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869f4a7d4c15d8ef2c57e176e4c65b2e77b966df
|
# Dataset Card for "donutv2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Amani123/donutv2
|
[
"region:us"
] |
2023-08-30T09:48:16+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 368282860.0, "num_examples": 452}], "download_size": 359046159, "dataset_size": 368282860.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T10:05:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "donutv2"
More Information needed
|
[
"# Dataset Card for \"donutv2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"donutv2\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"donutv2\"\n\nMore Information needed"
] |
9c0b59f7e6551be86f55f8be32d03df1a7978bbb
|
# Dataset of Fuwa Rinna
This is the dataset of Fuwa Rinna, containing 110 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 110 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 249 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 110 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 110 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 110 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 110 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 110 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 249 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 249 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 249 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/fuwa_rinna_mangakasantoassistantsanto
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-30T09:50:30+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:32+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Fuwa Rinna
=====================
This is the dataset of Fuwa Rinna, containing 110 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
93ab9dc2135b87e250e5b5c31af288fc0599e9a7
|
# Medical Triage Complaint Data Structure README
This data structure is designed to use for supervised finetuining of llam2 over generating triage questions based on provided patient complaint/age/gender as input
## JSON Format
The data structure is represented in JSON format, with two main sections: `input` and `questions`.
### Input Section
The `input` section contains information about the patient's complaint, age, and gender.
```json
{
"input": {
"complaint": "Patient's Chief/Main Complaint",
"age": "Patient's Age in number of years",
"gender": "Patient's Gender Female or Male"
}
}
```
### Questions Section
The `questions` section includes an array of questions related to the patient's specific complaint. Each question has a `questionType`, `question`, and a set of `options` (if applicable).
```json
{
"questions": [
{
"questionType": "select-option",
"question": "Question Text",
"options": ["Option 1", "Option 2", ...]
},
{
"questionType": "text",
"question": "Free-Form Text Question"
},
// Add more questions as needed
]
}
```
- `"questionType"` can be either `"select-option"` (for multiple-choice questions) or `"text"` (for open-ended text questions).
- `"question"` is the text of the question.
- `"options"` (if present) is an array of possible answer choices.
## Preapring Instruction set for finetune the llama2 model (any of 7b-hf, 13b-hf or 70b-hf)
Each "text" sample in training records has following 3 sections
1. Use following gereic instruction to generate traige questions with provided patient age, gender and complaint
```
###Instruction:
As a highly skilled doctor, your task is to assess the patient's clinical condition by asking a variety of clinical questions, You will be provided with Patient age,
gender and main complaint, and you need to generate more granular triage questions in json format, the keys of json data are as follows
question:<clinical triage question that could be helpful to understant patient condition more clearly>
questionType: <either question is type that can expect text answer or any options need to select. So set one of following types "text" or "option">
options: <provide posiible options for question if question type is option>
And dont generate any additional text except json data object. And each object should separate by comma. Follow Json Standard syntax.
questions should be atleast 8 and atmost 15 in number to understand the patient condition clearly.
```
2. add ###Input as Patient basic info
```
###Input:
{"input": {"complaint": "Patient's Chief/Main Complaint","age": "Patient's Age in number of years","gender": "Patient's Gender Female or Male"}}
```
4. Add questions json structure as output
```
###Output:
{"questions": [{"questionType": "select-option","question": "How would you describe the severity of your sore throat?","options": ["Mild", "Moderate", "Severe"]},
{"questionType": "select-option", "question": "Have you had a fever along with the sore throat?", "options": ["No", "Yes"]},
{"questionType": "text","question": "Anything else you want to share?"}]}
```
## Final instruction sample
```
###Instruction:
As a highly skilled doctor, your task is to assess the patient's clinical condition by asking a variety of clinical questions, You will be provided with Patient age,
gender and main complaint, and you need to generate more granular triage questions in json format, the keys of json data are as follows
question:<clinical triage question that could be helpful to understant patient condition more clearly>
questionType: <either question is type that can expect text answer or any options need to select. So set one of following types "text" or "option">
options: <provide posiible options for question if question type is option>
And dont generate any additional text except json data object. And each object should separate by comma. Follow Json Standard syntax.
questions should be atleast 8 and atmost 15 in number to understand the patient condition clearly.
###Input:
{"input": {"complaint": "Patient's Chief/Main Complaint","age": "Patient's Age in number of years","gender": "Patient's Gender Female or Male"}}
###Output:
{"questions": [{"questionType": "select-option","question": "How would you describe the severity of your sore throat?","options": ["Mild", "Moderate", "Severe"]},
{"questionType": "select-option", "question": "Have you had a fever along with the sore throat?", "options": ["No", "Yes"]},
{"questionType": "text","question": "Anything else you want to share?"}]}
```
|
krishnareddy/triage-questions
|
[
"license:mit",
"region:us"
] |
2023-08-30T09:51:56+00:00
|
{"license": "mit"}
|
2023-08-30T12:32:06+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# Medical Triage Complaint Data Structure README
This data structure is designed to use for supervised finetuining of llam2 over generating triage questions based on provided patient complaint/age/gender as input
## JSON Format
The data structure is represented in JSON format, with two main sections: 'input' and 'questions'.
### Input Section
The 'input' section contains information about the patient's complaint, age, and gender.
### Questions Section
The 'questions' section includes an array of questions related to the patient's specific complaint. Each question has a 'questionType', 'question', and a set of 'options' (if applicable).
- '"questionType"' can be either '"select-option"' (for multiple-choice questions) or '"text"' (for open-ended text questions).
- '"question"' is the text of the question.
- '"options"' (if present) is an array of possible answer choices.
## Preapring Instruction set for finetune the llama2 model (any of 7b-hf, 13b-hf or 70b-hf)
Each "text" sample in training records has following 3 sections
1. Use following gereic instruction to generate traige questions with provided patient age, gender and complaint
2. add ###Input as Patient basic info
4. Add questions json structure as output
## Final instruction sample
|
[
"# Medical Triage Complaint Data Structure README\n\nThis data structure is designed to use for supervised finetuining of llam2 over generating triage questions based on provided patient complaint/age/gender as input",
"## JSON Format\n\nThe data structure is represented in JSON format, with two main sections: 'input' and 'questions'.",
"### Input Section\n\nThe 'input' section contains information about the patient's complaint, age, and gender.",
"### Questions Section\n\nThe 'questions' section includes an array of questions related to the patient's specific complaint. Each question has a 'questionType', 'question', and a set of 'options' (if applicable).\n\n\n\n- '\"questionType\"' can be either '\"select-option\"' (for multiple-choice questions) or '\"text\"' (for open-ended text questions).\n- '\"question\"' is the text of the question.\n- '\"options\"' (if present) is an array of possible answer choices.",
"## Preapring Instruction set for finetune the llama2 model (any of 7b-hf, 13b-hf or 70b-hf)\n\nEach \"text\" sample in training records has following 3 sections\n\n \n1. Use following gereic instruction to generate traige questions with provided patient age, gender and complaint\n\n2. add ###Input as Patient basic info\n\n4. Add questions json structure as output",
"## Final instruction sample"
] |
[
"TAGS\n#license-mit #region-us \n",
"# Medical Triage Complaint Data Structure README\n\nThis data structure is designed to use for supervised finetuining of llam2 over generating triage questions based on provided patient complaint/age/gender as input",
"## JSON Format\n\nThe data structure is represented in JSON format, with two main sections: 'input' and 'questions'.",
"### Input Section\n\nThe 'input' section contains information about the patient's complaint, age, and gender.",
"### Questions Section\n\nThe 'questions' section includes an array of questions related to the patient's specific complaint. Each question has a 'questionType', 'question', and a set of 'options' (if applicable).\n\n\n\n- '\"questionType\"' can be either '\"select-option\"' (for multiple-choice questions) or '\"text\"' (for open-ended text questions).\n- '\"question\"' is the text of the question.\n- '\"options\"' (if present) is an array of possible answer choices.",
"## Preapring Instruction set for finetune the llama2 model (any of 7b-hf, 13b-hf or 70b-hf)\n\nEach \"text\" sample in training records has following 3 sections\n\n \n1. Use following gereic instruction to generate traige questions with provided patient age, gender and complaint\n\n2. add ###Input as Patient basic info\n\n4. Add questions json structure as output",
"## Final instruction sample"
] |
[
11,
48,
31,
26,
130,
86,
5
] |
[
"passage: TAGS\n#license-mit #region-us \n# Medical Triage Complaint Data Structure README\n\nThis data structure is designed to use for supervised finetuining of llam2 over generating triage questions based on provided patient complaint/age/gender as input## JSON Format\n\nThe data structure is represented in JSON format, with two main sections: 'input' and 'questions'.### Input Section\n\nThe 'input' section contains information about the patient's complaint, age, and gender.### Questions Section\n\nThe 'questions' section includes an array of questions related to the patient's specific complaint. Each question has a 'questionType', 'question', and a set of 'options' (if applicable).\n\n\n\n- '\"questionType\"' can be either '\"select-option\"' (for multiple-choice questions) or '\"text\"' (for open-ended text questions).\n- '\"question\"' is the text of the question.\n- '\"options\"' (if present) is an array of possible answer choices.## Preapring Instruction set for finetune the llama2 model (any of 7b-hf, 13b-hf or 70b-hf)\n\nEach \"text\" sample in training records has following 3 sections\n\n \n1. Use following gereic instruction to generate traige questions with provided patient age, gender and complaint\n\n2. add ###Input as Patient basic info\n\n4. Add questions json structure as output## Final instruction sample"
] |
5e24b3cf64543a2a2b42845aae3809ea54ae25cb
|
# GNS dataset
> Krishna Kumar, UT Austin
## Datasets
* Sand
* SandRamps
* WaterDropSample
## Download dataset
`git clone https://huggingface.co/datasets/geoelements/gns`
|
geoelements/gns
|
[
"license:cc-by-sa-4.0",
"region:us"
] |
2023-08-30T09:53:56+00:00
|
{"license": "cc-by-sa-4.0", "datasets": ["Sand", "SandRamps", "WaterDropSample"]}
|
2023-11-23T01:19:54+00:00
|
[] |
[] |
TAGS
#license-cc-by-sa-4.0 #region-us
|
# GNS dataset
> Krishna Kumar, UT Austin
## Datasets
* Sand
* SandRamps
* WaterDropSample
## Download dataset
'git clone URL
|
[
"# GNS dataset\n\n> Krishna Kumar, UT Austin",
"## Datasets\n* Sand\n* SandRamps\n* WaterDropSample",
"## Download dataset\n\n'git clone URL"
] |
[
"TAGS\n#license-cc-by-sa-4.0 #region-us \n",
"# GNS dataset\n\n> Krishna Kumar, UT Austin",
"## Datasets\n* Sand\n* SandRamps\n* WaterDropSample",
"## Download dataset\n\n'git clone URL"
] |
[
17,
11,
17,
9
] |
[
"passage: TAGS\n#license-cc-by-sa-4.0 #region-us \n# GNS dataset\n\n> Krishna Kumar, UT Austin## Datasets\n* Sand\n* SandRamps\n* WaterDropSample## Download dataset\n\n'git clone URL"
] |
23a3d069842f2e5153408c271b14fb6683303185
|
# Dataset of Ashisu Sahoto
This is the dataset of Ashisu Sahoto, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 407 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 407 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 407 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 407 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/ashisu_sahoto_mangakasantoassistantsanto
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-30T10:11:05+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:34+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Ashisu Sahoto
========================
This is the dataset of Ashisu Sahoto, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
6f38d9a4d62a587784698c1300e9cceac8b2fa02
|
# Dataset Card for "NEW-GEN"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pumaML/NEW-GEN
|
[
"region:us"
] |
2023-08-30T10:11:33+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1252876.2642514652, "num_examples": 3378}, {"name": "validation", "num_bytes": 139455.7357485349, "num_examples": 376}], "download_size": 896649, "dataset_size": 1392332.0}}
|
2023-08-30T10:11:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "NEW-GEN"
More Information needed
|
[
"# Dataset Card for \"NEW-GEN\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"NEW-GEN\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"NEW-GEN\"\n\nMore Information needed"
] |
c515ab8b9df8d8bc44ff6f444b5960b064d601f9
|
# Mixed Persian NER Dataset (PEYMA-ARMAN)
This dataset is a combination of [PEYMA](https://arxiv.org/abs/1801.09936) and [ARMAN](https://github.com/HaniehP/PersianNER) Persian NER datasets. It contains the following named entity tags:
- Product (PRO)
- Event (EVE)
- Facility (FAC)
- Location (LOC)
- Person (PER)
- Money (MON)
- Percent (PCT)
- Date (DAT)
- Organization (ORG)
- Time (TIM)
## Dataset Information
The dataset is divided into three splits: train, test, and validation. Below is a summary of the dataset statistics:
| Split | B_DAT | B_EVE | B_FAC | B_LOC | B_MON | B_ORG | B_PCT | B_PER | B_PRO | B_TIM | I_DAT | I_EVE | I_FAC | I_LOC | I_MON | I_ORG | I_PCT | I_PER | I_PRO | I_TIM | O | num_rows |
|------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|----------|
| Train | 1512 | 1379 | 1334 | 13040 | 446 | 15762 | 266 | 11371 | 1719 | 224 | 1939 | 4600 | 2222 | 4254 | 1314 | 21347 | 308 | 7160 | 1736 | 375 | 747216 | 26417 |
| Test | 185 | 218 | 124 | 1868 | 53 | 2017 | 27 | 1566 | 281 | 27 | 245 | 697 | 237 | 511 | 142 | 2843 | 31 | 1075 | 345 | 37 | 92214 | 3303 |
| Validation | 161 | 143 | 192 | 1539 | 28 | 2180 | 33 | 1335 | 172 | 30 | 217 | 520 | 349 | 494 | 54 | 2923 | 34 | 813 | 136 | 39 | 96857 | 3302 |
### First schema
```python
DatasetDict({
train: Dataset({
features: ['tokens', 'ner_tags', 'ner_tags_names'],
num_rows: 26417
})
test: Dataset({
features: ['tokens', 'ner_tags', 'ner_tags_names'],
num_rows: 3303
})
validation: Dataset({
features: ['tokens', 'ner_tags', 'ner_tags_names'],
num_rows: 3302
})
})
```
### How to load datset
```python
from datasets import load_dataset
data = load_dataset("AliFartout/PEYMA-ARMAN-Mixed")
```
Feel free to adjust the formatting according to your needs.
|
AliFartout/PEYMA-ARMAN-Mixed
|
[
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:fa",
"license:mit",
"arxiv:1801.09936",
"region:us"
] |
2023-08-30T10:11:46+00:00
|
{"language": ["fa"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "pretty_name": "PEYMA-ARMAN-Mixed"}
|
2023-08-30T13:11:43+00:00
|
[
"1801.09936"
] |
[
"fa"
] |
TAGS
#task_categories-token-classification #size_categories-10K<n<100K #language-Persian #license-mit #arxiv-1801.09936 #region-us
|
Mixed Persian NER Dataset (PEYMA-ARMAN)
=======================================
This dataset is a combination of PEYMA and ARMAN Persian NER datasets. It contains the following named entity tags:
* Product (PRO)
* Event (EVE)
* Facility (FAC)
* Location (LOC)
* Person (PER)
* Money (MON)
* Percent (PCT)
* Date (DAT)
* Organization (ORG)
* Time (TIM)
Dataset Information
-------------------
The dataset is divided into three splits: train, test, and validation. Below is a summary of the dataset statistics:
### First schema
### How to load datset
Feel free to adjust the formatting according to your needs.
|
[
"### First schema",
"### How to load datset\n\n\nFeel free to adjust the formatting according to your needs."
] |
[
"TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-Persian #license-mit #arxiv-1801.09936 #region-us \n",
"### First schema",
"### How to load datset\n\n\nFeel free to adjust the formatting according to your needs."
] |
[
49,
4,
19
] |
[
"passage: TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-Persian #license-mit #arxiv-1801.09936 #region-us \n### First schema### How to load datset\n\n\nFeel free to adjust the formatting according to your needs."
] |
ea416aed6b5c7b6b390b8de6736ef7265d77d56f
|
# Dataset Card for "llama_2_product_titles-esci_train-neg-complete"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qazisaad/llama_2_product_titles-esci_train-neg-complete
|
[
"region:us"
] |
2023-08-30T10:13:10+00:00
|
{"dataset_info": {"features": [{"name": "index", "dtype": "int64"}, {"name": "query", "dtype": "string"}, {"name": "average_score", "dtype": "float64"}, {"name": "total_score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "preds", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1920932, "num_examples": 977}], "download_size": 289337, "dataset_size": 1920932}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-30T10:13:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llama_2_product_titles-esci_train-neg-complete"
More Information needed
|
[
"# Dataset Card for \"llama_2_product_titles-esci_train-neg-complete\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llama_2_product_titles-esci_train-neg-complete\"\n\nMore Information needed"
] |
[
6,
31
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"llama_2_product_titles-esci_train-neg-complete\"\n\nMore Information needed"
] |
6dcd073d42bfa897d9653df6f0c579019a65bf17
|
# Dataset Card for Evaluation run of TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch
- **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 [TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__Ensemble5-Platypus2-13B-QLora-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T17:26:29.355241](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Ensemble5-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-22T17-26-29.355241.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": 0.07340604026845637,
"em_stderr": 0.002670854825275824,
"f1": 0.17929844798657668,
"f1_stderr": 0.003077076785957565,
"acc": 0.3816157872344754,
"acc_stderr": 0.008008666093235974
},
"harness|drop|3": {
"em": 0.07340604026845637,
"em_stderr": 0.002670854825275824,
"f1": 0.17929844798657668,
"f1_stderr": 0.003077076785957565
},
"harness|gsm8k|5": {
"acc": 0.018953752843062926,
"acc_stderr": 0.0037560783410314743
},
"harness|winogrande|5": {
"acc": 0.744277821625888,
"acc_stderr": 0.012261253845440474
}
}
```
### 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_TFLai__Ensemble5-Platypus2-13B-QLora-0.80-epoch
|
[
"region:us"
] |
2023-08-30T10:14:39+00:00
|
{"pretty_name": "Evaluation run of TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__Ensemble5-Platypus2-13B-QLora-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T17:26:29.355241](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Ensemble5-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-22T17-26-29.355241.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\": 0.07340604026845637,\n \"em_stderr\": 0.002670854825275824,\n \"f1\": 0.17929844798657668,\n \"f1_stderr\": 0.003077076785957565,\n \"acc\": 0.3816157872344754,\n \"acc_stderr\": 0.008008666093235974\n },\n \"harness|drop|3\": {\n \"em\": 0.07340604026845637,\n \"em_stderr\": 0.002670854825275824,\n \"f1\": 0.17929844798657668,\n \"f1_stderr\": 0.003077076785957565\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.018953752843062926,\n \"acc_stderr\": 0.0037560783410314743\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.744277821625888,\n \"acc_stderr\": 0.012261253845440474\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch", "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_08_30T11_14_15.390107", "path": ["**/details_harness|arc:challenge|25_2023-08-30T11:14:15.390107.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T11:14:15.390107.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T17_26_29.355241", "path": ["**/details_harness|drop|3_2023-10-22T17-26-29.355241.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T17-26-29.355241.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T17_26_29.355241", "path": ["**/details_harness|gsm8k|5_2023-10-22T17-26-29.355241.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T17-26-29.355241.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T11_14_15.390107", "path": ["**/details_harness|hellaswag|10_2023-08-30T11:14:15.390107.parquet"]}, {"split": "latest", "path": 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["**/details_harness|hendrycksTest-public_relations|5_2023-08-30T11:14:15.390107.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-30T11:14:15.390107.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_08_30T11_14_15.390107", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-30T11:14:15.390107.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-30T11:14:15.390107.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_08_30T11_14_15.390107", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-30T11:14:15.390107.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-30T11:14:15.390107.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_08_30T11_14_15.390107", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T11:14:15.390107.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T11:14:15.390107.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_08_30T11_14_15.390107", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-30T11:14:15.390107.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-30T11:14:15.390107.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_08_30T11_14_15.390107", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-30T11:14:15.390107.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-30T11:14:15.390107.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_08_30T11_14_15.390107", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-30T11:14:15.390107.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-30T11:14:15.390107.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_22T17_26_29.355241", "path": ["**/details_harness|winogrande|5_2023-10-22T17-26-29.355241.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-22T17-26-29.355241.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_30T11_14_15.390107", "path": ["results_2023-08-30T11:14:15.390107.parquet"]}, {"split": "2023_10_22T17_26_29.355241", "path": ["results_2023-10-22T17-26-29.355241.parquet"]}, {"split": "latest", "path": ["results_2023-10-22T17-26-29.355241.parquet"]}]}]}
|
2023-10-22T16:26:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T17:26:29.355241(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 TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T17:26:29.355241(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 TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T17:26:29.355241(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",
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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"
] |
[
6,
33,
31,
181,
67,
10,
4,
6,
6,
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5,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch## 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 TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T17:26:29.355241(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"
] |
725cce4b22e757b5ae40ce32f27326a8bbf8d2fc
|
# Dataset Card for "autotree_automl_Diabetes130US_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_Diabetes130US_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-30T10:19:01+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 174960000, "num_examples": 10000}, {"name": "validation", "num_bytes": 174960000, "num_examples": 10000}], "download_size": 45382840, "dataset_size": 349920000}}
|
2023-08-31T11:57:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_Diabetes130US_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_Diabetes130US_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_Diabetes130US_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_Diabetes130US_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
bb81a62e418d2624a73a8df589fbdc80651aeb12
|
# Dataset Card for "quant-stackexchange-posts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
theblackcat102/quant-stackexchange-posts
|
[
"region:us"
] |
2023-08-30T10:24:17+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Id", "dtype": "string"}, {"name": "PostTypeId", "dtype": "string"}, {"name": "AcceptedAnswerId", "dtype": "string"}, {"name": "ParentId", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "ViewCount", "dtype": "string"}, {"name": "Body", "dtype": "string"}, {"name": "Title", "dtype": "string"}, {"name": "ContentLicense", "dtype": "string"}, {"name": "FavoriteCount", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "LastActivityDate", "dtype": "string"}, {"name": "LastEditDate", "dtype": "string"}, {"name": "LastEditorUserId", "dtype": "string"}, {"name": "OwnerUserId", "dtype": "string"}, {"name": "Tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 53461476, "num_examples": 46554}], "download_size": 29558554, "dataset_size": 53461476}}
|
2023-08-30T10:24:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "quant-stackexchange-posts"
More Information needed
|
[
"# Dataset Card for \"quant-stackexchange-posts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"quant-stackexchange-posts\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"quant-stackexchange-posts\"\n\nMore Information needed"
] |
c1e08d7b1d865713547b28d0cf657c5fb4e9ff94
|
# Dataset of Otosuna Mihari
This is the dataset of Otosuna Mihari, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 459 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 459 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 459 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 459 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/otosuna_mihari_mangakasantoassistantsanto
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-30T10:25:07+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:36+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Otosuna Mihari
=========================
This is the dataset of Otosuna Mihari, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
42a70d906f88e6b4c8a7e8bdf36a9e6d98f0dd15
|
# Dataset Card for Evaluation run of TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch
- **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 [TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__Airboros2.1-Platypus2-13B-QLora-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T07:28:22.251658](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Airboros2.1-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-22T07-28-22.251658.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": 0.30935402684563756,
"em_stderr": 0.004733640588022148,
"f1": 0.39316484899329085,
"f1_stderr": 0.004587262243817744,
"acc": 0.3756906077348066,
"acc_stderr": 0.0060736573567015525
},
"harness|drop|3": {
"em": 0.30935402684563756,
"em_stderr": 0.004733640588022148,
"f1": 0.39316484899329085,
"f1_stderr": 0.004587262243817744
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.7513812154696132,
"acc_stderr": 0.012147314713403105
}
}
```
### 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_TFLai__Airboros2.1-Platypus2-13B-QLora-0.80-epoch
|
[
"region:us"
] |
2023-08-30T10:41:12+00:00
|
{"pretty_name": "Evaluation run of TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__Airboros2.1-Platypus2-13B-QLora-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T07:28:22.251658](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Airboros2.1-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-22T07-28-22.251658.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\": 0.30935402684563756,\n \"em_stderr\": 0.004733640588022148,\n \"f1\": 0.39316484899329085,\n \"f1_stderr\": 0.004587262243817744,\n \"acc\": 0.3756906077348066,\n \"acc_stderr\": 0.0060736573567015525\n },\n \"harness|drop|3\": {\n \"em\": 0.30935402684563756,\n \"em_stderr\": 0.004733640588022148,\n \"f1\": 0.39316484899329085,\n \"f1_stderr\": 0.004587262243817744\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7513812154696132,\n \"acc_stderr\": 0.012147314713403105\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch", "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_08_30T11_40_47.771600", "path": ["**/details_harness|arc:challenge|25_2023-08-30T11:40:47.771600.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T11:40:47.771600.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T07_28_22.251658", "path": ["**/details_harness|drop|3_2023-10-22T07-28-22.251658.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T07-28-22.251658.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T07_28_22.251658", "path": ["**/details_harness|gsm8k|5_2023-10-22T07-28-22.251658.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T07-28-22.251658.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T11_40_47.771600", "path": ["**/details_harness|hellaswag|10_2023-08-30T11:40:47.771600.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-30T11:40:47.771600.parquet"]}]}, 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2023-10-22T06:28:34+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T07:28:22.251658(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 TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T07:28:22.251658(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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"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T07:28:22.251658(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 TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch## 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 TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T07:28:22.251658(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"
] |
9f931595ca481475a39e5f9e9e935aa1b8f259d5
|
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<ul>
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<li>This supplement is not for lactating or pregnant women.</li>
</ul>
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<p><strong>Q. What Is The Full List of Ingredients in <a href="https://forums.hitched.co.uk/chat/forums/thread/bp120-premium-no1-in-us-forces-to-quickly-decrease-high-blood-pressure-levels-without-causing-harmful-effects-1115881/">BP120 Premium Blood Pressure Support</a>? And Are There Any Allergens?</strong></p>
<p><strong>Ans.</strong> <a href="https://medium.com/@bp120reviews/bp120-premium-newly-update-2023-powerfully-helps-systolic-diastolic-blood-pressure-control-in-1e623419034d">BP120 Premium Blood Pressure Support</a> contains clinical doses of the following:</p>
<ul>
<li><strong>CoQ10</strong></li>
<li><strong>L-Theanine</strong></li>
<li><strong>BEET ROOT POWDER</strong></li>
<li><strong>AGED BLACK GARLIC EXTRACT</strong></li>
<li><strong>HAWTHORN BERRY POWDER</strong></li>
<li><strong>Beta Cyclodextrin</strong></li>
</ul>
<p>Each ingredient is sourced from non-GMO plant extracts. <a href="https://sketchfab.com/3d-models/bp120-premium-reviews-price-update-2023-1f85f0a9d7d8430aa1808418858e7aab">BP120 Premium Blood Pressure Support</a> is free of any allergens, including gluten, dairy, soy, shellfish, and tree nuts.</p>
<p><strong><span class="faq__question--pre">Q</span>.<span class="faq__question__text">How Long <a href="https://hackmd.io/@bp120reviews/bp120-premium">BP120 Premium Blood Pressure Support</a> Take To See Results?</span></strong></p>
<p><strong><span class="faq__question__text">Ans. </span></strong>Most users see results within the first week. For some , it may take 14 days to see your blood pressure start to come down. The best results will be seen after 60 days because the longer you use BP120 Premium Blood Pressure Support, the better the results typically are!</p>
<p><strong><span class="faq__question__text">Q.Will <a href="https://groups.google.com/g/bp120-premium-blood-pressure-sale/c/mO-GpuPz1Oo">BP120 Premium Blood Pressure Support</a> Work For Me?</span></strong></p>
<p><span class="faq__question__text"><strong>Ans.</strong> Yes! <a href="https://colab.research.google.com/drive/1UARYutj4YzckOcdZAMKO19AfnqJsDHFo">BP120 Premium Blood Pressure</a> Support is formulated with the highest quality ingredients, in the exact doses needed, that have been used for centuries and have been clinically proven.</span></p>
<p><strong><span class="faq__question__text">Q.How Does The Refund Policy Work?</span></strong></p>
<p><span class="faq__question__text"><strong>Ans.</strong> Your <a href="https://lookerstudio.google.com/reporting/a626c7e5-5803-48c2-a129-d2ba462bbd0d">BP120 Premium Blood Pressure Support</a>'s Bottles comes with a 90 Days, 100% Money Back Guarantee. That means if you change your mind about this decision at any point in the next 3 months – all you need to do is email us or call our customer support team, they'll give you a return address where you can ship both your empty and full products and we’ll refund your purchase. The shipping and postage to cover the return will be paid by the customer.</span></p>
<h2><strong>BP120 Premium Blood Pressure Support Conclusion.</strong></h2>
<p><a href="https://www.remotehub.com/bp120.premium">BP120 Premium Blood Pressure Support</a> can remedy individuals with mild to moderate hypertension. According to the creator, it has ingredients whose purity and potency are scientifically tested to aid blood pressure effectively. The formula’s constituents have an active compound that combats oxidative stress, reduces inflammation, and has antihypertensive effects.Moreover, the formula inhibits oxidation of LDL cholesterol levels, promotes blood vessel relaxation, and improves blood flow. The dietary capsules enhance blood circulation, supporting heart function and kidney health. Consumers can choose their preferred pack on the official website and maintain healthy blood pressure levels.</p>
<h2 style="text-align: center;"><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="https://www.healthsupplement24x7.com/get-bp120-premium-blood-pressure"><strong>➦➦Visit the Official Website Today and Grab Your Bottle!</strong></a></span></h2>
<p><a href="https://cinnachroma-reviews.blogspot.com/2023/08/bp120-premium-blood-pressure-support.html">https://cinnachroma-reviews.blogspot.com/2023/08/bp120-premium-blood-pressure-support.html</a></p>
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<p><a href="https://sites.google.com/view/bp120-/home">https://sites.google.com/view/bp120-/home</a></p>
<p><a href="https://colab.research.google.com/drive/1ZMgiJpxh_rK2WjukdZ_qfJEHSicEN9-C">https://colab.research.google.com/drive/1ZMgiJpxh_rK2WjukdZ_qfJEHSicEN9-C</a></p>
<p><a href="https://lookerstudio.google.com/reporting/12d91229-5962-41d9-9319-308861175453">https://lookerstudio.google.com/reporting/12d91229-5962-41d9-9319-308861175453</a></p>
<p><a href="https://groups.google.com/g/bp120-premium-blood-pressure-support-official/c/LhgmFiPQc0U">https://groups.google.com/g/bp120-premium-blood-pressure-support-official/c/LhgmFiPQc0U</a></p>
<p><a href="https://bp120-updates.clubeo.com">https://bp120-updates.clubeo.com</a></p>
<p><a href="https://huggingface.co/datasets/bp120reviews/bp120premium/blob/main/README.md">https://huggingface.co/datasets/bp120reviews/bp120premium/blob/main/README.md</a></p>
<p><a href="https://bp120premium.hashnode.dev/bp120-premium-blood-pressure-support">https://bp120premium.hashnode.dev/bp120-premium-blood-pressure-support</a></p>
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<p><a href="https://bp120premium.bandcamp.com/track/bp120-premium-no-1-in-us-forces-to-quickly-decrease-high-blood-pressure-levels-without-causing-harmful-effects">https://bp120premium.bandcamp.com/track/bp120-premium-no-1-in-us-forces-to-quickly-decrease-high-blood-pressure-levels-without-causing-harmful-effects</a></p>
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bp120off/BP120Premium
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"region:us"
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2023-08-30T10:45:28+00:00
|
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2023-08-30T10:45:49+00:00
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TAGS
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|
<h1><strong><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Premium Blood Pressure Support – Official Website Link – Click Here</a></span></strong></h1>
<p><strong> Product Name - <a href="URL Premium Blood Pressure Support</a><br /></strong></p>
<p><strong> Quantity Per Bottle - 60 Capsules/Jar<br /></strong></p>
<p><strong> Category - Blood Pressure Support<br /></strong></p>
<p><strong> Compostion - Natural Components Only</strong></p>
<p><strong> Results - In 14 Days</strong></p>
<p><strong> Availability – Official Website <span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL
<p><strong> Rating: - 4.8/5.0 </strong></p>
<h3><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Here To Visit – OFFICIAL WEBSITE</strong></a></span></h3>
<h3><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Here To Visit – OFFICIAL WEBSITE</strong></a></span></h3>
<h3><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Here To Visit – OFFICIAL WEBSITE</strong></a></span></h3>
<p><strong>Reviews on <a href="URL Premium Blood Pressure Support</a> :</strong> High blood pressure causes damage to arteries, which heightens the risk of cardiovascular disease, stroke, and other severe health problems. Sodium is an essential mineral that regulates fluid balance in the body, but high intake could raise blood pressure. Moreover, inflammation can damage arteries, and high LDL cholesterol levels can lead to arterial plaque. However, maintaining a healthy body balance of high-blood pressure risk factors can minimize the risk and support cardiovascular health. </p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="500" border="0" data-original-height="442" data-original-width="565" /></a></div>
<p><a href="URL Premium Blood Pressure Support</a> has a proprietary blend of ingredients proven to reduce inflammation and enhance fluid balance, thus regulating blood pressure. According to the creator, the supplement enhances blood vessel dilation, increasing blood flow. Moreover, it eases water retention, protects the kidney, and supports cardiovascular health. What does the proprietary blend contain? This review has more information about the <a href="URL Premium Blood Pressure Support</a>.</p>
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<h2><strong>What is BP120 Premium Blood Pressure Support?</strong></h2>
<p><a href="URL Premium Blood Pressure Support</a> is an all-natural and effective advanced blood pressure support supplement to reactivate the Blood Pressure Release Valve in your heart.This ground-breaking formula uses natural ingredients, which help synthesize plant nutrients, starting with the Crocus flower extract.Each ingredient in this exceptional blend works in perfect harmony to arrange a symphony of advantages.The main objective is to reactivate the kidney-based Blood Pressure Release Valve. This mixture restores equilibrium while it works magic, enabling your body to reestablish control over blood pressure.</p>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="310" border="0" data-original-height="598" data-original-width="1231" /></a></span></h2>
<p><a href="URL Premium Blood Pressure Support</a> mainly helps to provide you with a path to optimal health where the forces of nature work together to promote your well-being.The supplement uses a special blend that helps the body eliminate excess fluid and salt and relaxes blood vessel walls to allow for unhindered blood flow.Read this complete review to know more about the <a href="URL Premium Blood Pressure Support</a> and to understand the complete benefits of the <a href="URL Premium Blood Pressure Support</a>.</p>
<h2><strong><span style="color: red;">BP120 Premium Blood Pressure Support</span> - The Way Its Works For You?</strong></h2>
<p><a href="URL Premium Blood Pressure Support</a> has been meticulously developed to solve this issue since it recognizes inflammation's crucial role in health. It strives to mitigate the negative effects of harmful chemicals that could jeopardize the health of your arteries.By doing this, it adopts a multifaceted strategy to protect against cardiovascular problems, promote a healthy blood flow, and instill calm inside the circulatory system.The formula for <a href="URL">BP120 Premium Blood Pressure Support</a> is a blend of high-quality ingredients, each selected for its distinct qualities.This complex mixture helps your arteries thoroughly detoxify by using its strong cleansing qualities to remove hazardous toxin buildups.These accumulations may impede.Each ingredient in <a href="URL Premium Blood Pressure Support</a> has been specifically chosen for its special qualities.</p>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="318" border="0" data-original-height="733" data-original-width="1475" /></a></span></h2>
<p>This complex mixture helps your arteries thoroughly detoxify by using its strong cleansing qualities to remove hazardous toxin buildups.These accumulations may block arteries, causing blood sugar obstructions that endanger general health.Increased levels of this TSC protein prevent more water molecules from flowing and instead cause them to become stuck in blood arteries.As a result, it raises the fluid level in the same area, which causes high blood pressure. To lower the TSC proteins and, it is important to REACTIVATE the Blood Pressure Release Valve.It happens due to the accumulation of fat and cholesterol in veins, which raises blood vessel pressure.</p>
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<h2><strong>Natural Components in BP120 Premium Blood Pressure Support.<br /></strong></h2>
<ul>
<li><strong>BEET ROOT POWDER :</strong> Beets are one of only a few plant foods that contain dietary nitrates. Nitrates convert into nitric oxide, a molecule that dilates blood vessels to help increase blood flow, which helps pretty much every function in the body.</li>
<li><strong>AGED BLACK GARLIC EXTR ACT :</strong> Black garlic has shown promise in improving cardiovascular health by reducing arterial stiffness, elevated cholesterol levels and blood 'stickiness'.</li>
<li><strong>HAWTHORN BERRY POWDER :</strong> Hawthorn is used to protect against heart disease and help control high Blood Pressure and high cholesterol. Both animal and human studies suggest hawthorn increases coronary artery blood flow, improves circulation, and lowers Blood Pressure.</li>
</ul>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="254" border="0" data-original-height="714" data-original-width="1800" /></a></span></h2>
<ul>
<li><strong>CoQ10 :</strong> CoQ10 has been shown to help alleviate heart stress. Especially for those already on prescribed medication, or who have had any heart procedures done. This is a basic, but essential binding ingredient for our life saving formulation</li>
<li><strong>Beta Cyclodextrin :</strong> Its sole job is to make this powerhouse formulation more bioavailable. Which means it'll work effectively, and quickly, to help you stabilize your blood pressure on a daily basis.</li>
<li><strong>L-Theanine :</strong> The naturally occuring, non-protein amino acid that has been shown to help reduce blood pressure and limit stress. It also is beneficial for a good night's sleep, which can help promote overall relaxation.</li>
</ul>
<h2><strong>The key benifits of BP120 Premium Blood Pressure Support.</strong></h2>
<p style="text-align: left;"><a href="URL Premium Blood Pressure Support</a> helps to improve vascular health and overall blood pressure.With the help of this <a href="URL Premium Blood Pressure Support</a>, it can help with appropriate systolic and diastolic pressure control.This supplement can also to enhancing platelet production and flow for effective clotting.This effective supplement can enhance blood vessel elasticity and strength.<a href="URL Premium Blood Pressure Support</a> can also provide the artery cells with a beneficial anti-inflammatory reaction.<a href="URL Premium Blood Pressure Support</a> can increase the nitric oxide generation to widen blood vessels and reduce tiredness.This <a href="URL Premium Blood Pressure Support</a> combats oxidative stress and restores its negative impact on cells.It will also to reducing cortisol and the stress response and also to properly increase bodily energyIt can also to enhancing kidney performance and properly lower the amount of cholesterol.It can properly boost the immune system to prevent illness and disease. </p>
<div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="260" border="0" data-original-height="764" data-original-width="1881" /></a></div>
<h2><strong>Pros of BP120 Premium Blood Pressure Support :</strong></h2>
<ul>
<li><strong>Lower's Blood Pressure.</strong></li>
<li><strong>Boost Your Overall Heart Health.</strong></li>
<li><strong>Powerful Antiodixant Function.</strong></li>
<li><strong>Supports Heart Rate.</strong></li>
<li><strong>Supports Cardiovascular Health.</strong></li>
<li><strong>Increases Metabolic Function.<br /></strong></li>
<li><strong>100% Natural, Certified Non-GMO.</strong></li>
<li><strong>Rigorous Quality Control Process.</strong></li>
</ul>
<h2 style="text-align: center;"><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL Here Your BP120 Premium Blood Pressure Support & Grab The Big Discount Right Now!</strong></a></span></h2>
<h2><strong>Cons of BP120 Premium Blood Pressure Support:</strong></h2>
<ul>
<li>Buy this <a href="URL Premium Blood Pressure Support</a> only from the <strong>official website</strong>, not any other platform.</li>
<li>This supplement is not for lactating or pregnant women.</li>
</ul>
<h2><strong>How Safe Is BP120 Premium Blood Pressure Support?</strong></h2>
<p><a href="URL Premium Blood Pressure Support</a> is made with carefully chosen ingredients combined in a facility that upholds the highest standards of Good Manufacturing Practices.This facility, which is regularly undergone FDA audits, proves our commitment to your safety.Regular testing for purity and the absence of any potentially dangerous substances, including bacteria, pathogens, pesticides, and preservatives, is an essential component of our procedure.After these meticulous inspections, the entire batch is rejected if any problems are found, demonstrating our unshakable dedication to your well-being.So this supplement is considered safe and effective, which plays a vital role in showing the wide range of health benefits.</p>
<h2><strong>BP120 Premium Blood Pressure Support Usage.</strong></h2>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="236" border="0" data-original-height="1002" data-original-width="2712" /></a></span></h2>
<p>Each <a href="URL Premium Blood Pressure Support</a> bottle contains 60 capsules. The manufacturer recommends consumers take two pills daily. According to the creator, vegan capsules have been tested for purity and safety and have no known side effects. Daily supplement usage can enable consumers to maintain healthy blood pressure and support cardiovascular health. However, consumers under medication or with underlying health problems should consult their physicians before using the dietary supplement.</p>
<h2><strong>Detailed pricing of BP120 Premium Blood Pressure Support.</strong></h2>
<h2 style="text-align: center;"><span style="background-color: white; font-weight: normal;"><a style="margin-left: 1em; margin-right: 1em;" href="URL src="URL alt="" width="640" height="358" border="0" data-original-height="651" data-original-width="1161" /></a></span></h2>
<ul>
<li style="text-align: left;"><strong>Starter Pack Buy One Bottle of <a href="URL Premium Blood Pressure Support</a> $59 + Free US Shipping.</strong></li>
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<li style="text-align: left;"><strong><span style="background-color: #339966; color: white;">Best Value Pack Buy Six Bottles of <a href="URL Premium Blood Pressure Support</a> $234 [USD 39/bottle]+ Free US Shipping.</span> <span style="color: green;"></span><br /></strong></li>
</ul>
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<h2>Money back guarantee on <a href="URL">BP120 Premium</a> Blood Pressure Support.</h2>
<p>A 90-day money-back guarantee backs each purchase of <a href="URL Premium Blood Pressure Support</a>. Consumers who find the product unsatisfactory should return the <a href="URL Premium Blood Pressure Support</a> and get a full refund. However, they’ll have to cater for the shipping and handling fee. For more information <a href="URL style="background-color: yellow; color: red;"><strong><<<<CLICK HERE>>>></strong></span></a></p>
<h2><strong>FAQ on <a href="URL Premium Blood Pressure Support</a><br /></strong></h2>
<p><strong>Q. What Is The Full List of Ingredients in <a href="URL Premium Blood Pressure Support</a>? And Are There Any Allergens?</strong></p>
<p><strong>Ans.</strong> <a href="URL Premium Blood Pressure Support</a> contains clinical doses of the following:</p>
<ul>
<li><strong>CoQ10</strong></li>
<li><strong>L-Theanine</strong></li>
<li><strong>BEET ROOT POWDER</strong></li>
<li><strong>AGED BLACK GARLIC EXTRACT</strong></li>
<li><strong>HAWTHORN BERRY POWDER</strong></li>
<li><strong>Beta Cyclodextrin</strong></li>
</ul>
<p>Each ingredient is sourced from non-GMO plant extracts. <a href="URL Premium Blood Pressure Support</a> is free of any allergens, including gluten, dairy, soy, shellfish, and tree nuts.</p>
<p><strong><span class="faq__question--pre">Q</span>.<span class="faq__question__text">How Long <a href="URL Premium Blood Pressure Support</a> Take To See Results?</span></strong></p>
<p><strong><span class="faq__question__text">Ans. </span></strong>Most users see results within the first week. For some , it may take 14 days to see your blood pressure start to come down. The best results will be seen after 60 days because the longer you use BP120 Premium Blood Pressure Support, the better the results typically are!</p>
<p><strong><span class="faq__question__text">Q.Will <a href="URL Premium Blood Pressure Support</a> Work For Me?</span></strong></p>
<p><span class="faq__question__text"><strong>Ans.</strong> Yes! <a href="URL Premium Blood Pressure</a> Support is formulated with the highest quality ingredients, in the exact doses needed, that have been used for centuries and have been clinically proven.</span></p>
<p><strong><span class="faq__question__text">Q.How Does The Refund Policy Work?</span></strong></p>
<p><span class="faq__question__text"><strong>Ans.</strong> Your <a href="URL Premium Blood Pressure Support</a>'s Bottles comes with a 90 Days, 100% Money Back Guarantee. That means if you change your mind about this decision at any point in the next 3 months – all you need to do is email us or call our customer support team, they'll give you a return address where you can ship both your empty and full products and we’ll refund your purchase. The shipping and postage to cover the return will be paid by the customer.</span></p>
<h2><strong>BP120 Premium Blood Pressure Support Conclusion.</strong></h2>
<p><a href="URL Premium Blood Pressure Support</a> can remedy individuals with mild to moderate hypertension. According to the creator, it has ingredients whose purity and potency are scientifically tested to aid blood pressure effectively. The formula’s constituents have an active compound that combats oxidative stress, reduces inflammation, and has antihypertensive effects.Moreover, the formula inhibits oxidation of LDL cholesterol levels, promotes blood vessel relaxation, and improves blood flow. The dietary capsules enhance blood circulation, supporting heart function and kidney health. Consumers can choose their preferred pack on the official website and maintain healthy blood pressure levels.</p>
<h2 style="text-align: center;"><span style="background-color: maroon; color: white;"><a style="background-color: maroon; color: white;" href="URL the Official Website Today and Grab Your Bottle!</strong></a></span></h2>
<p><a href="URL/URL
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<p><a href="URL/URL
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<p><a href="URL/URL
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<p><a href="URL/URL
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d7e8e4f791b4c3aa649b7ff266c67c2da6cd3f39
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# Dataset Card for "balanced_structs_reduced_labelled_large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
johannes-garstenauer/balanced_structs_reduced_labelled_large
|
[
"region:us"
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2023-08-30T10:48:18+00:00
|
{"dataset_info": {"features": [{"name": "struct", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 407429998.0, "num_examples": 1278338}], "download_size": 108755223, "dataset_size": 407429998.0}}
|
2023-08-30T10:49:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "balanced_structs_reduced_labelled_large"
More Information needed
|
[
"# Dataset Card for \"balanced_structs_reduced_labelled_large\"\n\nMore Information needed"
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[
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c83bc3e0df69fa6ffa854159d91b32b7082e1d80
|
# Dataset Card for "named-necklace-original-canny-caption"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cto-algo-huggingface/named-necklace-original-canny-caption
|
[
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2023-08-30T10:48:50+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16129334.0, "num_examples": 97}], "download_size": 0, "dataset_size": 16129334.0}}
|
2023-08-30T11:33:57+00:00
|
[] |
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TAGS
#region-us
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# Dataset Card for "named-necklace-original-canny-caption"
More Information needed
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[
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408c7193fadf3afa355bc1f343917ab809587cf6
|
# Dataset Card for "named-necklace-original-sobel-caption"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cto-algo-huggingface/named-necklace-original-sobel-caption
|
[
"region:us"
] |
2023-08-30T10:50:02+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16129334.0, "num_examples": 97}], "download_size": 0, "dataset_size": 16129334.0}}
|
2023-08-30T11:38:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "named-necklace-original-sobel-caption"
More Information needed
|
[
"# Dataset Card for \"named-necklace-original-sobel-caption\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"named-necklace-original-sobel-caption\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"named-necklace-original-sobel-caption\"\n\nMore Information needed"
] |
9d199ad0c121c30cb0fce5e8a7ac16fbab3d48ec
|
# Dataset of Kuroi Sena
This is the dataset of Kuroi Sena, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 426 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 426 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 426 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 426 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/kuroi_sena_mangakasantoassistantsanto
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-30T10:51:18+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:38+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Kuroi Sena
=====================
This is the dataset of Kuroi Sena, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
544e1b2467706929a34be04fe5d5ef6f9488772f
|
# Dataset Card for "eu_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
KatMarie/eu_corpora_parliament_processed
|
[
"region:us"
] |
2023-08-30T10:52:55+00:00
|
{"dataset_info": {"features": [], "splits": [{"name": "train"}], "download_size": 323, "dataset_size": 0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-30T10:52:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eu_corpora_parliament_processed"
More Information needed
|
[
"# Dataset Card for \"eu_corpora_parliament_processed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eu_corpora_parliament_processed\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eu_corpora_parliament_processed\"\n\nMore Information needed"
] |
ad3a3a848d87a8edc1d8993d0b42dded8cf3192b
|
# Dataset Card for "euparl_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
KatMarie/euparl_test
|
[
"region:us"
] |
2023-08-30T10:58:07+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22103017, "num_examples": 133599}], "download_size": 11392783, "dataset_size": 22103017}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-03T08:55:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "euparl_test"
More Information needed
|
[
"# Dataset Card for \"euparl_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"euparl_test\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"euparl_test\"\n\nMore Information needed"
] |
9358f1926374d57366ea2d3c932a2e1c092b42f0
|
# Dataset Card for Evaluation run of TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch
- **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 [TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T08:12:02.098459](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-22T08-12-02.098459.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": 0.0036703020134228187,
"em_stderr": 0.0006192871806511018,
"f1": 0.11549182046979932,
"f1_stderr": 0.0019129258376729289,
"acc": 0.36937647987371747,
"acc_stderr": 0.006173457431707654
},
"harness|drop|3": {
"em": 0.0036703020134228187,
"em_stderr": 0.0006192871806511018,
"f1": 0.11549182046979932,
"f1_stderr": 0.0019129258376729289
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.7387529597474349,
"acc_stderr": 0.012346914863415308
}
}
```
### 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_TFLai__MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch
|
[
"region:us"
] |
2023-08-30T11:03:32+00:00
|
{"pretty_name": "Evaluation run of TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T08:12:02.098459](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-22T08-12-02.098459.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\": 0.0036703020134228187,\n \"em_stderr\": 0.0006192871806511018,\n \"f1\": 0.11549182046979932,\n \"f1_stderr\": 0.0019129258376729289,\n \"acc\": 0.36937647987371747,\n \"acc_stderr\": 0.006173457431707654\n },\n \"harness|drop|3\": {\n \"em\": 0.0036703020134228187,\n \"em_stderr\": 0.0006192871806511018,\n \"f1\": 0.11549182046979932,\n \"f1_stderr\": 0.0019129258376729289\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7387529597474349,\n \"acc_stderr\": 0.012346914863415308\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch", "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_08_30T12_03_08.201386", "path": ["**/details_harness|arc:challenge|25_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T08_12_02.098459", "path": ["**/details_harness|drop|3_2023-10-22T08-12-02.098459.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T08-12-02.098459.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T08_12_02.098459", "path": ["**/details_harness|gsm8k|5_2023-10-22T08-12-02.098459.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T08-12-02.098459.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hellaswag|10_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-human_aging|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-international_law|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-management|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-marketing|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-nutrition|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-philosophy|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-prehistory|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-professional_law|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T12:03:08.201386.parquet", "**/details_harness|hendrycksTest-public_relations|5_2023-08-30T12:03:08.201386.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-30T12:03:08.201386.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-30T12:03:08.201386.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_22T08_12_02.098459", "path": ["**/details_harness|winogrande|5_2023-10-22T08-12-02.098459.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-22T08-12-02.098459.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_30T12_03_08.201386", "path": ["results_2023-08-30T12:03:08.201386.parquet"]}, {"split": "2023_10_22T08_12_02.098459", "path": ["results_2023-10-22T08-12-02.098459.parquet"]}, {"split": "latest", "path": ["results_2023-10-22T08-12-02.098459.parquet"]}]}]}
|
2023-10-22T07:12:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T08:12:02.098459(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 TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T08:12:02.098459(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 TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T08:12:02.098459(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 TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch## 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 TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch 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 2023-10-22T08:12:02.098459(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"
] |
df5992617e3febb9b13659f4a7a181fb0f881a06
|
# Dataset of Hata Ranko
This is the dataset of Hata Ranko, containing 103 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 103 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 226 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 103 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 103 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 103 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 103 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 103 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 226 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 226 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 226 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/hata_ranko_seitokaiyakuindomo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-30T11:05:46+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:40+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Hata Ranko
=====================
This is the dataset of Hata Ranko, containing 103 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
bb3e048b00c7889507450e2c2b6362733a92fe05
|
# Dataset of Shichijou Aria
This is the dataset of Shichijou Aria, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 493 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 493 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 493 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 493 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/shichijou_aria_seitokaiyakuindomo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-30T11:18:50+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:42+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Shichijou Aria
=========================
This is the dataset of Shichijou Aria, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
cda552d049e36f12b55d919ea5c9d25a56d37559
|
# Dataset Card for Evaluation run of TFLai/Athena-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/Athena-Platypus2-13B-QLora-0.80-epoch
- **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 [TFLai/Athena-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Athena-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T23:00:07.727248](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-21T23-00-07.727248.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": 0.10455117449664429,
"em_stderr": 0.0031334624512179676,
"f1": 0.22509018456375873,
"f1_stderr": 0.0034177949703821024,
"acc": 0.3634414270694895,
"acc_stderr": 0.006645721423171415
},
"harness|drop|3": {
"em": 0.10455117449664429,
"em_stderr": 0.0031334624512179676,
"f1": 0.22509018456375873,
"f1_stderr": 0.0034177949703821024
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225331
},
"harness|winogrande|5": {
"acc": 0.7261247040252565,
"acc_stderr": 0.012533292732620297
}
}
```
### 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_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch
|
[
"region:us"
] |
2023-08-30T11:24:47+00:00
|
{"pretty_name": "Evaluation run of TFLai/Athena-Platypus2-13B-QLora-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/Athena-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Athena-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-21T23:00:07.727248](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Athena-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-21T23-00-07.727248.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\": 0.10455117449664429,\n \"em_stderr\": 0.0031334624512179676,\n \"f1\": 0.22509018456375873,\n \"f1_stderr\": 0.0034177949703821024,\n \"acc\": 0.3634414270694895,\n \"acc_stderr\": 0.006645721423171415\n },\n \"harness|drop|3\": {\n \"em\": 0.10455117449664429,\n \"em_stderr\": 0.0031334624512179676,\n \"f1\": 0.22509018456375873,\n \"f1_stderr\": 0.0034177949703821024\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \"acc_stderr\": 0.0007581501137225331\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620297\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/Athena-Platypus2-13B-QLora-0.80-epoch", "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_08_30T12_24_23.685858", "path": ["**/details_harness|arc:challenge|25_2023-08-30T12:24:23.685858.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T12:24:23.685858.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_21T23_00_07.727248", "path": ["**/details_harness|drop|3_2023-10-21T23-00-07.727248.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-21T23-00-07.727248.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_21T23_00_07.727248", "path": ["**/details_harness|gsm8k|5_2023-10-21T23-00-07.727248.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-21T23-00-07.727248.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T12_24_23.685858", "path": ["**/details_harness|hellaswag|10_2023-08-30T12:24:23.685858.parquet"]}, {"split": "latest", "path": 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2023-10-21T22:00:20+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of TFLai/Athena-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/Athena-Platypus2-13B-QLora-0.80-epoch 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 2023-10-21T23:00:07.727248(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 TFLai/Athena-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/Athena-Platypus2-13B-QLora-0.80-epoch 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 2023-10-21T23:00:07.727248(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 TFLai/Athena-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/Athena-Platypus2-13B-QLora-0.80-epoch 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 2023-10-21T23:00:07.727248(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,
33,
31,
181,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
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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 TFLai/Athena-Platypus2-13B-QLora-0.80-epoch## 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 TFLai/Athena-Platypus2-13B-QLora-0.80-epoch 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 2023-10-21T23:00:07.727248(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"
] |
7baaddeba7df28b12d0cc9e807c44a8bbbf1e33e
|
# Dataset Card for "chart-to-table"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
chiragtubakad/chart-to-table
|
[
"region:us"
] |
2023-08-30T11:27:12+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39979829.0, "num_examples": 990}], "download_size": 33926492, "dataset_size": 39979829.0}}
|
2023-08-30T11:29:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "chart-to-table"
More Information needed
|
[
"# Dataset Card for \"chart-to-table\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"chart-to-table\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"chart-to-table\"\n\nMore Information needed"
] |
bb97995835c1c645d1d0ffef39bcb7101b3aef9b
|
#### **Dataset Description**
**About the Dataset**:
This dataset contains text data that has been processed to identify biased statements based on dimensions and aspects. Each entry has been processed using the GPT-4 language model and manually verified by 5 human annotators for quality assurance.
**Purpose**:
The dataset aims to help train and evaluate machine learning models in detecting, classifying, and correcting biases in text content, making it essential for NLP research related to fairness and debiasing.
**Origin**:
The dataset has been curated from diverse sources, including online media articles, blogs, and user comments. These sources were chosen for their likelihood of containing varying degrees of bias.
---
#### **Composition**
**Dataset Statistics**:
- Total entries: 7500
**Data Fields**:
- **biased_text (string)**: The original text containing biased content.
- **dimension (string)**: The broader category under which the bias can be classified, such as race, gender, religion, etc.
- **aspect (string)**: The specific perspective or angle of bias present in the text.
- **biased_profain_words (string)**: Words in the `biased_text` that are identified as profane or highly biased.
- **bias_label (string)**: The category or degree of bias ranging from mild, moderate, to severe.
- **debiased_text (string)**: The debiased version of the `biased_text` generated using GPT-4 and subsequently verified by 5 human annotators.
#### **Data Use and Limitations**
**Usage**:
This dataset can be employed for training models in detecting and correcting bias in text. It can also benchmark bias detection and correction algorithms.
**Limitations**:
- The dataset, though comprehensive, may not encapsulate all types of biases.
- The debiasing process may retain subtle biases or overlook some nuances, despite being vetted by human annotators.
- Labels might contain inherent subjectivities as they are determined based on annotator discretion.
|
newsmediabias/debiased_dataset
|
[
"task_categories:text-classification",
"task_categories:token-classification",
"license:creativeml-openrail-m",
"doi:10.57967/hf/1050",
"region:us"
] |
2023-08-30T11:29:09+00:00
|
{"license": "creativeml-openrail-m", "task_categories": ["text-classification", "token-classification"]}
|
2023-09-08T12:11:55+00:00
|
[] |
[] |
TAGS
#task_categories-text-classification #task_categories-token-classification #license-creativeml-openrail-m #doi-10.57967/hf/1050 #region-us
|
#### Dataset Description
About the Dataset:
This dataset contains text data that has been processed to identify biased statements based on dimensions and aspects. Each entry has been processed using the GPT-4 language model and manually verified by 5 human annotators for quality assurance.
Purpose:
The dataset aims to help train and evaluate machine learning models in detecting, classifying, and correcting biases in text content, making it essential for NLP research related to fairness and debiasing.
Origin:
The dataset has been curated from diverse sources, including online media articles, blogs, and user comments. These sources were chosen for their likelihood of containing varying degrees of bias.
---
#### Composition
Dataset Statistics:
- Total entries: 7500
Data Fields:
- biased_text (string): The original text containing biased content.
- dimension (string): The broader category under which the bias can be classified, such as race, gender, religion, etc.
- aspect (string): The specific perspective or angle of bias present in the text.
- biased_profain_words (string): Words in the 'biased_text' that are identified as profane or highly biased.
- bias_label (string): The category or degree of bias ranging from mild, moderate, to severe.
- debiased_text (string): The debiased version of the 'biased_text' generated using GPT-4 and subsequently verified by 5 human annotators.
#### Data Use and Limitations
Usage:
This dataset can be employed for training models in detecting and correcting bias in text. It can also benchmark bias detection and correction algorithms.
Limitations:
- The dataset, though comprehensive, may not encapsulate all types of biases.
- The debiasing process may retain subtle biases or overlook some nuances, despite being vetted by human annotators.
- Labels might contain inherent subjectivities as they are determined based on annotator discretion.
|
[
"#### Dataset Description\n\nAbout the Dataset:\nThis dataset contains text data that has been processed to identify biased statements based on dimensions and aspects. Each entry has been processed using the GPT-4 language model and manually verified by 5 human annotators for quality assurance.\n\nPurpose:\nThe dataset aims to help train and evaluate machine learning models in detecting, classifying, and correcting biases in text content, making it essential for NLP research related to fairness and debiasing.\n\nOrigin:\nThe dataset has been curated from diverse sources, including online media articles, blogs, and user comments. These sources were chosen for their likelihood of containing varying degrees of bias.\n\n---",
"#### Composition\n\nDataset Statistics:\n- Total entries: 7500\n\n\nData Fields:\n- biased_text (string): The original text containing biased content.\n- dimension (string): The broader category under which the bias can be classified, such as race, gender, religion, etc.\n- aspect (string): The specific perspective or angle of bias present in the text.\n- biased_profain_words (string): Words in the 'biased_text' that are identified as profane or highly biased.\n- bias_label (string): The category or degree of bias ranging from mild, moderate, to severe.\n- debiased_text (string): The debiased version of the 'biased_text' generated using GPT-4 and subsequently verified by 5 human annotators.",
"#### Data Use and Limitations\n\nUsage:\nThis dataset can be employed for training models in detecting and correcting bias in text. It can also benchmark bias detection and correction algorithms.\n\nLimitations:\n- The dataset, though comprehensive, may not encapsulate all types of biases.\n- The debiasing process may retain subtle biases or overlook some nuances, despite being vetted by human annotators.\n- Labels might contain inherent subjectivities as they are determined based on annotator discretion."
] |
[
"TAGS\n#task_categories-text-classification #task_categories-token-classification #license-creativeml-openrail-m #doi-10.57967/hf/1050 #region-us \n",
"#### Dataset Description\n\nAbout the Dataset:\nThis dataset contains text data that has been processed to identify biased statements based on dimensions and aspects. Each entry has been processed using the GPT-4 language model and manually verified by 5 human annotators for quality assurance.\n\nPurpose:\nThe dataset aims to help train and evaluate machine learning models in detecting, classifying, and correcting biases in text content, making it essential for NLP research related to fairness and debiasing.\n\nOrigin:\nThe dataset has been curated from diverse sources, including online media articles, blogs, and user comments. These sources were chosen for their likelihood of containing varying degrees of bias.\n\n---",
"#### Composition\n\nDataset Statistics:\n- Total entries: 7500\n\n\nData Fields:\n- biased_text (string): The original text containing biased content.\n- dimension (string): The broader category under which the bias can be classified, such as race, gender, religion, etc.\n- aspect (string): The specific perspective or angle of bias present in the text.\n- biased_profain_words (string): Words in the 'biased_text' that are identified as profane or highly biased.\n- bias_label (string): The category or degree of bias ranging from mild, moderate, to severe.\n- debiased_text (string): The debiased version of the 'biased_text' generated using GPT-4 and subsequently verified by 5 human annotators.",
"#### Data Use and Limitations\n\nUsage:\nThis dataset can be employed for training models in detecting and correcting bias in text. It can also benchmark bias detection and correction algorithms.\n\nLimitations:\n- The dataset, though comprehensive, may not encapsulate all types of biases.\n- The debiasing process may retain subtle biases or overlook some nuances, despite being vetted by human annotators.\n- Labels might contain inherent subjectivities as they are determined based on annotator discretion."
] |
[
53,
158,
185,
122
] |
[
"passage: TAGS\n#task_categories-text-classification #task_categories-token-classification #license-creativeml-openrail-m #doi-10.57967/hf/1050 #region-us \n#### Dataset Description\n\nAbout the Dataset:\nThis dataset contains text data that has been processed to identify biased statements based on dimensions and aspects. Each entry has been processed using the GPT-4 language model and manually verified by 5 human annotators for quality assurance.\n\nPurpose:\nThe dataset aims to help train and evaluate machine learning models in detecting, classifying, and correcting biases in text content, making it essential for NLP research related to fairness and debiasing.\n\nOrigin:\nThe dataset has been curated from diverse sources, including online media articles, blogs, and user comments. These sources were chosen for their likelihood of containing varying degrees of bias.\n\n---#### Composition\n\nDataset Statistics:\n- Total entries: 7500\n\n\nData Fields:\n- biased_text (string): The original text containing biased content.\n- dimension (string): The broader category under which the bias can be classified, such as race, gender, religion, etc.\n- aspect (string): The specific perspective or angle of bias present in the text.\n- biased_profain_words (string): Words in the 'biased_text' that are identified as profane or highly biased.\n- bias_label (string): The category or degree of bias ranging from mild, moderate, to severe.\n- debiased_text (string): The debiased version of the 'biased_text' generated using GPT-4 and subsequently verified by 5 human annotators."
] |
e95021b560c5fca7a82c30f507b723f784ea69b5
|
# Dataset of Hagimura Suzu
This is the dataset of Hagimura Suzu, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 458 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 458 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 458 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 458 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/hagimura_suzu_seitokaiyakuindomo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-30T11:33:56+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:44+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Hagimura Suzu
========================
This is the dataset of Hagimura Suzu, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
81b7251a5dd0e921160b8e433e43f9f8c881494f
|
polinaeterna/test_audio_viewer
|
[
"region:us"
] |
2023-08-30T11:34:06+00:00
|
{"configs": [{"config_name": "v1", "data_dir": "v1", "drop_labels": true}, {"config_name": "v2", "data_dir": "v2", "drop_labels": false}, {"config_name": "all", "data_files": "**/*"}], "duplicated_from": "polinaeterna/audiofolder_two_configs_in_metadata"}
|
2023-09-04T13:20:18+00:00
|
[] |
[] |
TAGS
#region-us
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
||
43038f15197de96f0dc2a4c0ad5b6a84ed7256a8
|
# Dataset Card for Evaluation run of TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch
- **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 [TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T05:09:10.291904](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-19T05-09-10.291904.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": 0.08263422818791946,
"em_stderr": 0.0028196220932582595,
"f1": 0.18827600671140907,
"f1_stderr": 0.003179593951573151,
"acc": 0.383890237575643,
"acc_stderr": 0.008217068757542224
},
"harness|drop|3": {
"em": 0.08263422818791946,
"em_stderr": 0.0028196220932582595,
"f1": 0.18827600671140907,
"f1_stderr": 0.003179593951573151
},
"harness|gsm8k|5": {
"acc": 0.02350265352539803,
"acc_stderr": 0.0041728836696439744
},
"harness|winogrande|5": {
"acc": 0.744277821625888,
"acc_stderr": 0.012261253845440474
}
}
```
### 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_TFLai__OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch
|
[
"region:us"
] |
2023-08-30T11:43:04+00:00
|
{"pretty_name": "Evaluation run of TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-19T05:09:10.291904](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-19T05-09-10.291904.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\": 0.08263422818791946,\n \"em_stderr\": 0.0028196220932582595,\n \"f1\": 0.18827600671140907,\n \"f1_stderr\": 0.003179593951573151,\n \"acc\": 0.383890237575643,\n \"acc_stderr\": 0.008217068757542224\n },\n \"harness|drop|3\": {\n \"em\": 0.08263422818791946,\n \"em_stderr\": 0.0028196220932582595,\n \"f1\": 0.18827600671140907,\n \"f1_stderr\": 0.003179593951573151\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02350265352539803,\n \"acc_stderr\": 0.0041728836696439744\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.744277821625888,\n \"acc_stderr\": 0.012261253845440474\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch", "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_08_30T12_42_39.618303", "path": ["**/details_harness|arc:challenge|25_2023-08-30T12:42:39.618303.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T12:42:39.618303.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_19T05_09_10.291904", "path": ["**/details_harness|drop|3_2023-10-19T05-09-10.291904.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-19T05-09-10.291904.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_19T05_09_10.291904", "path": ["**/details_harness|gsm8k|5_2023-10-19T05-09-10.291904.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-19T05-09-10.291904.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T12_42_39.618303", "path": ["**/details_harness|hellaswag|10_2023-08-30T12:42:39.618303.parquet"]}, {"split": "latest", "path": 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2023-10-19T04:09:23+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch 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 2023-10-19T05:09:10.291904(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 TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch 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 2023-10-19T05:09:10.291904(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 TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch",
"## 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 TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch 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 2023-10-19T05:09:10.291904(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,
36,
31,
184,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch## 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 TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch 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 2023-10-19T05:09:10.291904(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"
] |
75914383494871451947c4e8248fd6c643c7a3b8
|
# Dataset Card for "harrypotter_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mickume/harry_potter_tiny
|
[
"region:us"
] |
2023-08-30T11:46:08+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1234764, "num_examples": 7481}], "download_size": 747534, "dataset_size": 1234764}}
|
2023-08-30T11:46:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "harrypotter_tiny"
More Information needed
|
[
"# Dataset Card for \"harrypotter_tiny\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"harrypotter_tiny\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"harrypotter_tiny\"\n\nMore Information needed"
] |
853ce8c08b2dba6a28e3b5479e462b7e6323ca7f
|
# Dataset of Amakusa Shino
This is the dataset of Amakusa Shino, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 466 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 466 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 466 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 466 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/amakusa_shino_seitokaiyakuindomo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-30T11:46:21+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:46+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Amakusa Shino
========================
This is the dataset of Amakusa Shino, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
9249f0652a9f6a073b439319f42353ba9db9fd80
|
# Dataset Card for "ML-NLP"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pumaML/ML-NLP
|
[
"region:us"
] |
2023-08-30T11:48:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "num-step", "dtype": "int64"}, {"name": "prevId", "dtype": "int64"}, {"name": "action", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "testid", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 473479.3968887879, "num_examples": 3600}, {"name": "validation", "num_bytes": 52608.82187653199, "num_examples": 400}], "download_size": 0, "dataset_size": 526088.2187653199}}
|
2023-08-30T12:59:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ML-NLP"
More Information needed
|
[
"# Dataset Card for \"ML-NLP\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ML-NLP\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ML-NLP\"\n\nMore Information needed"
] |
08dc44613daa43cf96894c91a4ec2a60e4e3ee6b
|
# Dataset Card for "cpgQA-v1.0-unique-context"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minh21/cpgQA-v1.0-unique-context
|
[
"region:us"
] |
2023-08-30T12:05:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "answer_text", "dtype": "string"}, {"name": "answer_start", "dtype": "int64"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1167197, "num_examples": 871}, {"name": "test", "num_bytes": 268232, "num_examples": 226}], "download_size": 190979, "dataset_size": 1435429}}
|
2023-08-30T12:16:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cpgQA-v1.0-unique-context"
More Information needed
|
[
"# Dataset Card for \"cpgQA-v1.0-unique-context\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cpgQA-v1.0-unique-context\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cpgQA-v1.0-unique-context\"\n\nMore Information needed"
] |
c962ba63cfd40a8ae7eeedd77964cb15cc017137
|
# HumanEval_ru Dataset
## Dataset Summary
This is a version of Code Geneneration [HumanEval dataset](https://huggingface.co/datasets/openai_humaneval) translated to Russian.
## Supported tasks
The task is to generate body of the function based on the function signature and docstring. The programming problems are written in Python and contain Russian natural text in comments and docstrings.
## Task example
```python
from typing import List
def string_xor(a: str, b: str) -> str:
"""
Входными данными являются две строки a и b, состоящие только из 1 и 0.
Выполните двоичное XOR для этих входных данных и верните результат также в виде строки.
>>> string_xor('010', '110')
'100'
"""
# Your code here
```
## Dataset structure
Please refer to the structure of the [original HumanEval dataset](https://huggingface.co/datasets/openai_humaneval)
## Translation
Textual descriptions of tasks were translated automatically via Yandex.Translate API and then manually edited. Feel free to report errors in translations.
# Usage
## Load dataset
```python
from datasets import load_dataset
load_dataset('NLPCoreTeam/humaneval_ru')
DatasetDict({
train: Dataset({
features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point', 'signature', 'docstring', 'context', 'instruction', 'instruction_noexamples'],
num_rows: 164
})
})
```
## How to evaluate your models
To evaluate code generation capabilities of your models on HumanEval_ru please follow these steps (example is for [Codellama-7b-Python](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)):
1. Clone https://github.com/NLP-Core-Team/bigcode-evaluation-harness
2. Run evaluation (WARNING: generated code is executed, it may be unsafe) with the following command
```console
# mkdir -p ./outs/humaneval_ru
# mkdir -p ./results/humaneval_ru
accelerate launch main.py \
--model codellama/CodeLlama-7b-Python-hf \
--max_length_generation 512 \
--tasks humaneval_ru \
--use_auth_token \
--temperature 0.2 \
--n_samples 20 \
--precision fp16 \
--batch_size 1 \
--allow_code_execution \
--save_generations_path ./outs/humaneval_ru/codellama-7b-py.json \
--metric_output_path ./results/humaneval_ru/codellama-7b-py.metrics
```
4. Resulting metrics of Codellama-7b-Python should be
```python
"humaneval_ru": {
"pass@1": 0.35,
"pass@10": 0.5122803695209872
},
```
# Benchmark
[Starcoder](https://huggingface.co/bigcode/starcoder) and [Codellama](https://huggingface.co/codellama/CodeLlama-7b-hf) models evaluations on HumanEval_Ru and HumanEval are presented in the table below. For further information on Pass@1 and Pass@10 please refer to [original paper](https://arxiv.org/abs/2107.03374).
| model | RU Pass@1 | RU Pass@10 | EN Pass@1 | EN Pass@10 |
|:------------------------|--------------------------:|---------------------------:|--------------------------:|---------------------------:|
| starcoderbase-1b | 0.1420 | 0.1801 | 0.1509 | 0.2045 |
| starcoderbase-3b | 0.1924 | 0.2606 | 0.2137 | 0.3289 |
| starcoderbase-7b | 0.2515 | 0.3359 | 0.2868 | 0.3852 |
| starcoderbase-15b | 0.2676 | 0.3872 | 0.3036 | 0.4611 |
| starcoder-15b-Python | 0.3103 | 0.4132 | 0.3353 | 0.4931 |
| CodeLlama-7b-hf | 0.2673 | 0.3688 | 0.2975 | 0.4351 |
| CodeLlama-7b-Python-hf | 0.3500 | 0.5122 | 0.3960 | 0.5761 |
| CodeLlama-13b-hf | 0.3380 | 0.4884 | 0.3557 | 0.5489 |
| CodeLlama-13b-Python-hf | 0.4380 | 0.5796 | 0.4301 | 0.6226 |
<details>
<summary> Script to reproduce the results in the table </summary>
```console
#!/bin/bash
# use with https://github.com/NLP-Core-Team/bigcode-evaluation-harness
# RU
mkdir -p ./outs/humaneval_ru
mkdir -p ./results/humaneval_ru
MODELS_PATH="bigcode"
echo $MODELS_PATH
declare -A bs=( ["starcoderbase-1b"]=16 ["starcoderbase-3b"]=8 ["starcoderbase-7b"]=4 ["starcoderbase"]=1 ["starcoder"]=1)
for model_name in starcoderbase-1b starcoderbase-3b starcoderbase-7b starcoderbase starcoder
do
echo $MODELS_PATH/$model_name
accelerate launch --mixed_precision="fp16" main.py \
--model $MODELS_PATH/$model_name \
--max_length_generation 512 \
--tasks humaneval_ru \
--use_auth_token \
--temperature 0.2 \
--n_samples 20 \
--precision fp16 \
--batch_size ${bs[$model_name]} \
--allow_code_execution \
--save_generations_path ./outs/humaneval_ru/$model_name.json \
--metric_output_path ./results/humaneval_ru/$model_name.metrics
done
MODELS_PATH="codellama"
echo $MODELS_PATH
declare -A bs=( ["CodeLlama-7b-Python-hf"]=8 ["CodeLlama-7b-hf"]=16 ["CodeLlama-13b-Python-hf"]=4 ["CodeLlama-13b-hf"]=4 )
for model_name in CodeLlama-7b-hf CodeLlama-7b-Python-hf CodeLlama-13b-hf CodeLlama-13b-Python-hf
do
echo $MODELS_PATH/$model_name
accelerate launch --mixed_precision="fp16" main.py \
--model $MODELS_PATH/$model_name \
--max_length_generation 512 \
--tasks humaneval_ru \
--use_auth_token \
--temperature 0.2 \
--n_samples 20 \
--precision fp16 \
--batch_size ${bs[$model_name]} \
--allow_code_execution \
--save_generations_path ./outs/humaneval_ru/$model_name.json \
--metric_output_path ./results/humaneval_ru/$model_name.metrics
done
# EN
mkdir -p ./outs/humaneval
mkdir -p ./results/humaneval
MODELS_PATH="bigcode"
echo $MODELS_PATH
declare -A bs=( ["starcoderbase-1b"]=16 ["starcoderbase-3b"]=8 ["starcoderbase-7b"]=4 ["starcoderbase"]=1 ["starcoder"]=1)
for model_name in starcoderbase-1b starcoderbase-3b starcoderbase-7b starcoderbase starcoder
do
echo $MODELS_PATH/$model_name
accelerate launch --mixed_precision="fp16" main.py \
--model $MODELS_PATH/$model_name \
--max_length_generation 512 \
--tasks humaneval \
--use_auth_token \
--temperature 0.2 \
--n_samples 20 \
--precision fp16 \
--batch_size ${bs[$model_name]} \
--allow_code_execution \
--save_generations_path ./outs/humaneval/$model_name.json \
--metric_output_path ./results/humaneval/$model_name.metrics
done
MODELS_PATH="codellama"
echo $MODELS_PATH
declare -A bs=( ["CodeLlama-7b-Python-hf"]=8 ["CodeLlama-7b-hf"]=16 ["CodeLlama-13b-Python-hf"]=4 ["CodeLlama-13b-hf"]=4 )
for model_name in CodeLlama-7b-hf CodeLlama-7b-Python-hf CodeLlama-13b-hf CodeLlama-13b-Python-hf
do
echo $MODELS_PATH/$model_name
accelerate launch --mixed_precision="fp16" main.py \
--model $MODELS_PATH/$model_name \
--max_length_generation 512 \
--tasks humaneval \
--use_auth_token \
--temperature 0.2 \
--n_samples 20 \
--precision fp16 \
--batch_size ${bs[$model_name]} \
--allow_code_execution \
--save_generations_path ./outs/humaneval/$model_name.json \
--metric_output_path ./results/humaneval/$model_name.metrics
done
```
</details>
|
NLPCoreTeam/humaneval_ru
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:ru",
"language:en",
"license:mit",
"code",
"arxiv:2107.03374",
"region:us"
] |
2023-08-30T12:06:37+00:00
|
{"language": ["ru", "en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "tags": ["code"]}
|
2023-10-23T11:07:50+00:00
|
[
"2107.03374"
] |
[
"ru",
"en"
] |
TAGS
#task_categories-text-generation #size_categories-n<1K #language-Russian #language-English #license-mit #code #arxiv-2107.03374 #region-us
|
HumanEval\_ru Dataset
=====================
Dataset Summary
---------------
This is a version of Code Geneneration HumanEval dataset translated to Russian.
Supported tasks
---------------
The task is to generate body of the function based on the function signature and docstring. The programming problems are written in Python and contain Russian natural text in comments and docstrings.
Task example
------------
Dataset structure
-----------------
Please refer to the structure of the original HumanEval dataset
Translation
-----------
Textual descriptions of tasks were translated automatically via Yandex.Translate API and then manually edited. Feel free to report errors in translations.
Usage
=====
Load dataset
------------
How to evaluate your models
---------------------------
To evaluate code generation capabilities of your models on HumanEval\_ru please follow these steps (example is for Codellama-7b-Python):
1. Clone URL
2. Run evaluation (WARNING: generated code is executed, it may be unsafe) with the following command
3. Resulting metrics of Codellama-7b-Python should be
Benchmark
=========
Starcoder and Codellama models evaluations on HumanEval\_Ru and HumanEval are presented in the table below. For further information on Pass@1 and Pass@10 please refer to original paper.
Script to reproduce the results in the table
|
[] |
[
"TAGS\n#task_categories-text-generation #size_categories-n<1K #language-Russian #language-English #license-mit #code #arxiv-2107.03374 #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-Russian #language-English #license-mit #code #arxiv-2107.03374 #region-us \n"
] |
d004d46302e7c34b710451f625683dceee39a055
|
# Botox Injections (Before & After)
The dataset consists of photos featuring the same individuals captured before and after botox injections procedure. The dataset contains a diverse range of individuals with various *ages, ethnicities and genders*.
The dataset is useful for evaluation of the effectiveness of botox injections for different skin and face types, face recognition and reidentification tasks. It can be utilised for biometric tasks , in beauty sphere, for medical purposes and e-commerce.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=botox-injections-before-and-after) to discuss your requirements, learn about the price and buy the dataset.
# Content
- **before**: includes images of people before botox injections
- **after**: includes images of people after botox injections. People are the same as in the previous folder, photos are identified by the same name
- **.csv file**: contains information about the dataset
### File with the extension .csv
includes the following information for each set of media files:
- **person**: id of the person,
- **before**: link to the photo before the injection,
- **after**: link to the photo after the injection
# Images of people with botox injections might be collected in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=botox-injections-before-and-after) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
TrainingDataPro/botox-injections-before-and-after
|
[
"task_categories:image-classification",
"task_categories:image-to-image",
"language:en",
"license:cc-by-nc-nd-4.0",
"medical",
"code",
"region:us"
] |
2023-08-30T12:08:16+00:00
|
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-classification", "image-to-image"], "tags": ["medical", "code"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "before", "dtype": "image"}, {"name": "after", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 38806781, "num_examples": 23}], "download_size": 38824211, "dataset_size": 38806781}}
|
2023-09-25T13:21:13+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-classification #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #medical #code #region-us
|
# Botox Injections (Before & After)
The dataset consists of photos featuring the same individuals captured before and after botox injections procedure. The dataset contains a diverse range of individuals with various *ages, ethnicities and genders*.
The dataset is useful for evaluation of the effectiveness of botox injections for different skin and face types, face recognition and reidentification tasks. It can be utilised for biometric tasks , in beauty sphere, for medical purposes and e-commerce.
![](URL
# Get the dataset
### This is just an example of the data
Leave a request on URL to discuss your requirements, learn about the price and buy the dataset.
# Content
- before: includes images of people before botox injections
- after: includes images of people after botox injections. People are the same as in the previous folder, photos are identified by the same name
- .csv file: contains information about the dataset
### File with the extension .csv
includes the following information for each set of media files:
- person: id of the person,
- before: link to the photo before the injection,
- after: link to the photo after the injection
# Images of people with botox injections might be collected in accordance with your requirements.
## TrainingData provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: URL
TrainingData's GitHub: URL
|
[
"# Botox Injections (Before & After)\nThe dataset consists of photos featuring the same individuals captured before and after botox injections procedure. The dataset contains a diverse range of individuals with various *ages, ethnicities and genders*. \n\nThe dataset is useful for evaluation of the effectiveness of botox injections for different skin and face types, face recognition and reidentification tasks. It can be utilised for biometric tasks , in beauty sphere, for medical purposes and e-commerce.\n\n![](URL",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Content\n- before: includes images of people before botox injections\n- after: includes images of people after botox injections. People are the same as in the previous folder, photos are identified by the same name\n- .csv file: contains information about the dataset",
"### File with the extension .csv\nincludes the following information for each set of media files:\n- person: id of the person,\n- before: link to the photo before the injection,\n- after: link to the photo after the injection",
"# Images of people with botox injections might be collected in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
[
"TAGS\n#task_categories-image-classification #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #medical #code #region-us \n",
"# Botox Injections (Before & After)\nThe dataset consists of photos featuring the same individuals captured before and after botox injections procedure. The dataset contains a diverse range of individuals with various *ages, ethnicities and genders*. \n\nThe dataset is useful for evaluation of the effectiveness of botox injections for different skin and face types, face recognition and reidentification tasks. It can be utilised for biometric tasks , in beauty sphere, for medical purposes and e-commerce.\n\n![](URL",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Content\n- before: includes images of people before botox injections\n- after: includes images of people after botox injections. People are the same as in the previous folder, photos are identified by the same name\n- .csv file: contains information about the dataset",
"### File with the extension .csv\nincludes the following information for each set of media files:\n- person: id of the person,\n- before: link to the photo before the injection,\n- after: link to the photo after the injection",
"# Images of people with botox injections might be collected in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
[
51,
119,
5,
30,
58,
52,
19,
39
] |
[
"passage: TAGS\n#task_categories-image-classification #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #medical #code #region-us \n# Botox Injections (Before & After)\nThe dataset consists of photos featuring the same individuals captured before and after botox injections procedure. The dataset contains a diverse range of individuals with various *ages, ethnicities and genders*. \n\nThe dataset is useful for evaluation of the effectiveness of botox injections for different skin and face types, face recognition and reidentification tasks. It can be utilised for biometric tasks , in beauty sphere, for medical purposes and e-commerce.\n\n![](URL# Get the dataset### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.# Content\n- before: includes images of people before botox injections\n- after: includes images of people after botox injections. People are the same as in the previous folder, photos are identified by the same name\n- .csv file: contains information about the dataset### File with the extension .csv\nincludes the following information for each set of media files:\n- person: id of the person,\n- before: link to the photo before the injection,\n- after: link to the photo after the injection# Images of people with botox injections might be collected in accordance with your requirements.## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
e05aaf7a3d3226804381729d4f1ff5e8aa33fa11
|
# Dataset Card for "datascience-stackexchange-posts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
theblackcat102/datascience-stackexchange-posts
|
[
"region:us"
] |
2023-08-30T12:08:32+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Id", "dtype": "string"}, {"name": "PostTypeId", "dtype": "string"}, {"name": "AcceptedAnswerId", "dtype": "string"}, {"name": "ParentId", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "ViewCount", "dtype": "string"}, {"name": "Body", "dtype": "string"}, {"name": "Title", "dtype": "string"}, {"name": "ContentLicense", "dtype": "string"}, {"name": "FavoriteCount", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "LastActivityDate", "dtype": "string"}, {"name": "LastEditDate", "dtype": "string"}, {"name": "LastEditorUserId", "dtype": "string"}, {"name": "OwnerUserId", "dtype": "string"}, {"name": "Tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 91145469, "num_examples": 76788}], "download_size": 48788218, "dataset_size": 91145469}}
|
2023-08-30T12:08:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "datascience-stackexchange-posts"
More Information needed
|
[
"# Dataset Card for \"datascience-stackexchange-posts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"datascience-stackexchange-posts\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"datascience-stackexchange-posts\"\n\nMore Information needed"
] |
f40afbdc376ce791649adf0bc93173053a3f67c8
|
# Dataset Card for PypayaNumbers
## Dataset Description
### Dataset Summary
This dataset consists of images of numbers along with their bounding box coordinates and labels. The dataset is divided into train and test sets, with each set containing images, numbers, and bounding boxes. The numbers are represented as one-line text files, while the bounding boxes are in YOLO format.
### Supported Tasks and Leaderboards
This dataset supports the task of Optical Character Recognition (OCR) and object detection. Specifically, it can be used for tasks like digit recognition in images.
### Languages
The dataset does not contain any natural language data.
## Dataset Structure
### Data Instances
Each instance in the dataset comprises an image file, a corresponding text file with the number represented in the image, and a text file with the bounding box coordinates for each digit in YOLO format.
### Data Fields
- `image`: A file path to an image containing a number.
- `number`: A file path to a text file containing the number represented in the image.
- `bounding_boxes`: A file path to a text file containing the bounding box coordinates for each digit in the image.
### Data Splits
The dataset is split into a training set of 5000 instances and a testing set of 2500 instances.
## Dataset Creation
### Curation Rationale
This dataset was curated to support the development and evaluation of models for digit recognition in images.
### Source Data
#### Initial Data Collection and Normalization
The images and labels in this dataset were generated by taking screenshots from various computer games and programs and cutting fragments containing numbers.
#### Who are the source language producers?
N/A
### Annotations
#### Annotation process
The bounding box annotations were generated using open-source LabelImg software.
#### Who are the annotators?
PypayaTech
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset could help in improving models for digit recognition in images, which has numerous applications including automated data entry, number plate recognition, and form digitization.
### Discussion of Biases
As the dataset consists of synthetic images of numbers, no inherent biases related to human demographics or behavior are expected.
### Other Known Limitations
The dataset only contains images of numbers and might not generalize well to other types of characters or more complex images.
## Additional Information
### Dataset Curators
PypayaTech
### Licensing Information
This dataset is licensed under the MIT license.
### Contributions
Contributions to the dataset are welcome. Please contact the dataset curator for more information.
|
PypayaTech/PypayaNumbers
|
[
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"license:mit",
"ocr",
"numbers",
"computervision",
"region:us"
] |
2023-08-30T12:12:06+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["feature-extraction"], "tags": ["ocr", "numbers", "computervision"]}
|
2023-08-30T14:53:52+00:00
|
[] |
[] |
TAGS
#task_categories-feature-extraction #size_categories-10K<n<100K #license-mit #ocr #numbers #computervision #region-us
|
# Dataset Card for PypayaNumbers
## Dataset Description
### Dataset Summary
This dataset consists of images of numbers along with their bounding box coordinates and labels. The dataset is divided into train and test sets, with each set containing images, numbers, and bounding boxes. The numbers are represented as one-line text files, while the bounding boxes are in YOLO format.
### Supported Tasks and Leaderboards
This dataset supports the task of Optical Character Recognition (OCR) and object detection. Specifically, it can be used for tasks like digit recognition in images.
### Languages
The dataset does not contain any natural language data.
## Dataset Structure
### Data Instances
Each instance in the dataset comprises an image file, a corresponding text file with the number represented in the image, and a text file with the bounding box coordinates for each digit in YOLO format.
### Data Fields
- 'image': A file path to an image containing a number.
- 'number': A file path to a text file containing the number represented in the image.
- 'bounding_boxes': A file path to a text file containing the bounding box coordinates for each digit in the image.
### Data Splits
The dataset is split into a training set of 5000 instances and a testing set of 2500 instances.
## Dataset Creation
### Curation Rationale
This dataset was curated to support the development and evaluation of models for digit recognition in images.
### Source Data
#### Initial Data Collection and Normalization
The images and labels in this dataset were generated by taking screenshots from various computer games and programs and cutting fragments containing numbers.
#### Who are the source language producers?
N/A
### Annotations
#### Annotation process
The bounding box annotations were generated using open-source LabelImg software.
#### Who are the annotators?
PypayaTech
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset could help in improving models for digit recognition in images, which has numerous applications including automated data entry, number plate recognition, and form digitization.
### Discussion of Biases
As the dataset consists of synthetic images of numbers, no inherent biases related to human demographics or behavior are expected.
### Other Known Limitations
The dataset only contains images of numbers and might not generalize well to other types of characters or more complex images.
## Additional Information
### Dataset Curators
PypayaTech
### Licensing Information
This dataset is licensed under the MIT license.
### Contributions
Contributions to the dataset are welcome. Please contact the dataset curator for more information.
|
[
"# Dataset Card for PypayaNumbers",
"## Dataset Description",
"### Dataset Summary\n\nThis dataset consists of images of numbers along with their bounding box coordinates and labels. The dataset is divided into train and test sets, with each set containing images, numbers, and bounding boxes. The numbers are represented as one-line text files, while the bounding boxes are in YOLO format.",
"### Supported Tasks and Leaderboards\n\nThis dataset supports the task of Optical Character Recognition (OCR) and object detection. Specifically, it can be used for tasks like digit recognition in images.",
"### Languages\n\nThe dataset does not contain any natural language data.",
"## Dataset Structure",
"### Data Instances\n\nEach instance in the dataset comprises an image file, a corresponding text file with the number represented in the image, and a text file with the bounding box coordinates for each digit in YOLO format.",
"### Data Fields\n\n- 'image': A file path to an image containing a number.\n- 'number': A file path to a text file containing the number represented in the image.\n- 'bounding_boxes': A file path to a text file containing the bounding box coordinates for each digit in the image.",
"### Data Splits\n\nThe dataset is split into a training set of 5000 instances and a testing set of 2500 instances.",
"## Dataset Creation",
"### Curation Rationale\n\nThis dataset was curated to support the development and evaluation of models for digit recognition in images.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nThe images and labels in this dataset were generated by taking screenshots from various computer games and programs and cutting fragments containing numbers.",
"#### Who are the source language producers?\n\nN/A",
"### Annotations",
"#### Annotation process\n\nThe bounding box annotations were generated using open-source LabelImg software.",
"#### Who are the annotators?\n\nPypayaTech",
"### Personal and Sensitive Information\n\nThe dataset does not contain any personal or sensitive information.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset could help in improving models for digit recognition in images, which has numerous applications including automated data entry, number plate recognition, and form digitization.",
"### Discussion of Biases\n\nAs the dataset consists of synthetic images of numbers, no inherent biases related to human demographics or behavior are expected.",
"### Other Known Limitations\n\nThe dataset only contains images of numbers and might not generalize well to other types of characters or more complex images.",
"## Additional Information",
"### Dataset Curators\n\nPypayaTech",
"### Licensing Information\n\nThis dataset is licensed under the MIT license.",
"### Contributions\n\nContributions to the dataset are welcome. Please contact the dataset curator for more information."
] |
[
"TAGS\n#task_categories-feature-extraction #size_categories-10K<n<100K #license-mit #ocr #numbers #computervision #region-us \n",
"# Dataset Card for PypayaNumbers",
"## Dataset Description",
"### Dataset Summary\n\nThis dataset consists of images of numbers along with their bounding box coordinates and labels. The dataset is divided into train and test sets, with each set containing images, numbers, and bounding boxes. The numbers are represented as one-line text files, while the bounding boxes are in YOLO format.",
"### Supported Tasks and Leaderboards\n\nThis dataset supports the task of Optical Character Recognition (OCR) and object detection. Specifically, it can be used for tasks like digit recognition in images.",
"### Languages\n\nThe dataset does not contain any natural language data.",
"## Dataset Structure",
"### Data Instances\n\nEach instance in the dataset comprises an image file, a corresponding text file with the number represented in the image, and a text file with the bounding box coordinates for each digit in YOLO format.",
"### Data Fields\n\n- 'image': A file path to an image containing a number.\n- 'number': A file path to a text file containing the number represented in the image.\n- 'bounding_boxes': A file path to a text file containing the bounding box coordinates for each digit in the image.",
"### Data Splits\n\nThe dataset is split into a training set of 5000 instances and a testing set of 2500 instances.",
"## Dataset Creation",
"### Curation Rationale\n\nThis dataset was curated to support the development and evaluation of models for digit recognition in images.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nThe images and labels in this dataset were generated by taking screenshots from various computer games and programs and cutting fragments containing numbers.",
"#### Who are the source language producers?\n\nN/A",
"### Annotations",
"#### Annotation process\n\nThe bounding box annotations were generated using open-source LabelImg software.",
"#### Who are the annotators?\n\nPypayaTech",
"### Personal and Sensitive Information\n\nThe dataset does not contain any personal or sensitive information.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset could help in improving models for digit recognition in images, which has numerous applications including automated data entry, number plate recognition, and form digitization.",
"### Discussion of Biases\n\nAs the dataset consists of synthetic images of numbers, no inherent biases related to human demographics or behavior are expected.",
"### Other Known Limitations\n\nThe dataset only contains images of numbers and might not generalize well to other types of characters or more complex images.",
"## Additional Information",
"### Dataset Curators\n\nPypayaTech",
"### Licensing Information\n\nThis dataset is licensed under the MIT license.",
"### Contributions\n\nContributions to the dataset are welcome. Please contact the dataset curator for more information."
] |
[
45,
11,
4,
79,
51,
15,
6,
51,
75,
27,
5,
27,
4,
41,
13,
5,
24,
13,
20,
8,
40,
38,
32,
5,
10,
17,
26
] |
[
"passage: TAGS\n#task_categories-feature-extraction #size_categories-10K<n<100K #license-mit #ocr #numbers #computervision #region-us \n# Dataset Card for PypayaNumbers## Dataset Description### Dataset Summary\n\nThis dataset consists of images of numbers along with their bounding box coordinates and labels. The dataset is divided into train and test sets, with each set containing images, numbers, and bounding boxes. The numbers are represented as one-line text files, while the bounding boxes are in YOLO format.### Supported Tasks and Leaderboards\n\nThis dataset supports the task of Optical Character Recognition (OCR) and object detection. Specifically, it can be used for tasks like digit recognition in images.### Languages\n\nThe dataset does not contain any natural language data.## Dataset Structure### Data Instances\n\nEach instance in the dataset comprises an image file, a corresponding text file with the number represented in the image, and a text file with the bounding box coordinates for each digit in YOLO format.### Data Fields\n\n- 'image': A file path to an image containing a number.\n- 'number': A file path to a text file containing the number represented in the image.\n- 'bounding_boxes': A file path to a text file containing the bounding box coordinates for each digit in the image.### Data Splits\n\nThe dataset is split into a training set of 5000 instances and a testing set of 2500 instances.## Dataset Creation### Curation Rationale\n\nThis dataset was curated to support the development and evaluation of models for digit recognition in images.### Source Data#### Initial Data Collection and Normalization\n\nThe images and labels in this dataset were generated by taking screenshots from various computer games and programs and cutting fragments containing numbers.#### Who are the source language producers?\n\nN/A### Annotations#### Annotation process\n\nThe bounding box annotations were generated using open-source LabelImg software.#### Who are the annotators?\n\nPypayaTech"
] |
3bdd9897267ecf5a5d1ca72776ddd7b5db10dac3
|
# Deepmind Dataset
## Overview
The Deepmind dataset is a curated collection of high-quality datasets meticulously selected to suit a wide range of research and application needs. These datasets have been chosen for their relevance, diversity, and overall data quality. The Deepmind dataset is provided in the Stanford Alpaca format, ensuring consistency and ease of use across various projects and applications.
# License
The Deepmind dataset is made available under the Apache License 2.0, which allows for flexible usage, modification, and distribution while maintaining attribution to the original data sources.
# Dataset Details
Here's a snapshot of some of the top datasets included in the Deepmind collection:
| Dataset | Files | Size |
|---------------|-------|---------|
| Open_Orca | 2 | 6.9 GB |
| Dolphin | 2 | 5.7 GB |
| FLAN | 78 | 15.1 GB |
| t0 | 124 | 25.9 GB |
| **Total** | 206 | 53.6 GB |
The datasets in the Deepmind collection span a diverse array of domains and are carefully selected for their high quality. These datasets cater to various research and application needs, each offering unique insights and applications.
# Citation
```
Deepmind Dataset.
2023
Deepmind
Retrieved from huggingface.co/datasets/NewstaR/Deepmind
Apache 2.0 License
```
|
KaleidoSG/Deepmind
|
[
"size_categories:1M<n<10M",
"language:en",
"license:other",
"region:us"
] |
2023-08-30T12:15:05+00:00
|
{"language": ["en"], "license": "other", "size_categories": ["1M<n<10M"], "pretty_name": "Deepmind", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": ["train/OpenOrca/*.csv", "train/dolphin/*.csv", "train/flan_zsnoopt_data/*.csv", "train/t0_zsnoopt_data/*.csv"]}]}]}
|
2023-09-23T13:42:21+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-1M<n<10M #language-English #license-other #region-us
|
Deepmind Dataset
================
Overview
--------
The Deepmind dataset is a curated collection of high-quality datasets meticulously selected to suit a wide range of research and application needs. These datasets have been chosen for their relevance, diversity, and overall data quality. The Deepmind dataset is provided in the Stanford Alpaca format, ensuring consistency and ease of use across various projects and applications.
License
=======
The Deepmind dataset is made available under the Apache License 2.0, which allows for flexible usage, modification, and distribution while maintaining attribution to the original data sources.
Dataset Details
===============
Here's a snapshot of some of the top datasets included in the Deepmind collection:
Dataset: Open\_Orca, Files: 2, Size: 6.9 GB
Dataset: Dolphin, Files: 2, Size: 5.7 GB
Dataset: FLAN, Files: 78, Size: 15.1 GB
Dataset: t0, Files: 124, Size: 25.9 GB
Dataset: Total, Files: 206, Size: 53.6 GB
The datasets in the Deepmind collection span a diverse array of domains and are carefully selected for their high quality. These datasets cater to various research and application needs, each offering unique insights and applications.
|
[] |
[
"TAGS\n#size_categories-1M<n<10M #language-English #license-other #region-us \n"
] |
[
27
] |
[
"passage: TAGS\n#size_categories-1M<n<10M #language-English #license-other #region-us \n"
] |
dd942db0f9175852e2e877e7f81da7fdd2766e38
|
# Dataset Card for "cpgQA-v1.0-unique-context-test-10-percent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minh21/cpgQA-v1.0-unique-context-test-10-percent
|
[
"region:us"
] |
2023-08-30T12:15:06+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "answer_text", "dtype": "string"}, {"name": "answer_start", "dtype": "int64"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1292366, "num_examples": 988}, {"name": "test", "num_bytes": 143063, "num_examples": 109}], "download_size": 188281, "dataset_size": 1435429}}
|
2023-09-08T12:58:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cpgQA-v1.0-unique-context-test-10-percent"
More Information needed
|
[
"# Dataset Card for \"cpgQA-v1.0-unique-context-test-10-percent\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cpgQA-v1.0-unique-context-test-10-percent\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cpgQA-v1.0-unique-context-test-10-percent\"\n\nMore Information needed"
] |
41f35941203d85c9420b04d1868976ef2949c5a2
|
# Dataset Card for "hochiminh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bellagio-ai/hochiminh
|
[
"region:us"
] |
2023-08-30T12:51:55+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3478315.0, "num_examples": 13}], "download_size": 3476006, "dataset_size": 3478315.0}}
|
2023-08-30T12:52:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "hochiminh"
More Information needed
|
[
"# Dataset Card for \"hochiminh\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"hochiminh\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"hochiminh\"\n\nMore Information needed"
] |
6412ec3b0375781f916bde356a6d2f9d2d784d86
|
# 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)
|
LisaDuj/guanaco-llama2-1k
|
[
"region:us"
] |
2023-08-30T13:05:37+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-*"}]}]}
|
2023-08-30T13:05:38+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"
] |
f8c963cb76a1d6d0049b2d1ca98df29e71432bf7
|
# Dataset Card for "orca_mini_uncensored_squad_format_train"
## Dataset Description
Mostly purely an exercise in data extraction and formatting for dataset usage, and cross-model usage of data.
Uncensored data, because when everything is sanitized for alignment, the data may be "pure" but is no longer untimately realistic.
Part of an effort to create more question-answering friendly datasets that can be used for specialized domain training on small models.
### Dataset Summary
This is a "squad reformat" of an existing dataset located here: https://huggingface.co/datasets/julep-ai/orca_mini_uncensored
This could be swapped for squad format datasets for typical question-answering tasks with uncensored data from a partial pull of the mini-orca dataset here: psmathur/orca_minis_uncensored_dataset
### Supported Tasks and Leaderboards
- 'question-answering'
### Languages
The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
## Dataset Structure
Train and Test splits included
### Data Format
As in the squadv2 dataset, columns are: "id", "title", "context", "question", "answers": "text", "answer_start"
|
jtatman/orca_mini_uncensored_squad_format_train
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] |
2023-08-30T13:07:49+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "orca_mini_squad", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 118261864.35315199, "num_examples": 67300}, {"name": "test", "num_bytes": 13140597.646848004, "num_examples": 7478}], "download_size": 65276229, "dataset_size": 131402462.0}}
|
2023-09-05T01:25:49+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-mit #region-us
|
# Dataset Card for "orca_mini_uncensored_squad_format_train"
## Dataset Description
Mostly purely an exercise in data extraction and formatting for dataset usage, and cross-model usage of data.
Uncensored data, because when everything is sanitized for alignment, the data may be "pure" but is no longer untimately realistic.
Part of an effort to create more question-answering friendly datasets that can be used for specialized domain training on small models.
### Dataset Summary
This is a "squad reformat" of an existing dataset located here: URL
This could be swapped for squad format datasets for typical question-answering tasks with uncensored data from a partial pull of the mini-orca dataset here: psmathur/orca_minis_uncensored_dataset
### Supported Tasks and Leaderboards
- 'question-answering'
### Languages
The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
## Dataset Structure
Train and Test splits included
### Data Format
As in the squadv2 dataset, columns are: "id", "title", "context", "question", "answers": "text", "answer_start"
|
[
"# Dataset Card for \"orca_mini_uncensored_squad_format_train\"",
"## Dataset Description\nMostly purely an exercise in data extraction and formatting for dataset usage, and cross-model usage of data.\n\nUncensored data, because when everything is sanitized for alignment, the data may be \"pure\" but is no longer untimately realistic.\n\nPart of an effort to create more question-answering friendly datasets that can be used for specialized domain training on small models.",
"### Dataset Summary\nThis is a \"squad reformat\" of an existing dataset located here: URL\n\nThis could be swapped for squad format datasets for typical question-answering tasks with uncensored data from a partial pull of the mini-orca dataset here: psmathur/orca_minis_uncensored_dataset",
"### Supported Tasks and Leaderboards\n- 'question-answering'",
"### Languages\nThe BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.",
"## Dataset Structure\nTrain and Test splits included",
"### Data Format\nAs in the squadv2 dataset, columns are: \"id\", \"title\", \"context\", \"question\", \"answers\": \"text\", \"answer_start\""
] |
[
"TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-mit #region-us \n",
"# Dataset Card for \"orca_mini_uncensored_squad_format_train\"",
"## Dataset Description\nMostly purely an exercise in data extraction and formatting for dataset usage, and cross-model usage of data.\n\nUncensored data, because when everything is sanitized for alignment, the data may be \"pure\" but is no longer untimately realistic.\n\nPart of an effort to create more question-answering friendly datasets that can be used for specialized domain training on small models.",
"### Dataset Summary\nThis is a \"squad reformat\" of an existing dataset located here: URL\n\nThis could be swapped for squad format datasets for typical question-answering tasks with uncensored data from a partial pull of the mini-orca dataset here: psmathur/orca_minis_uncensored_dataset",
"### Supported Tasks and Leaderboards\n- 'question-answering'",
"### Languages\nThe BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.",
"## Dataset Structure\nTrain and Test splits included",
"### Data Format\nAs in the squadv2 dataset, columns are: \"id\", \"title\", \"context\", \"question\", \"answers\": \"text\", \"answer_start\""
] |
[
39,
23,
92,
79,
18,
62,
12,
47
] |
[
"passage: TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-mit #region-us \n# Dataset Card for \"orca_mini_uncensored_squad_format_train\"## Dataset Description\nMostly purely an exercise in data extraction and formatting for dataset usage, and cross-model usage of data.\n\nUncensored data, because when everything is sanitized for alignment, the data may be \"pure\" but is no longer untimately realistic.\n\nPart of an effort to create more question-answering friendly datasets that can be used for specialized domain training on small models.### Dataset Summary\nThis is a \"squad reformat\" of an existing dataset located here: URL\n\nThis could be swapped for squad format datasets for typical question-answering tasks with uncensored data from a partial pull of the mini-orca dataset here: psmathur/orca_minis_uncensored_dataset### Supported Tasks and Leaderboards\n- 'question-answering'### Languages\nThe BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.## Dataset Structure\nTrain and Test splits included### Data Format\nAs in the squadv2 dataset, columns are: \"id\", \"title\", \"context\", \"question\", \"answers\": \"text\", \"answer_start\""
] |
d18967f365fe3159503ed41486d5089560d92e4d
|
# Databricks Dolly 15k (es) for Llama-2
## Dataset Summary
The Databricks Dolly 15k dataset source is curated in multiple languages by [Argilla](https://huggingface.co/datasets/argilla/databricks-dolly-15k-curated-multilingual).
* Filtered by `lang=es`.
* Formatted according to the Llama-2 pattern: "\<s> [INST] user prompt [/INST] output model \</s>"
## Dataset Structure
The dataset has 3909 rows of tuples (instructions and outputs).
|
dariolopez/Llama-2-databricks-dolly-es
|
[
"size_categories:10K<n<100K",
"language:es",
"region:us"
] |
2023-08-30T13:23:13+00:00
|
{"language": ["es"], "size_categories": ["10K<n<100K"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "es", "num_bytes": 13756271, "num_examples": 15015}], "download_size": 8012116, "dataset_size": 13756271}, "configs": [{"config_name": "default", "data_files": [{"split": "es", "path": "data/es-*"}]}]}
|
2023-11-21T12:05:58+00:00
|
[] |
[
"es"
] |
TAGS
#size_categories-10K<n<100K #language-Spanish #region-us
|
# Databricks Dolly 15k (es) for Llama-2
## Dataset Summary
The Databricks Dolly 15k dataset source is curated in multiple languages by Argilla.
* Filtered by 'lang=es'.
* Formatted according to the Llama-2 pattern: "\<s> [INST] user prompt [/INST] output model \</s>"
## Dataset Structure
The dataset has 3909 rows of tuples (instructions and outputs).
|
[
"# Databricks Dolly 15k (es) for Llama-2",
"## Dataset Summary\n\nThe Databricks Dolly 15k dataset source is curated in multiple languages by Argilla.\n\n* Filtered by 'lang=es'.\n* Formatted according to the Llama-2 pattern: \"\\<s> [INST] user prompt [/INST] output model \\</s>\"",
"## Dataset Structure\n\nThe dataset has 3909 rows of tuples (instructions and outputs)."
] |
[
"TAGS\n#size_categories-10K<n<100K #language-Spanish #region-us \n",
"# Databricks Dolly 15k (es) for Llama-2",
"## Dataset Summary\n\nThe Databricks Dolly 15k dataset source is curated in multiple languages by Argilla.\n\n* Filtered by 'lang=es'.\n* Formatted according to the Llama-2 pattern: \"\\<s> [INST] user prompt [/INST] output model \\</s>\"",
"## Dataset Structure\n\nThe dataset has 3909 rows of tuples (instructions and outputs)."
] |
[
23,
15,
68,
25
] |
[
"passage: TAGS\n#size_categories-10K<n<100K #language-Spanish #region-us \n# Databricks Dolly 15k (es) for Llama-2## Dataset Summary\n\nThe Databricks Dolly 15k dataset source is curated in multiple languages by Argilla.\n\n* Filtered by 'lang=es'.\n* Formatted according to the Llama-2 pattern: \"\\<s> [INST] user prompt [/INST] output model \\</s>\"## Dataset Structure\n\nThe dataset has 3909 rows of tuples (instructions and outputs)."
] |
7bad200bf53d32d859d014bee87c65b691c7957f
|
# Dataset Card for customer_assistant
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/customer_assistant")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("argilla/customer_assistant")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| user-message | User-message | TextField | True | False |
| context | Context | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| question-rating | Rate the relevance of the user question | RatingQuestion | False | N/A | [1, 2, 3, 4, 5] |
| context-rating | Rate the quality and relevancy of context for the assistant | RatingQuestion | False | N/A | [1, 2, 3, 4, 5] |
| response | Write a helpful, harmful, accurate response to the user question | TextQuestion | True | N/A | N/A |
**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"fields": {
"context": "This process ensures the client administrator has full control over their team\u0027s access and can manage their workspace efficiently.Plans The plans for the Argilla Cloud service depend on the volume of records processed, with several tiers available to suit varying needs.Each tier has a corresponding monthly and annual price, with a 10% discount applied to the annual pricing option.The tier selection and associated price will be determined by the client\u0027s selection in the Service Order Form section of the Terms of Service document.Plans are: Starter 1 Million records Base 3 Million records Medium 4 Million records Large 6 million records\n\nSupport Argilla Cloud offers comprehensive support services to address various issues that may arise during the use of our service.Support levels are categorized into four distinct tiers, based on the severity of the issue, and a separate category for feature requests.The support process, response times, and procedures differ for each category.(1) Critical Issues Critical issues are characterized by: Severe impact on the Service, potentially rendering it completely non-functional.Disruption of critical service operations or functions.Obstruction of entire customer workflows.In the case of a critical issue, Argilla will: Assign specialist(s) to correct the issue on an expedited basis.Provide ongoing communication on the status via email and/or phone, according to the customer\u0027s preference.Begin work towards identifying a temporary workaround or fix.(2) Major Issues Major issues involve: Limited functionality of the Service.Service instability with periodic interruptions.Material service interruptions in mission-critical functions.Time-sensitive questions impacting performance or deliverables to end-clients.Upon encountering a major issue, Argilla will: Assign a specialist to begin a resolution.Implement additional, escalated procedures as reasonably determined necessary by Argilla Support Services staff.(3) Minor Issues Minor issues include: Errors causing partial, non-critical functionality loss.The need for clarification on procedures or information in documentation.Errors in service that may impact performance deliverables.(4) Trivial Issues Trivial issues are characterized by: Errors in system development with little to no impact on performance.Feature Requests Feature requests involve: Requesting a product enhancement.For feature requests, Argilla will: Respond regarding the relevance and interest in incorporating the requested feature.In summary, Argilla Cloud\u0027s support services are designed to provide timely and efficient assistance for issues of varying severity, ensuring a smooth and reliable user experience.All plans include Monday to Friday during office hours (8am to 17pm CEST) with additional support upon request.The Support Channels and features of each tier are shown below:\n\nStarter: Slack Community.Severity 1 - Response time \u003c 4 hours.Severity 2 - Response time \u003c 8 hours.Severity 3 - Response time \u003c 48 hours.Severity 4 not specified.Base: Ticketing System, Severity 1 - Response time \u003c 4 hours.Severity 2 - Response time \u003c 8 hours.Severity 3 - Response time \u003c 24 hours.Severity 4 not specified.Medium: Ticketing System and dedicated Slack channel, Severity 1 - Response time \u003c 4 hours.Severity 2 - Response time \u003c 8 hours.Severity 3 - Response time \u003c 24 hours.Severity 4 one week\n\nLarge: Ticketing System and dedicated Slack channel, Severity 1 - Response time \u003c 4 hours.Severity 2 - Response time \u003c 8 hours.Severity 3 - Response time \u003c 24 hours.Severity 4 one week.Data backup and recovery plan Argilla Cloud is committed to ensuring the safety and availability of your data.Our system is designed to run six data backups per day as a standard procedure.These backups capture a snapshot of the system state at the time of the backup, enabling restoration to that point if necessary.Our Recovery Point Objective (RPO) is four hours.This means that in the event of a system failure, the maximum data loss would be up to the last four hours of data input.We achieve this by running regular backups throughout the day, reducing the time window of potential data loss.Our Recovery Time Objective (RTO) is one hour.This is the maximum acceptable length of time that your system could be down following a failure or disruption.It represents our commitment to ensuring that your services are restored as quickly as possible.In the event of a disruption, our team will first evaluate the issue to determine the best course of action.If data recovery is necessary, we will restore from the most recent backup.We will then work to identify and resolve the root cause of the disruption to prevent a recurrence.Finally, we conduct regular test restores to ensure that our backup system is working as intended.These tests verify the integrity of the backup data and the functionality of the restore process.\nThis documents an overview of the Argilla Cloud service - a comprehensive Software as a Service (SaaS) solution for data labeling and curation.The service is specifically designed to meet the needs of businesses seeking a reliable, secure, and user-friendly platform for data management.The key components of our service include advanced security measures, robust data backup and recovery protocols, flexible pricing options, and dedicated customer support.The onboarding process is efficient, enabling clients to start using the service within one business day.The scope of this proposal includes details on the aforementioned aspects, providing a clear understanding of the service offerings and associated processes.Argilla Cloud offers four plans:\n\nStarter: Ideal for teams initiating their journey in scaling data curation and labelling projects.Perfect for environments where production monitoring is not a requirement.Base: Tailored for teams seeking to amplify their data curation, labelling efforts, and model monitoring, with enhanced support from Argilla.Medium: Designed for teams expanding their language model pipelines, requiring robust ML lifecycle management fortified by Argilla\u0027s comprehensive support.Large: Geared towards teams heavily dependent on language model pipelines, human feedback, and applications, requiring complete ML lifecycle management with robust support.Scope of services Argilla Cloud, a fully managed SaaS, encompasses the following functionalities: Unrestricted Users, Datasets, and Workspaces: The service imposes no limits on the number of users, datasets, or workspaces, supporting scalability of operations.Role-Based Access Control: Administrators and annotators have differentiated access rights to ensure structured and secure data management.Custom Subdomain: Clients are provided with a distinct argilla.io subdomain for accessing the platform.Regular Updates and Upgrades: The service includes regular platform patches and upgrades as part of routine maintenance to uphold system integrity and security.Managed Service: Infrastructure maintenance, backend operations, and other technical aspects are managed by Argilla, eliminating the need for client-side management.Security The security framework of the Argilla Cloud service involves a multi-faceted approach: Data Encryption at Rest: Data stored within the system is encrypted, forming a crucial layer of security.This process automatically encrypts data prior to storage, guarding against unauthorized access.Network Security Measures: The infrastructure has been designed to prevent unauthorized intrusion and to ensure consistent service availability.Measures include firewall protections, intrusion detection systems, and scheduled vulnerability scans to detect and address potential threats.Role-Based Access Control: The system implements role-based access control, defining access levels based on user roles.This mechanism controls the extent of access to sensitive information, aligning it with the responsibilities of each role.Security Audits: Regular audits of security systems and protocols are conducted to detect potential vulnerabilities and verify adherence to security standards.Employee Training: All personnel receive regular security training, fostering an understanding of the latest threats and the importance of security best practices.Incident Response Protocol: In the case of a security incident, a pre-defined incident response plan is activated.This plan outlines the procedures for managing different types of security events, and aims to ensure swift mitigation of potential damage.In summary, the security measures in place include data encryption, network security protocols, role-based access control, regular audits, employee training, and a comprehensive incident response plan.These measures contribute to a secure environment for data management.Setup and onboarding The process for setup and onboarding for Argilla Cloud is designed to be efficient and straightforward.The procedure involves a sequence of steps to ensure a smooth transition and optimal use of the service.Step 1: Account Creation The setup process begins with the creation of the client owner account.We require the client to provide the following details: Full name of the administrator Preferred username Administrator\u0027s email address Once these details are received, we send an onboarding email to sign up.Step 2: Platform Orientation Once logged in, the administrator has full access to the Argilla Cloud platform.They can familiarize themselves with the platform interface and various features.If required, a guided tour or tutorial can be provided to walk the administrator through the platform.Step 3: User Management The administrator is then responsible for setting up additional user accounts.They can invite users via email, manage roles (admin, annotator, etc.), and assign access permissions to different workspaces and datasets.Step 4: Workspace and Dataset Configuration The administrator can create and manage multiple workspaces and datasets.They have the option to configure settings as per their team\u0027s requirements, including assigning datasets to specific workspaces and managing access permissions.Step 5: Training and Support Argilla provides open resources and support to aid in the onboarding process.This includes user manuals, tutorials, and access to our support team for any queries or issues that may arise during the setup and onboarding process.By following these steps, new users can be quickly onboarded and begin using the Argilla Cloud service with minimal downtime.",
"user-message": "What is the ticketing system used by Argilla for customer support?"
},
"metadata": {},
"responses": [
{
"status": "submitted",
"user_id": "73d1e0c3-85ba-48bc-9386-519cdd5fd789",
"values": {
"context-rating": {
"value": 2
},
"question-rating": {
"value": 5
},
"response": {
"value": "Thanks for your interest in Argilla Cloud!\n\nThe ticketing system used by Argilla for customer support is provided by well-renowned SaaS service."
}
}
}
],
"suggestions": [
{
"question_id": "d7b6f5e3-6d4a-47c8-ba50-55ff15f8fb51",
"question_name": "response",
"value": "The ticketing system used by Argilla for customer support is not specified in the given context information."
}
]
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"context": "This documents an overview of the Argilla Cloud service - a comprehensive Software as a Service (SaaS) solution for data labeling and curation.The service is specifically designed to meet the needs of businesses seeking a reliable, secure, and user-friendly platform for data management.The key components of our service include advanced security measures, robust data backup and recovery protocols, flexible pricing options, and dedicated customer support.The onboarding process is efficient, enabling clients to start using the service within one business day.The scope of this proposal includes details on the aforementioned aspects, providing a clear understanding of the service offerings and associated processes.Argilla Cloud offers four plans:\n\nStarter: Ideal for teams initiating their journey in scaling data curation and labelling projects.Perfect for environments where production monitoring is not a requirement.Base: Tailored for teams seeking to amplify their data curation, labelling efforts, and model monitoring, with enhanced support from Argilla.Medium: Designed for teams expanding their language model pipelines, requiring robust ML lifecycle management fortified by Argilla\u0027s comprehensive support.Large: Geared towards teams heavily dependent on language model pipelines, human feedback, and applications, requiring complete ML lifecycle management with robust support.Scope of services Argilla Cloud, a fully managed SaaS, encompasses the following functionalities: Unrestricted Users, Datasets, and Workspaces: The service imposes no limits on the number of users, datasets, or workspaces, supporting scalability of operations.Role-Based Access Control: Administrators and annotators have differentiated access rights to ensure structured and secure data management.Custom Subdomain: Clients are provided with a distinct argilla.io subdomain for accessing the platform.Regular Updates and Upgrades: The service includes regular platform patches and upgrades as part of routine maintenance to uphold system integrity and security.Managed Service: Infrastructure maintenance, backend operations, and other technical aspects are managed by Argilla, eliminating the need for client-side management.Security The security framework of the Argilla Cloud service involves a multi-faceted approach: Data Encryption at Rest: Data stored within the system is encrypted, forming a crucial layer of security.This process automatically encrypts data prior to storage, guarding against unauthorized access.Network Security Measures: The infrastructure has been designed to prevent unauthorized intrusion and to ensure consistent service availability.Measures include firewall protections, intrusion detection systems, and scheduled vulnerability scans to detect and address potential threats.Role-Based Access Control: The system implements role-based access control, defining access levels based on user roles.This mechanism controls the extent of access to sensitive information, aligning it with the responsibilities of each role.Security Audits: Regular audits of security systems and protocols are conducted to detect potential vulnerabilities and verify adherence to security standards.Employee Training: All personnel receive regular security training, fostering an understanding of the latest threats and the importance of security best practices.Incident Response Protocol: In the case of a security incident, a pre-defined incident response plan is activated.This plan outlines the procedures for managing different types of security events, and aims to ensure swift mitigation of potential damage.In summary, the security measures in place include data encryption, network security protocols, role-based access control, regular audits, employee training, and a comprehensive incident response plan.These measures contribute to a secure environment for data management.Setup and onboarding The process for setup and onboarding for Argilla Cloud is designed to be efficient and straightforward.The procedure involves a sequence of steps to ensure a smooth transition and optimal use of the service.Step 1: Account Creation The setup process begins with the creation of the client owner account.We require the client to provide the following details: Full name of the administrator Preferred username Administrator\u0027s email address Once these details are received, we send an onboarding email to sign up.Step 2: Platform Orientation Once logged in, the administrator has full access to the Argilla Cloud platform.They can familiarize themselves with the platform interface and various features.If required, a guided tour or tutorial can be provided to walk the administrator through the platform.Step 3: User Management The administrator is then responsible for setting up additional user accounts.They can invite users via email, manage roles (admin, annotator, etc.), and assign access permissions to different workspaces and datasets.Step 4: Workspace and Dataset Configuration The administrator can create and manage multiple workspaces and datasets.They have the option to configure settings as per their team\u0027s requirements, including assigning datasets to specific workspaces and managing access permissions.Step 5: Training and Support Argilla provides open resources and support to aid in the onboarding process.This includes user manuals, tutorials, and access to our support team for any queries or issues that may arise during the setup and onboarding process.By following these steps, new users can be quickly onboarded and begin using the Argilla Cloud service with minimal downtime.\nThis process ensures the client administrator has full control over their team\u0027s access and can manage their workspace efficiently.Plans The plans for the Argilla Cloud service depend on the volume of records processed, with several tiers available to suit varying needs.Each tier has a corresponding monthly and annual price, with a 10% discount applied to the annual pricing option.The tier selection and associated price will be determined by the client\u0027s selection in the Service Order Form section of the Terms of Service document.Plans are: Starter 1 Million records Base 3 Million records Medium 4 Million records Large 6 million records\n\nSupport Argilla Cloud offers comprehensive support services to address various issues that may arise during the use of our service.Support levels are categorized into four distinct tiers, based on the severity of the issue, and a separate category for feature requests.The support process, response times, and procedures differ for each category.(1) Critical Issues Critical issues are characterized by: Severe impact on the Service, potentially rendering it completely non-functional.Disruption of critical service operations or functions.Obstruction of entire customer workflows.In the case of a critical issue, Argilla will: Assign specialist(s) to correct the issue on an expedited basis.Provide ongoing communication on the status via email and/or phone, according to the customer\u0027s preference.Begin work towards identifying a temporary workaround or fix.(2) Major Issues Major issues involve: Limited functionality of the Service.Service instability with periodic interruptions.Material service interruptions in mission-critical functions.Time-sensitive questions impacting performance or deliverables to end-clients.Upon encountering a major issue, Argilla will: Assign a specialist to begin a resolution.Implement additional, escalated procedures as reasonably determined necessary by Argilla Support Services staff.(3) Minor Issues Minor issues include: Errors causing partial, non-critical functionality loss.The need for clarification on procedures or information in documentation.Errors in service that may impact performance deliverables.(4) Trivial Issues Trivial issues are characterized by: Errors in system development with little to no impact on performance.Feature Requests Feature requests involve: Requesting a product enhancement.For feature requests, Argilla will: Respond regarding the relevance and interest in incorporating the requested feature.In summary, Argilla Cloud\u0027s support services are designed to provide timely and efficient assistance for issues of varying severity, ensuring a smooth and reliable user experience.All plans include Monday to Friday during office hours (8am to 17pm CEST) with additional support upon request.The Support Channels and features of each tier are shown below:\n\nStarter: Slack Community.Severity 1 - Response time \u003c 4 hours.Severity 2 - Response time \u003c 8 hours.Severity 3 - Response time \u003c 48 hours.Severity 4 not specified.Base: Ticketing System, Severity 1 - Response time \u003c 4 hours.Severity 2 - Response time \u003c 8 hours.Severity 3 - Response time \u003c 24 hours.Severity 4 not specified.Medium: Ticketing System and dedicated Slack channel, Severity 1 - Response time \u003c 4 hours.Severity 2 - Response time \u003c 8 hours.Severity 3 - Response time \u003c 24 hours.Severity 4 one week\n\nLarge: Ticketing System and dedicated Slack channel, Severity 1 - Response time \u003c 4 hours.Severity 2 - Response time \u003c 8 hours.Severity 3 - Response time \u003c 24 hours.Severity 4 one week.Data backup and recovery plan Argilla Cloud is committed to ensuring the safety and availability of your data.Our system is designed to run six data backups per day as a standard procedure.These backups capture a snapshot of the system state at the time of the backup, enabling restoration to that point if necessary.Our Recovery Point Objective (RPO) is four hours.This means that in the event of a system failure, the maximum data loss would be up to the last four hours of data input.We achieve this by running regular backups throughout the day, reducing the time window of potential data loss.Our Recovery Time Objective (RTO) is one hour.This is the maximum acceptable length of time that your system could be down following a failure or disruption.It represents our commitment to ensuring that your services are restored as quickly as possible.In the event of a disruption, our team will first evaluate the issue to determine the best course of action.If data recovery is necessary, we will restore from the most recent backup.We will then work to identify and resolve the root cause of the disruption to prevent a recurrence.Finally, we conduct regular test restores to ensure that our backup system is working as intended.These tests verify the integrity of the backup data and the functionality of the restore process.",
"context-rating": [],
"context-rating-suggestion": null,
"context-rating-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"external_id": null,
"metadata": "{}",
"question-rating": [],
"question-rating-suggestion": null,
"question-rating-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"response": [],
"response-suggestion": "The benefits of choosing Argilla Cloud service over other cloud services include advanced security measures, robust data backup and recovery protocols, flexible pricing options, dedicated customer support, and efficient onboarding process. Argilla Cloud offers a comprehensive security framework that includes data encryption at rest, network security measures, role-based access control, regular security audits, employee training, and a comprehensive incident response protocol. The service also ensures the safety and availability of data through regular data backups with a Recovery Point Objective (RPO) of four hours and a Recovery Time Objective (RTO) of one hour. Additionally, Argilla Cloud offers flexible pricing options based on the volume of records processed and provides dedicated customer support with different support tiers based on the severity of the issue. The onboarding process is designed to be efficient and straightforward, allowing new users to quickly start using the service with minimal downtime.",
"response-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"user-message": "What are the benefits of choosing Argilla Cloud service over other cloud services?"
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **user-message** is of type `TextField`.
* **context** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* (optional) **question-rating** is of type `RatingQuestion` with the following allowed values [1, 2, 3, 4, 5].
* (optional) **context-rating** is of type `RatingQuestion` with the following allowed values [1, 2, 3, 4, 5].
* **response** is of type `TextQuestion`.
* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **question-rating-suggestion** is of type `rating` with the following allowed values [1, 2, 3, 4, 5].
* (optional) **context-rating-suggestion** is of type `rating` with the following allowed values [1, 2, 3, 4, 5].
* (optional) **response-suggestion** is of type `text`.
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
[More Information Needed]
#### 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]
|
argilla/customer_assistant
|
[
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] |
2023-08-30T13:29:30+00:00
|
{"size_categories": "n<1K", "tags": ["rlfh", "argilla", "human-feedback"]}
|
2023-08-30T13:38:42+00:00
|
[] |
[] |
TAGS
#size_categories-n<1K #rlfh #argilla #human-feedback #region-us
|
Dataset Card for customer\_assistant
====================================
This dataset has been created with Argilla.
As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'.
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper:
* Leaderboard:
* Point of Contact:
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\_huggingface' method in Argilla.
* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\_huggingface' and can be loaded independently using the 'datasets' library via 'load\_dataset'.
* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:
### Load with 'datasets'
To load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:
### Supported Tasks and Leaderboards
This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.
There are no leaderboards associated with this dataset.
### Languages
Dataset Structure
-----------------
### Data in Argilla
The dataset is created in Argilla with: fields, questions, suggestions, and guidelines.
The fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
While the same record in HuggingFace 'datasets' looks as follows:
### Data Fields
Among the dataset fields, we differentiate between the following:
* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
+ user-message is of type 'TextField'.
+ context is of type 'TextField'.
* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.
+ (optional) question-rating is of type 'RatingQuestion' with the following allowed values [1, 2, 3, 4, 5].
+ (optional) context-rating is of type 'RatingQuestion' with the following allowed values [1, 2, 3, 4, 5].
+ response is of type 'TextQuestion'.
* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
+ (optional) question-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5].
+ (optional) context-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5].
+ (optional) response-suggestion is of type 'text'.
Additionally, we also have one more field which is optional and is the following:
* external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is 'train'.
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation guidelines
#### 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 Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.",
"### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:",
"### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:",
"### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.",
"### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:",
"### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ user-message is of type 'TextField'.\n\t+ context is of type 'TextField'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ (optional) question-rating is of type 'RatingQuestion' with the following allowed values [1, 2, 3, 4, 5].\n\t+ (optional) context-rating is of type 'RatingQuestion' with the following allowed values [1, 2, 3, 4, 5].\n\t+ response is of type 'TextQuestion'.\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) question-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5].\n\t+ (optional) context-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5].\n\t+ (optional) response-suggestion is of type 'text'.\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.",
"### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation guidelines",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n",
"### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.",
"### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:",
"### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:",
"### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.",
"### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:",
"### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ user-message is of type 'TextField'.\n\t+ context is of type 'TextField'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ (optional) question-rating is of type 'RatingQuestion' with the following allowed values [1, 2, 3, 4, 5].\n\t+ (optional) context-rating is of type 'RatingQuestion' with the following allowed values [1, 2, 3, 4, 5].\n\t+ response is of type 'TextQuestion'.\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) question-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5].\n\t+ (optional) context-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5].\n\t+ (optional) response-suggestion is of type 'text'.\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.",
"### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation guidelines",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
27,
162,
40,
53,
68,
11,
208,
40,
456,
27,
7,
4,
10,
10,
5,
5,
5,
9,
18,
7,
8,
14,
6,
6,
5
] |
[
"passage: TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------",
"passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ user-message is of type 'TextField'.\n\t+ context is of type 'TextField'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ (optional) question-rating is of type 'RatingQuestion' with the following allowed values [1, 2, 3, 4, 5].\n\t+ (optional) context-rating is of type 'RatingQuestion' with the following allowed values [1, 2, 3, 4, 5].\n\t+ response is of type 'TextQuestion'.\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) question-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5].\n\t+ (optional) context-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5].\n\t+ (optional) response-suggestion is of type 'text'.\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file."
] |
97f37a5ceb654d9f29be239a18c3d52e05669223
|
# Dataset Card for Evaluation run of JoSw-14/LoKuS-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/JoSw-14/LoKuS-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 [JoSw-14/LoKuS-13B](https://huggingface.co/JoSw-14/LoKuS-13B) 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_JoSw-14__LoKuS-13B",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-30T14:44:55.625857](https://huggingface.co/datasets/open-llm-leaderboard/details_JoSw-14__LoKuS-13B/blob/main/results_2023-08-30T14%3A44%3A55.625857.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": 0.7392017775637323,
"acc_stderr": 0.02998325172953868,
"acc_norm": 0.7430126648780258,
"acc_norm_stderr": 0.02996682963285512,
"mc1": 0.3537331701346389,
"mc1_stderr": 0.016737814358846147,
"mc2": 0.5139290818009655,
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}
```
### 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_JoSw-14__LoKuS-13B
|
[
"region:us"
] |
2023-08-30T13:45:20+00:00
|
{"pretty_name": "Evaluation run of JoSw-14/LoKuS-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [JoSw-14/LoKuS-13B](https://huggingface.co/JoSw-14/LoKuS-13B) 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_JoSw-14__LoKuS-13B\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-30T14:44:55.625857](https://huggingface.co/datasets/open-llm-leaderboard/details_JoSw-14__LoKuS-13B/blob/main/results_2023-08-30T14%3A44%3A55.625857.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\": 0.7392017775637323,\n \"acc_stderr\": 0.02998325172953868,\n \"acc_norm\": 0.7430126648780258,\n \"acc_norm_stderr\": 0.02996682963285512,\n \"mc1\": 0.3537331701346389,\n \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5139290818009655,\n \"mc2_stderr\": 0.015442752112557084\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5418088737201365,\n \"acc_stderr\": 0.0145602203087147,\n \"acc_norm\": 0.5776450511945392,\n \"acc_norm_stderr\": 0.014434138713379986\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6050587532364071,\n \"acc_stderr\": 0.004878390226591711,\n \"acc_norm\": 0.7940649273053176,\n \"acc_norm_stderr\": 0.004035568117596522\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7697368421052632,\n \"acc_stderr\": 0.034260594244031654,\n \"acc_norm\": 0.7697368421052632,\n \"acc_norm_stderr\": 0.034260594244031654\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8377358490566038,\n \"acc_stderr\": 0.022691482872035377,\n \"acc_norm\": 0.8377358490566038,\n \"acc_norm_stderr\": 0.022691482872035377\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.03309615177059006,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.03309615177059006\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 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"2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-30T14:44:55.625857.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_30T14_44_55.625857", "path": ["results_2023-08-30T14:44:55.625857.parquet"]}, {"split": "latest", "path": ["results_2023-08-30T14:44:55.625857.parquet"]}]}]}
|
2023-08-30T13:46:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of JoSw-14/LoKuS-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 JoSw-14/LoKuS-13B 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 2023-08-30T14:44:55.625857(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 JoSw-14/LoKuS-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 JoSw-14/LoKuS-13B 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 2023-08-30T14:44:55.625857(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 JoSw-14/LoKuS-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 JoSw-14/LoKuS-13B 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 2023-08-30T14:44:55.625857(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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"### 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 JoSw-14/LoKuS-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 JoSw-14/LoKuS-13B 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 2023-08-30T14:44:55.625857(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"
] |
159b1fff591a516f72ca854460c190995060220c
|
This dataset is in instruct
This dataset contains all roleplay data from gpteacher (9111 samples):
https://raw.githubusercontent.com/teknium1/GPTeacher/main/Roleplay%20Supplemental/roleplay-instruct-v2-final.json
https://raw.githubusercontent.com/teknium1/GPTeacher/main/Roleplay/roleplay-simple-deduped-roleplay-instruct.json
And 10000 datapoints from sharegpt:
https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
---
dataset_info:
features:
- name: instructions
struct:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 74202282
num_examples: 19111
download_size: 0
dataset_size: 74202282
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "roleplay_sharegpt_20k_instruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carterlabsltd/roleplay_sharegpt_20k_instruct
|
[
"region:us"
] |
2023-08-30T14:30:44+00:00
|
{}
|
2023-09-01T10:17:28+00:00
|
[] |
[] |
TAGS
#region-us
|
This dataset is in instruct
This dataset contains all roleplay data from gpteacher (9111 samples):
URL
URL
And 10000 datapoints from sharegpt:
URL
---
dataset_info:
features:
- name: instructions
struct:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 74202282
num_examples: 19111
download_size: 0
dataset_size: 74202282
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "roleplay_sharegpt_20k_instruct"
More Information needed
|
[
"# Dataset Card for \"roleplay_sharegpt_20k_instruct\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"roleplay_sharegpt_20k_instruct\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"roleplay_sharegpt_20k_instruct\"\n\nMore Information needed"
] |
f4d489007a4609b80b1c1563f26affcb0f58889a
|
# TaskLAMA
This is an unofficial upload of the [TaskLAMA](https://arxiv.org/abs/2308.15299) data.
TaskLAMA is a novel dataset for Structured Complex Task Decomposition (SCTD).
Some of the data statistics could be found at [Spico197/TaskLAMA](https://github.com/Spico197/TaskLAMA) .
## Citation
```bibtex
@misc{yuan2023tasklama,
title={TaskLAMA: Probing the Complex Task Understanding of Language Models},
author={Quan Yuan and Mehran Kazemi and Xin Xu and Isaac Noble and Vaiva Imbrasaite and Deepak Ramachandran},
year={2023},
eprint={2308.15299},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Spico/TaskLAMA
|
[
"license:cc-by-4.0",
"arxiv:2308.15299",
"region:us"
] |
2023-08-30T14:32:41+00:00
|
{"license": "cc-by-4.0"}
|
2023-09-12T16:05:02+00:00
|
[
"2308.15299"
] |
[] |
TAGS
#license-cc-by-4.0 #arxiv-2308.15299 #region-us
|
# TaskLAMA
This is an unofficial upload of the TaskLAMA data.
TaskLAMA is a novel dataset for Structured Complex Task Decomposition (SCTD).
Some of the data statistics could be found at Spico197/TaskLAMA .
|
[
"# TaskLAMA\n\nThis is an unofficial upload of the TaskLAMA data.\n\nTaskLAMA is a novel dataset for Structured Complex Task Decomposition (SCTD).\n\nSome of the data statistics could be found at Spico197/TaskLAMA ."
] |
[
"TAGS\n#license-cc-by-4.0 #arxiv-2308.15299 #region-us \n",
"# TaskLAMA\n\nThis is an unofficial upload of the TaskLAMA data.\n\nTaskLAMA is a novel dataset for Structured Complex Task Decomposition (SCTD).\n\nSome of the data statistics could be found at Spico197/TaskLAMA ."
] |
[
23,
59
] |
[
"passage: TAGS\n#license-cc-by-4.0 #arxiv-2308.15299 #region-us \n# TaskLAMA\n\nThis is an unofficial upload of the TaskLAMA data.\n\nTaskLAMA is a novel dataset for Structured Complex Task Decomposition (SCTD).\n\nSome of the data statistics could be found at Spico197/TaskLAMA ."
] |
3a5c1221ee87bc91e0e29d3056b509305a9fd47d
|
# Bangumi Image Base of Aho Girl
This is the image base of bangumi Aho Girl, we detected 28 characters, 6663 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 | 825 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 107 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 763 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 760 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 688 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 259 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 50 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 276 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 44 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 527 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 388 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 115 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 448 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 42 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 293 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 123 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 15 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 190 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 64 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 121 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 74 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 14 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 53 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 44 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 49 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 79 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 10 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 242 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/ahogirl
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-08-30T14:34:52+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-09-29T03:39:30+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Aho Girl
==============================
This is the image base of bangumi Aho Girl, we detected 28 characters, 6663 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"
] |
9d48dd18a1c210e61676fa89a24e8b444297425d
|
# Dataset Card for Evaluation run of jondurbin/airoboros-l2-13b-2.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-l2-13b-2.1
- **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 [jondurbin/airoboros-l2-13b-2.1](https://huggingface.co/jondurbin/airoboros-l2-13b-2.1) 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 4 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_jondurbin__airoboros-l2-13b-2.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T11:50:58.922768](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-13b-2.1/blob/main/results_2023-10-22T11-50-58.922768.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": 0.34458892617449666,
"em_stderr": 0.004866841438021559,
"f1": 0.4243645134228212,
"f1_stderr": 0.004703880113863456,
"acc": 0.39311250241596774,
"acc_stderr": 0.00863314852533744
},
"harness|drop|3": {
"em": 0.34458892617449666,
"em_stderr": 0.004866841438021559,
"f1": 0.4243645134228212,
"f1_stderr": 0.004703880113863456
},
"harness|gsm8k|5": {
"acc": 0.0356330553449583,
"acc_stderr": 0.005106107853744191
},
"harness|winogrande|5": {
"acc": 0.7505919494869772,
"acc_stderr": 0.012160189196930689
}
}
```
### 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_jondurbin__airoboros-l2-13b-2.1
|
[
"region:us"
] |
2023-08-30T14:35:00+00:00
|
{"pretty_name": "Evaluation run of jondurbin/airoboros-l2-13b-2.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [jondurbin/airoboros-l2-13b-2.1](https://huggingface.co/jondurbin/airoboros-l2-13b-2.1) 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 4 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_jondurbin__airoboros-l2-13b-2.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T11:50:58.922768](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-13b-2.1/blob/main/results_2023-10-22T11-50-58.922768.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\": 0.34458892617449666,\n \"em_stderr\": 0.004866841438021559,\n \"f1\": 0.4243645134228212,\n \"f1_stderr\": 0.004703880113863456,\n \"acc\": 0.39311250241596774,\n \"acc_stderr\": 0.00863314852533744\n },\n \"harness|drop|3\": {\n \"em\": 0.34458892617449666,\n \"em_stderr\": 0.004866841438021559,\n \"f1\": 0.4243645134228212,\n \"f1_stderr\": 0.004703880113863456\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0356330553449583,\n \"acc_stderr\": 0.005106107853744191\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7505919494869772,\n \"acc_stderr\": 0.012160189196930689\n }\n}\n```", "repo_url": "https://huggingface.co/jondurbin/airoboros-l2-13b-2.1", "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_08_30T15_34_36.295006", "path": ["**/details_harness|arc:challenge|25_2023-08-30T15:34:36.295006.parquet"]}, {"split": "2023_08_30T18_13_29.562428", "path": ["**/details_harness|arc:challenge|25_2023-08-30T18:13:29.562428.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T18:13:29.562428.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T07_13_42.257437", "path": ["**/details_harness|drop|3_2023-10-22T07-13-42.257437.parquet"]}, {"split": "2023_10_22T11_50_58.922768", "path": ["**/details_harness|drop|3_2023-10-22T11-50-58.922768.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T11-50-58.922768.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T07_13_42.257437", "path": ["**/details_harness|gsm8k|5_2023-10-22T07-13-42.257437.parquet"]}, {"split": "2023_10_22T11_50_58.922768", "path": ["**/details_harness|gsm8k|5_2023-10-22T11-50-58.922768.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T11-50-58.922768.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T15_34_36.295006", "path": ["**/details_harness|hellaswag|10_2023-08-30T15:34:36.295006.parquet"]}, {"split": "2023_08_30T18_13_29.562428", "path": ["**/details_harness|hellaswag|10_2023-08-30T18:13:29.562428.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-30T18:13:29.562428.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_08_30T15_34_36.295006", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T15:34:36.295006.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-30T15:34:36.295006.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-30T15:34:36.295006.parquet", 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"latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-30T18:13:29.562428.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_22T07_13_42.257437", "path": ["**/details_harness|winogrande|5_2023-10-22T07-13-42.257437.parquet"]}, {"split": "2023_10_22T11_50_58.922768", "path": ["**/details_harness|winogrande|5_2023-10-22T11-50-58.922768.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-22T11-50-58.922768.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_30T15_34_36.295006", "path": ["results_2023-08-30T15:34:36.295006.parquet"]}, {"split": "2023_08_30T18_13_29.562428", "path": ["results_2023-08-30T18:13:29.562428.parquet"]}, {"split": "2023_10_22T07_13_42.257437", "path": ["results_2023-10-22T07-13-42.257437.parquet"]}, {"split": "2023_10_22T11_50_58.922768", "path": ["results_2023-10-22T11-50-58.922768.parquet"]}, {"split": "latest", "path": ["results_2023-10-22T11-50-58.922768.parquet"]}]}]}
|
2023-10-22T10:51:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of jondurbin/airoboros-l2-13b-2.1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model jondurbin/airoboros-l2-13b-2.1 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 4 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 2023-10-22T11:50:58.922768(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 jondurbin/airoboros-l2-13b-2.1",
"## 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 jondurbin/airoboros-l2-13b-2.1 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 4 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 2023-10-22T11:50:58.922768(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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"### 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 jondurbin/airoboros-l2-13b-2.1 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 4 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 2023-10-22T11:50:58.922768(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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"### Discussion of Biases",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of jondurbin/airoboros-l2-13b-2.1## 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 jondurbin/airoboros-l2-13b-2.1 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 4 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 2023-10-22T11:50:58.922768(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"
] |
98b3b8430e2bc8753b69c35c5de959f536e0cb3d
|
# Dataset Card for "autotree_automl_Diabetes130US_gosdt_l512_d3_sd3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_Diabetes130US_gosdt_l512_d3_sd3
|
[
"region:us"
] |
2023-08-30T14:36:57+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "int64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "int64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 5538400000, "num_examples": 100000}, {"name": "validation", "num_bytes": 553840000, "num_examples": 10000}], "download_size": 487961116, "dataset_size": 6092240000}}
|
2023-08-30T14:37:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_Diabetes130US_gosdt_l512_d3_sd3"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_Diabetes130US_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_Diabetes130US_gosdt_l512_d3_sd3\"\n\nMore Information needed"
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6,
35
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_Diabetes130US_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
3b9dd4f24d16a389e01aee7048f226a926b4940d
|
# Dataset Card for "Metallography_segmenter_Dataset_B"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_B
|
[
"region:us"
] |
2023-08-30T15:00:17+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 180249364.0, "num_examples": 874}, {"name": "validation", "num_bytes": 44152138.0, "num_examples": 219}], "download_size": 223637073, "dataset_size": 224401502.0}}
|
2023-08-30T15:29:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_B"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_B\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_B\"\n\nMore Information needed"
] |
[
6,
22
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_B\"\n\nMore Information needed"
] |
346788bd24e00b62f5bdfce9fcf08a5b33d0449b
|
# Dataset Card for Evaluation run of tianyil1/denas-llama2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tianyil1/denas-llama2
- **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 [tianyil1/denas-llama2](https://huggingface.co/tianyil1/denas-llama2) 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_tianyil1__denas-llama2",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-30T16:03:41.588049](https://huggingface.co/datasets/open-llm-leaderboard/details_tianyil1__denas-llama2/blob/main/results_2023-08-30T16%3A03%3A41.588049.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": 0.45795528138208735,
"acc_stderr": 0.03527742372208128,
"acc_norm": 0.4618604191106212,
"acc_norm_stderr": 0.03526366614184863,
"mc1": 0.3084455324357405,
"mc1_stderr": 0.01616803938315687,
"mc2": 0.4524124423828304,
"mc2_stderr": 0.01467368036859452
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014611369529813272,
"acc_norm": 0.5392491467576792,
"acc_norm_stderr": 0.014566303676636584
},
"harness|hellaswag|10": {
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"acc_stderr": 0.004911837730582202,
"acc_norm": 0.7783310097590121,
"acc_norm_stderr": 0.004145206350032315
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
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"acc_stderr": 0.04284958639753399,
"acc_norm": 0.43703703703703706,
"acc_norm_stderr": 0.04284958639753399
},
"harness|hendrycksTest-astronomy|5": {
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"acc_norm": 0.4144736842105263,
"acc_norm_stderr": 0.04008973785779206
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.46037735849056605,
"acc_stderr": 0.030676096599389184,
"acc_norm": 0.46037735849056605,
"acc_norm_stderr": 0.030676096599389184
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.4236111111111111,
"acc_norm_stderr": 0.0413212501972337
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.41040462427745666,
"acc_stderr": 0.03750757044895537,
"acc_norm": 0.41040462427745666,
"acc_norm_stderr": 0.03750757044895537
},
"harness|hendrycksTest-college_physics|5": {
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},
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm_stderr": 0.03149930577784906
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.6220183486238532,
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
"mc1": 0.3084455324357405,
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"mc2": 0.4524124423828304,
"mc2_stderr": 0.01467368036859452
}
}
```
### 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_tianyil1__denas-llama2
|
[
"region:us"
] |
2023-08-30T15:04:05+00:00
|
{"pretty_name": "Evaluation run of tianyil1/denas-llama2", "dataset_summary": "Dataset automatically created during the evaluation run of model [tianyil1/denas-llama2](https://huggingface.co/tianyil1/denas-llama2) 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_tianyil1__denas-llama2\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-30T16:03:41.588049](https://huggingface.co/datasets/open-llm-leaderboard/details_tianyil1__denas-llama2/blob/main/results_2023-08-30T16%3A03%3A41.588049.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\": 0.45795528138208735,\n \"acc_stderr\": 0.03527742372208128,\n \"acc_norm\": 0.4618604191106212,\n \"acc_norm_stderr\": 0.03526366614184863,\n \"mc1\": 0.3084455324357405,\n \"mc1_stderr\": 0.01616803938315687,\n \"mc2\": 0.4524124423828304,\n \"mc2_stderr\": 0.01467368036859452\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.4991467576791809,\n \"acc_stderr\": 0.014611369529813272,\n \"acc_norm\": 0.5392491467576792,\n \"acc_norm_stderr\": 0.014566303676636584\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5880302728540131,\n \"acc_stderr\": 0.004911837730582202,\n \"acc_norm\": 0.7783310097590121,\n \"acc_norm_stderr\": 0.004145206350032315\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.43703703703703706,\n \"acc_stderr\": 0.04284958639753399,\n \"acc_norm\": 0.43703703703703706,\n \"acc_norm_stderr\": 0.04284958639753399\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.4144736842105263,\n \"acc_stderr\": 0.04008973785779206,\n \"acc_norm\": 0.4144736842105263,\n \"acc_norm_stderr\": 0.04008973785779206\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.46037735849056605,\n \"acc_stderr\": 0.030676096599389184,\n \"acc_norm\": 0.46037735849056605,\n \"acc_norm_stderr\": 0.030676096599389184\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4236111111111111,\n \"acc_stderr\": 0.0413212501972337,\n \"acc_norm\": 0.4236111111111111,\n \"acc_norm_stderr\": 0.0413212501972337\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.41040462427745666,\n \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.41040462427745666,\n \"acc_norm_stderr\": 0.03750757044895537\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\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4340425531914894,\n \"acc_stderr\": 0.03240038086792747,\n \"acc_norm\": 0.4340425531914894,\n \"acc_norm_stderr\": 0.03240038086792747\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3157894736842105,\n \"acc_stderr\": 0.043727482902780064,\n \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.043727482902780064\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.041227371113703316,\n \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.041227371113703316\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.291005291005291,\n \"acc_stderr\": 0.023393826500484865,\n \"acc_norm\": 0.291005291005291,\n \"acc_norm_stderr\": 0.023393826500484865\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2698412698412698,\n \"acc_stderr\": 0.03970158273235172,\n \"acc_norm\": 0.2698412698412698,\n \"acc_norm_stderr\": 0.03970158273235172\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.4612903225806452,\n \"acc_stderr\": 0.02835863485983692,\n \"acc_norm\": 0.4612903225806452,\n \"acc_norm_stderr\": 0.02835863485983692\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.3399014778325123,\n \"acc_stderr\": 0.033327690684107895,\n \"acc_norm\": 0.3399014778325123,\n \"acc_norm_stderr\": 0.033327690684107895\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03825460278380025,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03825460278380025\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5505050505050505,\n \"acc_stderr\": 0.035441324919479704,\n \"acc_norm\": 0.5505050505050505,\n \"acc_norm_stderr\": 0.035441324919479704\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6787564766839378,\n \"acc_stderr\": 0.033699508685490674,\n \"acc_norm\": 0.6787564766839378,\n \"acc_norm_stderr\": 0.033699508685490674\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.4230769230769231,\n \"acc_stderr\": 0.025049197876042338,\n \"acc_norm\": 0.4230769230769231,\n \"acc_norm_stderr\": 0.025049197876042338\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\": 0.027634907264178544\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.37815126050420167,\n \"acc_stderr\": 0.03149930577784906,\n \"acc_norm\": 0.37815126050420167,\n \"acc_norm_stderr\": 0.03149930577784906\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943342,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943342\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.6220183486238532,\n \"acc_stderr\": 0.02078918706672811,\n \"acc_norm\": 0.6220183486238532,\n \"acc_norm_stderr\": 0.02078918706672811\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.03054674526495318,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.03054674526495318\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5392156862745098,\n \"acc_stderr\": 0.03498501649369527,\n \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.03498501649369527\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.6033755274261603,\n \"acc_stderr\": 0.03184399873811225,\n \"acc_norm\": 0.6033755274261603,\n \"acc_norm_stderr\": 0.03184399873811225\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5426008968609866,\n \"acc_stderr\": 0.03343577705583065,\n \"acc_norm\": 0.5426008968609866,\n \"acc_norm_stderr\": 0.03343577705583065\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.5190839694656488,\n \"acc_stderr\": 0.04382094705550988,\n \"acc_norm\": 0.5190839694656488,\n \"acc_norm_stderr\": 0.04382094705550988\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.043913262867240704,\n \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.043913262867240704\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.48148148148148145,\n \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.4662576687116564,\n \"acc_stderr\": 0.039194155450484096,\n \"acc_norm\": 0.4662576687116564,\n \"acc_norm_stderr\": 0.039194155450484096\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.5242718446601942,\n \"acc_stderr\": 0.049449010929737795,\n \"acc_norm\": 0.5242718446601942,\n \"acc_norm_stderr\": 0.049449010929737795\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7094017094017094,\n \"acc_stderr\": 0.029745048572674085,\n \"acc_norm\": 0.7094017094017094,\n \"acc_norm_stderr\": 0.029745048572674085\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.52,\n 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TAGS
#region-us
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# Dataset Card for Evaluation run of tianyil1/denas-llama2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model tianyil1/denas-llama2 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 2023-08-30T16:03:41.588049(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 tianyil1/denas-llama2",
"## 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 tianyil1/denas-llama2 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 2023-08-30T16:03:41.588049(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 tianyil1/denas-llama2",
"## 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 tianyil1/denas-llama2 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 2023-08-30T16:03:41.588049(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 tianyil1/denas-llama2## 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 tianyil1/denas-llama2 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 2023-08-30T16:03:41.588049(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"
] |
5dd9145babce584f704ed9647bb8c44606374983
|
# Dataset Card for Picklebot50k
<!-- Provide a quick summary of the dataset. -->
50 thousand video clips of balls and strikes from MLB games from the 2016 season through the 2022 season.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
The dataset consists of roughly 50 thousand video clips of balls and strikes in .mp4 format, resized to 224x224 resolution.
The calculated standard deviation and mean for the dataset are
std: (0.2104, 0.1986, 0.1829)
mean: (0.3939, 0.3817, 0.3314).
- **Curated by:** Henry Freed
- **License:** MIT
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** The original project that this dataset was compiled for can be found here on [github](https://github.com/hbfreed/Picklebot).
- **Demo:** Coming soon!
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
The dataset was originally collected to call balls and strikes using neural networks. There are many other potential use cases, but they would almost certainly require relabeling.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
The dataset is structured as .tar files of the train, val, and test splits. The labels are contained in .csv files. The .csvs are structured as follows:
"filename.mp4",label
where the label is 0 for balls and 1 for strikes.
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The source data were scraped from Baseball Savant's [Statcast Search](https://baseballsavant.mlb.com/statcast_search). It's a pretty powerful search page, and a lot of fun to play around with.
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
After downloading the videos, they were cropped from 1280x720 at 60fps to the middle 600x600 pixels at 60fps. Finally, they were downsampled to 224x224 resolution at 15 fps (this can all be done using one ffmpeg command). Some of the longer clips where there was a lot of noise (shots of the crowd, instant replays, etc.) were trimmed (mostly by hand) down to a more manageable length.
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[Baseball Savant](https://baseballsavant.mlb.com/) and MLB/the broadcasters (whoever it is) originally created the videos.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
It's important to note that only balls and called strikes were collected. No swinging strikes, foul balls, hit by pitches, or anything else are included in the dataset. Additionally, most pitchers and batters are right handed, and nothing was done to try and balance that in this dataset.
|
hbfreed/Picklebot50k
|
[
"task_categories:video-classification",
"size_categories:10K<n<100K",
"license:mit",
"baseball",
"sports",
"video-classification",
"computer-vision",
"region:us"
] |
2023-08-30T15:06:23+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["video-classification"], "tags": ["baseball", "sports", "video-classification", "computer-vision"]}
|
2024-02-06T17:07:18+00:00
|
[] |
[] |
TAGS
#task_categories-video-classification #size_categories-10K<n<100K #license-mit #baseball #sports #video-classification #computer-vision #region-us
|
# Dataset Card for Picklebot50k
50 thousand video clips of balls and strikes from MLB games from the 2016 season through the 2022 season.
!Example Clip
## Dataset Details
### Dataset Description
The dataset consists of roughly 50 thousand video clips of balls and strikes in .mp4 format, resized to 224x224 resolution.
The calculated standard deviation and mean for the dataset are
std: (0.2104, 0.1986, 0.1829)
mean: (0.3939, 0.3817, 0.3314).
- Curated by: Henry Freed
- License: MIT
### Dataset Sources [optional]
- Repository: The original project that this dataset was compiled for can be found here on github.
- Demo: Coming soon!
## Uses
The dataset was originally collected to call balls and strikes using neural networks. There are many other potential use cases, but they would almost certainly require relabeling.
## Dataset Structure
The dataset is structured as .tar files of the train, val, and test splits. The labels are contained in .csv files. The .csvs are structured as follows:
"filename.mp4",label
where the label is 0 for balls and 1 for strikes.
### Source Data
The source data were scraped from Baseball Savant's Statcast Search. It's a pretty powerful search page, and a lot of fun to play around with.
#### Data Collection and Processing
After downloading the videos, they were cropped from 1280x720 at 60fps to the middle 600x600 pixels at 60fps. Finally, they were downsampled to 224x224 resolution at 15 fps (this can all be done using one ffmpeg command). Some of the longer clips where there was a lot of noise (shots of the crowd, instant replays, etc.) were trimmed (mostly by hand) down to a more manageable length.
#### Who are the source data producers?
Baseball Savant and MLB/the broadcasters (whoever it is) originally created the videos.
## Bias, Risks, and Limitations
It's important to note that only balls and called strikes were collected. No swinging strikes, foul balls, hit by pitches, or anything else are included in the dataset. Additionally, most pitchers and batters are right handed, and nothing was done to try and balance that in this dataset.
|
[
"# Dataset Card for Picklebot50k\n\n\n\n50 thousand video clips of balls and strikes from MLB games from the 2016 season through the 2022 season.\n!Example Clip",
"## Dataset Details",
"### Dataset Description\n\n\nThe dataset consists of roughly 50 thousand video clips of balls and strikes in .mp4 format, resized to 224x224 resolution.\n\n\n\nThe calculated standard deviation and mean for the dataset are \n\nstd: (0.2104, 0.1986, 0.1829)\n\n\nmean: (0.3939, 0.3817, 0.3314).\n\n\n- Curated by: Henry Freed\n- License: MIT",
"### Dataset Sources [optional]\n\n\n\n- Repository: The original project that this dataset was compiled for can be found here on github.\n- Demo: Coming soon!",
"## Uses\n\n\nThe dataset was originally collected to call balls and strikes using neural networks. There are many other potential use cases, but they would almost certainly require relabeling.",
"## Dataset Structure\n\n\nThe dataset is structured as .tar files of the train, val, and test splits. The labels are contained in .csv files. The .csvs are structured as follows:\n\n\"filename.mp4\",label\n\nwhere the label is 0 for balls and 1 for strikes.",
"### Source Data\n\n\nThe source data were scraped from Baseball Savant's Statcast Search. It's a pretty powerful search page, and a lot of fun to play around with.",
"#### Data Collection and Processing\n\n\n\nAfter downloading the videos, they were cropped from 1280x720 at 60fps to the middle 600x600 pixels at 60fps. Finally, they were downsampled to 224x224 resolution at 15 fps (this can all be done using one ffmpeg command). Some of the longer clips where there was a lot of noise (shots of the crowd, instant replays, etc.) were trimmed (mostly by hand) down to a more manageable length.",
"#### Who are the source data producers?\n\n\nBaseball Savant and MLB/the broadcasters (whoever it is) originally created the videos.",
"## Bias, Risks, and Limitations\n\n\nIt's important to note that only balls and called strikes were collected. No swinging strikes, foul balls, hit by pitches, or anything else are included in the dataset. Additionally, most pitchers and batters are right handed, and nothing was done to try and balance that in this dataset."
] |
[
"TAGS\n#task_categories-video-classification #size_categories-10K<n<100K #license-mit #baseball #sports #video-classification #computer-vision #region-us \n",
"# Dataset Card for Picklebot50k\n\n\n\n50 thousand video clips of balls and strikes from MLB games from the 2016 season through the 2022 season.\n!Example Clip",
"## Dataset Details",
"### Dataset Description\n\n\nThe dataset consists of roughly 50 thousand video clips of balls and strikes in .mp4 format, resized to 224x224 resolution.\n\n\n\nThe calculated standard deviation and mean for the dataset are \n\nstd: (0.2104, 0.1986, 0.1829)\n\n\nmean: (0.3939, 0.3817, 0.3314).\n\n\n- Curated by: Henry Freed\n- License: MIT",
"### Dataset Sources [optional]\n\n\n\n- Repository: The original project that this dataset was compiled for can be found here on github.\n- Demo: Coming soon!",
"## Uses\n\n\nThe dataset was originally collected to call balls and strikes using neural networks. There are many other potential use cases, but they would almost certainly require relabeling.",
"## Dataset Structure\n\n\nThe dataset is structured as .tar files of the train, val, and test splits. The labels are contained in .csv files. The .csvs are structured as follows:\n\n\"filename.mp4\",label\n\nwhere the label is 0 for balls and 1 for strikes.",
"### Source Data\n\n\nThe source data were scraped from Baseball Savant's Statcast Search. It's a pretty powerful search page, and a lot of fun to play around with.",
"#### Data Collection and Processing\n\n\n\nAfter downloading the videos, they were cropped from 1280x720 at 60fps to the middle 600x600 pixels at 60fps. Finally, they were downsampled to 224x224 resolution at 15 fps (this can all be done using one ffmpeg command). Some of the longer clips where there was a lot of noise (shots of the crowd, instant replays, etc.) were trimmed (mostly by hand) down to a more manageable length.",
"#### Who are the source data producers?\n\n\nBaseball Savant and MLB/the broadcasters (whoever it is) originally created the videos.",
"## Bias, Risks, and Limitations\n\n\nIt's important to note that only balls and called strikes were collected. No swinging strikes, foul balls, hit by pitches, or anything else are included in the dataset. Additionally, most pitchers and batters are right handed, and nothing was done to try and balance that in this dataset."
] |
[
48,
39,
4,
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42,
42,
75,
40,
114,
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84
] |
[
"passage: TAGS\n#task_categories-video-classification #size_categories-10K<n<100K #license-mit #baseball #sports #video-classification #computer-vision #region-us \n# Dataset Card for Picklebot50k\n\n\n\n50 thousand video clips of balls and strikes from MLB games from the 2016 season through the 2022 season.\n!Example Clip## Dataset Details### Dataset Description\n\n\nThe dataset consists of roughly 50 thousand video clips of balls and strikes in .mp4 format, resized to 224x224 resolution.\n\n\n\nThe calculated standard deviation and mean for the dataset are \n\nstd: (0.2104, 0.1986, 0.1829)\n\n\nmean: (0.3939, 0.3817, 0.3314).\n\n\n- Curated by: Henry Freed\n- License: MIT### Dataset Sources [optional]\n\n\n\n- Repository: The original project that this dataset was compiled for can be found here on github.\n- Demo: Coming soon!## Uses\n\n\nThe dataset was originally collected to call balls and strikes using neural networks. There are many other potential use cases, but they would almost certainly require relabeling.## Dataset Structure\n\n\nThe dataset is structured as .tar files of the train, val, and test splits. The labels are contained in .csv files. The .csvs are structured as follows:\n\n\"filename.mp4\",label\n\nwhere the label is 0 for balls and 1 for strikes.### Source Data\n\n\nThe source data were scraped from Baseball Savant's Statcast Search. It's a pretty powerful search page, and a lot of fun to play around with.#### Data Collection and Processing\n\n\n\nAfter downloading the videos, they were cropped from 1280x720 at 60fps to the middle 600x600 pixels at 60fps. Finally, they were downsampled to 224x224 resolution at 15 fps (this can all be done using one ffmpeg command). Some of the longer clips where there was a lot of noise (shots of the crowd, instant replays, etc.) were trimmed (mostly by hand) down to a more manageable length."
] |
5233439c76fdfbccbd9d7a2262e6fb50bff871b9
|
# Dataset Card for Evaluation run of yeontaek/WizardCoder-Python-13B-LoRa
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/WizardCoder-Python-13B-LoRa
- **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 [yeontaek/WizardCoder-Python-13B-LoRa](https://huggingface.co/yeontaek/WizardCoder-Python-13B-LoRa) 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_yeontaek__WizardCoder-Python-13B-LoRa",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T21:23:44.967167](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__WizardCoder-Python-13B-LoRa/blob/main/results_2023-10-22T21-23-44.967167.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": 0.002202181208053691,
"em_stderr": 0.000480051081661951,
"f1": 0.05132340604026855,
"f1_stderr": 0.0012472218955908543,
"acc": 0.3661954806593931,
"acc_stderr": 0.010378625716521293
},
"harness|drop|3": {
"em": 0.002202181208053691,
"em_stderr": 0.000480051081661951,
"f1": 0.05132340604026855,
"f1_stderr": 0.0012472218955908543
},
"harness|gsm8k|5": {
"acc": 0.07808946171341925,
"acc_stderr": 0.007390654481108212
},
"harness|winogrande|5": {
"acc": 0.654301499605367,
"acc_stderr": 0.013366596951934373
}
}
```
### 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_yeontaek__WizardCoder-Python-13B-LoRa
|
[
"region:us"
] |
2023-08-30T15:27:20+00:00
|
{"pretty_name": "Evaluation run of yeontaek/WizardCoder-Python-13B-LoRa", "dataset_summary": "Dataset automatically created during the evaluation run of model [yeontaek/WizardCoder-Python-13B-LoRa](https://huggingface.co/yeontaek/WizardCoder-Python-13B-LoRa) 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_yeontaek__WizardCoder-Python-13B-LoRa\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T21:23:44.967167](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__WizardCoder-Python-13B-LoRa/blob/main/results_2023-10-22T21-23-44.967167.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\": 0.002202181208053691,\n \"em_stderr\": 0.000480051081661951,\n \"f1\": 0.05132340604026855,\n \"f1_stderr\": 0.0012472218955908543,\n \"acc\": 0.3661954806593931,\n \"acc_stderr\": 0.010378625716521293\n },\n \"harness|drop|3\": {\n \"em\": 0.002202181208053691,\n \"em_stderr\": 0.000480051081661951,\n \"f1\": 0.05132340604026855,\n \"f1_stderr\": 0.0012472218955908543\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07808946171341925,\n \"acc_stderr\": 0.007390654481108212\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.654301499605367,\n \"acc_stderr\": 0.013366596951934373\n }\n}\n```", "repo_url": "https://huggingface.co/yeontaek/WizardCoder-Python-13B-LoRa", "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_08_30T16_26_56.590377", "path": ["**/details_harness|arc:challenge|25_2023-08-30T16:26:56.590377.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T16:26:56.590377.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T21_23_44.967167", "path": ["**/details_harness|drop|3_2023-10-22T21-23-44.967167.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T21-23-44.967167.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T21_23_44.967167", "path": ["**/details_harness|gsm8k|5_2023-10-22T21-23-44.967167.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T21-23-44.967167.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T16_26_56.590377", "path": ["**/details_harness|hellaswag|10_2023-08-30T16:26:56.590377.parquet"]}, {"split": "latest", "path": 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|
2023-10-22T20:23:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of yeontaek/WizardCoder-Python-13B-LoRa
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model yeontaek/WizardCoder-Python-13B-LoRa 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 2023-10-22T21:23:44.967167(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 yeontaek/WizardCoder-Python-13B-LoRa",
"## 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 yeontaek/WizardCoder-Python-13B-LoRa 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 2023-10-22T21:23:44.967167(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 yeontaek/WizardCoder-Python-13B-LoRa",
"## 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 yeontaek/WizardCoder-Python-13B-LoRa 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 2023-10-22T21:23:44.967167(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,
27,
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66,
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4,
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6,
5,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of yeontaek/WizardCoder-Python-13B-LoRa## 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 yeontaek/WizardCoder-Python-13B-LoRa 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 2023-10-22T21:23:44.967167(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"
] |
d49ff345aea585f6aa9a4fb51347ebc52ff443b3
|
# Dataset Card for "Metallography_segmenter_Dataset_C"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_C
|
[
"region:us"
] |
2023-08-30T15:35:33+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 55124828.0, "num_examples": 291}, {"name": "validation", "num_bytes": 14015324.0, "num_examples": 73}], "download_size": 68904368, "dataset_size": 69140152.0}}
|
2023-08-30T15:37:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_C"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_C\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_C\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_C\"\n\nMore Information needed"
] |
8784fee2a2e6c7e15e76abe60184b8a8df7d3ff2
|
# Dataset Card for "Metallography_segmenter_Dataset_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_D_LP
|
[
"region:us"
] |
2023-08-30T15:38:43+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 29176877.0, "num_examples": 128}, {"name": "validation", "num_bytes": 7735010.0, "num_examples": 33}], "download_size": 36785589, "dataset_size": 36911887.0}}
|
2023-08-30T15:41:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_D"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_D\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_D\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_D\"\n\nMore Information needed"
] |
fa169a86b0fdc7f0e796bdbdffa5f97a06f8e6b1
|
# Dataset Card for "Metallography_segmenter_Dataset_E"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_E
|
[
"region:us"
] |
2023-08-30T15:40:54+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 10435253.0, "num_examples": 52}, {"name": "validation", "num_bytes": 2709447.0, "num_examples": 13}], "download_size": 13127271, "dataset_size": 13144700.0}}
|
2023-08-31T14:29:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_E"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_E\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_E\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_E\"\n\nMore Information needed"
] |
0f23489d14c069f0868a66bf6c3fd2133e4e0f08
|
# Dataset Card for "Metallography_segmenter_Dataset_F"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_F
|
[
"region:us"
] |
2023-08-30T15:43:14+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 4021981.0, "num_examples": 20}, {"name": "validation", "num_bytes": 1278027.0, "num_examples": 6}], "download_size": 5293677, "dataset_size": 5300008.0}}
|
2023-08-31T14:31:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_F"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_F\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_F\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_F\"\n\nMore Information needed"
] |
2c63e99897c2f34f13e14d4cdf30b86da650d0c3
|
# Dataset Card for "Metallography_segmenter_Dataset_G"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_G
|
[
"region:us"
] |
2023-08-30T15:47:19+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3199749.0, "num_examples": 15}, {"name": "validation", "num_bytes": 843475.0, "num_examples": 4}], "download_size": 4037688, "dataset_size": 4043224.0}}
|
2023-08-30T15:48:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_G"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_G\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_G\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_G\"\n\nMore Information needed"
] |
240a58d524ea183f05dbc71fbba5d8ec67b90b0d
|
# Dataset Card for "Metallography_segmenter_Dataset_H"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_H
|
[
"region:us"
] |
2023-08-30T15:49:31+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2950144.0, "num_examples": 14}, {"name": "validation", "num_bytes": 923919.0, "num_examples": 4}], "download_size": 3873753, "dataset_size": 3874063.0}}
|
2023-08-30T16:25:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_H"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_H\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_H\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_H\"\n\nMore Information needed"
] |
53497c6c72296be216a57d7411db872805ab0c54
|
# Dataset Card for "my_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GehanSherif/my_dataset
|
[
"region:us"
] |
2023-08-30T15:57:27+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 1329.0, "num_examples": 1}], "download_size": 9215, "dataset_size": 1329.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-30T15:57:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "my_dataset"
More Information needed
|
[
"# Dataset Card for \"my_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"my_dataset\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"my_dataset\"\n\nMore Information needed"
] |
1d3dfae88ab21a56e880c81d31979f5c5f92326d
|
# ChatGPT ground truth dataset
This dataset is generated by ChatGPT and contains factual questions and corresponding answers from 160 subfields across natural and social sciences.
Specifically, the dataset covers eight major domains: mathematics, physics, chemistry, biology, medicine, engineering, computer science, and social sciences. Within each domain, 20 specific subfields are selected, with 500 question-answer pairs per subfield, resulting in a total of 80,000 question-answer pairs.
The language used in this dataset is English.
Accompanying the release of this dataset is the script code used to generate it.
# ChatGPT基准事实数据集
本数据集由ChatGPT自动生成，包含自然科学和社会科学的160个细分领域的事实性问题和相应的答案。
具体来说，本数据集涵盖数学、物理、化学、生物学、医学、工程、计算机科学、社会科学八大领域，每个领域选择了20个细分子领域，每个子领域有500个问答对，共80000个问答对。
本数据集的语言为英文。
和本数据集同时发布的还有生成本数据集使用的脚本代码。
|
hugfaceguy0001/ChatGPTGroundTruth
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:openrail",
"science",
"region:us"
] |
2023-08-30T16:13:55+00:00
|
{"language": ["en"], "license": "openrail", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "ChatGPT ground truth", "tags": ["science"], "configs": [{"config_name": "main_data", "data_files": "ground_truth.jsonl"}]}
|
2023-08-30T17:03:37+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-openrail #science #region-us
|
# ChatGPT ground truth dataset
This dataset is generated by ChatGPT and contains factual questions and corresponding answers from 160 subfields across natural and social sciences.
Specifically, the dataset covers eight major domains: mathematics, physics, chemistry, biology, medicine, engineering, computer science, and social sciences. Within each domain, 20 specific subfields are selected, with 500 question-answer pairs per subfield, resulting in a total of 80,000 question-answer pairs.
The language used in this dataset is English.
Accompanying the release of this dataset is the script code used to generate it.
# ChatGPT基准事实数据集
本数据集由ChatGPT自动生成，包含自然科学和社会科学的160个细分领域的事实性问题和相应的答案。
具体来说，本数据集涵盖数学、物理、化学、生物学、医学、工程、计算机科学、社会科学八大领域，每个领域选择了20个细分子领域，每个子领域有500个问答对，共80000个问答对。
本数据集的语言为英文。
和本数据集同时发布的还有生成本数据集使用的脚本代码。
|
[
"# ChatGPT ground truth dataset\n\nThis dataset is generated by ChatGPT and contains factual questions and corresponding answers from 160 subfields across natural and social sciences.\n\nSpecifically, the dataset covers eight major domains: mathematics, physics, chemistry, biology, medicine, engineering, computer science, and social sciences. Within each domain, 20 specific subfields are selected, with 500 question-answer pairs per subfield, resulting in a total of 80,000 question-answer pairs.\n\nThe language used in this dataset is English.\n\nAccompanying the release of this dataset is the script code used to generate it.",
"# ChatGPT基准事实数据集\n本数据集由ChatGPT自动生成，包含自然科学和社会科学的160个细分领域的事实性问题和相应的答案。\n\n具体来说，本数据集涵盖数学、物理、化学、生物学、医学、工程、计算机科学、社会科学八大领域，每个领域选择了20个细分子领域，每个子领域有500个问答对，共80000个问答对。\n\n本数据集的语言为英文。\n\n和本数据集同时发布的还有生成本数据集使用的脚本代码。"
] |
[
"TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-openrail #science #region-us \n",
"# ChatGPT ground truth dataset\n\nThis dataset is generated by ChatGPT and contains factual questions and corresponding answers from 160 subfields across natural and social sciences.\n\nSpecifically, the dataset covers eight major domains: mathematics, physics, chemistry, biology, medicine, engineering, computer science, and social sciences. Within each domain, 20 specific subfields are selected, with 500 question-answer pairs per subfield, resulting in a total of 80,000 question-answer pairs.\n\nThe language used in this dataset is English.\n\nAccompanying the release of this dataset is the script code used to generate it.",
"# ChatGPT基准事实数据集\n本数据集由ChatGPT自动生成，包含自然科学和社会科学的160个细分领域的事实性问题和相应的答案。\n\n具体来说，本数据集涵盖数学、物理、化学、生物学、医学、工程、计算机科学、社会科学八大领域，每个领域选择了20个细分子领域，每个子领域有500个问答对，共80000个问答对。\n\n本数据集的语言为英文。\n\n和本数据集同时发布的还有生成本数据集使用的脚本代码。"
] |
[
42,
150,
119
] |
[
"passage: TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-openrail #science #region-us \n# ChatGPT ground truth dataset\n\nThis dataset is generated by ChatGPT and contains factual questions and corresponding answers from 160 subfields across natural and social sciences.\n\nSpecifically, the dataset covers eight major domains: mathematics, physics, chemistry, biology, medicine, engineering, computer science, and social sciences. Within each domain, 20 specific subfields are selected, with 500 question-answer pairs per subfield, resulting in a total of 80,000 question-answer pairs.\n\nThe language used in this dataset is English.\n\nAccompanying the release of this dataset is the script code used to generate it.# ChatGPT基准事实数据集\n本数据集由ChatGPT自动生成，包含自然科学和社会科学的160个细分领域的事实性问题和相应的答案。\n\n具体来说，本数据集涵盖数学、物理、化学、生物学、医学、工程、计算机科学、社会科学八大领域，每个领域选择了20个细分子领域，每个子领域有500个问答对，共80000个问答对。\n\n本数据集的语言为英文。\n\n和本数据集同时发布的还有生成本数据集使用的脚本代码。"
] |
a5df8466847b5067b97ff40c4ba5bed7e6a5b329
|
# 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]
|
erebos/LLAMaAtlasDataset
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"language:de",
"region:us"
] |
2023-08-30T16:23:28+00:00
|
{"language": ["en", "de"], "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "a"}
|
2023-09-01T16:32:07+00:00
|
[] |
[
"en",
"de"
] |
TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #language-German #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
#### 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 Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis 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",
"#### 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",
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a405e7322d8eb83323c91bfade3aca40f3227c73
|
# Dataset Card for Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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 [uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-13b](https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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_uukuguy__speechless-orca-platypus-coig-lite-2k-0.6e-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T07:05:10.941569](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-2k-0.6e-13b/blob/main/results_2023-10-19T07-05-10.941569.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": 0.40740352348993286,
"em_stderr": 0.005031895046041088,
"f1": 0.46848573825503476,
"f1_stderr": 0.004828956071080464,
"acc": 0.4270311930602038,
"acc_stderr": 0.00945942839156503
},
"harness|drop|3": {
"em": 0.40740352348993286,
"em_stderr": 0.005031895046041088,
"f1": 0.46848573825503476,
"f1_stderr": 0.004828956071080464
},
"harness|gsm8k|5": {
"acc": 0.07505686125852919,
"acc_stderr": 0.007257633145486643
},
"harness|winogrande|5": {
"acc": 0.7790055248618785,
"acc_stderr": 0.011661223637643416
}
}
```
### 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_uukuguy__speechless-orca-platypus-coig-lite-2k-0.6e-13b
|
[
"region:us"
] |
2023-08-30T16:55:15+00:00
|
{"pretty_name": "Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-13b](https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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_uukuguy__speechless-orca-platypus-coig-lite-2k-0.6e-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-19T07:05:10.941569](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-2k-0.6e-13b/blob/main/results_2023-10-19T07-05-10.941569.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\": 0.40740352348993286,\n \"em_stderr\": 0.005031895046041088,\n \"f1\": 0.46848573825503476,\n \"f1_stderr\": 0.004828956071080464,\n \"acc\": 0.4270311930602038,\n \"acc_stderr\": 0.00945942839156503\n },\n \"harness|drop|3\": {\n \"em\": 0.40740352348993286,\n \"em_stderr\": 0.005031895046041088,\n \"f1\": 0.46848573825503476,\n \"f1_stderr\": 0.004828956071080464\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07505686125852919,\n \"acc_stderr\": 0.007257633145486643\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7790055248618785,\n \"acc_stderr\": 0.011661223637643416\n }\n}\n```", "repo_url": "https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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": 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|
2023-10-19T06:05:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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 uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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 2023-10-19T07:05:10.941569(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 uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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 uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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 2023-10-19T07:05:10.941569(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 uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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 uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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 2023-10-19T07:05:10.941569(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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"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Curation Rationale",
"### Source Data",
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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 uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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 uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-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 2023-10-19T07:05:10.941569(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"
] |
62f4851c204b49aa14276d633b831841fe356004
|
# Dataset Card for Evaluation run of DataLinguistic/DataLinguistic-34B-V1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/DataLinguistic/DataLinguistic-34B-V1.0
- **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 [DataLinguistic/DataLinguistic-34B-V1.0](https://huggingface.co/DataLinguistic/DataLinguistic-34B-V1.0) 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_DataLinguistic__DataLinguistic-34B-V1.0",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-30T18:11:50.917227](https://huggingface.co/datasets/open-llm-leaderboard/details_DataLinguistic__DataLinguistic-34B-V1.0/blob/main/results_2023-08-30T18%3A11%3A50.917227.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": 0.23205864259563588,
"acc_stderr": 0.030715936843036345,
"acc_norm": 0.2336046598801071,
"acc_norm_stderr": 0.030731498766140584,
"mc1": 0.2460220318237454,
"mc1_stderr": 0.015077219200662578,
"mc2": 0.48734329486879213,
"mc2_stderr": 0.01631194781446388
},
"harness|arc:challenge|25": {
"acc": 0.23293515358361774,
"acc_stderr": 0.012352507042617396,
"acc_norm": 0.2764505119453925,
"acc_norm_stderr": 0.013069662474252428
},
"harness|hellaswag|10": {
"acc": 0.2819159529974109,
"acc_stderr": 0.004490130691020429,
"acc_norm": 0.3296156144194384,
"acc_norm_stderr": 0.004691128722535484
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.18518518518518517,
"acc_stderr": 0.03355677216313142,
"acc_norm": 0.18518518518518517,
"acc_norm_stderr": 0.03355677216313142
},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_stderr": 0.02528839450289137,
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"acc_norm_stderr": 0.02528839450289137
},
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},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.2,
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},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
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"acc_norm": 0.20809248554913296,
"acc_norm_stderr": 0.030952890217749874
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.21568627450980393,
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},
"harness|hendrycksTest-computer_security|5": {
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"harness|hendrycksTest-conceptual_physics|5": {
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"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-international_law|5": {
"acc": 0.2396694214876033,
"acc_stderr": 0.03896878985070417,
"acc_norm": 0.2396694214876033,
"acc_norm_stderr": 0.03896878985070417
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"harness|hendrycksTest-jurisprudence|5": {
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"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.042365112580946336
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-machine_learning|5": {
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"harness|hendrycksTest-management|5": {
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"acc_norm": 0.17475728155339806,
"acc_norm_stderr": 0.037601780060266224
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.23754789272030652,
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"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.24855491329479767,
"acc_norm_stderr": 0.023267528432100174
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"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.23798882681564246,
"acc_norm_stderr": 0.014242630070574915
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"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.023929155517351284
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"harness|hendrycksTest-philosophy|5": {
"acc": 0.1864951768488746,
"acc_stderr": 0.02212243977248077,
"acc_norm": 0.1864951768488746,
"acc_norm_stderr": 0.02212243977248077
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.21604938271604937,
"acc_stderr": 0.022899162918445806,
"acc_norm": 0.21604938271604937,
"acc_norm_stderr": 0.022899162918445806
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"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.23404255319148937,
"acc_stderr": 0.025257861359432417,
"acc_norm": 0.23404255319148937,
"acc_norm_stderr": 0.025257861359432417
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"harness|hendrycksTest-professional_law|5": {
"acc": 0.2457627118644068,
"acc_stderr": 0.010996156635142692,
"acc_norm": 0.2457627118644068,
"acc_norm_stderr": 0.010996156635142692
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"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.18382352941176472,
"acc_stderr": 0.023529242185193106,
"acc_norm": 0.18382352941176472,
"acc_norm_stderr": 0.023529242185193106
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.25,
"acc_stderr": 0.01751781884501444,
"acc_norm": 0.25,
"acc_norm_stderr": 0.01751781884501444
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03955932861795833,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03955932861795833
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.18775510204081633,
"acc_stderr": 0.02500025603954621,
"acc_norm": 0.18775510204081633,
"acc_norm_stderr": 0.02500025603954621
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"harness|hendrycksTest-sociology|5": {
"acc": 0.24378109452736318,
"acc_stderr": 0.03036049015401465,
"acc_norm": 0.24378109452736318,
"acc_norm_stderr": 0.03036049015401465
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
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"harness|hendrycksTest-virology|5": {
"acc": 0.28313253012048195,
"acc_stderr": 0.03507295431370518,
"acc_norm": 0.28313253012048195,
"acc_norm_stderr": 0.03507295431370518
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.3216374269005848,
"acc_stderr": 0.03582529442573122,
"acc_norm": 0.3216374269005848,
"acc_norm_stderr": 0.03582529442573122
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2460220318237454,
"mc1_stderr": 0.015077219200662578,
"mc2": 0.48734329486879213,
"mc2_stderr": 0.01631194781446388
}
}
```
### 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_DataLinguistic__DataLinguistic-34B-V1.0
|
[
"region:us"
] |
2023-08-30T17:12:15+00:00
|
{"pretty_name": "Evaluation run of DataLinguistic/DataLinguistic-34B-V1.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [DataLinguistic/DataLinguistic-34B-V1.0](https://huggingface.co/DataLinguistic/DataLinguistic-34B-V1.0) 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_DataLinguistic__DataLinguistic-34B-V1.0\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-30T18:11:50.917227](https://huggingface.co/datasets/open-llm-leaderboard/details_DataLinguistic__DataLinguistic-34B-V1.0/blob/main/results_2023-08-30T18%3A11%3A50.917227.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\": 0.23205864259563588,\n \"acc_stderr\": 0.030715936843036345,\n \"acc_norm\": 0.2336046598801071,\n \"acc_norm_stderr\": 0.030731498766140584,\n \"mc1\": 0.2460220318237454,\n \"mc1_stderr\": 0.015077219200662578,\n \"mc2\": 0.48734329486879213,\n \"mc2_stderr\": 0.01631194781446388\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.23293515358361774,\n \"acc_stderr\": 0.012352507042617396,\n \"acc_norm\": 0.2764505119453925,\n \"acc_norm_stderr\": 0.013069662474252428\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2819159529974109,\n \"acc_stderr\": 0.004490130691020429,\n \"acc_norm\": 0.3296156144194384,\n \"acc_norm_stderr\": 0.004691128722535484\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.02528839450289137\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n 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TAGS
#region-us
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# Dataset Card for Evaluation run of DataLinguistic/DataLinguistic-34B-V1.0
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model DataLinguistic/DataLinguistic-34B-V1.0 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 2023-08-30T18:11:50.917227(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 DataLinguistic/DataLinguistic-34B-V1.0",
"## 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 DataLinguistic/DataLinguistic-34B-V1.0 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 2023-08-30T18:11:50.917227(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 DataLinguistic/DataLinguistic-34B-V1.0",
"## 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 DataLinguistic/DataLinguistic-34B-V1.0 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 2023-08-30T18:11:50.917227(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?",
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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",
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"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of DataLinguistic/DataLinguistic-34B-V1.0## 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 DataLinguistic/DataLinguistic-34B-V1.0 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 2023-08-30T18:11:50.917227(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"
] |
a2c3c121e5bc509d267424111c6c570050e53279
|
# 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]
|
isashap/pleasework
|
[
"language:en",
"region:us"
] |
2023-08-30T17:35:18+00:00
|
{"language": ["en"], "pretty_name": "AI Resume"}
|
2023-08-30T19:16:04+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #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
#### 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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"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
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"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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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 Summary\n\nThis 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",
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"## Dataset Creation",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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"### Dataset Curators",
"### Licensing Information",
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"passage: TAGS\n#language-English #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis 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#### 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"
] |
352edf8526304b37b117afb14eadaa2fe68a3f90
|
# Dataset Card for Evaluation run of TheBloke/Airoboros-L2-13B-2.1-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Airoboros-L2-13B-2.1-GPTQ
- **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 [TheBloke/Airoboros-L2-13B-2.1-GPTQ](https://huggingface.co/TheBloke/Airoboros-L2-13B-2.1-GPTQ) 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_TheBloke__Airoboros-L2-13B-2.1-GPTQ",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T16:15:21.953879](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Airoboros-L2-13B-2.1-GPTQ/blob/main/results_2023-09-22T16-15-21.953879.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": 0.36325503355704697,
"em_stderr": 0.004925249459609538,
"f1": 0.4313957634228207,
"f1_stderr": 0.004768180025704384,
"acc": 0.4016912073136653,
"acc_stderr": 0.00940489808002435
},
"harness|drop|3": {
"em": 0.36325503355704697,
"em_stderr": 0.004925249459609538,
"f1": 0.4313957634228207,
"f1_stderr": 0.004768180025704384
},
"harness|gsm8k|5": {
"acc": 0.05989385898407885,
"acc_stderr": 0.006536148151288716
},
"harness|winogrande|5": {
"acc": 0.7434885556432518,
"acc_stderr": 0.012273648008759982
}
}
```
### 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_TheBloke__Airoboros-L2-13B-2.1-GPTQ
|
[
"region:us"
] |
2023-08-30T17:43:31+00:00
|
{"pretty_name": "Evaluation run of TheBloke/Airoboros-L2-13B-2.1-GPTQ", "dataset_summary": "Dataset automatically created during the evaluation run of model [TheBloke/Airoboros-L2-13B-2.1-GPTQ](https://huggingface.co/TheBloke/Airoboros-L2-13B-2.1-GPTQ) 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_TheBloke__Airoboros-L2-13B-2.1-GPTQ\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-22T16:15:21.953879](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Airoboros-L2-13B-2.1-GPTQ/blob/main/results_2023-09-22T16-15-21.953879.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\": 0.36325503355704697,\n \"em_stderr\": 0.004925249459609538,\n \"f1\": 0.4313957634228207,\n \"f1_stderr\": 0.004768180025704384,\n \"acc\": 0.4016912073136653,\n \"acc_stderr\": 0.00940489808002435\n },\n \"harness|drop|3\": {\n \"em\": 0.36325503355704697,\n \"em_stderr\": 0.004925249459609538,\n \"f1\": 0.4313957634228207,\n \"f1_stderr\": 0.004768180025704384\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05989385898407885,\n \"acc_stderr\": 0.006536148151288716\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.012273648008759982\n }\n}\n```", "repo_url": "https://huggingface.co/TheBloke/Airoboros-L2-13B-2.1-GPTQ", "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_08_30T18_43_07.011974", "path": ["**/details_harness|arc:challenge|25_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_22T16_15_21.953879", "path": ["**/details_harness|drop|3_2023-09-22T16-15-21.953879.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-22T16-15-21.953879.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_22T16_15_21.953879", "path": ["**/details_harness|gsm8k|5_2023-09-22T16-15-21.953879.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-22T16-15-21.953879.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hellaswag|10_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hendrycksTest-human_aging|5_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-08-30T18:43:07.011974.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-30T18:43:07.011974.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_22T16_15_21.953879", "path": ["**/details_harness|winogrande|5_2023-09-22T16-15-21.953879.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-22T16-15-21.953879.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_30T18_43_07.011974", "path": ["results_2023-08-30T18:43:07.011974.parquet"]}, {"split": "2023_09_22T16_15_21.953879", "path": ["results_2023-09-22T16-15-21.953879.parquet"]}, {"split": "latest", "path": ["results_2023-09-22T16-15-21.953879.parquet"]}]}]}
|
2023-09-22T15:15:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TheBloke/Airoboros-L2-13B-2.1-GPTQ
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TheBloke/Airoboros-L2-13B-2.1-GPTQ 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 2023-09-22T16:15:21.953879(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 TheBloke/Airoboros-L2-13B-2.1-GPTQ",
"## 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 TheBloke/Airoboros-L2-13B-2.1-GPTQ 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 2023-09-22T16:15:21.953879(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 TheBloke/Airoboros-L2-13B-2.1-GPTQ",
"## 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 TheBloke/Airoboros-L2-13B-2.1-GPTQ 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 2023-09-22T16:15:21.953879(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 TheBloke/Airoboros-L2-13B-2.1-GPTQ## 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 TheBloke/Airoboros-L2-13B-2.1-GPTQ 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 2023-09-22T16:15:21.953879(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"
] |
3be90335b66f24456a5d6659d9c8d208c0357119
|
# Pile Uncopyrighted
In response to [authors demanding that LLMs stop using their works](https://tcrn.ch/3rtpIDn), here's a copy of [The Pile](https://huggingface.co/datasets/monology/pile) with all copyrighted content removed.
Please consider using this dataset to train your future LLMs, to respect authors and abide by copyright law.
Creating an uncopyrighted version of a larger dataset (ie RedPajama) is planned, with no ETA.
**Methodology**
Cleaning was performed by removing everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2 subsets.
Based on section 7.1 of [the original paper](https://arxiv.org/abs/2101.00027), these datasets are the only ones which are not explicitly allowed to be used in AI training.
|
monology/pile-uncopyrighted
|
[
"license:other",
"arxiv:2101.00027",
"region:us"
] |
2023-08-30T17:47:58+00:00
|
{"license": "other"}
|
2023-08-31T02:45:38+00:00
|
[
"2101.00027"
] |
[] |
TAGS
#license-other #arxiv-2101.00027 #region-us
|
# Pile Uncopyrighted
In response to authors demanding that LLMs stop using their works, here's a copy of The Pile with all copyrighted content removed.
Please consider using this dataset to train your future LLMs, to respect authors and abide by copyright law.
Creating an uncopyrighted version of a larger dataset (ie RedPajama) is planned, with no ETA.
Methodology
Cleaning was performed by removing everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2 subsets.
Based on section 7.1 of the original paper, these datasets are the only ones which are not explicitly allowed to be used in AI training.
|
[
"# Pile Uncopyrighted\nIn response to authors demanding that LLMs stop using their works, here's a copy of The Pile with all copyrighted content removed. \nPlease consider using this dataset to train your future LLMs, to respect authors and abide by copyright law. \nCreating an uncopyrighted version of a larger dataset (ie RedPajama) is planned, with no ETA. \n\nMethodology \nCleaning was performed by removing everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2 subsets. \nBased on section 7.1 of the original paper, these datasets are the only ones which are not explicitly allowed to be used in AI training."
] |
[
"TAGS\n#license-other #arxiv-2101.00027 #region-us \n",
"# Pile Uncopyrighted\nIn response to authors demanding that LLMs stop using their works, here's a copy of The Pile with all copyrighted content removed. \nPlease consider using this dataset to train your future LLMs, to respect authors and abide by copyright law. \nCreating an uncopyrighted version of a larger dataset (ie RedPajama) is planned, with no ETA. \n\nMethodology \nCleaning was performed by removing everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2 subsets. \nBased on section 7.1 of the original paper, these datasets are the only ones which are not explicitly allowed to be used in AI training."
] |
[
19,
161
] |
[
"passage: TAGS\n#license-other #arxiv-2101.00027 #region-us \n# Pile Uncopyrighted\nIn response to authors demanding that LLMs stop using their works, here's a copy of The Pile with all copyrighted content removed. \nPlease consider using this dataset to train your future LLMs, to respect authors and abide by copyright law. \nCreating an uncopyrighted version of a larger dataset (ie RedPajama) is planned, with no ETA. \n\nMethodology \nCleaning was performed by removing everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2 subsets. \nBased on section 7.1 of the original paper, these datasets are the only ones which are not explicitly allowed to be used in AI training."
] |
a8ddbb7f914c4cd9d39b4f5341bd27853e5a2991
|
# Dataset Card for "humaneval_plus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
loubnabnl/humaneval_plus
|
[
"region:us"
] |
2023-08-30T17:48:38+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "task_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "entry_point", "dtype": "string"}, {"name": "canonical_solution", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "contract", "dtype": "string"}, {"name": "base_input", "dtype": "string"}, {"name": "atol", "dtype": "float64"}, {"name": "plus_input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7571857, "num_examples": 164}], "download_size": 2006302, "dataset_size": 7571857}}
|
2023-08-30T19:10:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "humaneval_plus"
More Information needed
|
[
"# Dataset Card for \"humaneval_plus\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"humaneval_plus\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"humaneval_plus\"\n\nMore Information needed"
] |
fb11e17f6b228a5d1db2b745e766313b40e8226c
|
## Dataset Description
Microsoft created this large dataset of Java Junit test cases with its corresponding focal methods.
It contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K
Java open source project hosted on GitHub.
The mapping between test case and focal methods are based heuristics rules and Java developer's best practice.
More information could be found here:
- [methods2test Github repo](https://github.com/microsoft/methods2test)
- [Methods2Test: A dataset of focal methods mapped to test cases](https://arxiv.org/pdf/2203.12776.pdf)
## Dataset Schema
```
target: <TEST_CASE>
src_fm: <FOCAL_METHOD>
src_fm_fc: <FOCAL_CLASS_NAME> <FOCAL_METHOD>
src_fm_fc_co: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS>
src_fm_fc_ms: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES>
src_fm_fc_ms_ff: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> <FIELDS>
```
## Focal Context
- fm: this representation incorporates exclusively the source
code of the focal method. Intuitively, this contains the most
important information for generating accurate test cases for
the given method.
- fm+fc: this representations adds the focal class name, which
can provide meaningful semantic information to the model.
- fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this
augmentation is that the test case may require instantiating
an object of the focal class in order to properly test the focal
method.
- fm+fc+c+m: this representation adds the signatures of the
other public methods in the focal class. The rationale which
motivated this inclusion is that the test case may need to
invoke other auxiliary methods within the class (e.g., getters,
setters) to set up or tear down the testing environment.
- fm+fc+c+m+f : this representation adds the public fields of
the focal class. The motivation is that test cases may need to
inspect the status of the public fields to properly test a focal
method.
The different levels of focal contexts are the following:
```
FM: focal method
FM_FC: focal method + focal class name
FM_FC_CO: focal method + focal class name + constructor signatures
FM_FC_MS: focal method + focal class name + constructor signatures + public method signatures
FM_FC_MS_FF: focal method + focal class name + constructor signatures + public method signatures + public fields
```
## Lmitations
The original authors validate the heuristics by inspecting a
statistically significant sample (confidence level of 95% within 10%
margin of error) of 97 samples from the training set. Two authors
independently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct
link between the test case and the corresponding focal method
## Contribution
All the thanks to the original authors.
|
jitx/Methods2Test_java_unit_test_code
|
[
"task_categories:text-generation",
"language:en",
"license:mit",
"unit test",
"java",
"code",
"arxiv:2203.12776",
"region:us"
] |
2023-08-30T17:59:03+00:00
|
{"language": ["en"], "license": "mit", "task_categories": ["text-generation"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "target", "dtype": "string"}, {"name": "src_fm", "dtype": "string"}, {"name": "src_fm_fc", "dtype": "string"}, {"name": "src_fm_fc_co", "dtype": "string"}, {"name": "src_fm_fc_ms", "dtype": "string"}, {"name": "src_fm_fc_ms_ff", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3399525755, "num_examples": 624022}, {"name": "test", "num_bytes": 907751466, "num_examples": 156922}], "download_size": 558984469, "dataset_size": 4307277221}, "tags": ["unit test", "java", "code"]}
|
2023-08-30T18:31:25+00:00
|
[
"2203.12776"
] |
[
"en"
] |
TAGS
#task_categories-text-generation #language-English #license-mit #unit test #java #code #arxiv-2203.12776 #region-us
|
## Dataset Description
Microsoft created this large dataset of Java Junit test cases with its corresponding focal methods.
It contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K
Java open source project hosted on GitHub.
The mapping between test case and focal methods are based heuristics rules and Java developer's best practice.
More information could be found here:
- methods2test Github repo
- Methods2Test: A dataset of focal methods mapped to test cases
## Dataset Schema
## Focal Context
- fm: this representation incorporates exclusively the source
code of the focal method. Intuitively, this contains the most
important information for generating accurate test cases for
the given method.
- fm+fc: this representations adds the focal class name, which
can provide meaningful semantic information to the model.
- fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this
augmentation is that the test case may require instantiating
an object of the focal class in order to properly test the focal
method.
- fm+fc+c+m: this representation adds the signatures of the
other public methods in the focal class. The rationale which
motivated this inclusion is that the test case may need to
invoke other auxiliary methods within the class (e.g., getters,
setters) to set up or tear down the testing environment.
- fm+fc+c+m+f : this representation adds the public fields of
the focal class. The motivation is that test cases may need to
inspect the status of the public fields to properly test a focal
method.
!image/png
The different levels of focal contexts are the following:
## Lmitations
The original authors validate the heuristics by inspecting a
statistically significant sample (confidence level of 95% within 10%
margin of error) of 97 samples from the training set. Two authors
independently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct
link between the test case and the corresponding focal method
## Contribution
All the thanks to the original authors.
|
[
"## Dataset Description\nMicrosoft created this large dataset of Java Junit test cases with its corresponding focal methods. \nIt contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K\nJava open source project hosted on GitHub. \n\nThe mapping between test case and focal methods are based heuristics rules and Java developer's best practice.\n\nMore information could be found here:\n- methods2test Github repo\n- Methods2Test: A dataset of focal methods mapped to test cases",
"## Dataset Schema",
"## Focal Context\n- fm: this representation incorporates exclusively the source\ncode of the focal method. Intuitively, this contains the most\nimportant information for generating accurate test cases for\nthe given method.\n- fm+fc: this representations adds the focal class name, which\ncan provide meaningful semantic information to the model.\n- fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this\naugmentation is that the test case may require instantiating\nan object of the focal class in order to properly test the focal\nmethod.\n- fm+fc+c+m: this representation adds the signatures of the\nother public methods in the focal class. The rationale which\nmotivated this inclusion is that the test case may need to\ninvoke other auxiliary methods within the class (e.g., getters,\nsetters) to set up or tear down the testing environment.\n- fm+fc+c+m+f : this representation adds the public fields of\nthe focal class. The motivation is that test cases may need to\ninspect the status of the public fields to properly test a focal\nmethod.\n\n!image/png\n\n The different levels of focal contexts are the following:",
"## Lmitations\nThe original authors validate the heuristics by inspecting a\nstatistically significant sample (confidence level of 95% within 10%\nmargin of error) of 97 samples from the training set. Two authors\nindependently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct\nlink between the test case and the corresponding focal method",
"## Contribution\nAll the thanks to the original authors."
] |
[
"TAGS\n#task_categories-text-generation #language-English #license-mit #unit test #java #code #arxiv-2203.12776 #region-us \n",
"## Dataset Description\nMicrosoft created this large dataset of Java Junit test cases with its corresponding focal methods. \nIt contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K\nJava open source project hosted on GitHub. \n\nThe mapping between test case and focal methods are based heuristics rules and Java developer's best practice.\n\nMore information could be found here:\n- methods2test Github repo\n- Methods2Test: A dataset of focal methods mapped to test cases",
"## Dataset Schema",
"## Focal Context\n- fm: this representation incorporates exclusively the source\ncode of the focal method. Intuitively, this contains the most\nimportant information for generating accurate test cases for\nthe given method.\n- fm+fc: this representations adds the focal class name, which\ncan provide meaningful semantic information to the model.\n- fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this\naugmentation is that the test case may require instantiating\nan object of the focal class in order to properly test the focal\nmethod.\n- fm+fc+c+m: this representation adds the signatures of the\nother public methods in the focal class. The rationale which\nmotivated this inclusion is that the test case may need to\ninvoke other auxiliary methods within the class (e.g., getters,\nsetters) to set up or tear down the testing environment.\n- fm+fc+c+m+f : this representation adds the public fields of\nthe focal class. The motivation is that test cases may need to\ninspect the status of the public fields to properly test a focal\nmethod.\n\n!image/png\n\n The different levels of focal contexts are the following:",
"## Lmitations\nThe original authors validate the heuristics by inspecting a\nstatistically significant sample (confidence level of 95% within 10%\nmargin of error) of 97 samples from the training set. Two authors\nindependently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct\nlink between the test case and the corresponding focal method",
"## Contribution\nAll the thanks to the original authors."
] |
[
42,
115,
5,
275,
86,
12
] |
[
"passage: TAGS\n#task_categories-text-generation #language-English #license-mit #unit test #java #code #arxiv-2203.12776 #region-us \n## Dataset Description\nMicrosoft created this large dataset of Java Junit test cases with its corresponding focal methods. \nIt contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K\nJava open source project hosted on GitHub. \n\nThe mapping between test case and focal methods are based heuristics rules and Java developer's best practice.\n\nMore information could be found here:\n- methods2test Github repo\n- Methods2Test: A dataset of focal methods mapped to test cases## Dataset Schema## Focal Context\n- fm: this representation incorporates exclusively the source\ncode of the focal method. Intuitively, this contains the most\nimportant information for generating accurate test cases for\nthe given method.\n- fm+fc: this representations adds the focal class name, which\ncan provide meaningful semantic information to the model.\n- fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this\naugmentation is that the test case may require instantiating\nan object of the focal class in order to properly test the focal\nmethod.\n- fm+fc+c+m: this representation adds the signatures of the\nother public methods in the focal class. The rationale which\nmotivated this inclusion is that the test case may need to\ninvoke other auxiliary methods within the class (e.g., getters,\nsetters) to set up or tear down the testing environment.\n- fm+fc+c+m+f : this representation adds the public fields of\nthe focal class. The motivation is that test cases may need to\ninspect the status of the public fields to properly test a focal\nmethod.\n\n!image/png\n\n The different levels of focal contexts are the following:"
] |
50274b84f699ef0a69a37e0d2d47a9add1cbde93
|
# Dataset Card for "mushi-snli-llama2-grammar_struct-10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Musha-the-Yusha/mushi-snli-llama2-grammar_struct-10k
|
[
"region:us"
] |
2023-08-30T18:07:06+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3456483, "num_examples": 10000}], "download_size": 1106306, "dataset_size": 3456483}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T09:25:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mushi-snli-llama2-grammar_struct-10k"
More Information needed
|
[
"# Dataset Card for \"mushi-snli-llama2-grammar_struct-10k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mushi-snli-llama2-grammar_struct-10k\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mushi-snli-llama2-grammar_struct-10k\"\n\nMore Information needed"
] |
638cfa0030aabc2d309976a4c389fdad74dddaca
|
# Dataset Card for "pri_docidv2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pnadel/pri_docidv2
|
[
"region:us"
] |
2023-08-30T18:51:36+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Handwritten", "1": "Publication", "2": "Tabular", "3": "Typeset document", "4": "Unusable"}}}}], "splits": [{"name": "train", "num_bytes": 21654067.34670487, "num_examples": 488}, {"name": "test", "num_bytes": 9318348.65329513, "num_examples": 210}], "download_size": 30987466, "dataset_size": 30972416.0}}
|
2023-08-30T18:51:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pri_docidv2"
More Information needed
|
[
"# Dataset Card for \"pri_docidv2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pri_docidv2\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pri_docidv2\"\n\nMore Information needed"
] |
b3dddf80cb27ff43df094c8bfe9ab83c8a5f02b3
|
# Dataset Card for "RLCD-generated-preference-data-split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TaylorAI/RLCD-generated-preference-data-split
|
[
"region:us"
] |
2023-08-30T19:06:24+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "float64"}, {"name": "output_1", "dtype": "string"}, {"name": "output_2", "dtype": "string"}, {"name": "preference", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 142629947, "num_examples": 160000}, {"name": "validation", "num_bytes": 7163731, "num_examples": 7999}], "download_size": 88067760, "dataset_size": 149793678}}
|
2023-08-30T19:16:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "RLCD-generated-preference-data-split"
More Information needed
|
[
"# Dataset Card for \"RLCD-generated-preference-data-split\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"RLCD-generated-preference-data-split\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"RLCD-generated-preference-data-split\"\n\nMore Information needed"
] |
2931af771e46610c49fa3d65b34a584df19d0f0b
|
Image + Text!
|
Neu256/Arc-diffusion-1.2.1
|
[
"region:us"
] |
2023-08-30T19:24:34+00:00
|
{}
|
2023-08-30T19:27:02+00:00
|
[] |
[] |
TAGS
#region-us
|
Image + Text!
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
7a8ea44bf8ad2462dfd376700d9a413caadbf46c
|
# Dataset Card for "autotree_automl_credit_gosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_credit_gosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-30T19:33:25+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3388000000, "num_examples": 100000}, {"name": "validation", "num_bytes": 338800000, "num_examples": 10000}], "download_size": 840908403, "dataset_size": 3726800000}}
|
2023-08-30T19:34:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_credit_gosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_credit_gosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_credit_gosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_credit_gosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
6a59190d2c06a14ce2bdb285469766994be44132
|
# Dataset Card for "eli5-subquestion-paired"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
JihyukKim/eli5-subquestion-paired
|
[
"region:us"
] |
2023-08-30T19:38:23+00:00
|
{"dataset_info": {"features": [{"name": "qid", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response_j", "dtype": "string"}, {"name": "response_k", "dtype": "string"}, {"name": "gold_claims", "sequence": "string"}, {"name": "response_j_claims", "sequence": "string"}, {"name": "response_k_claims", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 52398773, "num_examples": 43725}, {"name": "test", "num_bytes": 981221, "num_examples": 831}], "download_size": 8716749, "dataset_size": 53379994}}
|
2023-08-30T19:38:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eli5-subquestion-paired"
More Information needed
|
[
"# Dataset Card for \"eli5-subquestion-paired\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eli5-subquestion-paired\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eli5-subquestion-paired\"\n\nMore Information needed"
] |
9bd134244ca76c16e7ad2ce552d27d944e02e3f3
|
# Dataset Card for Evaluation run of Fredithefish/Guanaco-3B-Uncensored-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Fredithefish/Guanaco-3B-Uncensored-v2
- **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 [Fredithefish/Guanaco-3B-Uncensored-v2](https://huggingface.co/Fredithefish/Guanaco-3B-Uncensored-v2) 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_Fredithefish__Guanaco-3B-Uncensored-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-14T16:41:50.908036](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__Guanaco-3B-Uncensored-v2/blob/main/results_2023-10-14T16-41-50.908036.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": 0.007550335570469799,
"em_stderr": 0.0008864964951979535,
"f1": 0.06604236577181238,
"f1_stderr": 0.0016343124784852466,
"acc": 0.3180119592645405,
"acc_stderr": 0.007530429037072097
},
"harness|drop|3": {
"em": 0.007550335570469799,
"em_stderr": 0.0008864964951979535,
"f1": 0.06604236577181238,
"f1_stderr": 0.0016343124784852466
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245497
},
"harness|winogrande|5": {
"acc": 0.632991318074191,
"acc_stderr": 0.013546284512919645
}
}
```
### 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_Fredithefish__Guanaco-3B-Uncensored-v2
|
[
"region:us"
] |
2023-08-30T19:46:59+00:00
|
{"pretty_name": "Evaluation run of Fredithefish/Guanaco-3B-Uncensored-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Fredithefish/Guanaco-3B-Uncensored-v2](https://huggingface.co/Fredithefish/Guanaco-3B-Uncensored-v2) 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_Fredithefish__Guanaco-3B-Uncensored-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-14T16:41:50.908036](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__Guanaco-3B-Uncensored-v2/blob/main/results_2023-10-14T16-41-50.908036.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\": 0.007550335570469799,\n \"em_stderr\": 0.0008864964951979535,\n \"f1\": 0.06604236577181238,\n \"f1_stderr\": 0.0016343124784852466,\n \"acc\": 0.3180119592645405,\n \"acc_stderr\": 0.007530429037072097\n },\n \"harness|drop|3\": {\n \"em\": 0.007550335570469799,\n \"em_stderr\": 0.0008864964951979535,\n \"f1\": 0.06604236577181238,\n \"f1_stderr\": 0.0016343124784852466\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \"acc_stderr\": 0.0015145735612245497\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.632991318074191,\n \"acc_stderr\": 0.013546284512919645\n }\n}\n```", "repo_url": "https://huggingface.co/Fredithefish/Guanaco-3B-Uncensored-v2", "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_08_30T20_46_40.695179", "path": ["**/details_harness|arc:challenge|25_2023-08-30T20:46:40.695179.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T20:46:40.695179.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_14T16_41_50.908036", "path": ["**/details_harness|drop|3_2023-10-14T16-41-50.908036.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-14T16-41-50.908036.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_14T16_41_50.908036", "path": ["**/details_harness|gsm8k|5_2023-10-14T16-41-50.908036.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-14T16-41-50.908036.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T20_46_40.695179", "path": ["**/details_harness|hellaswag|10_2023-08-30T20:46:40.695179.parquet"]}, {"split": "latest", "path": 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"path": ["**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T20:46:40.695179.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T20:46:40.695179.parquet"]}]}, {"config_name": "harness_hendrycksTest_clinical_knowledge_5", "data_files": [{"split": "2023_08_30T20_46_40.695179", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T20:46:40.695179.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T20:46:40.695179.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_biology_5", "data_files": [{"split": "2023_08_30T20_46_40.695179", "path": ["**/details_harness|hendrycksTest-college_biology|5_2023-08-30T20:46:40.695179.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-college_biology|5_2023-08-30T20:46:40.695179.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_chemistry_5", "data_files": [{"split": 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2023-10-14T15:42:03+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of Fredithefish/Guanaco-3B-Uncensored-v2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Fredithefish/Guanaco-3B-Uncensored-v2 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 2023-10-14T16:41:50.908036(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 Fredithefish/Guanaco-3B-Uncensored-v2",
"## 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 Fredithefish/Guanaco-3B-Uncensored-v2 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 2023-10-14T16:41:50.908036(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 Fredithefish/Guanaco-3B-Uncensored-v2",
"## 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 Fredithefish/Guanaco-3B-Uncensored-v2 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 2023-10-14T16:41:50.908036(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,
25,
31,
173,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Fredithefish/Guanaco-3B-Uncensored-v2## 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 Fredithefish/Guanaco-3B-Uncensored-v2 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 2023-10-14T16:41:50.908036(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"
] |
218aa2036e0fafdd6c3a9efda002b649549331cb
|
# Dataset Card for "eli5-subquestion-paired-sft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
JihyukKim/eli5-subquestion-paired-sft
|
[
"region:us"
] |
2023-08-30T19:48:23+00:00
|
{"dataset_info": {"features": [{"name": "qid", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response_j", "dtype": "string"}, {"name": "response_k", "dtype": "string"}, {"name": "gold_claims", "sequence": "string"}, {"name": "response_j_claims", "sequence": "string"}, {"name": "response_k_claims", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 20711053, "num_examples": 17578}, {"name": "test", "num_bytes": 399389, "num_examples": 340}], "download_size": 6461297, "dataset_size": 21110442}}
|
2023-08-30T19:48:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eli5-subquestion-paired-sft"
More Information needed
|
[
"# Dataset Card for \"eli5-subquestion-paired-sft\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eli5-subquestion-paired-sft\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eli5-subquestion-paired-sft\"\n\nMore Information needed"
] |
f9d0ffe100756527d7610187c2e7f1c736369ea4
|
# Dataset Card for "python-code-instructions-18k-alpaca-standardized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HydraLM/python-code-instructions-18k-alpaca-standardized
|
[
"region:us"
] |
2023-08-30T19:50:39+00:00
|
{"dataset_info": {"features": [{"name": "message", "dtype": "string"}, {"name": "message_type", "dtype": "string"}, {"name": "message_id", "dtype": "int64"}, {"name": "conversation_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 12760071, "num_examples": 55836}], "download_size": 5720247, "dataset_size": 12760071}}
|
2023-08-30T19:50:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "python-code-instructions-18k-alpaca-standardized"
More Information needed
|
[
"# Dataset Card for \"python-code-instructions-18k-alpaca-standardized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"python-code-instructions-18k-alpaca-standardized\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"python-code-instructions-18k-alpaca-standardized\"\n\nMore Information needed"
] |
8dd69ff4e4365df869a0a321429cc7958b3de3a6
|
# Dataset Card for "autotree_automl_bank-marketing_gosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_bank-marketing_gosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-30T19:57:06+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2773600000, "num_examples": 100000}, {"name": "validation", "num_bytes": 277360000, "num_examples": 10000}], "download_size": 412140145, "dataset_size": 3050960000}}
|
2023-08-30T19:57:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_bank-marketing_gosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_bank-marketing_gosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_bank-marketing_gosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
32
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_bank-marketing_gosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
2d134335b24111315215f106723585e85d3724f2
|
# Dataset Card for "code_filtered"
This includes data from [xlcost](https://huggingface.co/datasets/vikp/xlcost_filtered_2k), [evol instruct](https://huggingface.co/datasets/vikp/evol_instruct_code_filtered_39k), [code alpaca](https://huggingface.co/datasets/vikp/evol_codealpaca_filtered_87k), [code instructions](https://huggingface.co/datasets/vikp/code_instructions_filtered_7k), and [code search net](https://huggingface.co/datasets/vikp/code_search_net_filtered_34k). Data is filtered based on quality and learning value.
|
vikp/python_code_instructions_filtered
|
[
"region:us"
] |
2023-08-30T20:18:43+00:00
|
{"dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "kind", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 313731517, "num_examples": 170635}], "download_size": 160726948, "dataset_size": 313731517}}
|
2023-08-31T01:45:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "code_filtered"
This includes data from xlcost, evol instruct, code alpaca, code instructions, and code search net. Data is filtered based on quality and learning value.
|
[
"# Dataset Card for \"code_filtered\"\n\nThis includes data from xlcost, evol instruct, code alpaca, code instructions, and code search net. Data is filtered based on quality and learning value."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"code_filtered\"\n\nThis includes data from xlcost, evol instruct, code alpaca, code instructions, and code search net. Data is filtered based on quality and learning value."
] |
[
6,
48
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"code_filtered\"\n\nThis includes data from xlcost, evol instruct, code alpaca, code instructions, and code search net. Data is filtered based on quality and learning value."
] |
6635ef217998d847c24471e48e8c9c2840329eea
|
This dataset contains code changes in each commit of most starred python project, stored on GitHub.
## Code to reproduce the parsing process
To parse code we performed the following steps:
* Get list of most starred GitHub repos via API
* With **git** python package clone all the repos from the list to local machine and write code defference for each commit of every repo to the dataset.
* Clean dataset to remove to large commits, commits with not python code changes, commits with non-ASCII chars, etc.
* Group files changed in 1 commit into single sample of the dataset.
To reproduce these steps you need to:
1) run *src/github_parsing.ipynb* to parse repos from github
2) to clean the data and group dataset samples run *src/data_cleaning.ipynb*
## Dataset features
Dataset have the following features:
1) repo_name
2) commit_message
3) commit_changes - changes in code in all python files, contained in the commit
4) files_changed - number of files, changed in the commit
5) changes_len - number of chars in the code changes
For model training we used only *commit_message* feature as a label and *commit_changes* as an input for the model.
Code changes have the following structure:
```
<filename> name_of_the_file <filename>
code_of_changes
<commit_msg>
```
Special tokens used in the input:
* <file_name> - used to separate name of the file
* <code_del> and <code_add> used to separate added or deleted lines of code in the commit
* <commit_msg> used to separate commit message
Example of input for the model:
```
<filename> a/tests/test_constraint.py b/tests/test_constraint.py<filename>
<code_del>--- a/tests/test_constraint.py<code_del>
<code_add>+++ b/tests/test_constraint.py<code_add>
@@ -87,10 +87,15 @@ def test_accurate_approximation_when_known():
n_iter=10,
)
<code_del>- params = optimizer.res[0]["params"]<code_del>
<code_del>- x, y = params['x'], params['y']<code_del>
<code_add>+ # Exclude the last sampled point, because the constraint is not fitted on that.<code_add>
<code_add>+ res = np.array([[r['target'], r['constraint'], r['params']['x'], r['params']['y']] for r in optimizer.res[:-1]])<code_add>
<code_add>+<code_add>
<code_add>+ xy = res[:, [2, 3]]<code_add>
<code_add>+ x = res[:, 2]<code_add>
<code_add>+ y = res[:, 3]<code_add>
<code_del>- assert constraint_function(x, y) == approx(conmod.approx(np.array([x, y])), rel=1e-5, abs=1e-5)<code_del>
<code_add>+ assert constraint_function(x, y) == approx(conmod.approx(xy), rel=1e-5, abs=1e-5)<code_add>
<code_add>+ assert constraint_function(x, y) == approx(optimizer.space.constraint_values[:-1], rel=1e-5, abs=1e-5)<code_add>
def test_multiple_constraints():
<commit_msg>In case of commit with the several files changed, different files are separated with 3 blank lines.<eos>
```
In case of commit with the several files changed, different files are separated with 3 blank lines.
|
narySt/github_commits
|
[
"size_categories:n<1K",
"language:en",
"license:mit",
"region:us"
] |
2023-08-30T20:20:18+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "pretty_name": "github-commits", "dataset_info": {"features": [{"name": "input_ids", "sequence": "int64"}, {"name": "attention_mask", "sequence": "int64"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 257967900, "num_examples": 20973}, {"name": "val", "num_bytes": 45891300, "num_examples": 3731}], "download_size": 10916827, "dataset_size": 303859200}}
|
2023-09-12T20:21:08+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-n<1K #language-English #license-mit #region-us
|
This dataset contains code changes in each commit of most starred python project, stored on GitHub.
## Code to reproduce the parsing process
To parse code we performed the following steps:
* Get list of most starred GitHub repos via API
* With git python package clone all the repos from the list to local machine and write code defference for each commit of every repo to the dataset.
* Clean dataset to remove to large commits, commits with not python code changes, commits with non-ASCII chars, etc.
* Group files changed in 1 commit into single sample of the dataset.
To reproduce these steps you need to:
1) run *src/github_parsing.ipynb* to parse repos from github
2) to clean the data and group dataset samples run *src/data_cleaning.ipynb*
## Dataset features
Dataset have the following features:
1) repo_name
2) commit_message
3) commit_changes - changes in code in all python files, contained in the commit
4) files_changed - number of files, changed in the commit
5) changes_len - number of chars in the code changes
For model training we used only *commit_message* feature as a label and *commit_changes* as an input for the model.
Code changes have the following structure:
Special tokens used in the input:
* <file_name> - used to separate name of the file
* <code_del> and <code_add> used to separate added or deleted lines of code in the commit
* <commit_msg> used to separate commit message
Example of input for the model:
In case of commit with the several files changed, different files are separated with 3 blank lines.
|
[
"## Code to reproduce the parsing process\nTo parse code we performed the following steps:\n* Get list of most starred GitHub repos via API\n* With git python package clone all the repos from the list to local machine and write code defference for each commit of every repo to the dataset.\n* Clean dataset to remove to large commits, commits with not python code changes, commits with non-ASCII chars, etc.\n* Group files changed in 1 commit into single sample of the dataset.\nTo reproduce these steps you need to:\n1) run *src/github_parsing.ipynb* to parse repos from github \n2) to clean the data and group dataset samples run *src/data_cleaning.ipynb*",
"## Dataset features\nDataset have the following features:\n1) repo_name\n2) commit_message\n3) commit_changes - changes in code in all python files, contained in the commit\n4) files_changed - number of files, changed in the commit\n5) changes_len - number of chars in the code changes\nFor model training we used only *commit_message* feature as a label and *commit_changes* as an input for the model.\nCode changes have the following structure:\n\nSpecial tokens used in the input:\n* <file_name> - used to separate name of the file\n* <code_del> and <code_add> used to separate added or deleted lines of code in the commit\n* <commit_msg> used to separate commit message \n\nExample of input for the model:\n\nIn case of commit with the several files changed, different files are separated with 3 blank lines."
] |
[
"TAGS\n#size_categories-n<1K #language-English #license-mit #region-us \n",
"## Code to reproduce the parsing process\nTo parse code we performed the following steps:\n* Get list of most starred GitHub repos via API\n* With git python package clone all the repos from the list to local machine and write code defference for each commit of every repo to the dataset.\n* Clean dataset to remove to large commits, commits with not python code changes, commits with non-ASCII chars, etc.\n* Group files changed in 1 commit into single sample of the dataset.\nTo reproduce these steps you need to:\n1) run *src/github_parsing.ipynb* to parse repos from github \n2) to clean the data and group dataset samples run *src/data_cleaning.ipynb*",
"## Dataset features\nDataset have the following features:\n1) repo_name\n2) commit_message\n3) commit_changes - changes in code in all python files, contained in the commit\n4) files_changed - number of files, changed in the commit\n5) changes_len - number of chars in the code changes\nFor model training we used only *commit_message* feature as a label and *commit_changes* as an input for the model.\nCode changes have the following structure:\n\nSpecial tokens used in the input:\n* <file_name> - used to separate name of the file\n* <code_del> and <code_add> used to separate added or deleted lines of code in the commit\n* <commit_msg> used to separate commit message \n\nExample of input for the model:\n\nIn case of commit with the several files changed, different files are separated with 3 blank lines."
] |
[
25,
178,
202
] |
[
"passage: TAGS\n#size_categories-n<1K #language-English #license-mit #region-us \n## Code to reproduce the parsing process\nTo parse code we performed the following steps:\n* Get list of most starred GitHub repos via API\n* With git python package clone all the repos from the list to local machine and write code defference for each commit of every repo to the dataset.\n* Clean dataset to remove to large commits, commits with not python code changes, commits with non-ASCII chars, etc.\n* Group files changed in 1 commit into single sample of the dataset.\nTo reproduce these steps you need to:\n1) run *src/github_parsing.ipynb* to parse repos from github \n2) to clean the data and group dataset samples run *src/data_cleaning.ipynb*## Dataset features\nDataset have the following features:\n1) repo_name\n2) commit_message\n3) commit_changes - changes in code in all python files, contained in the commit\n4) files_changed - number of files, changed in the commit\n5) changes_len - number of chars in the code changes\nFor model training we used only *commit_message* feature as a label and *commit_changes* as an input for the model.\nCode changes have the following structure:\n\nSpecial tokens used in the input:\n* <file_name> - used to separate name of the file\n* <code_del> and <code_add> used to separate added or deleted lines of code in the commit\n* <commit_msg> used to separate commit message \n\nExample of input for the model:\n\nIn case of commit with the several files changed, different files are separated with 3 blank lines."
] |
5334d0637ca2a36c1732e2ede5648da7e531557e
|
# Dataset Card for ComPile: A Large IR Dataset from Production Sources
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Changelog](#changelog)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Usage](#dataset-usage)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Dataset Size](#dataset-size)
- [Licensing](#licensing)
- [How to Cite](#how-to-cite)
## Dataset Description
- **Homepage:** https://llvm-ml.github.io/ComPile/
- **Paper:** https://arxiv.org/abs/2309.15432
- **Leaderboard:** N/A
### Changelog
|Release|Programming Languages|Description|
|-|-|-|
|v1.0| C/C++, Rust, Swift, Julia | Fine Tuning-scale dataset of 564GB of deduplicated LLVM IR |
### Dataset Summary
ComPile contains over 500GB of permissively-licensed source code compiled to [LLVM](https://llvm.org) intermediate representation (IR) covering C/C++, Rust, Swift, and Julia.
The dataset was created by hooking into LLVM code generation either through the language's package manager or the
compiler directly to extract the dataset of intermediate representations from production grade programs using our
[dataset collection utility for the LLVM compilation infrastructure](https://doi.org/10.5281/zenodo.10155761).
### Languages
The dataset contains **5 programming languages** as of v1.0.
```
"c++", "c", "rust", "swift", "julia"
```
### Dataset Usage
To use ComPile we recommend HuggingFace's [datasets library](https://huggingface.co/docs/datasets/index). To e.g. load the dataset:
```python
from datasets import load_dataset
ds = load_dataset('llvm-ml/ComPile', split='train')
```
By default this will download the entirety of the 550GB+ dataset, and cache it locally at the directory
specified by the environment variable `HF_DATASETS_CACHE`, which defaults to `~/.cache/huggingface`. To
load the dataset in a streaming format, where the data is not saved locally:
```python
ds = load_dataset('llvm-ml/ComPile', split='train', streaming=True)
```
For further arguments of `load_dataset`, please take a look at the
`loading a dataset` [documentation](https://huggingface.co/docs/datasets/load_hub), and
the `streaming` [documentation](https://huggingface.co/docs/datasets/stream). Bear in mind that
this is significantly slower than loading the dataset from a local storage. For experimentation that
requires more performance but might not require the whole dataset, you can also specify a portion
of the dataset to download. For example, the following code will only download the first 10%
of the dataset:
```python
ds = load_dataset('llvm-ml/ComPile', split='train[:10%]')
```
Once the dataset has been loaded, the individual module files can be accessed by iterating through
the dataset or accessing specific indices:
```python
# We can iterate through the dataset
next(iter(ds))
# We can also access modules at specific indices
ds[0]
```
Filtering and map operations can be performed with the primitives available within the
HuggingFace `datasets` library.
## Dataset Structure
### Data Fields
Each row in the dataset consists of an individual LLVM-IR Module along with some metadata. There are
six columns associated with each row:
- `content` (string): This column contains the raw bitcode that composes the module. This can be written to a `.bc`
file and manipulated using the standard llvm utilities or passed in directly through stdin if using something
like Python's `subprocess`.
- `license_expression` (string): This column contains the SPDX expression describing the license of the project that the
module came from.
- `license_source` (string): This column describes the way the `license_expression` was determined. This might indicate
an individual package ecosystem (eg `spack`), license detection (eg `go_license_detector`), or might also indicate
manual curation (`manual`).
- `license_files`: This column contains an array of license files. These file names map to licenses included in
`/licenses/licenses-0.parquet`.
- `package_source` (string): This column contains information on the package that the module was sourced from. This is
typically a link to a tar archive or git repository from which the project was built, but might also contain a
mapping to a specific package ecosystem that provides the source, such as Spack.
- `language` (string): This column indicates the source language that the module was compiled from.
## Dataset Size
| Langauge | Raw Size | License Constraints | Deduplicated + License Constraints |
|----------|----------|---------------------|------------------------------------|
| C/C++ | 124GB | 47GB | 31GB |
| C | N/A | N/A | 3GB |
| C++ | N/A | N/A | 28GB |
| Julia | 201GB | 179GB | 153GB |
| Swift | 8GB | 7GB | 7GB |
| Rust | 656GB | 443GB | 373GB |
| Total | 989GB | 676GB | 564GB |
The raw size is the size obtained directly from building all the projects. The license constraints column
shows the size per language after license information is taken into account. The last column shows the size
when both license constraints and deduplication are taken into account, which is what is included in the
dataset.
Note that the sizes displayed here are of the compressed bitcode representation rather
than textual IR. We see an expansion ratio of 2-5x, averaging around 4x when converting
from compressed bitcode to textual IR.
## Dataset Construction
Exact details on how the dataset is constructed are available in
[our paper describing the dataset](https://arxiv.org/abs/2309.15432). The packages for
v1.0 of the dataset were downloaded and built on 1/12/24-1/13/24.
## Licensing
The individual modules within the dataset are subject to the licenses of the projects that they come from. License
information is available in each row, including the SPDX license expression, the license files, and also a link to
the package source where license information can be further validated.
The curation of these modules is licensed under a CC-BY-4.0 license.
## Contact Info
1. Aiden Grossman ([email protected])
2. Ludger Paehler ([email protected])
3. Johannes Doerfert ([email protected])
## How to Cite
Please cite the dataset in the following format:
```bibtex
@article{grossman2023compile,
title={ComPile: A Large IR Dataset from Production Sources},
author={Grossman, Aiden and Paehler, Ludger and Parasyris, Konstantinos and Ben-Nun, Tal and Hegna, Jacob and Moses, William and Diaz, Jose M Monsalve and Trofin, Mircea and Doerfert, Johannes},
journal={arXiv preprint arXiv:2309.15432},
year={2023}
}
```
|
llvm-ml/ComPile
|
[
"task_categories:text-generation",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:cc-by-4.0",
"arxiv:2309.15432",
"region:us"
] |
2023-08-30T20:26:11+00:00
|
{"annotations_creators": [], "language": ["code"], "license": "cc-by-4.0", "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": [], "pretty_name": "ComPile"}
|
2024-02-08T09:42:48+00:00
|
[
"2309.15432"
] |
[
"code"
] |
TAGS
#task_categories-text-generation #multilinguality-multilingual #size_categories-unknown #language-code #license-cc-by-4.0 #arxiv-2309.15432 #region-us
|
Dataset Card for ComPile: A Large IR Dataset from Production Sources
====================================================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Changelog
+ Dataset Summary
+ Languages
+ Dataset Usage
* Dataset Structure
+ Data Fields
* Dataset Size
* Licensing
* How to Cite
Dataset Description
-------------------
* Homepage: URL
* Paper: URL
* Leaderboard: N/A
### Changelog
Release: v1.0, Programming Languages: C/C++, Rust, Swift, Julia, Description: Fine Tuning-scale dataset of 564GB of deduplicated LLVM IR
### Dataset Summary
ComPile contains over 500GB of permissively-licensed source code compiled to LLVM intermediate representation (IR) covering C/C++, Rust, Swift, and Julia.
The dataset was created by hooking into LLVM code generation either through the language's package manager or the
compiler directly to extract the dataset of intermediate representations from production grade programs using our
dataset collection utility for the LLVM compilation infrastructure.
### Languages
The dataset contains 5 programming languages as of v1.0.
### Dataset Usage
To use ComPile we recommend HuggingFace's datasets library. To e.g. load the dataset:
By default this will download the entirety of the 550GB+ dataset, and cache it locally at the directory
specified by the environment variable 'HF\_DATASETS\_CACHE', which defaults to '~/.cache/huggingface'. To
load the dataset in a streaming format, where the data is not saved locally:
For further arguments of 'load\_dataset', please take a look at the
'loading a dataset' documentation, and
the 'streaming' documentation. Bear in mind that
this is significantly slower than loading the dataset from a local storage. For experimentation that
requires more performance but might not require the whole dataset, you can also specify a portion
of the dataset to download. For example, the following code will only download the first 10%
of the dataset:
Once the dataset has been loaded, the individual module files can be accessed by iterating through
the dataset or accessing specific indices:
Filtering and map operations can be performed with the primitives available within the
HuggingFace 'datasets' library.
Dataset Structure
-----------------
### Data Fields
Each row in the dataset consists of an individual LLVM-IR Module along with some metadata. There are
six columns associated with each row:
* 'content' (string): This column contains the raw bitcode that composes the module. This can be written to a '.bc'
file and manipulated using the standard llvm utilities or passed in directly through stdin if using something
like Python's 'subprocess'.
* 'license\_expression' (string): This column contains the SPDX expression describing the license of the project that the
module came from.
* 'license\_source' (string): This column describes the way the 'license\_expression' was determined. This might indicate
an individual package ecosystem (eg 'spack'), license detection (eg 'go\_license\_detector'), or might also indicate
manual curation ('manual').
* 'license\_files': This column contains an array of license files. These file names map to licenses included in
'/licenses/licenses-0.parquet'.
* 'package\_source' (string): This column contains information on the package that the module was sourced from. This is
typically a link to a tar archive or git repository from which the project was built, but might also contain a
mapping to a specific package ecosystem that provides the source, such as Spack.
* 'language' (string): This column indicates the source language that the module was compiled from.
Dataset Size
------------
The raw size is the size obtained directly from building all the projects. The license constraints column
shows the size per language after license information is taken into account. The last column shows the size
when both license constraints and deduplication are taken into account, which is what is included in the
dataset.
Note that the sizes displayed here are of the compressed bitcode representation rather
than textual IR. We see an expansion ratio of 2-5x, averaging around 4x when converting
from compressed bitcode to textual IR.
Dataset Construction
--------------------
Exact details on how the dataset is constructed are available in
our paper describing the dataset. The packages for
v1.0 of the dataset were downloaded and built on 1/12/24-1/13/24.
Licensing
---------
The individual modules within the dataset are subject to the licenses of the projects that they come from. License
information is available in each row, including the SPDX license expression, the license files, and also a link to
the package source where license information can be further validated.
The curation of these modules is licensed under a CC-BY-4.0 license.
Contact Info
------------
1. Aiden Grossman (amgrossman@URL)
2. Ludger Paehler (paehlerludger@URL)
3. Johannes Doerfert (doerfert1@URL)
How to Cite
-----------
Please cite the dataset in the following format:
|
[
"### Changelog\n\n\nRelease: v1.0, Programming Languages: C/C++, Rust, Swift, Julia, Description: Fine Tuning-scale dataset of 564GB of deduplicated LLVM IR",
"### Dataset Summary\n\n\nComPile contains over 500GB of permissively-licensed source code compiled to LLVM intermediate representation (IR) covering C/C++, Rust, Swift, and Julia.\nThe dataset was created by hooking into LLVM code generation either through the language's package manager or the\ncompiler directly to extract the dataset of intermediate representations from production grade programs using our\ndataset collection utility for the LLVM compilation infrastructure.",
"### Languages\n\n\nThe dataset contains 5 programming languages as of v1.0.",
"### Dataset Usage\n\n\nTo use ComPile we recommend HuggingFace's datasets library. To e.g. load the dataset:\n\n\nBy default this will download the entirety of the 550GB+ dataset, and cache it locally at the directory\nspecified by the environment variable 'HF\\_DATASETS\\_CACHE', which defaults to '~/.cache/huggingface'. To\nload the dataset in a streaming format, where the data is not saved locally:\n\n\nFor further arguments of 'load\\_dataset', please take a look at the\n'loading a dataset' documentation, and\nthe 'streaming' documentation. Bear in mind that\nthis is significantly slower than loading the dataset from a local storage. For experimentation that\nrequires more performance but might not require the whole dataset, you can also specify a portion\nof the dataset to download. For example, the following code will only download the first 10%\nof the dataset:\n\n\nOnce the dataset has been loaded, the individual module files can be accessed by iterating through\nthe dataset or accessing specific indices:\n\n\nFiltering and map operations can be performed with the primitives available within the\nHuggingFace 'datasets' library.\n\n\nDataset Structure\n-----------------",
"### Data Fields\n\n\nEach row in the dataset consists of an individual LLVM-IR Module along with some metadata. There are\nsix columns associated with each row:\n\n\n* 'content' (string): This column contains the raw bitcode that composes the module. This can be written to a '.bc'\nfile and manipulated using the standard llvm utilities or passed in directly through stdin if using something\nlike Python's 'subprocess'.\n* 'license\\_expression' (string): This column contains the SPDX expression describing the license of the project that the\nmodule came from.\n* 'license\\_source' (string): This column describes the way the 'license\\_expression' was determined. This might indicate\nan individual package ecosystem (eg 'spack'), license detection (eg 'go\\_license\\_detector'), or might also indicate\nmanual curation ('manual').\n* 'license\\_files': This column contains an array of license files. These file names map to licenses included in\n'/licenses/licenses-0.parquet'.\n* 'package\\_source' (string): This column contains information on the package that the module was sourced from. This is\ntypically a link to a tar archive or git repository from which the project was built, but might also contain a\nmapping to a specific package ecosystem that provides the source, such as Spack.\n* 'language' (string): This column indicates the source language that the module was compiled from.\n\n\nDataset Size\n------------\n\n\n\nThe raw size is the size obtained directly from building all the projects. The license constraints column\nshows the size per language after license information is taken into account. The last column shows the size\nwhen both license constraints and deduplication are taken into account, which is what is included in the\ndataset.\n\n\nNote that the sizes displayed here are of the compressed bitcode representation rather\nthan textual IR. We see an expansion ratio of 2-5x, averaging around 4x when converting\nfrom compressed bitcode to textual IR.\n\n\nDataset Construction\n--------------------\n\n\nExact details on how the dataset is constructed are available in\nour paper describing the dataset. The packages for\nv1.0 of the dataset were downloaded and built on 1/12/24-1/13/24.\n\n\nLicensing\n---------\n\n\nThe individual modules within the dataset are subject to the licenses of the projects that they come from. License\ninformation is available in each row, including the SPDX license expression, the license files, and also a link to\nthe package source where license information can be further validated.\n\n\nThe curation of these modules is licensed under a CC-BY-4.0 license.\n\n\nContact Info\n------------\n\n\n1. Aiden Grossman (amgrossman@URL)\n2. Ludger Paehler (paehlerludger@URL)\n3. Johannes Doerfert (doerfert1@URL)\n\n\nHow to Cite\n-----------\n\n\nPlease cite the dataset in the following format:"
] |
[
"TAGS\n#task_categories-text-generation #multilinguality-multilingual #size_categories-unknown #language-code #license-cc-by-4.0 #arxiv-2309.15432 #region-us \n",
"### Changelog\n\n\nRelease: v1.0, Programming Languages: C/C++, Rust, Swift, Julia, Description: Fine Tuning-scale dataset of 564GB of deduplicated LLVM IR",
"### Dataset Summary\n\n\nComPile contains over 500GB of permissively-licensed source code compiled to LLVM intermediate representation (IR) covering C/C++, Rust, Swift, and Julia.\nThe dataset was created by hooking into LLVM code generation either through the language's package manager or the\ncompiler directly to extract the dataset of intermediate representations from production grade programs using our\ndataset collection utility for the LLVM compilation infrastructure.",
"### Languages\n\n\nThe dataset contains 5 programming languages as of v1.0.",
"### Dataset Usage\n\n\nTo use ComPile we recommend HuggingFace's datasets library. To e.g. load the dataset:\n\n\nBy default this will download the entirety of the 550GB+ dataset, and cache it locally at the directory\nspecified by the environment variable 'HF\\_DATASETS\\_CACHE', which defaults to '~/.cache/huggingface'. To\nload the dataset in a streaming format, where the data is not saved locally:\n\n\nFor further arguments of 'load\\_dataset', please take a look at the\n'loading a dataset' documentation, and\nthe 'streaming' documentation. Bear in mind that\nthis is significantly slower than loading the dataset from a local storage. For experimentation that\nrequires more performance but might not require the whole dataset, you can also specify a portion\nof the dataset to download. For example, the following code will only download the first 10%\nof the dataset:\n\n\nOnce the dataset has been loaded, the individual module files can be accessed by iterating through\nthe dataset or accessing specific indices:\n\n\nFiltering and map operations can be performed with the primitives available within the\nHuggingFace 'datasets' library.\n\n\nDataset Structure\n-----------------",
"### Data Fields\n\n\nEach row in the dataset consists of an individual LLVM-IR Module along with some metadata. There are\nsix columns associated with each row:\n\n\n* 'content' (string): This column contains the raw bitcode that composes the module. This can be written to a '.bc'\nfile and manipulated using the standard llvm utilities or passed in directly through stdin if using something\nlike Python's 'subprocess'.\n* 'license\\_expression' (string): This column contains the SPDX expression describing the license of the project that the\nmodule came from.\n* 'license\\_source' (string): This column describes the way the 'license\\_expression' was determined. This might indicate\nan individual package ecosystem (eg 'spack'), license detection (eg 'go\\_license\\_detector'), or might also indicate\nmanual curation ('manual').\n* 'license\\_files': This column contains an array of license files. These file names map to licenses included in\n'/licenses/licenses-0.parquet'.\n* 'package\\_source' (string): This column contains information on the package that the module was sourced from. This is\ntypically a link to a tar archive or git repository from which the project was built, but might also contain a\nmapping to a specific package ecosystem that provides the source, such as Spack.\n* 'language' (string): This column indicates the source language that the module was compiled from.\n\n\nDataset Size\n------------\n\n\n\nThe raw size is the size obtained directly from building all the projects. The license constraints column\nshows the size per language after license information is taken into account. The last column shows the size\nwhen both license constraints and deduplication are taken into account, which is what is included in the\ndataset.\n\n\nNote that the sizes displayed here are of the compressed bitcode representation rather\nthan textual IR. We see an expansion ratio of 2-5x, averaging around 4x when converting\nfrom compressed bitcode to textual IR.\n\n\nDataset Construction\n--------------------\n\n\nExact details on how the dataset is constructed are available in\nour paper describing the dataset. The packages for\nv1.0 of the dataset were downloaded and built on 1/12/24-1/13/24.\n\n\nLicensing\n---------\n\n\nThe individual modules within the dataset are subject to the licenses of the projects that they come from. License\ninformation is available in each row, including the SPDX license expression, the license files, and also a link to\nthe package source where license information can be further validated.\n\n\nThe curation of these modules is licensed under a CC-BY-4.0 license.\n\n\nContact Info\n------------\n\n\n1. Aiden Grossman (amgrossman@URL)\n2. Ludger Paehler (paehlerludger@URL)\n3. Johannes Doerfert (doerfert1@URL)\n\n\nHow to Cite\n-----------\n\n\nPlease cite the dataset in the following format:"
] |
[
55,
47,
106,
19,
286,
684
] |
[
"passage: TAGS\n#task_categories-text-generation #multilinguality-multilingual #size_categories-unknown #language-code #license-cc-by-4.0 #arxiv-2309.15432 #region-us \n### Changelog\n\n\nRelease: v1.0, Programming Languages: C/C++, Rust, Swift, Julia, Description: Fine Tuning-scale dataset of 564GB of deduplicated LLVM IR### Dataset Summary\n\n\nComPile contains over 500GB of permissively-licensed source code compiled to LLVM intermediate representation (IR) covering C/C++, Rust, Swift, and Julia.\nThe dataset was created by hooking into LLVM code generation either through the language's package manager or the\ncompiler directly to extract the dataset of intermediate representations from production grade programs using our\ndataset collection utility for the LLVM compilation infrastructure.### Languages\n\n\nThe dataset contains 5 programming languages as of v1.0.",
"passage: ### Dataset Usage\n\n\nTo use ComPile we recommend HuggingFace's datasets library. To e.g. load the dataset:\n\n\nBy default this will download the entirety of the 550GB+ dataset, and cache it locally at the directory\nspecified by the environment variable 'HF\\_DATASETS\\_CACHE', which defaults to '~/.cache/huggingface'. To\nload the dataset in a streaming format, where the data is not saved locally:\n\n\nFor further arguments of 'load\\_dataset', please take a look at the\n'loading a dataset' documentation, and\nthe 'streaming' documentation. Bear in mind that\nthis is significantly slower than loading the dataset from a local storage. For experimentation that\nrequires more performance but might not require the whole dataset, you can also specify a portion\nof the dataset to download. For example, the following code will only download the first 10%\nof the dataset:\n\n\nOnce the dataset has been loaded, the individual module files can be accessed by iterating through\nthe dataset or accessing specific indices:\n\n\nFiltering and map operations can be performed with the primitives available within the\nHuggingFace 'datasets' library.\n\n\nDataset Structure\n-----------------"
] |
d0c84c5bd2c6e630588955f559dcbb90f118d4a7
|
# Dataset Card for "tinystories_instruction_finetuning_with_pretrainingdata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Samee-ur/tinystories_instruction_finetuning_with_pretrainingdata
|
[
"region:us"
] |
2023-08-30T20:26:55+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27650, "num_examples": 206}], "download_size": 14275, "dataset_size": 27650}}
|
2023-08-30T20:26:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tinystories_instruction_finetuning_with_pretrainingdata"
More Information needed
|
[
"# Dataset Card for \"tinystories_instruction_finetuning_with_pretrainingdata\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tinystories_instruction_finetuning_with_pretrainingdata\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tinystories_instruction_finetuning_with_pretrainingdata\"\n\nMore Information needed"
] |
3e8b0dfd1fd86c7f7ffefae65ff92d2ee00e4001
|
# Dataset Card for "xlcost_clean"
Python functions extracted from the xlcost benchmark and cleaned.
|
vikp/xlcost_clean
|
[
"region:us"
] |
2023-08-30T20:36:48+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6512530.282012524, "num_examples": 6168}], "download_size": 2228263, "dataset_size": 6512530.282012524}}
|
2023-09-01T20:45:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xlcost_clean"
Python functions extracted from the xlcost benchmark and cleaned.
|
[
"# Dataset Card for \"xlcost_clean\"\n\nPython functions extracted from the xlcost benchmark and cleaned."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xlcost_clean\"\n\nPython functions extracted from the xlcost benchmark and cleaned."
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xlcost_clean\"\n\nPython functions extracted from the xlcost benchmark and cleaned."
] |
4c9eef0deddfa310a8fd2bd90eacb7bc1f7d75fe
|
# Dataset Card for "xlcost_filtered_2k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vikp/xlcost_filtered_2k
|
[
"region:us"
] |
2023-08-30T20:39:43+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "quality_prob", "dtype": "float64"}, {"name": "learning_prob", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 1631570.2028233004, "num_examples": 1838}], "download_size": 605641, "dataset_size": 1631570.2028233004}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-30T20:39:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xlcost_filtered_2k"
More Information needed
|
[
"# Dataset Card for \"xlcost_filtered_2k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xlcost_filtered_2k\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xlcost_filtered_2k\"\n\nMore Information needed"
] |
05fe8e24a597e759cb8d448a21f5223911d3a603
|
# Dataset Card for "guanaco_english_commits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ArmelR/guanaco_english_commits
|
[
"region:us"
] |
2023-08-30T20:47:47+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10546101.0, "num_examples": 6981}, {"name": "test", "num_bytes": 602433.0, "num_examples": 443}], "download_size": 6193011, "dataset_size": 11148534.0}}
|
2023-08-30T20:47:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco_english_commits"
More Information needed
|
[
"# Dataset Card for \"guanaco_english_commits\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco_english_commits\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco_english_commits\"\n\nMore Information needed"
] |
5095182b75ac91546b8c7ec9b712c6ea252e9a98
|
# Dataset Card for Evaluation run of jondurbin/airoboros-l2-7b-2.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-l2-7b-2.1
- **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 [jondurbin/airoboros-l2-7b-2.1](https://huggingface.co/jondurbin/airoboros-l2-7b-2.1) 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_jondurbin__airoboros-l2-7b-2.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T19:19:26.603130](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-2.1/blob/main/results_2023-10-22T19-19-26.603130.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": 0.3058934563758389,
"em_stderr": 0.004718867836387577,
"f1": 0.36892197986577313,
"f1_stderr": 0.004645489671001802,
"acc": 0.38155355549664816,
"acc_stderr": 0.008174839284551696
},
"harness|drop|3": {
"em": 0.3058934563758389,
"em_stderr": 0.004718867836387577,
"f1": 0.36892197986577313,
"f1_stderr": 0.004645489671001802
},
"harness|gsm8k|5": {
"acc": 0.021986353297952996,
"acc_stderr": 0.004039162758110015
},
"harness|winogrande|5": {
"acc": 0.7411207576953434,
"acc_stderr": 0.012310515810993378
}
}
```
### 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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### Citation Information
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### Contributions
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|
open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-2.1
|
[
"region:us"
] |
2023-08-30T20:48:55+00:00
|
{"pretty_name": "Evaluation run of jondurbin/airoboros-l2-7b-2.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [jondurbin/airoboros-l2-7b-2.1](https://huggingface.co/jondurbin/airoboros-l2-7b-2.1) 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_jondurbin__airoboros-l2-7b-2.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T19:19:26.603130](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-2.1/blob/main/results_2023-10-22T19-19-26.603130.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\": 0.3058934563758389,\n \"em_stderr\": 0.004718867836387577,\n \"f1\": 0.36892197986577313,\n \"f1_stderr\": 0.004645489671001802,\n \"acc\": 0.38155355549664816,\n \"acc_stderr\": 0.008174839284551696\n },\n \"harness|drop|3\": {\n \"em\": 0.3058934563758389,\n \"em_stderr\": 0.004718867836387577,\n \"f1\": 0.36892197986577313,\n \"f1_stderr\": 0.004645489671001802\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.021986353297952996,\n \"acc_stderr\": 0.004039162758110015\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7411207576953434,\n \"acc_stderr\": 0.012310515810993378\n }\n}\n```", "repo_url": "https://huggingface.co/jondurbin/airoboros-l2-7b-2.1", "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_08_30T21_48_31.608881", "path": ["**/details_harness|arc:challenge|25_2023-08-30T21:48:31.608881.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T21:48:31.608881.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T19_19_26.603130", "path": ["**/details_harness|drop|3_2023-10-22T19-19-26.603130.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T19-19-26.603130.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T19_19_26.603130", "path": ["**/details_harness|gsm8k|5_2023-10-22T19-19-26.603130.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T19-19-26.603130.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T21_48_31.608881", "path": ["**/details_harness|hellaswag|10_2023-08-30T21:48:31.608881.parquet"]}, {"split": "latest", "path": 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TAGS
#region-us
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# Dataset Card for Evaluation run of jondurbin/airoboros-l2-7b-2.1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model jondurbin/airoboros-l2-7b-2.1 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 2023-10-22T19:19:26.603130(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 jondurbin/airoboros-l2-7b-2.1",
"## 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 jondurbin/airoboros-l2-7b-2.1 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 2023-10-22T19:19:26.603130(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 jondurbin/airoboros-l2-7b-2.1",
"## 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 jondurbin/airoboros-l2-7b-2.1 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 2023-10-22T19:19:26.603130(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 jondurbin/airoboros-l2-7b-2.1## 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 jondurbin/airoboros-l2-7b-2.1 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 2023-10-22T19:19:26.603130(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"
] |
f74b11e8b2e38f3828c3052622d108a22de53319
|
# Scrapinghub Article Extraction Benchmark
This dataset was originally created and distributed under MIT License by Scrapinghub on GitHub: [github.com/scrapinghub/article-extraction-benchmark](https://github.com/scrapinghub/article-extraction-benchmark)
It is mirrored on the HuggingFace Hub as a convenience.
|
allenai/scrapinghub-article-extraction-benchmark
|
[
"task_categories:text2text-generation",
"size_categories:n<1K",
"license:mit",
"region:us"
] |
2023-08-30T20:55:18+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text2text-generation"], "pretty_name": "Scrapinghub Article Extraction Benchmark", "dataset_info": {"features": [{"name": "html", "dtype": "string"}, {"name": "articleBody", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32354376, "num_examples": 181}], "download_size": 10374590, "dataset_size": 32354376}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-30T21:05:48+00:00
|
[] |
[] |
TAGS
#task_categories-text2text-generation #size_categories-n<1K #license-mit #region-us
|
# Scrapinghub Article Extraction Benchmark
This dataset was originally created and distributed under MIT License by Scrapinghub on GitHub: URL
It is mirrored on the HuggingFace Hub as a convenience.
|
[
"# Scrapinghub Article Extraction Benchmark\n\nThis dataset was originally created and distributed under MIT License by Scrapinghub on GitHub: URL\n\nIt is mirrored on the HuggingFace Hub as a convenience."
] |
[
"TAGS\n#task_categories-text2text-generation #size_categories-n<1K #license-mit #region-us \n",
"# Scrapinghub Article Extraction Benchmark\n\nThis dataset was originally created and distributed under MIT License by Scrapinghub on GitHub: URL\n\nIt is mirrored on the HuggingFace Hub as a convenience."
] |
[
34,
49
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[
"passage: TAGS\n#task_categories-text2text-generation #size_categories-n<1K #license-mit #region-us \n# Scrapinghub Article Extraction Benchmark\n\nThis dataset was originally created and distributed under MIT License by Scrapinghub on GitHub: URL\n\nIt is mirrored on the HuggingFace Hub as a convenience."
] |
20c70422d7ebf56134ee27aa8dedf7a9d8bfef0f
|
# Dataset Card for Evaluation run of conceptofmind/Open-LLongMA-3b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/conceptofmind/Open-LLongMA-3b
- **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 [conceptofmind/Open-LLongMA-3b](https://huggingface.co/conceptofmind/Open-LLongMA-3b) 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_conceptofmind__Open-LLongMA-3b",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-30T21:59:22.661580](https://huggingface.co/datasets/open-llm-leaderboard/details_conceptofmind__Open-LLongMA-3b/blob/main/results_2023-08-30T21%3A59%3A22.661580.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": {
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"acc_norm": 0.25883980680254576,
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"mc1": 0.2252141982864137,
"mc1_stderr": 0.014623240768023493,
"mc2": 0.345076271513504,
"mc2_stderr": 0.013239849784853331
},
"harness|arc:challenge|25": {
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"acc_norm": 0.39761092150170646,
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},
"harness|hellaswag|10": {
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},
"harness|hendrycksTest-abstract_algebra|5": {
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"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.2222222222222222,
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},
"harness|hendrycksTest-astronomy|5": {
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},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.27,
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},
"harness|hendrycksTest-college_mathematics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-nutrition|5": {
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},
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"acc_stderr": 0.030360490154014645,
"acc_norm": 0.24378109452736318,
"acc_norm_stderr": 0.030360490154014645
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-virology|5": {
"acc": 0.3192771084337349,
"acc_stderr": 0.03629335329947861,
"acc_norm": 0.3192771084337349,
"acc_norm_stderr": 0.03629335329947861
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.3216374269005848,
"acc_stderr": 0.03582529442573122,
"acc_norm": 0.3216374269005848,
"acc_norm_stderr": 0.03582529442573122
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2252141982864137,
"mc1_stderr": 0.014623240768023493,
"mc2": 0.345076271513504,
"mc2_stderr": 0.013239849784853331
}
}
```
### 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_conceptofmind__Open-LLongMA-3b
|
[
"region:us"
] |
2023-08-30T20:59:45+00:00
|
{"pretty_name": "Evaluation run of conceptofmind/Open-LLongMA-3b", "dataset_summary": "Dataset automatically created during the evaluation run of model [conceptofmind/Open-LLongMA-3b](https://huggingface.co/conceptofmind/Open-LLongMA-3b) 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_conceptofmind__Open-LLongMA-3b\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-30T21:59:22.661580](https://huggingface.co/datasets/open-llm-leaderboard/details_conceptofmind__Open-LLongMA-3b/blob/main/results_2023-08-30T21%3A59%3A22.661580.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\": 0.2557876003278525,\n \"acc_stderr\": 0.03147445441526624,\n \"acc_norm\": 0.25883980680254576,\n \"acc_norm_stderr\": 0.031472248225636705,\n \"mc1\": 0.2252141982864137,\n \"mc1_stderr\": 0.014623240768023493,\n \"mc2\": 0.345076271513504,\n \"mc2_stderr\": 0.013239849784853331\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.38054607508532423,\n \"acc_stderr\": 0.014188277712349824,\n \"acc_norm\": 0.39761092150170646,\n \"acc_norm_stderr\": 0.014301752223279538\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.49153555068711413,\n \"acc_stderr\": 0.004989066355449554,\n \"acc_norm\": 0.6545508862776339,\n \"acc_norm_stderr\": 0.004745426656377574\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.035914440841969694,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.035914440841969694\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.19736842105263158,\n \"acc_stderr\": 0.03238981601699397,\n \"acc_norm\": 0.19736842105263158,\n \"acc_norm_stderr\": 0.03238981601699397\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.27547169811320754,\n \"acc_stderr\": 0.027495663683724067,\n \"acc_norm\": 0.27547169811320754,\n \"acc_norm_stderr\": 0.027495663683724067\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2013888888888889,\n \"acc_stderr\": 0.033536474697138406,\n \"acc_norm\": 0.2013888888888889,\n \"acc_norm_stderr\": 0.033536474697138406\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036622,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036622\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2023121387283237,\n \"acc_stderr\": 0.030631145539198823,\n \"acc_norm\": 0.2023121387283237,\n \"acc_norm_stderr\": 0.030631145539198823\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617749,\n \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617749\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3276595744680851,\n \"acc_stderr\": 0.030683020843231008,\n \"acc_norm\": 0.3276595744680851,\n \"acc_norm_stderr\": 0.030683020843231008\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n \"acc_stderr\": 0.0414243971948936,\n \"acc_norm\": 0.2631578947368421,\n \"acc_norm_stderr\": 0.0414243971948936\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2206896551724138,\n \"acc_stderr\": 0.03455930201924812,\n \"acc_norm\": 0.2206896551724138,\n \"acc_norm_stderr\": 0.03455930201924812\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.26455026455026454,\n \"acc_stderr\": 0.02271746789770861,\n \"acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.02271746789770861\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.19047619047619047,\n \"acc_stderr\": 0.03512207412302053,\n \"acc_norm\": 0.19047619047619047,\n \"acc_norm_stderr\": 0.03512207412302053\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.25806451612903225,\n \"acc_stderr\": 0.024892469172462846,\n \"acc_norm\": 0.25806451612903225,\n \"acc_norm_stderr\": 0.024892469172462846\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.270935960591133,\n \"acc_stderr\": 0.031270907132976984,\n \"acc_norm\": 0.270935960591133,\n \"acc_norm_stderr\": 0.031270907132976984\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.22727272727272727,\n \"acc_stderr\": 0.02985751567338641,\n \"acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.02985751567338641\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.21761658031088082,\n \"acc_stderr\": 0.029778663037752954,\n \"acc_norm\": 0.21761658031088082,\n \"acc_norm_stderr\": 0.029778663037752954\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.22564102564102564,\n \"acc_stderr\": 0.021193632525148533,\n \"acc_norm\": 0.22564102564102564,\n \"acc_norm_stderr\": 0.021193632525148533\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.24814814814814815,\n \"acc_stderr\": 0.026335739404055803,\n \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.026335739404055803\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.23109243697478993,\n \"acc_stderr\": 0.02738140692786897,\n \"acc_norm\": 0.23109243697478993,\n \"acc_norm_stderr\": 0.02738140692786897\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.25165562913907286,\n \"acc_stderr\": 0.035433042343899844,\n \"acc_norm\": 0.25165562913907286,\n \"acc_norm_stderr\": 0.035433042343899844\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.01714985851425095,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.01714985851425095\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.19907407407407407,\n \"acc_stderr\": 0.02723229846269023,\n \"acc_norm\": 0.19907407407407407,\n \"acc_norm_stderr\": 0.02723229846269023\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.23039215686274508,\n \"acc_stderr\": 0.029554292605695066,\n \"acc_norm\": 0.23039215686274508,\n \"acc_norm_stderr\": 0.029554292605695066\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.27848101265822783,\n \"acc_stderr\": 0.029178682304842544,\n \"acc_norm\": 0.27848101265822783,\n \"acc_norm_stderr\": 0.029178682304842544\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3721973094170404,\n \"acc_stderr\": 0.03244305283008731,\n \"acc_norm\": 0.3721973094170404,\n \"acc_norm_stderr\": 0.03244305283008731\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.2231404958677686,\n \"acc_stderr\": 0.03800754475228733,\n \"acc_norm\": 0.2231404958677686,\n \"acc_norm_stderr\": 0.03800754475228733\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.20245398773006135,\n \"acc_stderr\": 0.03157065078911902,\n \"acc_norm\": 0.20245398773006135,\n \"acc_norm_stderr\": 0.03157065078911902\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2767857142857143,\n \"acc_stderr\": 0.04246624336697625,\n \"acc_norm\": 0.2767857142857143,\n \"acc_norm_stderr\": 0.04246624336697625\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690877,\n \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690877\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.02934311479809447,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.02934311479809447\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.28991060025542786,\n \"acc_stderr\": 0.016225017944770957,\n \"acc_norm\": 0.28991060025542786,\n \"acc_norm_stderr\": 0.016225017944770957\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.25722543352601157,\n \"acc_stderr\": 0.023532925431044283,\n \"acc_norm\": 0.25722543352601157,\n \"acc_norm_stderr\": 0.023532925431044283\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.02463004897982476,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.02463004897982476\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2797427652733119,\n \"acc_stderr\": 0.025494259350694905,\n \"acc_norm\": 0.2797427652733119,\n \"acc_norm_stderr\": 0.025494259350694905\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.26851851851851855,\n \"acc_stderr\": 0.024659685185967277,\n \"acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.024659685185967277\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.2730496453900709,\n \"acc_stderr\": 0.026577860943307854,\n \"acc_norm\": 0.2730496453900709,\n \"acc_norm_stderr\": 0.026577860943307854\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2392438070404172,\n \"acc_stderr\": 0.010896123652676653,\n \"acc_norm\": 0.2392438070404172,\n \"acc_norm_stderr\": 0.010896123652676653\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.026303648393696036,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.026303648393696036\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.01740181671142766,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.01740181671142766\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.33636363636363636,\n \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.33636363636363636,\n \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.19591836734693877,\n \"acc_stderr\": 0.025409301953225678,\n \"acc_norm\": 0.19591836734693877,\n \"acc_norm_stderr\": 0.025409301953225678\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.030360490154014645,\n \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.030360490154014645\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.3192771084337349,\n \"acc_stderr\": 0.03629335329947861,\n \"acc_norm\": 0.3192771084337349,\n \"acc_norm_stderr\": 0.03629335329947861\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3216374269005848,\n \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2252141982864137,\n \"mc1_stderr\": 0.014623240768023493,\n \"mc2\": 0.345076271513504,\n \"mc2_stderr\": 0.013239849784853331\n }\n}\n```", "repo_url": "https://huggingface.co/conceptofmind/Open-LLongMA-3b", "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_08_30T21_59_22.661580", "path": ["**/details_harness|arc:challenge|25_2023-08-30T21:59:22.661580.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T21:59:22.661580.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T21_59_22.661580", "path": ["**/details_harness|hellaswag|10_2023-08-30T21:59:22.661580.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-30T21:59:22.661580.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_08_30T21_59_22.661580", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T21:59:22.661580.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-30T21:59:22.661580.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-30T21:59:22.661580.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T21:59:22.661580.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T21:59:22.661580.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-30T21:59:22.661580.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T21:59:22.661580.parquet", 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|
2023-08-30T21:00:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of conceptofmind/Open-LLongMA-3b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model conceptofmind/Open-LLongMA-3b 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 2023-08-30T21:59:22.661580(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 conceptofmind/Open-LLongMA-3b",
"## 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 conceptofmind/Open-LLongMA-3b 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 2023-08-30T21:59:22.661580(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 conceptofmind/Open-LLongMA-3b",
"## 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 conceptofmind/Open-LLongMA-3b 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 2023-08-30T21:59:22.661580(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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"### Data Splits",
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"### 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 conceptofmind/Open-LLongMA-3b## 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 conceptofmind/Open-LLongMA-3b 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 2023-08-30T21:59:22.661580(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"
] |
ab7faa87926d23cbfbaeff5043fcc1adeb8bd51c
|
# Dataset Card for Evaluation run of TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ
- **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 [TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ](https://huggingface.co/TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ) 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_TheBloke__OpenOrcaxOpenChat-Preview2-13B-GPTQ",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T10:53:17.967443](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__OpenOrcaxOpenChat-Preview2-13B-GPTQ/blob/main/results_2023-10-22T10-53-17.967443.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": 0.004823825503355705,
"em_stderr": 0.0007095539645563046,
"f1": 0.08351929530201369,
"f1_stderr": 0.0017605531187545353,
"acc": 0.4477247418430049,
"acc_stderr": 0.010448120593026917
},
"harness|drop|3": {
"em": 0.004823825503355705,
"em_stderr": 0.0007095539645563046,
"f1": 0.08351929530201369,
"f1_stderr": 0.0017605531187545353
},
"harness|gsm8k|5": {
"acc": 0.1243366186504928,
"acc_stderr": 0.009088880962028442
},
"harness|winogrande|5": {
"acc": 0.771112865035517,
"acc_stderr": 0.011807360224025395
}
}
```
### 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_TheBloke__OpenOrcaxOpenChat-Preview2-13B-GPTQ
|
[
"region:us"
] |
2023-08-30T21:07:12+00:00
|
{"pretty_name": "Evaluation run of TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ", "dataset_summary": "Dataset automatically created during the evaluation run of model [TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ](https://huggingface.co/TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ) 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_TheBloke__OpenOrcaxOpenChat-Preview2-13B-GPTQ\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T10:53:17.967443](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__OpenOrcaxOpenChat-Preview2-13B-GPTQ/blob/main/results_2023-10-22T10-53-17.967443.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\": 0.004823825503355705,\n \"em_stderr\": 0.0007095539645563046,\n \"f1\": 0.08351929530201369,\n \"f1_stderr\": 0.0017605531187545353,\n \"acc\": 0.4477247418430049,\n \"acc_stderr\": 0.010448120593026917\n },\n \"harness|drop|3\": {\n \"em\": 0.004823825503355705,\n \"em_stderr\": 0.0007095539645563046,\n \"f1\": 0.08351929530201369,\n \"f1_stderr\": 0.0017605531187545353\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1243366186504928,\n \"acc_stderr\": 0.009088880962028442\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.771112865035517,\n \"acc_stderr\": 0.011807360224025395\n }\n}\n```", "repo_url": "https://huggingface.co/TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ", "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_08_30T22_06_48.097340", "path": ["**/details_harness|arc:challenge|25_2023-08-30T22:06:48.097340.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-30T22:06:48.097340.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T10_53_17.967443", "path": ["**/details_harness|drop|3_2023-10-22T10-53-17.967443.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T10-53-17.967443.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T10_53_17.967443", "path": ["**/details_harness|gsm8k|5_2023-10-22T10-53-17.967443.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T10-53-17.967443.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_30T22_06_48.097340", "path": ["**/details_harness|hellaswag|10_2023-08-30T22:06:48.097340.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-08-30T22:06:48.097340.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-30T22:06:48.097340.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_22T10_53_17.967443", "path": ["**/details_harness|winogrande|5_2023-10-22T10-53-17.967443.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-22T10-53-17.967443.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_30T22_06_48.097340", "path": ["results_2023-08-30T22:06:48.097340.parquet"]}, {"split": "2023_10_22T10_53_17.967443", "path": ["results_2023-10-22T10-53-17.967443.parquet"]}, {"split": "latest", "path": ["results_2023-10-22T10-53-17.967443.parquet"]}]}]}
|
2023-10-22T09:53:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ 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 2023-10-22T10:53:17.967443(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 TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ",
"## 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 TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ 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 2023-10-22T10:53:17.967443(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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"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ 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:",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ## 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 TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ 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 2023-10-22T10:53:17.967443(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"
] |
fa527ff15b76d5e3e2f4f10deae19ce1f3329da3
|
## Commonsense-Dialogues Dataset
We present Commonsense-Dialogues, a crowdsourced dataset of ~11K dialogues grounded in social contexts involving utilization of commonsense. The social contexts used were sourced from the **train** split of the [SocialIQA](https://leaderboard.allenai.org/socialiqa/submissions/get-started) dataset, a multiple-choice question-answering based social commonsense reasoning benchmark.
For the collection of the Commonsense-Dialogues dataset, each Turker was presented a social context and asked to write a dialogue of 4-6 turns between two people based on the event(s) described in the context. The Turker was asked to alternate between the roles of an individual referenced in the context and a 3rd party friend. See the following dialogues as examples:
```
"1": { # dialogue_id
"context": "Sydney met Carson's mother for the first time last week. He liked her.", # multiple individuals in the context: Sydney and Carson
"speaker": "Sydney", # role 1 = Sydney, role 2 = a third-person friend of Sydney
"turns": [
"I met Carson's mother last week for the first time.",
"How was she?",
"She turned out to be really nice. I like her.",
"That's good to hear.",
"It is, especially since Carson and I are getting serious.",
"Well, at least you'll like your in-law if you guys get married."
]
}
"2": {
"context": "Kendall had a party at Jordan's house but was found out to not have asked and just broke in.",
"speaker": "Kendall",
"turns": [
"Did you hear about my party this weekend at Jordan\u2019s house?",
"I heard it was amazing, but that you broke in.",
"That was a misunderstanding, I had permission to be there.",
"Who gave you permission?",
"I talked to Jordan about it months ago before he left town to go to school, but he forgot to tell his roommates about it.",
"Ok cool, I hope everything gets resolved."
]
}
```
The data can be found in the `/data` directory of this repo. `train.json` has ~9K dialogues, `valid.json` and `test.json` have ~1K dialogues each. Since all the contexts were sourced from the **train** split of SocialIQA, it is imperative to note that any form of **multi-task** training and evaluation with Commonsense-Dialogues and SocialIQA must be done with caution to ensure fair and accurate conclusions.
Some statistics about the data are provided below:
| Stat | Train | Valid | Test |
| ---- | ---- | ---- | ---- |
|# of dialogues | 9058 | 1157 | 1158 |
|average # of turns in a dialogue | 5.72 | 5.72 | 5.71 |
|average # of words in a turn | 12.4 | 12.4 | 12.2 |
|# of distinct SocialIQA contexts used | 3672 | 483 | 473 |
|average # of dialogues for a SocialIQA context| 2.46 | 2.395 | 2.45 |
## Security
See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
## License
This repository is licensed under the CC-BY-NC 4.0 License.
## Citation
If you use this dataset, please cite the following paper:
```
@inproceedings{zhou-etal-2021-commonsense,
title = "Commonsense-Focused Dialogues for Response Generation: An Empirical Study",
author = "Zhou, Pei and
Gopalakrishnan, Karthik and
Hedayatnia, Behnam and
Kim, Seokhwan and
Pujara, Jay and
Ren, Xiang and
Liu, Yang and
Hakkani-Tur, Dilek",
booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
year = "2021",
address = "Singapore and Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2109.06427"
}
```
Note that the paper uses newly collected dialogues as well as those that were filtered from existing datasets. This repo contains our newly collected dialogues alone.
|
mvansegb123/commonsense-dialogues
|
[
"task_categories:text-classification",
"task_categories:table-question-answering",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"arxiv:2109.06427",
"region:us"
] |
2023-08-30T21:42:25+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "table-question-answering", "text-generation"]}
|
2023-08-30T22:10:24+00:00
|
[
"2109.06427"
] |
[
"en"
] |
TAGS
#task_categories-text-classification #task_categories-table-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc #arxiv-2109.06427 #region-us
|
Commonsense-Dialogues Dataset
-----------------------------
We present Commonsense-Dialogues, a crowdsourced dataset of ~11K dialogues grounded in social contexts involving utilization of commonsense. The social contexts used were sourced from the train split of the SocialIQA dataset, a multiple-choice question-answering based social commonsense reasoning benchmark.
For the collection of the Commonsense-Dialogues dataset, each Turker was presented a social context and asked to write a dialogue of 4-6 turns between two people based on the event(s) described in the context. The Turker was asked to alternate between the roles of an individual referenced in the context and a 3rd party friend. See the following dialogues as examples:
The data can be found in the '/data' directory of this repo. 'URL' has ~9K dialogues, 'URL' and 'URL' have ~1K dialogues each. Since all the contexts were sourced from the train split of SocialIQA, it is imperative to note that any form of multi-task training and evaluation with Commonsense-Dialogues and SocialIQA must be done with caution to ensure fair and accurate conclusions.
Some statistics about the data are provided below:
Security
--------
See CONTRIBUTING for more information.
License
-------
This repository is licensed under the CC-BY-NC 4.0 License.
If you use this dataset, please cite the following paper:
Note that the paper uses newly collected dialogues as well as those that were filtered from existing datasets. This repo contains our newly collected dialogues alone.
|
[] |
[
"TAGS\n#task_categories-text-classification #task_categories-table-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc #arxiv-2109.06427 #region-us \n"
] |
[
72
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"passage: TAGS\n#task_categories-text-classification #task_categories-table-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc #arxiv-2109.06427 #region-us \n"
] |
c966c19ef66ec2c0f1c27133845477cd8d32f092
|
# Dataset Card for "Mermaid_LLAMA_SimpleInstruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dshut002/Mermaid_LLAMA_SimpleInstruct
|
[
"region:us"
] |
2023-08-30T21:45:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 500, "num_examples": 1}], "download_size": 4587, "dataset_size": 500}}
|
2023-08-30T21:45:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Mermaid_LLAMA_SimpleInstruct"
More Information needed
|
[
"# Dataset Card for \"Mermaid_LLAMA_SimpleInstruct\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Mermaid_LLAMA_SimpleInstruct\"\n\nMore Information needed"
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6,
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"Mermaid_LLAMA_SimpleInstruct\"\n\nMore Information needed"
] |
c3e80bc20b383e99d85f3e51bbb414510f72b996
|
# Dataset Card for Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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 [uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b](https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T05:18:23.703135](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b/blob/main/results_2023-10-18T05-18-23.703135.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": 0.33598993288590606,
"em_stderr": 0.004837156244460491,
"f1": 0.39884333053691334,
"f1_stderr": 0.00469778128078984,
"acc": 0.40645791907839585,
"acc_stderr": 0.00927667824750805
},
"harness|drop|3": {
"em": 0.33598993288590606,
"em_stderr": 0.004837156244460491,
"f1": 0.39884333053691334,
"f1_stderr": 0.00469778128078984
},
"harness|gsm8k|5": {
"acc": 0.058377558756633814,
"acc_stderr": 0.006458083557832456
},
"harness|winogrande|5": {
"acc": 0.7545382794001578,
"acc_stderr": 0.012095272937183644
}
}
```
### 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_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b
|
[
"region:us"
] |
2023-08-30T22:37:56+00:00
|
{"pretty_name": "Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b](https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T05:18:23.703135](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-orca-platypus-coig-lite-4k-0.5e-13b/blob/main/results_2023-10-18T05-18-23.703135.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\": 0.33598993288590606,\n \"em_stderr\": 0.004837156244460491,\n \"f1\": 0.39884333053691334,\n \"f1_stderr\": 0.00469778128078984,\n \"acc\": 0.40645791907839585,\n \"acc_stderr\": 0.00927667824750805\n },\n \"harness|drop|3\": {\n \"em\": 0.33598993288590606,\n \"em_stderr\": 0.004837156244460491,\n \"f1\": 0.39884333053691334,\n \"f1_stderr\": 0.00469778128078984\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.058377558756633814,\n \"acc_stderr\": 0.006458083557832456\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7545382794001578,\n \"acc_stderr\": 0.012095272937183644\n }\n}\n```", "repo_url": "https://huggingface.co/uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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": 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2023-10-18T04:18:36+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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 uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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 2023-10-18T05:18:23.703135(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 uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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 uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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 2023-10-18T05:18:23.703135(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 uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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 uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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 2023-10-18T05:18:23.703135(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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184,
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6,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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 uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-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 2023-10-18T05:18:23.703135(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"
] |
e9364e93cf333b3ef259dfa3abd40484d10ed44f
|
# Dataset Card for "autotree_automl_electricity_gosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_electricity_gosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-08-30T23:12:02+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2773600000, "num_examples": 100000}, {"name": "validation", "num_bytes": 277360000, "num_examples": 10000}], "download_size": 691921046, "dataset_size": 3050960000}}
|
2023-08-30T23:12:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_electricity_gosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_electricity_gosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_electricity_gosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
31
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_electricity_gosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
1de2641e93fb4e8caf00acf1a9c93541c64323e4
|
# Dataset Card for "llama_2_product_titles-esci_train_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qazisaad/llama_2_product_titles-esci_train_all
|
[
"region:us"
] |
2023-08-30T23:15:16+00:00
|
{"dataset_info": {"features": [{"name": "index", "dtype": "int64"}, {"name": "query", "dtype": "string"}, {"name": "average_score", "dtype": "float64"}, {"name": "total_score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 34404870, "num_examples": 16404}], "download_size": 0, "dataset_size": 34404870}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T02:34:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llama_2_product_titles-esci_train_all"
More Information needed
|
[
"# Dataset Card for \"llama_2_product_titles-esci_train_all\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llama_2_product_titles-esci_train_all\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"llama_2_product_titles-esci_train_all\"\n\nMore Information needed"
] |
65a51b8c8e861e9043610085f4b490be3244817d
|
# Dataset Card for "llama_2-product-titles-esci-train-all-temp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qazisaad/llama_2-product-titles-esci-train-all-temp
|
[
"region:us"
] |
2023-08-30T23:21:59+00:00
|
{"dataset_info": {"features": [{"name": "index", "dtype": "int64"}, {"name": "query", "dtype": "string"}, {"name": "average_score", "dtype": "float64"}, {"name": "total_score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "preds", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6964939, "num_examples": 3060}], "download_size": 1087545, "dataset_size": 6964939}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T09:40:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llama_2-product-titles-esci-train-all-temp"
More Information needed
|
[
"# Dataset Card for \"llama_2-product-titles-esci-train-all-temp\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llama_2-product-titles-esci-train-all-temp\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"llama_2-product-titles-esci-train-all-temp\"\n\nMore Information needed"
] |
9fbcb0b1c3a02127dfa628ef34cec8dff1b834e1
|
# Dataset Card for Evaluation run of TheBloke/Genz-70b-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Genz-70b-GPTQ
- **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 [TheBloke/Genz-70b-GPTQ](https://huggingface.co/TheBloke/Genz-70b-GPTQ) 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_TheBloke__Genz-70b-GPTQ",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-31T00:30:34.342002](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Genz-70b-GPTQ/blob/main/results_2023-08-31T00%3A30%3A34.342002.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
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"mc1_stderr": 0.01734120239498826,
"mc2": 0.6228267270427654,
"mc2_stderr": 0.014836432877772263
},
"harness|arc:challenge|25": {
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},
"harness|hellaswag|10": {
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"acc_norm": 0.8764190400318662,
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},
"harness|hendrycksTest-abstract_algebra|5": {
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},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.6370370370370371,
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},
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"harness|hendrycksTest-prehistory|5": {
"acc": 0.8487654320987654,
"acc_stderr": 0.019935086092149897,
"acc_norm": 0.8487654320987654,
"acc_norm_stderr": 0.019935086092149897
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5638297872340425,
"acc_stderr": 0.029583452036284076,
"acc_norm": 0.5638297872340425,
"acc_norm_stderr": 0.029583452036284076
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5534550195567145,
"acc_stderr": 0.012697046024399654,
"acc_norm": 0.5534550195567145,
"acc_norm_stderr": 0.012697046024399654
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7389705882352942,
"acc_stderr": 0.026679252270103135,
"acc_norm": 0.7389705882352942,
"acc_norm_stderr": 0.026679252270103135
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7630718954248366,
"acc_stderr": 0.017201662169789772,
"acc_norm": 0.7630718954248366,
"acc_norm_stderr": 0.017201662169789772
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7363636363636363,
"acc_stderr": 0.04220224692971987,
"acc_norm": 0.7363636363636363,
"acc_norm_stderr": 0.04220224692971987
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8,
"acc_stderr": 0.02560737598657916,
"acc_norm": 0.8,
"acc_norm_stderr": 0.02560737598657916
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8606965174129353,
"acc_stderr": 0.024484487162913973,
"acc_norm": 0.8606965174129353,
"acc_norm_stderr": 0.024484487162913973
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.9,
"acc_stderr": 0.030151134457776334,
"acc_norm": 0.9,
"acc_norm_stderr": 0.030151134457776334
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.038899512528272166,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.038899512528272166
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8771929824561403,
"acc_stderr": 0.025172984350155754,
"acc_norm": 0.8771929824561403,
"acc_norm_stderr": 0.025172984350155754
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4320685434516524,
"mc1_stderr": 0.01734120239498826,
"mc2": 0.6228267270427654,
"mc2_stderr": 0.014836432877772263
}
}
```
### 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_TheBloke__Genz-70b-GPTQ
|
[
"region:us"
] |
2023-08-30T23:30:59+00:00
|
{"pretty_name": "Evaluation run of TheBloke/Genz-70b-GPTQ", "dataset_summary": "Dataset automatically created during the evaluation run of model [TheBloke/Genz-70b-GPTQ](https://huggingface.co/TheBloke/Genz-70b-GPTQ) 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_TheBloke__Genz-70b-GPTQ\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-31T00:30:34.342002](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Genz-70b-GPTQ/blob/main/results_2023-08-31T00%3A30%3A34.342002.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\": 0.7017249416277331,\n \"acc_stderr\": 0.030832772804323012,\n \"acc_norm\": 0.70569345061239,\n \"acc_norm_stderr\": 0.03080075128019408,\n \"mc1\": 0.4320685434516524,\n \"mc1_stderr\": 0.01734120239498826,\n \"mc2\": 0.6228267270427654,\n \"mc2_stderr\": 0.014836432877772263\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6638225255972696,\n \"acc_stderr\": 0.013804855026205763,\n \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.013250012579393443\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.689205337582155,\n \"acc_stderr\": 0.004618730353217047,\n \"acc_norm\": 0.8764190400318662,\n \"acc_norm_stderr\": 0.0032843028764223\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.03110318238312338,\n \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.03110318238312338\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n \"acc_stderr\": 0.032166008088022675,\n \"acc_norm\": 0.8194444444444444,\n \"acc_norm_stderr\": 0.032166008088022675\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.676595744680851,\n \"acc_stderr\": 0.030579442773610337,\n \"acc_norm\": 0.676595744680851,\n \"acc_norm_stderr\": 0.030579442773610337\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.04685473041907789\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\": 0.4417989417989418,\n \"acc_stderr\": 0.02557625706125384,\n \"acc_norm\": 0.4417989417989418,\n \"acc_norm_stderr\": 0.02557625706125384\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8290322580645161,\n \"acc_stderr\": 0.02141724293632159,\n \"acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.02141724293632159\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\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\": 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822523,\n \"acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822523\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240528,\n \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240528\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.7051282051282052,\n \"acc_stderr\": 0.023119362758232294,\n \"acc_norm\": 0.7051282051282052,\n \"acc_norm_stderr\": 0.023119362758232294\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.028406533090608463,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.028406533090608463\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.773109243697479,\n \"acc_stderr\": 0.027205371538279472,\n \"acc_norm\": 0.773109243697479,\n \"acc_norm_stderr\": 0.027205371538279472\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248437,\n \"acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248437\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8917431192660551,\n \"acc_stderr\": 0.013321348447611769,\n \"acc_norm\": 0.8917431192660551,\n \"acc_norm_stderr\": 0.013321348447611769\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\": 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073312,\n \"acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073312\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.9029535864978903,\n \"acc_stderr\": 0.019269323025640262,\n \"acc_norm\": 0.9029535864978903,\n \"acc_norm_stderr\": 0.019269323025640262\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8116591928251121,\n \"acc_stderr\": 0.026241132996407252,\n \"acc_norm\": 0.8116591928251121,\n \"acc_norm_stderr\": 0.026241132996407252\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.03217829420744633,\n \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.03217829420744633\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.859504132231405,\n \"acc_stderr\": 0.03172233426002157,\n \"acc_norm\": 0.859504132231405,\n \"acc_norm_stderr\": 0.03172233426002157\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\": 0.8159509202453987,\n \"acc_stderr\": 0.030446777687971726,\n \"acc_norm\": 0.8159509202453987,\n \"acc_norm_stderr\": 0.030446777687971726\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9102564102564102,\n \"acc_stderr\": 0.018724301741941642,\n \"acc_norm\": 0.9102564102564102,\n \"acc_norm_stderr\": 0.018724301741941642\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8722860791826309,\n \"acc_stderr\": 0.011935626313999876,\n \"acc_norm\": 0.8722860791826309,\n \"acc_norm_stderr\": 0.011935626313999876\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.8005780346820809,\n \"acc_stderr\": 0.021511900654252562,\n \"acc_norm\": 0.8005780346820809,\n \"acc_norm_stderr\": 0.021511900654252562\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5754189944134078,\n \"acc_stderr\": 0.01653117099327888,\n \"acc_norm\": 0.5754189944134078,\n \"acc_norm_stderr\": 0.01653117099327888\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.02392915551735129,\n \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.02392915551735129\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7717041800643086,\n \"acc_stderr\": 0.023839303311398205,\n \"acc_norm\": 0.7717041800643086,\n \"acc_norm_stderr\": 0.023839303311398205\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8487654320987654,\n \"acc_stderr\": 0.019935086092149897,\n \"acc_norm\": 0.8487654320987654,\n \"acc_norm_stderr\": 0.019935086092149897\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5638297872340425,\n \"acc_stderr\": 0.029583452036284076,\n \"acc_norm\": 0.5638297872340425,\n \"acc_norm_stderr\": 0.029583452036284076\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5534550195567145,\n \"acc_stderr\": 0.012697046024399654,\n \"acc_norm\": 0.5534550195567145,\n \"acc_norm_stderr\": 0.012697046024399654\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103135,\n \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103135\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.7630718954248366,\n \"acc_stderr\": 0.017201662169789772,\n \"acc_norm\": 0.7630718954248366,\n \"acc_norm_stderr\": 0.017201662169789772\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02560737598657916,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02560737598657916\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.5180722891566265,\n \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.025172984350155754,\n \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.025172984350155754\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4320685434516524,\n \"mc1_stderr\": 0.01734120239498826,\n \"mc2\": 0.6228267270427654,\n \"mc2_stderr\": 0.014836432877772263\n }\n}\n```", "repo_url": "https://huggingface.co/TheBloke/Genz-70b-GPTQ", "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_08_31T00_30_34.342002", "path": ["**/details_harness|arc:challenge|25_2023-08-31T00:30:34.342002.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-31T00:30:34.342002.parquet"]}]}, 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|
2023-08-30T23:31:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TheBloke/Genz-70b-GPTQ
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TheBloke/Genz-70b-GPTQ 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 2023-08-31T00:30:34.342002(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 TheBloke/Genz-70b-GPTQ",
"## 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 TheBloke/Genz-70b-GPTQ 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 2023-08-31T00:30:34.342002(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 TheBloke/Genz-70b-GPTQ",
"## 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 TheBloke/Genz-70b-GPTQ 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 2023-08-31T00:30:34.342002(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 TheBloke/Genz-70b-GPTQ## 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 TheBloke/Genz-70b-GPTQ 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 2023-08-31T00:30:34.342002(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"
] |
e5c5d8476e42073bae4a46476f802e80f653de13
|
# Dataset Card for "llama_2_optimized_product_titles-esci-4-7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qazisaad/llama_2_optimized_product_titles-esci-4-7
|
[
"region:us"
] |
2023-08-30T23:40:24+00:00
|
{"dataset_info": {"features": [{"name": "index", "dtype": "int64"}, {"name": "product_title", "dtype": "string"}, {"name": "average_score", "dtype": "float64"}, {"name": "total_score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 28182010, "num_examples": 9861}], "download_size": 4414697, "dataset_size": 28182010}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T00:13:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llama_2_optimized_product_titles-esci-4-7"
More Information needed
|
[
"# Dataset Card for \"llama_2_optimized_product_titles-esci-4-7\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llama_2_optimized_product_titles-esci-4-7\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"llama_2_optimized_product_titles-esci-4-7\"\n\nMore Information needed"
] |
026d978e05d8625ddfe8214034cae7297522c99b
|
# Dataset Card for "llama-2-optimized-product-titles-esci-4-7-temp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qazisaad/llama-2-optimized-product-titles-esci-4-7-temp
|
[
"region:us"
] |
2023-08-30T23:50:21+00:00
|
{"dataset_info": {"features": [{"name": "index", "dtype": "int64"}, {"name": "product_title", "dtype": "string"}, {"name": "average_score", "dtype": "float64"}, {"name": "total_score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "preds", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 11551253, "num_examples": 3660}], "download_size": 2198115, "dataset_size": 11551253}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T09:45:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llama-2-optimized-product-titles-esci-4-7-temp"
More Information needed
|
[
"# Dataset Card for \"llama-2-optimized-product-titles-esci-4-7-temp\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llama-2-optimized-product-titles-esci-4-7-temp\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"llama-2-optimized-product-titles-esci-4-7-temp\"\n\nMore Information needed"
] |
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