
quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus
is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.
Please refer to our paper for further details.
### Dataset Structure
This dataset contains message trees. Each message tree has an initial prompt message as the root node,
which can have multiple child messages as replies, and these child messages can have multiple replies.
All messages have a role property: this can either be "assistant" or "prompter". The roles in
conversation threads from prompt to leaf node strictly alternate between "prompter" and "assistant".
This version of the dataset contains data collected on the URL website until April 12 2023.
### JSON Example: Message
For readability, the following JSON examples are shown formatted with indentation on multiple lines.
Objects are stored without indentation (on single lines) in the actual jsonl files.
### JSON Example: Conversation Tree
For readability, only a subset of the message properties is shown here.
Please refer to oasst-data for
details about the data structure and Python code to read and write jsonl files containing oasst data objects.
If you would like to explore the dataset yourself you can find a
'getting-started'
notebook in the 'notebooks/openassistant-oasst1' folder of the LAION-AI/Open-Assistant
github repository.
## Main Dataset Files
Conversation data is provided either as nested messages in trees (extension '.URL')
or as a flat list (table) of messages (extension '.URL').
### Ready For Export Trees
Trees in 'ready_for_export' state without spam and deleted messages including message labels.
The oasst_ready-trees file usually is sufficient for supervised fine-tuning (SFT) & reward model (RM) training.
### All Trees
All trees, including those in states 'prompt_lottery_waiting' (trees that consist of only one message, namely the initial prompt),
'aborted_low_grade' (trees that stopped growing because the messages had low quality), and 'halted_by_moderator'.
### Supplemental Exports: Spam & Prompts
These are messages which were deleted or have a negative review result ('"review_result": false').
Besides low quality, a frequent reason for message deletion is a wrong language tag.
These are all the kept initial prompt messages with positive review result (no spam) of trees in 'ready_for_export' or 'prompt_lottery_waiting' state.
### Using the Huggingface Datasets
While HF datasets is ideal for tabular datasets, it is not a natural fit for nested data structures like the OpenAssistant conversation trees.
Nevertheless, we make all messages which can also be found in the file '2023-04-12_oasst_ready.URL' available in parquet as train/validation splits.
These are directly loadable by Huggingface Datasets.
To load the oasst1 train & validation splits use:
The messages appear in depth-first order of the message trees.
Full conversation trees can be reconstructed from the flat messages table by using the 'parent_id'
and 'message_id' properties to identify the parent-child relationship of messages. The 'message_tree_id'
and 'tree_state' properties (only present in flat messages files) can be used to find all messages of a message tree or to select trees by their state.
### Languages
OpenAssistant Conversations incorporates 35 different languages with a distribution of messages as follows:
Languages with over 1000 messages
- English: 71956
- Spanish: 43061
- Russian: 9089
- German: 5279
- Chinese: 4962
- French: 4251
- Thai: 3042
- Portuguese (Brazil): 2969
- Catalan: 2260
- Korean: 1553
- Ukrainian: 1352
- Italian: 1320
- Japanese: 1018
<details>
<summary><b>Languages with under 1000 messages</b></summary>
<ul>
<li>Vietnamese: 952</li>
<li>Basque: 947</li>
<li>Polish: 886</li>
<li>Hungarian: 811</li>
<li>Arabic: 666</li>
<li>Dutch: 628</li>
<li>Swedish: 512</li>
<li>Turkish: 454</li>
<li>Finnish: 386</li>
<li>Czech: 372</li>
<li>Danish: 358</li>
<li>Galician: 339</li>
<li>Hebrew: 255</li>
<li>Romanian: 200</li>
<li>Norwegian Bokmål: 133</li>
<li>Indonesian: 115</li>
<li>Bulgarian: 95</li>
<li>Bengali: 82</li>
<li>Persian: 72</li>
<li>Greek: 66</li>
<li>Esperanto: 59</li>
<li>Slovak: 19</li>
</ul>
</details>
## Contact
- Discord Open Assistant Discord Server
- GitHub: LAION-AI/Open-Assistant
- E-Mail: open-assistant@URL
|
[
"# Chat Fine-tuning Dataset - Guanaco Style\nThis dataset allows for fine-tuning chat models using \"### Human:\" AND \"### Assistant\" as the beginning and end of sequence tokens.\n\nPreparation:\n\n1. The dataset is cloned from TimDettmers, which itself is a subset of the Open Assistant dataset, which you can find here. This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.\n1. The dataset was then slightly adjusted to:\n - if a row of data ends with an assistant response, then \"### Human\" was additionally added to the end of that row of data.\n\nDetails of the root dataset follow, copied from that repo:",
"# OpenAssistant Conversations Dataset (OASST1)",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL",
"### Dataset Summary\n\nIn an effort to democratize research on large-scale alignment, we release OpenAssistant \nConversations (OASST1), a human-generated, human-annotated assistant-style conversation \ncorpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 \nquality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus \nis a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.\n\nPlease refer to our paper for further details.",
"### Dataset Structure\n\nThis dataset contains message trees. Each message tree has an initial prompt message as the root node, \nwhich can have multiple child messages as replies, and these child messages can have multiple replies. \n\nAll messages have a role property: this can either be \"assistant\" or \"prompter\". The roles in \nconversation threads from prompt to leaf node strictly alternate between \"prompter\" and \"assistant\".\n\nThis version of the dataset contains data collected on the URL website until April 12 2023.",
"### JSON Example: Message\n\nFor readability, the following JSON examples are shown formatted with indentation on multiple lines.\nObjects are stored without indentation (on single lines) in the actual jsonl files.",
"### JSON Example: Conversation Tree\n\nFor readability, only a subset of the message properties is shown here.\n\n\n\nPlease refer to oasst-data for\ndetails about the data structure and Python code to read and write jsonl files containing oasst data objects.\n\nIf you would like to explore the dataset yourself you can find a \n'getting-started' \nnotebook in the 'notebooks/openassistant-oasst1' folder of the LAION-AI/Open-Assistant\ngithub repository.",
"## Main Dataset Files\n\nConversation data is provided either as nested messages in trees (extension '.URL') \nor as a flat list (table) of messages (extension '.URL').",
"### Ready For Export Trees\n\n\nTrees in 'ready_for_export' state without spam and deleted messages including message labels.\nThe oasst_ready-trees file usually is sufficient for supervised fine-tuning (SFT) & reward model (RM) training.",
"### All Trees\n\nAll trees, including those in states 'prompt_lottery_waiting' (trees that consist of only one message, namely the initial prompt),\n'aborted_low_grade' (trees that stopped growing because the messages had low quality), and 'halted_by_moderator'.",
"### Supplemental Exports: Spam & Prompts\n\nThese are messages which were deleted or have a negative review result ('\"review_result\": false').\nBesides low quality, a frequent reason for message deletion is a wrong language tag.\n\n\nThese are all the kept initial prompt messages with positive review result (no spam) of trees in 'ready_for_export' or 'prompt_lottery_waiting' state.",
"### Using the Huggingface Datasets\n\nWhile HF datasets is ideal for tabular datasets, it is not a natural fit for nested data structures like the OpenAssistant conversation trees.\nNevertheless, we make all messages which can also be found in the file '2023-04-12_oasst_ready.URL' available in parquet as train/validation splits. \nThese are directly loadable by Huggingface Datasets.\n\nTo load the oasst1 train & validation splits use:\n\n\n\nThe messages appear in depth-first order of the message trees.\n\nFull conversation trees can be reconstructed from the flat messages table by using the 'parent_id' \nand 'message_id' properties to identify the parent-child relationship of messages. The 'message_tree_id' \nand 'tree_state' properties (only present in flat messages files) can be used to find all messages of a message tree or to select trees by their state.",
"### Languages\n\nOpenAssistant Conversations incorporates 35 different languages with a distribution of messages as follows:\n\nLanguages with over 1000 messages\n- English: 71956\n- Spanish: 43061\n- Russian: 9089\n- German: 5279\n- Chinese: 4962\n- French: 4251\n- Thai: 3042\n- Portuguese (Brazil): 2969\n- Catalan: 2260\n- Korean: 1553\n- Ukrainian: 1352\n- Italian: 1320\n- Japanese: 1018\n\n<details>\n <summary><b>Languages with under 1000 messages</b></summary>\n <ul>\n <li>Vietnamese: 952</li>\n <li>Basque: 947</li>\n <li>Polish: 886</li>\n <li>Hungarian: 811</li>\n <li>Arabic: 666</li>\n <li>Dutch: 628</li>\n <li>Swedish: 512</li>\n <li>Turkish: 454</li>\n <li>Finnish: 386</li>\n <li>Czech: 372</li>\n <li>Danish: 358</li>\n <li>Galician: 339</li>\n <li>Hebrew: 255</li>\n <li>Romanian: 200</li>\n <li>Norwegian Bokmål: 133</li>\n <li>Indonesian: 115</li>\n <li>Bulgarian: 95</li>\n <li>Bengali: 82</li>\n <li>Persian: 72</li>\n <li>Greek: 66</li>\n <li>Esperanto: 59</li>\n <li>Slovak: 19</li>\n </ul>\n</details>",
"## Contact\n\n- Discord Open Assistant Discord Server\n- GitHub: LAION-AI/Open-Assistant\n- E-Mail: open-assistant@URL"
] |
[
"TAGS\n#size_categories-1K<n<10k #language-English #language-Spanish #language-Russian #language-German #language-Polish #language-Thai #language-Vietnamese #language-Swedish #language-Bengali #language-Danish #language-Hebrew #language-Italian #language-Persian #language-Slovak #language-Indonesian #language-Norwegian Bokmål #language-Modern Greek (1453-) #language-Dutch #language-Hungarian #language-Basque #language-Chinese #language-Esperanto #language-Japanese #language-Catalan #language-Czech #language-Bulgarian #language-Finnish #language-Portuguese #language-Turkish #language-Romanian #language-Arabic #language-Ukrainian #language-Galician #language-French #language-Korean #license-apache-2.0 #human-feedback #llama-2 #arxiv-2304.07327 #region-us \n",
"# Chat Fine-tuning Dataset - Guanaco Style\nThis dataset allows for fine-tuning chat models using \"### Human:\" AND \"### Assistant\" as the beginning and end of sequence tokens.\n\nPreparation:\n\n1. The dataset is cloned from TimDettmers, which itself is a subset of the Open Assistant dataset, which you can find here. This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.\n1. The dataset was then slightly adjusted to:\n - if a row of data ends with an assistant response, then \"### Human\" was additionally added to the end of that row of data.\n\nDetails of the root dataset follow, copied from that repo:",
"# OpenAssistant Conversations Dataset (OASST1)",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL",
"### Dataset Summary\n\nIn an effort to democratize research on large-scale alignment, we release OpenAssistant \nConversations (OASST1), a human-generated, human-annotated assistant-style conversation \ncorpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 \nquality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus \nis a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.\n\nPlease refer to our paper for further details.",
"### Dataset Structure\n\nThis dataset contains message trees. Each message tree has an initial prompt message as the root node, \nwhich can have multiple child messages as replies, and these child messages can have multiple replies. \n\nAll messages have a role property: this can either be \"assistant\" or \"prompter\". The roles in \nconversation threads from prompt to leaf node strictly alternate between \"prompter\" and \"assistant\".\n\nThis version of the dataset contains data collected on the URL website until April 12 2023.",
"### JSON Example: Message\n\nFor readability, the following JSON examples are shown formatted with indentation on multiple lines.\nObjects are stored without indentation (on single lines) in the actual jsonl files.",
"### JSON Example: Conversation Tree\n\nFor readability, only a subset of the message properties is shown here.\n\n\n\nPlease refer to oasst-data for\ndetails about the data structure and Python code to read and write jsonl files containing oasst data objects.\n\nIf you would like to explore the dataset yourself you can find a \n'getting-started' \nnotebook in the 'notebooks/openassistant-oasst1' folder of the LAION-AI/Open-Assistant\ngithub repository.",
"## Main Dataset Files\n\nConversation data is provided either as nested messages in trees (extension '.URL') \nor as a flat list (table) of messages (extension '.URL').",
"### Ready For Export Trees\n\n\nTrees in 'ready_for_export' state without spam and deleted messages including message labels.\nThe oasst_ready-trees file usually is sufficient for supervised fine-tuning (SFT) & reward model (RM) training.",
"### All Trees\n\nAll trees, including those in states 'prompt_lottery_waiting' (trees that consist of only one message, namely the initial prompt),\n'aborted_low_grade' (trees that stopped growing because the messages had low quality), and 'halted_by_moderator'.",
"### Supplemental Exports: Spam & Prompts\n\nThese are messages which were deleted or have a negative review result ('\"review_result\": false').\nBesides low quality, a frequent reason for message deletion is a wrong language tag.\n\n\nThese are all the kept initial prompt messages with positive review result (no spam) of trees in 'ready_for_export' or 'prompt_lottery_waiting' state.",
"### Using the Huggingface Datasets\n\nWhile HF datasets is ideal for tabular datasets, it is not a natural fit for nested data structures like the OpenAssistant conversation trees.\nNevertheless, we make all messages which can also be found in the file '2023-04-12_oasst_ready.URL' available in parquet as train/validation splits. \nThese are directly loadable by Huggingface Datasets.\n\nTo load the oasst1 train & validation splits use:\n\n\n\nThe messages appear in depth-first order of the message trees.\n\nFull conversation trees can be reconstructed from the flat messages table by using the 'parent_id' \nand 'message_id' properties to identify the parent-child relationship of messages. The 'message_tree_id' \nand 'tree_state' properties (only present in flat messages files) can be used to find all messages of a message tree or to select trees by their state.",
"### Languages\n\nOpenAssistant Conversations incorporates 35 different languages with a distribution of messages as follows:\n\nLanguages with over 1000 messages\n- English: 71956\n- Spanish: 43061\n- Russian: 9089\n- German: 5279\n- Chinese: 4962\n- French: 4251\n- Thai: 3042\n- Portuguese (Brazil): 2969\n- Catalan: 2260\n- Korean: 1553\n- Ukrainian: 1352\n- Italian: 1320\n- Japanese: 1018\n\n<details>\n <summary><b>Languages with under 1000 messages</b></summary>\n <ul>\n <li>Vietnamese: 952</li>\n <li>Basque: 947</li>\n <li>Polish: 886</li>\n <li>Hungarian: 811</li>\n <li>Arabic: 666</li>\n <li>Dutch: 628</li>\n <li>Swedish: 512</li>\n <li>Turkish: 454</li>\n <li>Finnish: 386</li>\n <li>Czech: 372</li>\n <li>Danish: 358</li>\n <li>Galician: 339</li>\n <li>Hebrew: 255</li>\n <li>Romanian: 200</li>\n <li>Norwegian Bokmål: 133</li>\n <li>Indonesian: 115</li>\n <li>Bulgarian: 95</li>\n <li>Bengali: 82</li>\n <li>Persian: 72</li>\n <li>Greek: 66</li>\n <li>Esperanto: 59</li>\n <li>Slovak: 19</li>\n </ul>\n</details>",
"## Contact\n\n- Discord Open Assistant Discord Server\n- GitHub: LAION-AI/Open-Assistant\n- E-Mail: open-assistant@URL"
] |
[
239,
171,
15,
18,
120,
120,
51,
117,
46,
66,
74,
99,
221,
381,
36
] |
[
"passage: TAGS\n#size_categories-1K<n<10k #language-English #language-Spanish #language-Russian #language-German #language-Polish #language-Thai #language-Vietnamese #language-Swedish #language-Bengali #language-Danish #language-Hebrew #language-Italian #language-Persian #language-Slovak #language-Indonesian #language-Norwegian Bokmål #language-Modern Greek (1453-) #language-Dutch #language-Hungarian #language-Basque #language-Chinese #language-Esperanto #language-Japanese #language-Catalan #language-Czech #language-Bulgarian #language-Finnish #language-Portuguese #language-Turkish #language-Romanian #language-Arabic #language-Ukrainian #language-Galician #language-French #language-Korean #license-apache-2.0 #human-feedback #llama-2 #arxiv-2304.07327 #region-us \n# Chat Fine-tuning Dataset - Guanaco Style\nThis dataset allows for fine-tuning chat models using \"### Human:\" AND \"### Assistant\" as the beginning and end of sequence tokens.\n\nPreparation:\n\n1. The dataset is cloned from TimDettmers, which itself is a subset of the Open Assistant dataset, which you can find here. This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.\n1. The dataset was then slightly adjusted to:\n - if a row of data ends with an assistant response, then \"### Human\" was additionally added to the end of that row of data.\n\nDetails of the root dataset follow, copied from that repo:# OpenAssistant Conversations Dataset (OASST1)## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL",
"passage: ### Dataset Summary\n\nIn an effort to democratize research on large-scale alignment, we release OpenAssistant \nConversations (OASST1), a human-generated, human-annotated assistant-style conversation \ncorpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 \nquality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus \nis a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.\n\nPlease refer to our paper for further details.### Dataset Structure\n\nThis dataset contains message trees. Each message tree has an initial prompt message as the root node, \nwhich can have multiple child messages as replies, and these child messages can have multiple replies. \n\nAll messages have a role property: this can either be \"assistant\" or \"prompter\". The roles in \nconversation threads from prompt to leaf node strictly alternate between \"prompter\" and \"assistant\".\n\nThis version of the dataset contains data collected on the URL website until April 12 2023.### JSON Example: Message\n\nFor readability, the following JSON examples are shown formatted with indentation on multiple lines.\nObjects are stored without indentation (on single lines) in the actual jsonl files.### JSON Example: Conversation Tree\n\nFor readability, only a subset of the message properties is shown here.\n\n\n\nPlease refer to oasst-data for\ndetails about the data structure and Python code to read and write jsonl files containing oasst data objects.\n\nIf you would like to explore the dataset yourself you can find a \n'getting-started' \nnotebook in the 'notebooks/openassistant-oasst1' folder of the LAION-AI/Open-Assistant\ngithub repository.## Main Dataset Files\n\nConversation data is provided either as nested messages in trees (extension '.URL') \nor as a flat list (table) of messages (extension '.URL').### Ready For Export Trees\n\n\nTrees in 'ready_for_export' state without spam and deleted messages including message labels.\nThe oasst_ready-trees file usually is sufficient for supervised fine-tuning (SFT) & reward model (RM) training.### All Trees\n\nAll trees, including those in states 'prompt_lottery_waiting' (trees that consist of only one message, namely the initial prompt),\n'aborted_low_grade' (trees that stopped growing because the messages had low quality), and 'halted_by_moderator'.",
"passage: ### Supplemental Exports: Spam & Prompts\n\nThese are messages which were deleted or have a negative review result ('\"review_result\": false').\nBesides low quality, a frequent reason for message deletion is a wrong language tag.\n\n\nThese are all the kept initial prompt messages with positive review result (no spam) of trees in 'ready_for_export' or 'prompt_lottery_waiting' state.### Using the Huggingface Datasets\n\nWhile HF datasets is ideal for tabular datasets, it is not a natural fit for nested data structures like the OpenAssistant conversation trees.\nNevertheless, we make all messages which can also be found in the file '2023-04-12_oasst_ready.URL' available in parquet as train/validation splits. \nThese are directly loadable by Huggingface Datasets.\n\nTo load the oasst1 train & validation splits use:\n\n\n\nThe messages appear in depth-first order of the message trees.\n\nFull conversation trees can be reconstructed from the flat messages table by using the 'parent_id' \nand 'message_id' properties to identify the parent-child relationship of messages. The 'message_tree_id' \nand 'tree_state' properties (only present in flat messages files) can be used to find all messages of a message tree or to select trees by their state."
] |
02590f3423ec36b47f452403903bf1aae361f457
|
# Dataset Card for "5f48a05c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/5f48a05c
|
[
"region:us"
] |
2023-10-04T11:28:36+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 176, "num_examples": 10}], "download_size": 1365, "dataset_size": 176}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T11:28:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "5f48a05c"
More Information needed
|
[
"# Dataset Card for \"5f48a05c\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"5f48a05c\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"5f48a05c\"\n\nMore Information needed"
] |
e9c87aa7347a7cf20fee7ea814f9ec54873332e4
|
This dataset collected from various sources,
Once I obtained the urls for youtube videos, I used langchain with YoutubeLoader function to get text of videos.
Source of data: https://github.com/talesmarra/youtube_data_analysis
tasks: summarization, named entity recognition,
|
umarigan/youtube_scripts
|
[
"task_categories:zero-shot-classification",
"task_categories:summarization",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-10-04T11:39:01+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["zero-shot-classification", "summarization"]}
|
2023-10-04T11:46:54+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-zero-shot-classification #task_categories-summarization #language-English #license-apache-2.0 #region-us
|
This dataset collected from various sources,
Once I obtained the urls for youtube videos, I used langchain with YoutubeLoader function to get text of videos.
Source of data: URL
tasks: summarization, named entity recognition,
|
[] |
[
"TAGS\n#task_categories-zero-shot-classification #task_categories-summarization #language-English #license-apache-2.0 #region-us \n"
] |
[
41
] |
[
"passage: TAGS\n#task_categories-zero-shot-classification #task_categories-summarization #language-English #license-apache-2.0 #region-us \n"
] |
8cf5e28fa303914d910efd02b3235cb4ddfa5986
|
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
apacheotom/guanaco-llama2-1k
|
[
"region:us"
] |
2023-10-04T12:04:46+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966693, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T12:04:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
818c797d7edd6c8f4b2865b3ecea6929184b3809
|
# Dataset Card for "synpre_set_1M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/synpre_set_1M
|
[
"region:us"
] |
2023-10-04T12:12:37+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1218382220, "num_examples": 1000000}, {"name": "validation", "num_bytes": 12163626, "num_examples": 10000}], "download_size": 8496414, "dataset_size": 1230545846}}
|
2023-10-04T12:26:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "synpre_set_1M"
More Information needed
|
[
"# Dataset Card for \"synpre_set_1M\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"synpre_set_1M\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"synpre_set_1M\"\n\nMore Information needed"
] |
b02cf3038fb62dd4a0227fb086942655cfe0c319
|
# Bangumi Image Base of Hyōka
This is the image base of bangumi Hyōka, we detected 33 characters, 3456 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1026 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 57 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 548 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 41 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 85 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 312 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 51 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 48 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 25 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 8 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 45 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 17 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 77 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 687 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 34 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 26 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 18 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 15 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 15 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 10 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 22 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 12 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 16 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 9 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 5 | [Download](25/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 26 | 11 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 6 | [Download](27/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 28 | 9 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 9 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 5 | [Download](30/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 31 | 9 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 171 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/hyoka
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T12:28:27+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T14:09:35+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Hyōka
===========================
This is the image base of bangumi Hyōka, we detected 33 characters, 3456 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
6a50751e126ba3a05ba002ef8ed6e013baeab09b
|
# Dataset Card for Dataset Name
## Dataset Description
### Dataset Summary
This dataset is a deduplicated version of [sharegpt4](https://huggingface.co/datasets/shibing624/sharegpt_gpt4).
<br>The deduplication process has two steps:<br>
1. The literal duplicates (both input and outputs) are removed
2. The remaining (5749) instances are embedded with the [SentenceTransformer library](https://www.sbert.net/) ("paraphrase-multilingual-mpnet-base-v2" model).
Then, we compute the cosine similarity among all the possible pairs, and consider paraphrases those pairs with a similarity > 0.95. For each paraphrase group, we only retain one element.
The resulting dataset has 5139 elements.
### Languages
The dataset includes several languages, but the vast majority of it is in English. Roughly 600 instances are in more than one language, as detected by [langdetect](https://pypi.org/project/langdetect/).
The languages that appear across the dataset, together with the number of instances they appear in, follow:
<details>
<summary>Language Distribution</summary>
en 4053<br>
zh-cn 423<br>
ko 333<br>
fr 168<br>
ja 151<br>
es 142<br>
no 110<br>
et 97<br>
de 81<br>
ca 78<br>
vi 63<br>
fi 52<br>
zh-tw 47<br>
pt 42<br>
tl 39<br>
ru 24<br>
he 24<br>
id 23<br>
it 22<br>
sv 21<br>
pl 16<br>
nl 16<br>
th 15<br>
ro 11<br>
da 9<br>
tr 8<br>
cs 8<br>
hr 6<br>
uk 5<br>
af 5<br>
ar 4<br>
bg 3<br>
cy 2<br>
sk 2<br>
hu 2<br>
so 2<br>
bn 1<br>
sl 1<br>
hi 1<br>
sw 1<br>
lv 1<br>
el 1<br>
</details>
### Data Fields
Each instance has two fields:
- 'input': one turn of a human-bot conversation, initiated by a human. It starts with 'Human: ', and it ends with 'Assistant: '
- 'output': the bot reply
|
CaterinaLac/sharegpt-deduplicated
|
[
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:en",
"language:zh",
"language:ko",
"language:fr",
"language:ja",
"language:es",
"language:no",
"language:et",
"language:de",
"language:ca",
"language:vi",
"language:fi",
"license:apache-2.0",
"region:us"
] |
2023-10-04T12:31:41+00:00
|
{"language": ["en", "zh", "ko", "fr", "ja", "es", "no", "et", "de", "ca", "vi", "fi"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["conversational"]}
|
2023-10-04T13:40:39+00:00
|
[] |
[
"en",
"zh",
"ko",
"fr",
"ja",
"es",
"no",
"et",
"de",
"ca",
"vi",
"fi"
] |
TAGS
#task_categories-conversational #size_categories-1K<n<10K #language-English #language-Chinese #language-Korean #language-French #language-Japanese #language-Spanish #language-Norwegian #language-Estonian #language-German #language-Catalan #language-Vietnamese #language-Finnish #license-apache-2.0 #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
### Dataset Summary
This dataset is a deduplicated version of sharegpt4.
<br>The deduplication process has two steps:<br>
1. The literal duplicates (both input and outputs) are removed
2. The remaining (5749) instances are embedded with the SentenceTransformer library ("paraphrase-multilingual-mpnet-base-v2" model).
Then, we compute the cosine similarity among all the possible pairs, and consider paraphrases those pairs with a similarity > 0.95. For each paraphrase group, we only retain one element.
The resulting dataset has 5139 elements.
### Languages
The dataset includes several languages, but the vast majority of it is in English. Roughly 600 instances are in more than one language, as detected by langdetect.
The languages that appear across the dataset, together with the number of instances they appear in, follow:
<details>
<summary>Language Distribution</summary>
en 4053<br>
zh-cn 423<br>
ko 333<br>
fr 168<br>
ja 151<br>
es 142<br>
no 110<br>
et 97<br>
de 81<br>
ca 78<br>
vi 63<br>
fi 52<br>
zh-tw 47<br>
pt 42<br>
tl 39<br>
ru 24<br>
he 24<br>
id 23<br>
it 22<br>
sv 21<br>
pl 16<br>
nl 16<br>
th 15<br>
ro 11<br>
da 9<br>
tr 8<br>
cs 8<br>
hr 6<br>
uk 5<br>
af 5<br>
ar 4<br>
bg 3<br>
cy 2<br>
sk 2<br>
hu 2<br>
so 2<br>
bn 1<br>
sl 1<br>
hi 1<br>
sw 1<br>
lv 1<br>
el 1<br>
</details>
### Data Fields
Each instance has two fields:
- 'input': one turn of a human-bot conversation, initiated by a human. It starts with 'Human: ', and it ends with 'Assistant: '
- 'output': the bot reply
|
[
"# Dataset Card for Dataset Name",
"## Dataset Description",
"### Dataset Summary\nThis dataset is a deduplicated version of sharegpt4. \n<br>The deduplication process has two steps:<br>\n1. The literal duplicates (both input and outputs) are removed\n2. The remaining (5749) instances are embedded with the SentenceTransformer library (\"paraphrase-multilingual-mpnet-base-v2\" model).\nThen, we compute the cosine similarity among all the possible pairs, and consider paraphrases those pairs with a similarity > 0.95. For each paraphrase group, we only retain one element.\nThe resulting dataset has 5139 elements.",
"### Languages\n\nThe dataset includes several languages, but the vast majority of it is in English. Roughly 600 instances are in more than one language, as detected by langdetect. \nThe languages that appear across the dataset, together with the number of instances they appear in, follow: \n<details>\n <summary>Language Distribution</summary>\n en 4053<br>\nzh-cn 423<br>\nko 333<br>\nfr 168<br>\nja 151<br>\nes 142<br>\nno 110<br>\net 97<br>\nde 81<br>\nca 78<br>\nvi 63<br>\nfi 52<br>\nzh-tw 47<br>\npt 42<br>\ntl 39<br>\nru 24<br>\nhe 24<br>\nid 23<br>\nit 22<br>\nsv 21<br>\npl 16<br>\nnl 16<br>\nth 15<br>\nro 11<br>\nda 9<br>\ntr 8<br>\ncs 8<br>\nhr 6<br>\nuk 5<br>\naf 5<br>\nar 4<br>\nbg 3<br>\ncy 2<br>\nsk 2<br>\nhu 2<br>\nso 2<br>\nbn 1<br>\nsl 1<br>\nhi 1<br>\nsw 1<br>\nlv 1<br>\nel 1<br>\n\n</details>",
"### Data Fields\nEach instance has two fields: \n- 'input': one turn of a human-bot conversation, initiated by a human. It starts with 'Human: ', and it ends with 'Assistant: '\n- 'output': the bot reply"
] |
[
"TAGS\n#task_categories-conversational #size_categories-1K<n<10K #language-English #language-Chinese #language-Korean #language-French #language-Japanese #language-Spanish #language-Norwegian #language-Estonian #language-German #language-Catalan #language-Vietnamese #language-Finnish #license-apache-2.0 #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description",
"### Dataset Summary\nThis dataset is a deduplicated version of sharegpt4. \n<br>The deduplication process has two steps:<br>\n1. The literal duplicates (both input and outputs) are removed\n2. The remaining (5749) instances are embedded with the SentenceTransformer library (\"paraphrase-multilingual-mpnet-base-v2\" model).\nThen, we compute the cosine similarity among all the possible pairs, and consider paraphrases those pairs with a similarity > 0.95. For each paraphrase group, we only retain one element.\nThe resulting dataset has 5139 elements.",
"### Languages\n\nThe dataset includes several languages, but the vast majority of it is in English. Roughly 600 instances are in more than one language, as detected by langdetect. \nThe languages that appear across the dataset, together with the number of instances they appear in, follow: \n<details>\n <summary>Language Distribution</summary>\n en 4053<br>\nzh-cn 423<br>\nko 333<br>\nfr 168<br>\nja 151<br>\nes 142<br>\nno 110<br>\net 97<br>\nde 81<br>\nca 78<br>\nvi 63<br>\nfi 52<br>\nzh-tw 47<br>\npt 42<br>\ntl 39<br>\nru 24<br>\nhe 24<br>\nid 23<br>\nit 22<br>\nsv 21<br>\npl 16<br>\nnl 16<br>\nth 15<br>\nro 11<br>\nda 9<br>\ntr 8<br>\ncs 8<br>\nhr 6<br>\nuk 5<br>\naf 5<br>\nar 4<br>\nbg 3<br>\ncy 2<br>\nsk 2<br>\nhu 2<br>\nso 2<br>\nbn 1<br>\nsl 1<br>\nhi 1<br>\nsw 1<br>\nlv 1<br>\nel 1<br>\n\n</details>",
"### Data Fields\nEach instance has two fields: \n- 'input': one turn of a human-bot conversation, initiated by a human. It starts with 'Human: ', and it ends with 'Assistant: '\n- 'output': the bot reply"
] |
[
100,
8,
4,
151,
312,
62
] |
[
"passage: TAGS\n#task_categories-conversational #size_categories-1K<n<10K #language-English #language-Chinese #language-Korean #language-French #language-Japanese #language-Spanish #language-Norwegian #language-Estonian #language-German #language-Catalan #language-Vietnamese #language-Finnish #license-apache-2.0 #region-us \n# Dataset Card for Dataset Name## Dataset Description### Dataset Summary\nThis dataset is a deduplicated version of sharegpt4. \n<br>The deduplication process has two steps:<br>\n1. The literal duplicates (both input and outputs) are removed\n2. The remaining (5749) instances are embedded with the SentenceTransformer library (\"paraphrase-multilingual-mpnet-base-v2\" model).\nThen, we compute the cosine similarity among all the possible pairs, and consider paraphrases those pairs with a similarity > 0.95. For each paraphrase group, we only retain one element.\nThe resulting dataset has 5139 elements."
] |
825f3023a39485bf81733d4c957f404fc69a8717
|
# DiscoEval Benchmark Datasets
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Sources](#dataset-sources)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Benchmark Creators](#benchmark-creators)
- [Citation Information](#citation-information)
- [Loading Data Examples](#loading-data-examples)
- [Loading Data for Sentence Positioning Task with the Arxiv data source](#loading-data-for-sentence-positioning-task-with-the-arxiv-data-source)
### Dataset Description
- **Repository:** [DiscoEval repository](https://github.com/ZeweiChu/DiscoEval)
- **Paper:** [Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations](https://arxiv.org/pdf/1909.00142)
### Dataset Summary
The DiscoEval is an English-language Benchmark that contains a test suite of 7
tasks to evaluate whether sentence representations include semantic information
relevant to discourse processing. The benchmark datasets offer a collection of
tasks designed to evaluate natural language understanding models in the context
of discourse analysis and coherence.
### Dataset Sources
- **Arxiv**: A repository of scientific papers and research articles.
- **Wikipedia**: An extensive online encyclopedia with articles on diverse topics.
- **Rocstory**: A dataset consisting of fictional stories.
- **Ubuntu IRC channel**: Conversational data extracted from the Ubuntu Internet Relay Chat (IRC) channel.
- **PeerRead**: A dataset of scientific papers frequently used for discourse-related tasks.
- **RST Discourse Treebank**: A dataset annotated with Rhetorical Structure Theory (RST) discourse relations.
- **Penn Discourse Treebank**: Another dataset with annotated discourse relations, facilitating the study of discourse structure.
### Supported Tasks
1. **Sentence Positioning**
- **Datasets Sources**: Arxiv, Wikipedia, Rocstory
- **Description**: Determine the correct placement of a sentence within a given context of five sentences. To form the input when training classifiers encode the five sentences to vector representations \\(x_i\\). As input to the classfier we include \\(x_1\\) and the contcatination of \\(x_1 - x_i\\) for all \\(i\\): \\([x_1, x_1 - x_2, x_1-x_3,x_1-x_4,x_1-x_5]\\)
2. **Binary Sentence Ordering**
- **Datasets Sources**: Arxiv, Wikipedia, Rocstory
- **Description**: Determining whether two sentences are in the correct consecutive order, identifying the more coherent structure. To form the input when training classifiers, we concatenate the embeddings of both sentences with their element-wise difference: \\([x_1, x_2, x_1-x_2]\\)
3. **Discourse Coherence**
- **Datasets Sources**: Ubuntu IRC channel, Wikipedia
- **Description**: Determine whether a sequence of six sentences form a coherent paragraph. To form the input when training classifiers, encode all sentences to vector representations and concatenate all of them: \\([x_1, x_2, x_3, x_4, x_5, x_6]\\)
4. **Sentence Section Prediction**
- **Datasets Sources**: Constructed from PeerRead
- **Description**: Determine the section or category to which a sentence belongs within a scientific paper, based on the content and context. To form the input when training classifiers, simply input the sentence embedding.
5. **Discourse Relations**
- **Datasets Sources**: RST Discourse Treebank, Penn Discourse Treebank
- **Description**: Identify and classify discourse relations between sentences or text segments, helping to reveal the structure and flow of discourse. To form the input when training classifiers, refer to the [original paper](https://arxiv.org/pdf/1909.00142) for instructions
### Languages
The text in all datasets is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
All tasks are classification tasks, and they differ by the number of sentences per example and the type of label.
An example from the Sentence Positioning task would look as follows:
```
{
'sentence_1': 'Dan was overweight as well.',
'sentence_2': 'Dan's parents were overweight.',
'sentence_3': 'The doctors told his parents it was unhealthy.',
'sentence_4': 'His parents understood and decided to make a change.',
'sentence_5': 'They got themselves and Dan on a diet.'
'label': '1'
}
```
The label is '1' since the first sentence should go at position number 1 (counting from zero)
Another example from the Binary Sentence Ordering task would look as follows:
```
{
'sentence_1': 'When she walked in, she felt awkward.',
'sentence_2': 'Janet decided to go to her high school's party.',
'label': '0'
}
```
The label is '0' because this is not the correct order of the sentences. It should be sentence_2 and then sentence_1.
For more examples, you can refer the [original paper]((https://arxiv.org/pdf/1909.00142).
### Data Fields
In this benchmark, all data fields are string, including the labels.
### Data Splits
The data is split into training, validation and test set for each of the tasks in the benchmark.
| Task and Dataset | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| Sentence Positioning: Arxiv| 10000 | 4000 | 4000|
| Sentence Positioning: Rocstory| 10000 | 4000 | 4000|
| Sentence Positioning: Wiki| 10000 | 4000 | 4000|
| Binary Sentence Ordering: Arxiv| 20000 | 8000 | 8000|
| Binary Sentence Ordering: Rocstory| 20000 | 8000 | 8000|
| Binary Sentence Ordering: Wiki| 20000 | 8000 | 8000|
| Discourse Coherence: Chat| 5816 | 1834 | 2418|
| Discourse Coherence: Wiki| 10000 | 4000 | 4000|
| Sentence Section Prediction | 10000 | 4000 | 4000 |
| Discourse Relation: Penn Discourse Tree Bank: Implicit | 8693 | 2972 | 3024 |
| Discourse Relation: Penn Discourse Tree Bank: Explicit | 9383 | 3613 | 3758 |
| Discourse Relation: RST Discourse Tree Bank | 17051 | 2045 | 2308 |
## Additional Information
### Benchmark Creators
This benchmark was created by Mingda Chen, Zewei Chu and Kevin Gimpel during work done at the University of Chicago and the Toyota Technologival Institute at Chicago.
### Citation Information
```
@inproceedings{mchen-discoeval-19,
title = {Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations},
author = {Mingda Chen and Zewei Chu and Kevin Gimpel},
booktitle = {Proc. of {EMNLP}},
year={2019}
}
```
## Loading Data Examples
### Loading Data for Sentence Positioning Task with the Arxiv data source
```python
from datasets import load_dataset
# Load the Sentence Positioning dataset
dataset = load_dataset(path="OfekGlick/DiscoEval", name="SParxiv")
# Access the train, validation, and test splits
train_data = dataset["train"]
validation_data = dataset["validation"]
test_data = dataset["test"]
# Example usage: Print the first few training examples
for example in train_data[:5]:
print(example)
```
The other possible inputs for the `name` parameter are:
`SParxiv`, `SProcstory`, `SPwiki`, `SSPabs`, `PDTB-I`, `PDTB-E`, `BSOarxiv`, `BSOrocstory`, `BSOwiki`, `DCchat`, `DCwiki`, `RST`
|
PrincipledPreTraining/DiscoEval
|
[
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:bsd",
"Discourse",
"Discourse Evaluation",
"NLP",
"arxiv:1909.00142",
"region:us"
] |
2023-10-04T12:34:43+00:00
|
{"language": ["en"], "license": "bsd", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "pretty_name": "DiscoEval", "tags": ["Discourse", "Discourse Evaluation", "NLP"]}
|
2023-10-22T08:46:18+00:00
|
[
"1909.00142"
] |
[
"en"
] |
TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-bsd #Discourse #Discourse Evaluation #NLP #arxiv-1909.00142 #region-us
|
DiscoEval Benchmark Datasets
============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Dataset Sources
+ Supported Tasks
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Additional Information
+ Benchmark Creators
+ Citation Information
* Loading Data Examples
+ Loading Data for Sentence Positioning Task with the Arxiv data source
### Dataset Description
* Repository: DiscoEval repository
* Paper: Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations
### Dataset Summary
The DiscoEval is an English-language Benchmark that contains a test suite of 7
tasks to evaluate whether sentence representations include semantic information
relevant to discourse processing. The benchmark datasets offer a collection of
tasks designed to evaluate natural language understanding models in the context
of discourse analysis and coherence.
### Dataset Sources
* Arxiv: A repository of scientific papers and research articles.
* Wikipedia: An extensive online encyclopedia with articles on diverse topics.
* Rocstory: A dataset consisting of fictional stories.
* Ubuntu IRC channel: Conversational data extracted from the Ubuntu Internet Relay Chat (IRC) channel.
* PeerRead: A dataset of scientific papers frequently used for discourse-related tasks.
* RST Discourse Treebank: A dataset annotated with Rhetorical Structure Theory (RST) discourse relations.
* Penn Discourse Treebank: Another dataset with annotated discourse relations, facilitating the study of discourse structure.
### Supported Tasks
1. Sentence Positioning
* Datasets Sources: Arxiv, Wikipedia, Rocstory
* Description: Determine the correct placement of a sentence within a given context of five sentences. To form the input when training classifiers encode the five sentences to vector representations \(x\_i\). As input to the classfier we include \(x\_1\) and the contcatination of \(x\_1 - x\_i\) for all \(i\): \([x\_1, x\_1 - x\_2, x\_1-x\_3,x\_1-x\_4,x\_1-x\_5]\)
2. Binary Sentence Ordering
* Datasets Sources: Arxiv, Wikipedia, Rocstory
* Description: Determining whether two sentences are in the correct consecutive order, identifying the more coherent structure. To form the input when training classifiers, we concatenate the embeddings of both sentences with their element-wise difference: \([x\_1, x\_2, x\_1-x\_2]\)
3. Discourse Coherence
* Datasets Sources: Ubuntu IRC channel, Wikipedia
* Description: Determine whether a sequence of six sentences form a coherent paragraph. To form the input when training classifiers, encode all sentences to vector representations and concatenate all of them: \([x\_1, x\_2, x\_3, x\_4, x\_5, x\_6]\)
4. Sentence Section Prediction
* Datasets Sources: Constructed from PeerRead
* Description: Determine the section or category to which a sentence belongs within a scientific paper, based on the content and context. To form the input when training classifiers, simply input the sentence embedding.
5. Discourse Relations
* Datasets Sources: RST Discourse Treebank, Penn Discourse Treebank
* Description: Identify and classify discourse relations between sentences or text segments, helping to reveal the structure and flow of discourse. To form the input when training classifiers, refer to the original paper for instructions
### Languages
The text in all datasets is in English. The associated BCP-47 code is 'en'.
Dataset Structure
-----------------
### Data Instances
All tasks are classification tasks, and they differ by the number of sentences per example and the type of label.
An example from the Sentence Positioning task would look as follows:
The label is '1' since the first sentence should go at position number 1 (counting from zero)
Another example from the Binary Sentence Ordering task would look as follows:
The label is '0' because this is not the correct order of the sentences. It should be sentence\_2 and then sentence\_1.
For more examples, you can refer the original paper.
### Data Fields
In this benchmark, all data fields are string, including the labels.
### Data Splits
The data is split into training, validation and test set for each of the tasks in the benchmark.
Additional Information
----------------------
### Benchmark Creators
This benchmark was created by Mingda Chen, Zewei Chu and Kevin Gimpel during work done at the University of Chicago and the Toyota Technologival Institute at Chicago.
Loading Data Examples
---------------------
### Loading Data for Sentence Positioning Task with the Arxiv data source
The other possible inputs for the 'name' parameter are:
'SParxiv', 'SProcstory', 'SPwiki', 'SSPabs', 'PDTB-I', 'PDTB-E', 'BSOarxiv', 'BSOrocstory', 'BSOwiki', 'DCchat', 'DCwiki', 'RST'
|
[
"### Dataset Description\n\n\n* Repository: DiscoEval repository\n* Paper: Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations",
"### Dataset Summary\n\n\nThe DiscoEval is an English-language Benchmark that contains a test suite of 7\ntasks to evaluate whether sentence representations include semantic information\nrelevant to discourse processing. The benchmark datasets offer a collection of\ntasks designed to evaluate natural language understanding models in the context\nof discourse analysis and coherence.",
"### Dataset Sources\n\n\n* Arxiv: A repository of scientific papers and research articles.\n* Wikipedia: An extensive online encyclopedia with articles on diverse topics.\n* Rocstory: A dataset consisting of fictional stories.\n* Ubuntu IRC channel: Conversational data extracted from the Ubuntu Internet Relay Chat (IRC) channel.\n* PeerRead: A dataset of scientific papers frequently used for discourse-related tasks.\n* RST Discourse Treebank: A dataset annotated with Rhetorical Structure Theory (RST) discourse relations.\n* Penn Discourse Treebank: Another dataset with annotated discourse relations, facilitating the study of discourse structure.",
"### Supported Tasks\n\n\n1. Sentence Positioning\n\n\n\t* Datasets Sources: Arxiv, Wikipedia, Rocstory\n\t* Description: Determine the correct placement of a sentence within a given context of five sentences. To form the input when training classifiers encode the five sentences to vector representations \\(x\\_i\\). As input to the classfier we include \\(x\\_1\\) and the contcatination of \\(x\\_1 - x\\_i\\) for all \\(i\\): \\([x\\_1, x\\_1 - x\\_2, x\\_1-x\\_3,x\\_1-x\\_4,x\\_1-x\\_5]\\)\n2. Binary Sentence Ordering\n\n\n\t* Datasets Sources: Arxiv, Wikipedia, Rocstory\n\t* Description: Determining whether two sentences are in the correct consecutive order, identifying the more coherent structure. To form the input when training classifiers, we concatenate the embeddings of both sentences with their element-wise difference: \\([x\\_1, x\\_2, x\\_1-x\\_2]\\)\n3. Discourse Coherence\n\n\n\t* Datasets Sources: Ubuntu IRC channel, Wikipedia\n\t* Description: Determine whether a sequence of six sentences form a coherent paragraph. To form the input when training classifiers, encode all sentences to vector representations and concatenate all of them: \\([x\\_1, x\\_2, x\\_3, x\\_4, x\\_5, x\\_6]\\)\n4. Sentence Section Prediction\n\n\n\t* Datasets Sources: Constructed from PeerRead\n\t* Description: Determine the section or category to which a sentence belongs within a scientific paper, based on the content and context. To form the input when training classifiers, simply input the sentence embedding.\n5. Discourse Relations\n\n\n\t* Datasets Sources: RST Discourse Treebank, Penn Discourse Treebank\n\t* Description: Identify and classify discourse relations between sentences or text segments, helping to reveal the structure and flow of discourse. To form the input when training classifiers, refer to the original paper for instructions",
"### Languages\n\n\nThe text in all datasets is in English. The associated BCP-47 code is 'en'.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAll tasks are classification tasks, and they differ by the number of sentences per example and the type of label.\n\n\nAn example from the Sentence Positioning task would look as follows:\n\n\nThe label is '1' since the first sentence should go at position number 1 (counting from zero)\n\n\nAnother example from the Binary Sentence Ordering task would look as follows:\n\n\nThe label is '0' because this is not the correct order of the sentences. It should be sentence\\_2 and then sentence\\_1.\n\n\nFor more examples, you can refer the original paper.",
"### Data Fields\n\n\nIn this benchmark, all data fields are string, including the labels.",
"### Data Splits\n\n\nThe data is split into training, validation and test set for each of the tasks in the benchmark.\n\n\n\nAdditional Information\n----------------------",
"### Benchmark Creators\n\n\nThis benchmark was created by Mingda Chen, Zewei Chu and Kevin Gimpel during work done at the University of Chicago and the Toyota Technologival Institute at Chicago.\n\n\nLoading Data Examples\n---------------------",
"### Loading Data for Sentence Positioning Task with the Arxiv data source\n\n\nThe other possible inputs for the 'name' parameter are:\n'SParxiv', 'SProcstory', 'SPwiki', 'SSPabs', 'PDTB-I', 'PDTB-E', 'BSOarxiv', 'BSOrocstory', 'BSOwiki', 'DCchat', 'DCwiki', 'RST'"
] |
[
"TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-bsd #Discourse #Discourse Evaluation #NLP #arxiv-1909.00142 #region-us \n",
"### Dataset Description\n\n\n* Repository: DiscoEval repository\n* Paper: Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations",
"### Dataset Summary\n\n\nThe DiscoEval is an English-language Benchmark that contains a test suite of 7\ntasks to evaluate whether sentence representations include semantic information\nrelevant to discourse processing. The benchmark datasets offer a collection of\ntasks designed to evaluate natural language understanding models in the context\nof discourse analysis and coherence.",
"### Dataset Sources\n\n\n* Arxiv: A repository of scientific papers and research articles.\n* Wikipedia: An extensive online encyclopedia with articles on diverse topics.\n* Rocstory: A dataset consisting of fictional stories.\n* Ubuntu IRC channel: Conversational data extracted from the Ubuntu Internet Relay Chat (IRC) channel.\n* PeerRead: A dataset of scientific papers frequently used for discourse-related tasks.\n* RST Discourse Treebank: A dataset annotated with Rhetorical Structure Theory (RST) discourse relations.\n* Penn Discourse Treebank: Another dataset with annotated discourse relations, facilitating the study of discourse structure.",
"### Supported Tasks\n\n\n1. Sentence Positioning\n\n\n\t* Datasets Sources: Arxiv, Wikipedia, Rocstory\n\t* Description: Determine the correct placement of a sentence within a given context of five sentences. To form the input when training classifiers encode the five sentences to vector representations \\(x\\_i\\). As input to the classfier we include \\(x\\_1\\) and the contcatination of \\(x\\_1 - x\\_i\\) for all \\(i\\): \\([x\\_1, x\\_1 - x\\_2, x\\_1-x\\_3,x\\_1-x\\_4,x\\_1-x\\_5]\\)\n2. Binary Sentence Ordering\n\n\n\t* Datasets Sources: Arxiv, Wikipedia, Rocstory\n\t* Description: Determining whether two sentences are in the correct consecutive order, identifying the more coherent structure. To form the input when training classifiers, we concatenate the embeddings of both sentences with their element-wise difference: \\([x\\_1, x\\_2, x\\_1-x\\_2]\\)\n3. Discourse Coherence\n\n\n\t* Datasets Sources: Ubuntu IRC channel, Wikipedia\n\t* Description: Determine whether a sequence of six sentences form a coherent paragraph. To form the input when training classifiers, encode all sentences to vector representations and concatenate all of them: \\([x\\_1, x\\_2, x\\_3, x\\_4, x\\_5, x\\_6]\\)\n4. Sentence Section Prediction\n\n\n\t* Datasets Sources: Constructed from PeerRead\n\t* Description: Determine the section or category to which a sentence belongs within a scientific paper, based on the content and context. To form the input when training classifiers, simply input the sentence embedding.\n5. Discourse Relations\n\n\n\t* Datasets Sources: RST Discourse Treebank, Penn Discourse Treebank\n\t* Description: Identify and classify discourse relations between sentences or text segments, helping to reveal the structure and flow of discourse. To form the input when training classifiers, refer to the original paper for instructions",
"### Languages\n\n\nThe text in all datasets is in English. The associated BCP-47 code is 'en'.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAll tasks are classification tasks, and they differ by the number of sentences per example and the type of label.\n\n\nAn example from the Sentence Positioning task would look as follows:\n\n\nThe label is '1' since the first sentence should go at position number 1 (counting from zero)\n\n\nAnother example from the Binary Sentence Ordering task would look as follows:\n\n\nThe label is '0' because this is not the correct order of the sentences. It should be sentence\\_2 and then sentence\\_1.\n\n\nFor more examples, you can refer the original paper.",
"### Data Fields\n\n\nIn this benchmark, all data fields are string, including the labels.",
"### Data Splits\n\n\nThe data is split into training, validation and test set for each of the tasks in the benchmark.\n\n\n\nAdditional Information\n----------------------",
"### Benchmark Creators\n\n\nThis benchmark was created by Mingda Chen, Zewei Chu and Kevin Gimpel during work done at the University of Chicago and the Toyota Technologival Institute at Chicago.\n\n\nLoading Data Examples\n---------------------",
"### Loading Data for Sentence Positioning Task with the Arxiv data source\n\n\nThe other possible inputs for the 'name' parameter are:\n'SParxiv', 'SProcstory', 'SPwiki', 'SSPabs', 'PDTB-I', 'PDTB-E', 'BSOarxiv', 'BSOrocstory', 'BSOwiki', 'DCchat', 'DCwiki', 'RST'"
] |
[
60,
40,
78,
162,
515,
33,
129,
21,
34,
48,
104
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-bsd #Discourse #Discourse Evaluation #NLP #arxiv-1909.00142 #region-us \n### Dataset Description\n\n\n* Repository: DiscoEval repository\n* Paper: Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations### Dataset Summary\n\n\nThe DiscoEval is an English-language Benchmark that contains a test suite of 7\ntasks to evaluate whether sentence representations include semantic information\nrelevant to discourse processing. The benchmark datasets offer a collection of\ntasks designed to evaluate natural language understanding models in the context\nof discourse analysis and coherence.### Dataset Sources\n\n\n* Arxiv: A repository of scientific papers and research articles.\n* Wikipedia: An extensive online encyclopedia with articles on diverse topics.\n* Rocstory: A dataset consisting of fictional stories.\n* Ubuntu IRC channel: Conversational data extracted from the Ubuntu Internet Relay Chat (IRC) channel.\n* PeerRead: A dataset of scientific papers frequently used for discourse-related tasks.\n* RST Discourse Treebank: A dataset annotated with Rhetorical Structure Theory (RST) discourse relations.\n* Penn Discourse Treebank: Another dataset with annotated discourse relations, facilitating the study of discourse structure."
] |
e05b86bd9a806770348a301398593485478258c7
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
BruceHU/mmser
|
[
"region:us"
] |
2023-10-04T12:40:35+00:00
|
{}
|
2023-10-04T12:47:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
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#### Who are the annotators?
### Personal and Sensitive Information
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### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
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### Dataset Curators
### Licensing Information
### Contributions
|
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6d493545f650153ba544d7022b23579d06b8f5ce
|
# Dataset Card for "qa_wikipedia_sentence_transformer_negative_farming"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
legacy107/qa_wikipedia_sentence_transformer_negative_farming
|
[
"region:us"
] |
2023-10-04T12:45:45+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "negatives", "sequence": "string"}, {"name": "positive", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 147665416, "num_examples": 27742}, {"name": "test", "num_bytes": 18591659, "num_examples": 3468}, {"name": "validation", "num_bytes": 18443101, "num_examples": 3458}], "download_size": 37917812, "dataset_size": 184700176}}
|
2023-10-04T12:45:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "qa_wikipedia_sentence_transformer_negative_farming"
More Information needed
|
[
"# Dataset Card for \"qa_wikipedia_sentence_transformer_negative_farming\"\n\nMore Information needed"
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[
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"passage: TAGS\n#region-us \n# Dataset Card for \"qa_wikipedia_sentence_transformer_negative_farming\"\n\nMore Information needed"
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174c68eaa7d6aa4d7bf1cde4c06a51b7d6fb7e56
|
# Dataset Card for "ff0ba7a6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/ff0ba7a6
|
[
"region:us"
] |
2023-10-04T12:47:34+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 224, "num_examples": 10}], "download_size": 1359, "dataset_size": 224}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T12:47:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ff0ba7a6"
More Information needed
|
[
"# Dataset Card for \"ff0ba7a6\"\n\nMore Information needed"
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7618157ea1d4a20bddfb9d02648079e92a7b4e0b
|
# Human Resource Management System (HRMS) Dataset
## Overview
The Human Resource Management System (HRMS) Dataset is a collection of questions and answers related to various aspects of HRMS. It has been generated for educational purposes and is intended to be used for training and testing question-answering models in the field of Human Resources and Management.
## License
This dataset is provided under the [Apache License, Version 2.0](LICENSE). You are free to use, modify, and distribute this dataset in accordance with the terms and conditions of the Apache License.
## Contents
- `hrms_dataset.csv`: The main dataset file containing the following columns:
- `question`: Questions related to HRMS.
- `tag`: High-level categorization of the questions.
- `subtag`: Sub-categorization of the questions (optional).
- `answer`: Corresponding answers to the questions.
## Purpose
The purpose of this dataset is to facilitate the development and evaluation of question-answering models for HRMS-related queries. It can be used for various NLP and machine learning tasks, including but not limited to:
- Question-Answering Systems
- Text Classification
- NLP Model Training
## Usage
Researchers and developers can utilize this dataset to:
- Train and fine-tune NLP models for question-answering tasks.
- Conduct experiments and evaluations in the field of HRMS.
- Enhance natural language understanding and generation capabilities.
## Citation
If you use this dataset in your work or research, we kindly request that you cite it as follows:
We also extend our appreciation to the open-source community and [Any Relevant Libraries or Tools] for their support in the development of this dataset.
# We also extend our appreciation to the open-source community and [Any Relevant Libraries or Tools] for their support in the development of this dataset.
license: apache-2.0
task_categories:
- table-question-answering
- zero-shot-classification
- summarization
- text-generation
- text-to-speech
- sentence-similarity
- conversational
- question-answering
- text-classification
- text2text-generation
pretty_name: HRMS Question-Answering Dataset
## Acknowledgments
We would like to express our gratitude to the following individuals and organizations for their contributions to the Human Resource Management System (HRMS) Dataset:
- Contributor Name: Bhavya Shah
- Another Contributor Name: Jr. Data Scientist
|
Bhavya-123/HRMS_Datahub
|
[
"region:us"
] |
2023-10-04T12:49:09+00:00
|
{}
|
2023-10-04T12:51:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Human Resource Management System (HRMS) Dataset
## Overview
The Human Resource Management System (HRMS) Dataset is a collection of questions and answers related to various aspects of HRMS. It has been generated for educational purposes and is intended to be used for training and testing question-answering models in the field of Human Resources and Management.
## License
This dataset is provided under the Apache License, Version 2.0. You are free to use, modify, and distribute this dataset in accordance with the terms and conditions of the Apache License.
## Contents
- 'hrms_dataset.csv': The main dataset file containing the following columns:
- 'question': Questions related to HRMS.
- 'tag': High-level categorization of the questions.
- 'subtag': Sub-categorization of the questions (optional).
- 'answer': Corresponding answers to the questions.
## Purpose
The purpose of this dataset is to facilitate the development and evaluation of question-answering models for HRMS-related queries. It can be used for various NLP and machine learning tasks, including but not limited to:
- Question-Answering Systems
- Text Classification
- NLP Model Training
## Usage
Researchers and developers can utilize this dataset to:
- Train and fine-tune NLP models for question-answering tasks.
- Conduct experiments and evaluations in the field of HRMS.
- Enhance natural language understanding and generation capabilities.
If you use this dataset in your work or research, we kindly request that you cite it as follows:
We also extend our appreciation to the open-source community and [Any Relevant Libraries or Tools] for their support in the development of this dataset.
# We also extend our appreciation to the open-source community and [Any Relevant Libraries or Tools] for their support in the development of this dataset.
license: apache-2.0
task_categories:
- table-question-answering
- zero-shot-classification
- summarization
- text-generation
- text-to-speech
- sentence-similarity
- conversational
- question-answering
- text-classification
- text2text-generation
pretty_name: HRMS Question-Answering Dataset
## Acknowledgments
We would like to express our gratitude to the following individuals and organizations for their contributions to the Human Resource Management System (HRMS) Dataset:
- Contributor Name: Bhavya Shah
- Another Contributor Name: Jr. Data Scientist
|
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"# We also extend our appreciation to the open-source community and [Any Relevant Libraries or Tools] for their support in the development of this dataset.\n\nlicense: apache-2.0\ntask_categories:\n- table-question-answering\n- zero-shot-classification\n- summarization\n- text-generation\n- text-to-speech\n- sentence-similarity\n- conversational\n- question-answering\n- text-classification\n- text2text-generation\npretty_name: HRMS Question-Answering Dataset",
"## Acknowledgments\n\nWe would like to express our gratitude to the following individuals and organizations for their contributions to the Human Resource Management System (HRMS) Dataset:\n\n- Contributor Name: Bhavya Shah\n- Another Contributor Name: Jr. Data Scientist"
] |
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"## License\n\nThis dataset is provided under the Apache License, Version 2.0. You are free to use, modify, and distribute this dataset in accordance with the terms and conditions of the Apache License.",
"## Contents\n\n- 'hrms_dataset.csv': The main dataset file containing the following columns:\n - 'question': Questions related to HRMS.\n - 'tag': High-level categorization of the questions.\n - 'subtag': Sub-categorization of the questions (optional).\n - 'answer': Corresponding answers to the questions.",
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"# We also extend our appreciation to the open-source community and [Any Relevant Libraries or Tools] for their support in the development of this dataset.\n\nlicense: apache-2.0\ntask_categories:\n- table-question-answering\n- zero-shot-classification\n- summarization\n- text-generation\n- text-to-speech\n- sentence-similarity\n- conversational\n- question-answering\n- text-classification\n- text2text-generation\npretty_name: HRMS Question-Answering Dataset",
"## Acknowledgments\n\nWe would like to express our gratitude to the following individuals and organizations for their contributions to the Human Resource Management System (HRMS) Dataset:\n\n- Contributor Name: Bhavya Shah\n- Another Contributor Name: Jr. Data Scientist"
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"passage: TAGS\n#region-us \n# Human Resource Management System (HRMS) Dataset## Overview\n\nThe Human Resource Management System (HRMS) Dataset is a collection of questions and answers related to various aspects of HRMS. It has been generated for educational purposes and is intended to be used for training and testing question-answering models in the field of Human Resources and Management.## License\n\nThis dataset is provided under the Apache License, Version 2.0. You are free to use, modify, and distribute this dataset in accordance with the terms and conditions of the Apache License.## Contents\n\n- 'hrms_dataset.csv': The main dataset file containing the following columns:\n - 'question': Questions related to HRMS.\n - 'tag': High-level categorization of the questions.\n - 'subtag': Sub-categorization of the questions (optional).\n - 'answer': Corresponding answers to the questions.## Purpose\n\nThe purpose of this dataset is to facilitate the development and evaluation of question-answering models for HRMS-related queries. It can be used for various NLP and machine learning tasks, including but not limited to:\n- Question-Answering Systems\n- Text Classification\n- NLP Model Training## Usage\n\nResearchers and developers can utilize this dataset to:\n- Train and fine-tune NLP models for question-answering tasks.\n- Conduct experiments and evaluations in the field of HRMS.\n- Enhance natural language understanding and generation capabilities.\n\nIf you use this dataset in your work or research, we kindly request that you cite it as follows:\n\n\n \nWe also extend our appreciation to the open-source community and [Any Relevant Libraries or Tools] for their support in the development of this dataset."
] |
8d6b3b80311c67ee5473d4065f222b8aef053620
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: griffin/redress-clinical-hallucination-generator
* Dataset: StanBienaives/french-open-fiscal-texts
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@zein](https://huggingface.co/zein) for evaluating this model.
|
autoevaluate/autoeval-eval-StanBienaives__french-open-fiscal-texts-default-b348f4-26735144900
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:55:51+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["StanBienaives/french-open-fiscal-texts"], "eval_info": {"task": "summarization", "model": "griffin/redress-clinical-hallucination-generator", "metrics": ["accuracy"], "dataset_name": "StanBienaives/french-open-fiscal-texts", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "summary", "target": "solution"}}}
|
2023-10-04T13:08:04+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: griffin/redress-clinical-hallucination-generator
* Dataset: StanBienaives/french-open-fiscal-texts
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @zein for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: griffin/redress-clinical-hallucination-generator\n* Dataset: StanBienaives/french-open-fiscal-texts\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zein for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: griffin/redress-clinical-hallucination-generator\n* Dataset: StanBienaives/french-open-fiscal-texts\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zein for evaluating this model."
] |
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"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: griffin/redress-clinical-hallucination-generator\n* Dataset: StanBienaives/french-open-fiscal-texts\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @zein for evaluating this model."
] |
a0749a5e78c3538a4b526189501144d29a0500b1
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distill-pegasus-xsum-16-4
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kaprerna135](https://huggingface.co/kaprerna135) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-a0cc45-27486144906
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:56:35+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "sshleifer/distill-pegasus-xsum-16-4", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T13:14:24+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distill-pegasus-xsum-16-4
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kaprerna135 for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distill-pegasus-xsum-16-4\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distill-pegasus-xsum-16-4\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
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] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distill-pegasus-xsum-16-4\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
307dc3492d0e018e55501a1d4e755eb0d9f9c0e5
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/bigbird-pegasus-large-bigpatent
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kaprerna135](https://huggingface.co/kaprerna135) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-a0cc45-27486144907
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:56:43+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "google/bigbird-pegasus-large-bigpatent", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T15:38:20+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: google/bigbird-pegasus-large-bigpatent
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kaprerna135 for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/bigbird-pegasus-large-bigpatent\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/bigbird-pegasus-large-bigpatent\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
13,
90,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: google/bigbird-pegasus-large-bigpatent\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
d6edd473b7f518229e46818631479ffc1d4d89f4
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kaprerna135](https://huggingface.co/kaprerna135) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-b01992-27495144908
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:56:51+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "sysresearch101/t5-large-finetuned-xsum-cnn", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T13:28:43+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kaprerna135 for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
13,
99,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
e50fbf0f5dedbe20d6cbb9a74f29191a9783a10b
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kaprerna135](https://huggingface.co/kaprerna135) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-35af0a-27496144909
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:56:58+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "sysresearch101/t5-large-finetuned-xsum-cnn", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T13:28:59+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: sysresearch101/t5-large-finetuned-xsum-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kaprerna135 for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
13,
99,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sysresearch101/t5-large-finetuned-xsum-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
5d20708132d544fe7fcf97e93f897ff5170f3f4e
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-base-book-summary
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kaprerna135](https://huggingface.co/kaprerna135) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-35af0a-27496144910
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:57:07+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "pszemraj/led-base-book-summary", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T19:24:55+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-base-book-summary
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kaprerna135 for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-base-book-summary\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-base-book-summary\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
13,
91,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-base-book-summary\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
926958f9a9357ce501b36f59bd80cbc53c556ead
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-large-book-summary
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kaprerna135](https://huggingface.co/kaprerna135) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-35af0a-27496144911
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:57:13+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "pszemraj/led-large-book-summary", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T16:41:18+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-large-book-summary
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kaprerna135 for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-large-book-summary\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-large-book-summary\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
13,
92,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-large-book-summary\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
a85eecaa2a6f9de0ccb556bcdfb8c0d91dcf5a1a
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-base-book-summary
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kaprerna135](https://huggingface.co/kaprerna135) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-1c6815-27497144912
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:57:22+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "pszemraj/led-base-book-summary", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T15:47:34+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-base-book-summary
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kaprerna135 for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-base-book-summary\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-base-book-summary\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
13,
87,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-base-book-summary\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
5adc63c924e2e2fe0e6da90f1869b0b3aab6f775
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-large-book-summary
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kaprerna135](https://huggingface.co/kaprerna135) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-1c6815-27497144913
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:57:29+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "pszemraj/led-large-book-summary", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T16:30:38+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-large-book-summary
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kaprerna135 for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-large-book-summary\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-large-book-summary\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
[
13,
88,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/led-large-book-summary\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kaprerna135 for evaluating this model."
] |
52ab8cede5448f1efc47ad06738b2035e39ecce8
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/tglobal-large-booksum-WIP4-r1
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-25089b-27589144917
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:58:02+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "pszemraj/tglobal-large-booksum-WIP4-r1", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T18:18:47+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: pszemraj/tglobal-large-booksum-WIP4-r1
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @pszemraj for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/tglobal-large-booksum-WIP4-r1\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @pszemraj for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/tglobal-large-booksum-WIP4-r1\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @pszemraj for evaluating this model."
] |
[
13,
97,
16
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: pszemraj/tglobal-large-booksum-WIP4-r1\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pszemraj for evaluating this model."
] |
f067cc6c30baf31be2797985d94cb40474f2faee
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: phpaiola/ptt5-base-summ-temario
* Dataset: zeroshot/twitter-financial-news-topic
* Config: zeroshot--twitter-financial-news-topic
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@peterdevathala](https://huggingface.co/peterdevathala) for evaluating this model.
|
autoevaluate/autoeval-eval-zeroshot__twitter-financial-news-topic-zeroshot__twitte-178919-28982144928
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:59:37+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["zeroshot/twitter-financial-news-topic"], "eval_info": {"task": "summarization", "model": "phpaiola/ptt5-base-summ-temario", "metrics": ["bertscore"], "dataset_name": "zeroshot/twitter-financial-news-topic", "dataset_config": "zeroshot--twitter-financial-news-topic", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:04:01+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: phpaiola/ptt5-base-summ-temario
* Dataset: zeroshot/twitter-financial-news-topic
* Config: zeroshot--twitter-financial-news-topic
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @peterdevathala for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: phpaiola/ptt5-base-summ-temario\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: phpaiola/ptt5-base-summ-temario\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
[
13,
110,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: phpaiola/ptt5-base-summ-temario\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
7248502afaedfd94267bb40b962831c3c7d78992
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: zeroshot/twitter-financial-news-topic
* Config: zeroshot--twitter-financial-news-topic
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@peterdevathala](https://huggingface.co/peterdevathala) for evaluating this model.
|
autoevaluate/autoeval-eval-zeroshot__twitter-financial-news-topic-zeroshot__twitte-178919-28982144929
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:59:46+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["zeroshot/twitter-financial-news-topic"], "eval_info": {"task": "summarization", "model": "facebook/bart-large-cnn", "metrics": ["bertscore"], "dataset_name": "zeroshot/twitter-financial-news-topic", "dataset_config": "zeroshot--twitter-financial-news-topic", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:56:56+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: zeroshot/twitter-financial-news-topic
* Config: zeroshot--twitter-financial-news-topic
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @peterdevathala for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
[
13,
104,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
78106cd0ffb02f20eb3ca88d35df47be0463141e
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: phpaiola/ptt5-base-summ-temario
* Dataset: zeroshot/twitter-financial-news-topic
* Config: zeroshot--twitter-financial-news-topic
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@peterdevathala](https://huggingface.co/peterdevathala) for evaluating this model.
|
autoevaluate/autoeval-eval-zeroshot__twitter-financial-news-topic-zeroshot__twitte-e590a9-28983144930
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T12:59:56+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["zeroshot/twitter-financial-news-topic"], "eval_info": {"task": "summarization", "model": "phpaiola/ptt5-base-summ-temario", "metrics": ["bertscore"], "dataset_name": "zeroshot/twitter-financial-news-topic", "dataset_config": "zeroshot--twitter-financial-news-topic", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:04:12+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: phpaiola/ptt5-base-summ-temario
* Dataset: zeroshot/twitter-financial-news-topic
* Config: zeroshot--twitter-financial-news-topic
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @peterdevathala for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: phpaiola/ptt5-base-summ-temario\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: phpaiola/ptt5-base-summ-temario\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
[
13,
110,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: phpaiola/ptt5-base-summ-temario\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
f31466416a91ec376fee65899f323772f0f183cf
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: zeroshot/twitter-financial-news-topic
* Config: zeroshot--twitter-financial-news-topic
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@peterdevathala](https://huggingface.co/peterdevathala) for evaluating this model.
|
autoevaluate/autoeval-eval-zeroshot__twitter-financial-news-topic-zeroshot__twitte-e590a9-28983144931
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T13:00:06+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["zeroshot/twitter-financial-news-topic"], "eval_info": {"task": "summarization", "model": "facebook/bart-large-cnn", "metrics": ["bertscore"], "dataset_name": "zeroshot/twitter-financial-news-topic", "dataset_config": "zeroshot--twitter-financial-news-topic", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:54:14+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: zeroshot/twitter-financial-news-topic
* Config: zeroshot--twitter-financial-news-topic
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @peterdevathala for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
[
13,
104,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: facebook/bart-large-cnn\n* Dataset: zeroshot/twitter-financial-news-topic\n* Config: zeroshot--twitter-financial-news-topic\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @peterdevathala for evaluating this model."
] |
4a3fc3d4348e7cf94e51dad7f50eddf192073146
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-sentiment-latest
* Dataset: tweet_eval
* Config: sentiment
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ericbugin](https://huggingface.co/ericbugin) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-sentiment-be35d9-30474144941
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T13:01:37+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "cardiffnlp/twitter-roberta-base-sentiment-latest", "metrics": [], "dataset_name": "tweet_eval", "dataset_config": "sentiment", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:02:43+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-sentiment-latest
* Dataset: tweet_eval
* Config: sentiment
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @ericbugin for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-sentiment-latest\n* Dataset: tweet_eval\n* Config: sentiment\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @ericbugin for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-sentiment-latest\n* Dataset: tweet_eval\n* Config: sentiment\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @ericbugin for evaluating this model."
] |
[
13,
97,
17
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-sentiment-latest\n* Dataset: tweet_eval\n* Config: sentiment\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ericbugin for evaluating this model."
] |
40465878cec93467aaf8389a120ca7e7662b9509
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@[email protected]](https://huggingface.co/[email protected]) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-offensive-736f56-30712144944
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T13:02:08+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "cardiffnlp/twitter-roberta-base-2021-124m-offensive", "metrics": ["bertscore"], "dataset_name": "tweet_eval", "dataset_config": "offensive", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:03:15+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @fabeelaalirawther@URL for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
13,
100,
21
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
21ef23cb4f4b0060766bc4bd593e7ede64f9d113
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@[email protected]](https://huggingface.co/[email protected]) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-offensive-93ad2d-30713144950
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T13:03:05+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "cardiffnlp/twitter-roberta-base-2021-124m-offensive", "metrics": ["bertscore"], "dataset_name": "tweet_eval", "dataset_config": "offensive", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:04:12+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @fabeelaalirawther@URL for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
13,
100,
21
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
820ac7a0e2bf09755dd214d9f0e80ad5340f2959
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: elozano/tweet_offensive_eval
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@[email protected]](https://huggingface.co/[email protected]) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-offensive-93ad2d-30713144953
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T13:03:37+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "elozano/tweet_offensive_eval", "metrics": ["bertscore"], "dataset_name": "tweet_eval", "dataset_config": "offensive", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:04:45+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Multi-class Text Classification
* Model: elozano/tweet_offensive_eval
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @fabeelaalirawther@URL for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: elozano/tweet_offensive_eval\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: elozano/tweet_offensive_eval\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
13,
92,
21
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: elozano/tweet_offensive_eval\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
9a10a793121be6a87fcd5e210fa979c47b5bfc24
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: elozano/tweet_offensive_eval
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@[email protected]](https://huggingface.co/[email protected]) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-offensive-736f56-30712144947
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T13:03:38+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "elozano/tweet_offensive_eval", "metrics": ["bertscore"], "dataset_name": "tweet_eval", "dataset_config": "offensive", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:04:47+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Multi-class Text Classification
* Model: elozano/tweet_offensive_eval
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @fabeelaalirawther@URL for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: elozano/tweet_offensive_eval\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: elozano/tweet_offensive_eval\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
13,
92,
21
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: elozano/tweet_offensive_eval\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
c0031bcad36210cfc4480aad5c8ca5339c9884df
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/roberta-base-offensive
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@[email protected]](https://huggingface.co/[email protected]) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-offensive-f58805-30720144955
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T13:03:57+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "cardiffnlp/roberta-base-offensive", "metrics": ["bertscore"], "dataset_name": "tweet_eval", "dataset_config": "offensive", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:05:09+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/roberta-base-offensive
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @fabeelaalirawther@URL for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/roberta-base-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/roberta-base-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
13,
93,
21
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/roberta-base-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
a18787a69096b3c40931e23e78c4b2b7199cf9ea
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@[email protected]](https://huggingface.co/[email protected]) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-offensive-f58805-30720144956
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T13:04:07+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "cardiffnlp/twitter-roberta-base-2021-124m-offensive", "metrics": ["bertscore"], "dataset_name": "tweet_eval", "dataset_config": "offensive", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T13:05:10+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @fabeelaalirawther@URL for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
[
13,
100,
21
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive\n* Dataset: tweet_eval\n* Config: offensive\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @fabeelaalirawther@URL for evaluating this model."
] |
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