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50a06885204f7b45e953033eb08904c5a0d5bafe
|
## STR-2022: Dataset Description
The dataset consists of 5500 English sentence pairs that are scored and ranked on a relatedness scale ranging from 0 (least related) to 1 (most related).
## Loading the Dataset
- The sentence pairs, and associated scores, are in the file sem_text_rel_ranked.csv in the root directory. The CSV file can be read using:
```python
import pandas as pd
str = pd.read_csv('sem_text_rel_ranked.csv')
row = str.loc[0]
sent1, sent2 = row['Text'].split("\n")
score = row['Score']
```
- Relevant columns:
- Text: Sentence pair, separated by the newline character.
- Score: The semantic relatedness score between 0 and 1.
- Additionally:
- the SourceID column indicates the source dataset from which the sentence pair was drawn (see Table 2 of our paper)
- The SubsetID column indicates the sampling strategy used for the source dataset
- and the PairID is a unique identifier for each pair that also indicates its Source and Subset.
## Why Semantic Relatedness?
Closeness of meaning can be of two kinds: semantic relatedness and semantic similarity. Two sentences are considered semantically similar when they have a paraphrasal or entailment relation, whereas relatedness accounts for all of the commonalities that can exist between two sentences. Semantic relatedness is central to textual coherence and narrative structure. Automatically determining semantic relatedness has many applications such as question answering, plagiarism detection, text generation (say in personal assistants and chat bots), and summarization.
Prior NLP work has focused on semantic similarity (a small subset of semantic relatedness), largely because of a dearth of datasets. In this paper, we present the first manually annotated dataset of sentence--sentence semantic relatedness. It includes fine-grained scores of relatedness from 0 (least related) to 1 (most related) for 5,500 English sentence pairs. The sentences are taken from diverse sources and thus also have diverse sentence structures, varying amounts of lexical overlap, and varying formality.
## Comparative Annotations and Best-Worst Scaling
Most existing sentence-sentence similarity datasets were annotated, one item at a time, using coarse rating labels such as integer values between 1 and 5\@ representing coarse degrees of closeness. It is well documented that such approaches suffer from inter- and intra-annotator inconsistency, scale region bias, and issues arising due to the fixed granularity.
The relatedness scores for our dataset were, instead, obtained using a __comparative__ annotation schema. In comparative annotations, two (or more) items are presented together and the annotator has to determine which is greater with respect to the metric of interest.
Specifically, we use Best-Worst Scaling, a comparative annotation method}, which has been shown to produce reliable scores with fewer annotations in other NLP tasks. We use scripts from https://saifmohammad.com/WebPages/BestWorst.html to obtain relatedness scores from our annotations.
## Citing our work
Please use the following BibTex entry to cite us if you use our dataset or any of the [associated analyses](https://arxiv.org/abs/2110.04845):
```
@inproceedings{abdalla2023makes,
title={What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study},
author={Abdalla, Mohamed and Vishnubhotla, Krishnapriya and Mohammad, Saif M.},
year={2023},
address = {Dubrovnik, Croatia},
publisher = "Association for Computational Linguistics",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume"
}
```
## Ethics Statement
Any dataset of semantic relatedness entails several ethical considerations. We talk about this in Section 10 of [our paper](https://arxiv.org/abs/2110.04845).
## Relevant Links
- [GitHub repository](https://github.com/Priya22/semantic-textual-relatedness)
- [Zenodo page](https://zenodo.org/record/7599667)
## Creators
- [Mohamed Abdalla](https://www.cs.toronto.edu/~msa/index_all.html) (University of Toronto)
- [Krishnapriya Vishnubhotla](https://priya22.github.io/) (University of Toronto)
- [Saif M. Mohammad](http://saifmohammad.com/) (National Research Council Canada)
**Contact**: [email protected], [email protected], [email protected]
|
vkpriya/str-2022
|
[
"task_categories:sentence-similarity",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"#sentence-relatedness",
"#semantic-similarity",
"#semantic-relatedness",
"arxiv:2110.04845",
"region:us"
] |
2023-02-06T17:16:38+00:00
|
{"language": ["en"], "license": "other", "size_categories": ["1K<n<10K"], "task_categories": ["sentence-similarity"], "pretty_name": "str-2022", "tags": ["#sentence-relatedness", "#semantic-similarity", "#semantic-relatedness"]}
|
2023-02-06T17:50:10+00:00
|
898a4636a13c66b4c1db9140121b186af5df0493
|
# Dataset Card for "quora"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.kaggle.com/c/quora-question-pairs](https://www.kaggle.com/c/quora-question-pairs)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 55.48 MB
- **Size of the generated dataset:** 55.46 MB
- **Total amount of disk used:** 110.94 MB
### Dataset Summary
The Quora dataset is composed of question pairs, and the task is to determine if the questions are paraphrases of each other (have the same meaning).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 55.48 MB
- **Size of the generated dataset:** 55.46 MB
- **Total amount of disk used:** 110.94 MB
### Data Fields
The data fields are the same among all splits.
### Data Splits
| name |train |
|-------|-----:|
|default|404290|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[Quora Term of Service](https://www.quora.com/about/tos), no commercial use.
### Citation Information
Unknown.
|
chenghao/quora_questions
|
[
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] |
2023-02-06T17:16:39+00:00
|
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": "other", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification"], "pretty_name": "Quora Questions", "dataset_info": {"features": [{"name": "questions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51635953, "num_examples": 808580}], "download_size": 31079310, "dataset_size": 51635953}}
|
2023-02-06T17:23:12+00:00
|
d8d0b40f63f236102567026dd7594963187c5df2
|
luigisaetta/atco2_only_augmented
|
[
"task_categories:automatic-speech-recognition",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"atc",
"asr",
"en",
"region:us"
] |
2023-02-06T18:04:51+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["automatic-speech-recognition"], "pretty_name": "atco2 augmented", "tags": ["atc", "asr", "en"]}
|
2023-02-06T18:53:32+00:00
|
|
4a3a9ec81cd41dd081478e6a29bc2b663069792b
|
# Dataset Card for PlotQA
## Dataset Description
- **PlotQA from here:** [PlotQA](https://github.com/NiteshMethani/PlotQA)
### Dataset Summary
PlotQA is a VQA dataset with 28.9 million question-answer pairs grounded over 224,377 plots on data from real-world sources and questions based on crowd-sourced question templates.
## Dataset Structure
### Data Fields
List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
- `image`: PIL image of a plot
- `text`: string of json data 'models'. See notes below.
From [here](https://github.com/NiteshMethani/PlotQA/blob/master/PlotQA_Dataset.md):
'models': It is a list of dictionaries. Depending on the type of the plot (single or 2,3,4-multi), the length of the dictionary can vary from 1 to 4. Each dictionary contains the following keys-
name: Label corresponding to the datapoint.
color: Color corresponding to the `name` datapoint.
bboxes: Bounding boxes corresponding to the `name` datapoints in the plot.
label: label corresponding to the datapoint which will appear as the legend (same as the `name` field).
x: x-value of the datapoints.
y: y-value of the datapoints.
[json2token](https://github.com/clovaai/donut/blob/b317b4bbf1eecec7c62e7666f2097e1e90a6b441/donut/model.py#L495) function was used to convert json to string.
The new tokens are already loaded in plotQA processor:
```
from transformers import DonutProcessor
processor = DonutProcessor.from_pretrained("[achang/donut-plotqa-trained](https://huggingface.co/achang/donut-plotqa-trained)")
```
### Data Splits
```
validation: Dataset({
features: ['image', 'text'],
num_rows: 33650
})
train: Dataset({
features: ['image', 'text'],
num_rows: 157070
})
test: Dataset({
features: ['image', 'text'],
num_rows: 33657
})
```
## Misc
Dataset Creation, Annotations, Considerations for Using the Data, Social Impact of Dataset, Additional Information, Licensing Information look at [plotQA](https://github.com/NiteshMethani/PlotQA)
### Citation Information
Please cite the following if you use the PlotQA dataset in your work:
```
@InProceedings{Methani_2020_WACV,
author = {Methani, Nitesh and Ganguly, Pritha and Khapra, Mitesh M. and Kumar, Pratyush},
title = {PlotQA: Reasoning over Scientific Plots},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}
```
|
achang/plot_qa
|
[
"task_categories:visual-question-answering",
"language:en",
"license:cc",
"plotQA",
"region:us"
] |
2023-02-06T18:51:17+00:00
|
{"language": ["en"], "license": "cc", "task_categories": ["visual-question-answering"], "pretty_name": "PlotQA", "tags": ["plotQA"]}
|
2023-02-12T01:20:56+00:00
|
d0eaa5d51a30d3a4b328753b07ec3b01e2da44b0
| ERROR: type should be string, got "\nhttps://youtu.be/8FS0oUB-eCI" |
ChristophSchuhmann/wikipedia-3sentence-level-retrieval-index
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-06T19:06:30+00:00
|
{"license": "apache-2.0"}
|
2023-02-06T19:23:05+00:00
|
62f56cf31884d7fe3417393b843279f3a4fa74f0
|
# Dataset Card for "OxfordPets_test_facebook_opt_6.7b_Attributes_Caption_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_6.7b_Attributes_Caption_ns_3669
|
[
"region:us"
] |
2023-02-06T19:22:10+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 121141370.375, "num_examples": 3669}, {"name": "fewshot_1_bs_16", "num_bytes": 122187472.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 124265353.375, "num_examples": 3669}], "download_size": 358500289, "dataset_size": 367594196.125}}
|
2023-02-06T20:12:12+00:00
|
b49ebb467fb96b1480e07ea89398fa57ce01312d
|
# Dataset Card for "OxfordPets_test_facebook_opt_6.7b_Visclues_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_6.7b_Visclues_ns_3669
|
[
"region:us"
] |
2023-02-06T19:32:27+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 121471156.375, "num_examples": 3669}, {"name": "fewshot_1_bs_16", "num_bytes": 122822613.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 125537094.375, "num_examples": 3669}], "download_size": 359034680, "dataset_size": 369830864.125}}
|
2023-02-06T20:32:16+00:00
|
aa55ad1b76a2cb717e2e6230471419172faebce6
|
lingfeng0410/Abdominal_Organ_Segmentation
|
[
"license:mit",
"region:us"
] |
2023-02-06T20:38:48+00:00
|
{"license": "mit"}
|
2023-02-06T20:38:48+00:00
|
|
d8aab1945cd4bfa3457d941d8582f8fb380d4375
|
dfcihale/Abdominal_Organ_Segmentation
|
[
"license:mit",
"region:us"
] |
2023-02-06T20:40:54+00:00
|
{"license": "mit"}
|
2023-02-06T20:40:54+00:00
|
|
6a7c3281bf85095260f4bc6710b4b6d435d53f09
|
# Dataset Card for "test_americanas"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ricardo-filho/test_americanas
|
[
"region:us"
] |
2023-02-06T20:53:18+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 83378, "num_examples": 961}], "download_size": 50417, "dataset_size": 83378}}
|
2023-02-06T20:53:20+00:00
|
870efbba61b4f77fd99624215e69913853c7bd82
|
jlmarrugom/cif_imgs
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-06T20:58:35+00:00
|
{"license": "apache-2.0"}
|
2023-03-22T17:44:02+00:00
|
|
0dc4c326436b30f901b484ecaabb0962d7a97ca7
|
https://github.com/facebookresearch/dadc-limit
```
@inproceedings{Wallace2022Dynamic,
Title = {Analyzing Dynamic Adversarial Training Data in the Limit},
Author = {Eric Wallace and Adina Williams and Robin Jia and Douwe Kiela},
Booktitle={Findings of the Association for Computational Linguistics},
Year = {2022}
}
```
|
tasksource/dadc-limit-nli
|
[
"task_ids:natural-language-inference",
"language:en",
"license:cc",
"region:us"
] |
2023-02-06T21:03:12+00:00
|
{"language": ["en"], "license": "cc", "task_ids": ["natural-language-inference"]}
|
2023-05-31T07:45:20+00:00
|
b31987214f50aa27d0a5425ad0f216384b9e1302
|
hamdan07/UltraSound-lung
|
[
"license:openrail",
"region:us"
] |
2023-02-06T22:04:51+00:00
|
{"license": "openrail"}
|
2023-02-06T22:07:03+00:00
|
|
c49bb5bbfc47be587c7dc1df0da10c3d1d44bf94
|
# Dataset Card for "SNLI-VE_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/SNLI-VE_train
|
[
"region:us"
] |
2023-02-07T00:32:32+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "filename", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}, {"name": "id", "dtype": "int64"}, {"name": "id_image", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 73634118251.385, "num_examples": 529527}], "download_size": 27853612384, "dataset_size": 73634118251.385}}
|
2023-02-07T23:21:35+00:00
|
136197d1fdd50e82e7cb3228fc2cace5b8fe208a
|
# Dataset Card for 🥤SODA Synthetic Dialogue
## Dataset Description
- **Repository:**
[Code](https://github.com/LAION-AI/Open-Assistant/openassistant/datasets/soda_synthetic_dialogue/)
## Dataset Summary
🥤SODA Synthetic Dialogue is a set of synthetic dialogues between Assistant and
User. In each conversation, User asks Assistant to perform summarization or
story generation tasks based on a snippet of an existing dialogue, story, or
from a title or theme.
This data was created by synthesizing the dialogues in
[🥤Soda](https://huggingface.co/datasets/allenai/soda) and applying a set of
templates to generate the conversation. The original research paper can be found
[here](https://arxiv.org/pdf/2212.10465v1.pdf).
Example:
```
User: Can you write the next few lines of dialogue for this scene:
Cornell: I know what you're thinking, Dontrell. I can sense your feelings and know exactly what you're going to say or do next. You're thinking about how much you miss your family, and how you wish you could see them again. You're wondering if there's anything you can do to make that happen. And you're also feeling a little guilty, because you know that if you could see them again, it would only be for a short while before they died.
Dontrell: Cornell, how can you possibly know all that? It's like you can read my mind!
Assistant: Sure, the next dialogue for this scene could be:
Cornell: I told you, I can sense your feelings. It's like I have a connection to your thoughts and emotions. I'm not sure why or how it works, but ever since we met, I've been able to understand what you're thinking and feeling pretty easily.
Dontrell: Wow...that's amazing! And a little bit scary too. But it does explain why we've always been so close - even though we've only known each other for a few months.
Cornell: Yeah, I guess our connection is pretty strong. But don't worry, Dontrell - I'll never use it to manipulate or control you in any way. I promise."
Dontrell: That's a relief! I was starting to feel a little bit like you could read my mind, and it was kind of creepy. But if you promise not to use your abilities to control me, then I'm okay with it.
Cornell: I swear, Dontrell. I'll never use my abilities to control or manipulate you in any way. You have my word.
User: And a title?
Assistant: Sure, a title for this dialogue could be:
Cornell is intuitive. Cornell reads Dontrell's mind.
User: How about a short description?
Assistant: Sure, a short description for this dialogue could be:
Cornell knows what Dontrell is thinking. He can sense her feelings and know exactly what she is going to say or do next.
User: What would be one theme of this story?
Assistant: One theme of this story could be:
being intuitive
```
## Usage
The dataset contains one configuration, `dialogue_modeling`, which has a single
text `conversation` feature.
## Source data
The script to synthesize this dataset was originally created by
[ontocord](https://github.com/ontocord) in
[this Colab notebook](https://colab.research.google.com/drive/1Sw3px5dP8whdqT7QMNoqwmqIasZkMbJi?usp=sharing)
and prepared for Hugging Face by
[Jeffrey Quesnelle](https://github.com/jquesnelle/).
## Citation
Please cite our work if you find the resources in this repository useful:
```
@article{ontocord2023sodasynth,
author = {ontocord and Jeffrey Quesnelle},
title = {SODA Synthetic Dialogue},
year = {2023}
}
```
|
emozilla/soda_synthetic_dialogue
|
[
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|allenai/soda",
"language:en",
"license:mit",
"open-assistant",
"conversations-summarization",
"arxiv:2212.10465",
"region:us"
] |
2023-02-07T01:55:49+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["extended|allenai/soda"], "task_categories": ["conversational"], "task_ids": ["dialogue-generation"], "pretty_name": "SODA Synthetic Dialogue", "tags": ["open-assistant", "conversations-summarization"], "dataset_info": {"features": [{"name": "conversation", "dtype": "string"}], "config_name": "soda_synthetic_dialogue_dialogue_modeling", "splits": [{"name": "train", "num_bytes": 1524941476, "num_examples": 1191582}, {"name": "test", "num_bytes": 190565707, "num_examples": 148968}, {"name": "validation", "num_bytes": 187161878, "num_examples": 146346}], "download_size": 1952926579, "dataset_size": 1902669061}}
|
2023-02-07T03:54:33+00:00
|
17501f72697cf8018aaf496162e9dc1408a64e67
|
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage: https://towardsdatascience.com/counsel-chat-bootstrapping-high-quality-therapy-data-971b419f33da**
- **Repository: https://github.com/nbertagnolli/counsel-chat**
- **Paper: https://towardsdatascience.com/counsel-chat-bootstrapping-high-quality-therapy-data-971b419f33da**
- **Leaderboard: NA**
- **Point of Contact: nbertagnolli**
### Dataset Summary
Scrape of Counselchat.com's forum. CounselChat.com is an example of an expert community.
It is a platform to help counselors build their reputation and make meaningful contact with potential clients.
On the site, therapists respond to questions posed by clients, and users can like responses that they find most helpful.
It’s a nice idea and lends itself to some interesting data. This data contains expert responses by licensed clinicialns
to questions posed by individuals.
### Supported Tasks and Leaderboards
NA
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
* questionID — A unique question identifier which is distinct for every question
* questionTitle — The title of the question on counsel chat
* questionText — The body of the individual’s question to counselors
* questionLink — A URL to the last location of that question (might not still be active)
* topic — The topic the question was listed under
* therapistInfo — The summary of each therapist, usually a name and specialty
* therapistURL — a link to the therapist’s bio on counselchat
* answerText — The therapist response to the question
* upvotes — The number of upvotes the answerText received
* split — The data split for training, validation, and testing.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
There is a lack of high quality open source mental health data available for study in NLP. Most datasets revolve around
forums like Reddit, which can provide great insights, but don't capture the type of language often used by counselors.
This dataset seeks to help bridge that gap and provide some additional data of counselors interacting with patients in
need.
### Source Data
The dataset was scraped on 20220401 from counselchat.com.
#### Initial Data Collection and Normalization
The dataset was scraped on 20220401 from counselchat.com. The data is in it's raw form and has not been normalized.
#### Who are the source language producers?
The text was written by licensed counselors in the United States and annonymous individuals.
### Annotations
The dataset does not contain any additional annotations.
### Personal and Sensitive Information
This data is not anonymized, so individuals' names can be found in the dataset. CounselChat.com allows therapists to advertise for their clinics
by providing sound publicly available advise. The therapist names have been kept as part of the original dataset.
## 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
nicolas bertagnolli
### Licensing Information
MIT
### Citation Information
Bertagnolli, N. Counsel Chat: Bootstrapping High-Quality Therapy Data. Available online: https://towardsdatascience.com/counsel-chat-bootstrapping-high-quality-therapy-data-971b419f33da
```
@misc{bertagnolli2020counsel,
title={Counsel chat: Bootstrapping high-quality therapy data},
author={Bertagnolli, Nicolas},
year={2020},
publisher={Towards Data Science. https://towardsdatascience. com/counsel-chat~…}
}
```
### Contributions
Thanks to [@nbertagnolli](https://github.com/nbertagnolli) for adding this dataset.
|
nbertagnolli/counsel-chat
|
[
"region:us"
] |
2023-02-07T03:51:53+00:00
|
{}
|
2023-06-17T16:55:38+00:00
|
156692bc3200992729e7d071d96283bc53e13d71
|
nateraw/fuego-20230206-230622-6f8a56
|
[
"fuego",
"region:us"
] |
2023-02-07T04:06:23+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230206-230622-6f8a56", "status": "done", "script": "run_lora_finetuning.py", "requirements_file": "requirements.txt", "space_id": "nateraw/fuego-20230206-230622-6f8a56", "space_hardware": "t4-small"}}
|
2023-02-07T04:55:33+00:00
|
|
c7badfe00b1477ce672f50ee353b3b4129ee3fa0
|
# Dataset Card for "pytorch-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
BigTimeCoderSean/pytorch-issues
|
[
"region:us"
] |
2023-02-07T04:10:39+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "dtype": "null"}, {"name": "comments", "dtype": "int64"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1524374.916, "num_examples": 492}], "download_size": 540539, "dataset_size": 1524374.916}}
|
2023-02-07T06:38:06+00:00
|
fa19528ee0b05304746e821bb5024be49ac5e980
|
# Dataset Card for "350k_dataset_health_ar_en_th_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HealthTeam/350k_dataset_health_ar_en_th_tokenized
|
[
"region:us"
] |
2023-02-07T04:20:00+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 744995253, "num_examples": 1075552}, {"name": "validation", "num_bytes": 26752860, "num_examples": 40432}, {"name": "test", "num_bytes": 26752860, "num_examples": 40432}], "download_size": 294054766, "dataset_size": 798500973}}
|
2023-02-07T04:20:31+00:00
|
b8c74066b9c187507e65d2372e9e7e0642825da7
|
sakhawat/tesyt
|
[
"license:mit",
"region:us"
] |
2023-02-07T04:35:38+00:00
|
{"license": "mit"}
|
2023-07-03T01:41:16+00:00
|
|
9dfff02b71779e3ecf7ac4e6bbe2ed7c9ea485de
|
Martineberle/CS_CP_Ti_mechanical_props
|
[
"Cold spray",
"CP Titanium",
"mechanical properties",
"spray parameters",
"region:us"
] |
2023-02-07T06:25:03+00:00
|
{"tags": ["Cold spray", "CP Titanium", "mechanical properties", "spray parameters"]}
|
2023-02-07T06:36:33+00:00
|
|
f80e02d4629a2311b413bddc172150b5723c559a
|
mble/verToSemVer
|
[
"license:odbl",
"region:us"
] |
2023-02-07T07:03:09+00:00
|
{"license": "odbl"}
|
2023-02-07T17:40:34+00:00
|
|
755fcfdb2d707587d97338f2318b919f1bb3d38a
|
pavanBuduguppa/abcdv1.1_nsp
|
[
"license:gpl-2.0",
"region:us"
] |
2023-02-07T07:53:37+00:00
|
{"license": "gpl-2.0"}
|
2023-02-07T08:03:02+00:00
|
|
d963778e31e7e0ed2329c0f4cbe493be532f0e71
|
---
task_categories:
- image-segmentation
tags:
- Earth Observation
|
burakekim/mapinwild
|
[
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"Earth Observation",
"region:us"
] |
2023-02-07T08:23:28+00:00
|
{"size_categories": ["1K<n<10K"], "task_categories": ["image-segmentation"], "pretty_name": "MapInWild", "tags": ["Earth Observation"]}
|
2023-09-25T08:43:12+00:00
|
81c8662aabd34f1c7456883b317c6b6097cfe2bb
|
# Dataset Card for Unsilencing Colonial Archives via Automated Entity Recognition
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/budh333/UnSilence_VOC/tree/v1.3
- **Repository:** https://doi.org/10.5281/zenodo.6958524
- **Paper:** https://arxiv.org/abs/2210.02194
- **Leaderboard:**
- **Point of Contact:** * [Mrinalini Luthra](mailto:[email protected])
### Dataset Summary
**Note**: this data card was adapted from documentation and a [data card](https://github.com/budh333/UnSilence_VOC/blob/main/Datacard.pdf) written by the creators of the dataset.
> Colonial archives are at the center of increased interest from a variety of perspectives, as they contain traces of historically marginalized people. Unfortunately, like most archives, they remain difficult to access due to significant persisting barriers. We focus here on one of them: the biases to be found in historical findings aids, such as indices of person names, which remain in use to this day. In colonial archives, indexes can perpetrate silences by omitting to include mentions of historically marginalized persons. In order to overcome such limitation and pluralize the scope of existing finding aids, we propose using automated entity recognition. To this end, we contribute a fit-for-purpose annotation typology and apply it on the colonial archive of the Dutch East India Company (VOC). We release a corpus of nearly 70,000 annotations as a shared task, for which we provide strong baselines using state-of-the-art neural network models.
This dataset is based on the digitized collection of the Dutch East India
Company (VOC) Testaments under the custody of the Dutch National
Archives. These testaments of VOC-servants are mainly from the 18th
century, for the most part drawn up in the Asian VOC-settlements and
to a lesser extent on the VOC ships and in the Republic. The
testaments have a fixed order in the text structure and the language is
18th century Dutch.
The dataset has 68,429 annotations spanning over 79,797 tokens
across 2193 unique pages. 47% of the total annotations correspond to
entities and 53% to attributes of those entities. Of the 32,203 entity
annotations, 11,715 (36.3%) correspond to instances that represent
persons with associated attributes of gender, legal status and notarial
role, 4,510 (14%) correspond to instances of places, 1,080 (3.5%)
correspond to organizations with attribute beneficiary and 14,898
(46.2%) correspond to proper names (of places, organizations and
persons).
### Supported Tasks and Leaderboards
- named-entity-recognition: This dataset can be used to train a model for Named Entity Recognition. In particular, the dataset was designed to detect mentions of people in archival documents.
### Languages
The dataset contains 18th Century Dutch. The text in the dataset was produced via handwritten text recognition so contains some errors.
## Dataset Structure
### Data Instances
Each datapoint refers to a central entity that can be a person, place, organization or proper name or their attributes such as gender, legal
status and notarial role of a person.
### Data Fields
- tokens: tokens being annotated
- NE-MAIN: main entity type, i.e. person, place, Organization, ProperName
- NE-PER-NAME: person name entity
- NE-PER-GENDER: When the mention of a person is followed or preceded by
trigger words which reveal their gender, the text is annotated
as a Person with the appropriate value of the attribute Gender.
When a person is mentioned without a gender trigger word,
their gender is marked as Unspecified.
- NE-PER-LEGAL-STATUS: legal status where known, i.e. Free(d), Enslaved, Unspecified
- NE-PER-ROLE: The attribute Role refers to roles specific to testaments in notarial archives, which may take exactly one of the following
values: Testator, Notary, Witness, Beneficiary, Acting Notary, Testator Beneficiary or Other
- NE-ORG-BENEFICIARY: Organizations have the attribute Beneficiary which can take the value Yes or No depending on
whether the testator decides an organization to be their beneficiary.
- MISC: other annotations not fitting into the above labels.
- document_id: id for the document being annotated
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
This dataset was created for training entity recognition models to create more inclusive content based indexes on the collection of VOC testaments.
### Source Data
#### Initial Data Collection and Normalization
This dataset is based on the digitized collection of the Dutch East India
Company (VOC) Testaments under the custody of the Dutch National
Archives.
#### Who are the source language producers?
[More Information Needed]
### Annotations
| Entity | # | % |
|--------------|--------|------|
| Person | 11,715 | 36.4 |
| Place | 4,510 | 14 |
| Organization | 1,080 | 3.4 |
| ProperName | 14,898 | 46.2 |
#### Annotation process
Annotations were created as a shared annotation task on the Brat annotation software. Annotations were created by highlighting the relevant span of
text and choosing its entity type and where applicable exactly one attribute value through a drop down menu. To tag the same span as two entities, the span must be selected two times and labelled accordingly. For example: ‘Adam Domingo’ has been labelled twice as a Person and ProperName.
#### Who are the annotators?
### 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
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
|
biglam/unsilence_voc
|
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"size_categories:1K<n<10K",
"language:nl",
"license:cc-by-4.0",
"lam ",
"arxiv:2210.02194",
"region:us"
] |
2023-02-07T09:33:34+00:00
|
{"language": ["nl"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "Unsilencing Colonial Archives via Automated Entity Recognition", "dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "NE-MAIN", "sequence": {"class_label": {"names": {"0": "B-Organization", "1": "B-Organization,B-Place", "2": "B-Organization,I-Person", "3": "B-Organization,I-Place", "4": "B-Person", "5": "B-Person,B-Place", "6": "B-Person,I-Place", "7": "B-Place", "8": "I-Organization", "9": "I-Organization,B-Place", "10": "I-Organization,I-Person", "11": "I-Organization,I-Person,B-Place", "12": "I-Organization,I-Person,I-Place", "13": "I-Organization,I-Place", "14": "I-Person", "15": "I-Person,B-Place", "16": "I-Person,I-Place", "17": "I-Place", "18": "O"}}}}, {"name": "NE-PER-NAME", "sequence": {"class_label": {"names": {"0": "I-ProperName", "1": "O", "2": "B-ProperName", "3": ""}}}}, {"name": "NE-PER-GENDER", "sequence": {"class_label": {"names": {"0": "B-Group", "1": "B-Man", "2": "B-Man,B-Unspecified", "3": "B-Man,I-Woman", "4": "B-Unspecified", "5": "B-Unspecified,I-Woman", "6": "B-Woman", "7": "I-Group", "8": "I-Man", "9": "I-Man,I-Unspecified", "10": "I-Man,I-Woman", "11": "I-Unspecified", "12": "I-Unspecified,I-Woman", "13": "I-Woman", "14": "NE-PER-GENDER", "15": "O"}}}}, {"name": "NE-PER-LEGAL-STATUS", "sequence": {"class_label": {"names": {"0": "B-Enslaved", "1": "B-Freed", "2": "B-Unspecified", "3": "I-Enslaved", "4": "I-Freed", "5": "I-Unspecified", "6": "NE-PER-LEGAL-STATUS", "7": "O"}}}}, {"name": "NE-PER-ROLE", "sequence": {"class_label": {"names": {"0": "B-Acting_Notary", "1": "B-Beneficiary", "2": "B-Notary", "3": "B-Other", "4": "B-Testator", "5": "B-Testator_Beneficiary", "6": "B-Witness", "7": "I-Acting_Notary", "8": "I-Beneficiary", "9": "I-Beneficiary,B-Other", "10": "I-Beneficiary,I-Other", "11": "I-Notary", "12": "I-Other", "13": "I-Testator", "14": "I-Testator_Beneficiary", "15": "I-Witness", "16": "NE-PER-ROLE", "17": "O"}}}}, {"name": "NE-ORG-BENEFICIARY", "sequence": {"class_label": {"names": {"0": "B-No", "1": "B-Yes", "2": "I-No", "3": "I-Yes", "4": "NE-ORG-BENEFICIARY", "5": "O"}}}}, {"name": "MISC", "dtype": "string"}, {"name": "document_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31436367, "num_examples": 2199}], "download_size": 2148172, "dataset_size": 31436367}, "tags": ["lam "], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-16T10:33:53+00:00
|
5f714e5f5470dd32a8957b8ff397303bca764207
|
Hunterlin/test
|
[
"license:openrail",
"region:us"
] |
2023-02-07T10:10:46+00:00
|
{"license": "openrail"}
|
2023-02-08T10:30:56+00:00
|
|
acfd45f22d4638f6b7264886ac0f3d8456f32316
|
# Danbooru 2021 SQLite
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is the metadata of danbooru 2021 dataset in SQLite format.
https://gwern.net/danbooru2021
### 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]
|
KBlueLeaf/Danbooru2021-SQLite
|
[
"task_categories:text-generation",
"task_categories:zero-shot-classification",
"size_categories:1M<n<10M",
"region:us"
] |
2023-02-07T10:31:58+00:00
|
{"size_categories": ["1M<n<10M"], "task_categories": ["text-generation", "zero-shot-classification"]}
|
2023-02-25T13:32:36+00:00
|
8ed740fa66c9d97dd3ffe0b7c4197f7c71d1983c
|
<strong>Lexicon of words for investigating the political personalization phenomenon in Italian language</strong></br>
List of 3,303 personalizing words in Italian language, annotated with the corresponding sentiment classification as referred to political offices.</br>
Words are group by category: Moral and behavioral, Physical, Social and economic.
<strong>Citation info and BibTeX entry</strong></br>
<a href="" target="_blank"></a>
```bibtex
@article{Bru2023,
title={Combining NLP techniques and statistical modeling to analyze gender gaps in the mediated personalization of politics},
author={Brugnoli, Emanuele and Simone, Rosaria and Delmastro, Marco},
journal={},
year={2023},
volume={}
}
```
|
brema76/political_personalization_it
|
[
"license:mit",
"region:us"
] |
2023-02-07T10:46:42+00:00
|
{"license": "mit"}
|
2023-02-07T11:00:36+00:00
|
854788e4a332543769f895eb7c8b3afb8622ab82
|
# Dataset Card for "dreambooth-lego"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Zenodia/dreambooth-lego
|
[
"region:us"
] |
2023-02-07T12:17:23+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 14731012.0, "num_examples": 12}], "download_size": 14673354, "dataset_size": 14731012.0}}
|
2023-02-07T12:17:30+00:00
|
5c1297a9eb6c91714ea77c0d4ac5aca9b6a952e5
|
# Speaker embeddings extracted from CMU ARCTIC
There is one `.npy` file for each utterance in the dataset, 7931 files in total. The speaker embeddings are 512-element X-vectors.
The [CMU ARCTIC](http://www.festvox.org/cmu_arctic/) dataset divides the utterances among the following speakers:
- bdl (US male)
- slt (US female)
- jmk (Canadian male)
- awb (Scottish male)
- rms (US male)
- clb (US female)
- ksp (Indian male)
The X-vectors were extracted using [this script](https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/manifest/utils/prep_cmu_arctic_spkemb.py), which uses the `speechbrain/spkrec-xvect-voxceleb` model.
Usage:
```python
from datasets import load_dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = embeddings_dataset[7306]["xvector"]
speaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0)
```
|
Matthijs/cmu-arctic-xvectors
|
[
"task_categories:text-to-speech",
"task_categories:audio-to-audio",
"license:mit",
"region:us"
] |
2023-02-07T12:39:22+00:00
|
{"license": "mit", "task_categories": ["text-to-speech", "audio-to-audio"], "pretty_name": "CMU ARCTIC X-Vectors"}
|
2023-02-07T14:04:48+00:00
|
8ccd836cbbe1e32c8d9f4a0859e09753ee6797e9
|
# Dataset Card for Dataset Name
## Dataset Description
### Dataset Summary
The dataset provides a mapping between [TV Tropes](https://tvtropes.org/) and [IMDb](https://www.imdb.com/) entries for about 10K movies.
### Supported Tasks and Leaderboards
No particular task is supported, because the dataset is intended to enrich other datasets
(for instance, [Movielens](https://grouplens.org/datasets/movielens/))
### Languages
Movie titles are provided in English the way they are present on TV Tropes.
## Dataset Structure
The dataset is comprised of two columns: `tvtropes` and `imdb`. The former can be used to access the movie's page on TV Tropes
using the following url template: `https://tvtropes.org/pmwiki/pmwiki.php/Film/{tvtropes}`. The latter can be used to acces the
movie's page on IMDb using the following url template `https://www.imdb.com/title/tt{imdb}/` (note that the imbd id has to be
padded to the left with zeroes if it contains less than seven digits).
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
There is only one split. The data is not meant to be used directly for training/evaluation.
## Dataset Creation
The dataset was created semi-automatically: [Cinemagoer](https://github.com/cinemagoer/cinemagoer) was used to query IMDb for movies with
matching titles, then the resulting output was manually verified.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
### Annotations
#### Annotation process
The annotation only involved manually verifying that a given TV Tropes entry corresponds to a correct IMDb entry.
However, some are inaccuracies may be still present due to the sheer volume of data.
#### Who are the annotators?
The correctness of the mapping was verified by me (Aleksei Dorkin) personally.
### Personal and Sensitive Information
Not applicable.
## Considerations for Using the Data
### Social Impact of Dataset
Not applicable. The impact may potentially come from the data that is linked using the dataset.
### Discussion of Biases
Not applicable. The datasets only links together entries for movies on different web resources.
### Other Known Limitations
One potential limitation of the dataset may come from the way TV Tropes pages are organized. It is not always possible to automatically
distinguish a page related to a franchise in general and a paged related to a particular movie in a franchise.
Another thing to consider is that it's pretty common for movies to share the exact same title. At the same time, they can also be quite
similar in content. Thus, it's not always trivial to distinguish movies with the same title if no year is given.
As a result, some inaccuries of this nature may be present in the data.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC0
### Citation Information
[More Information Needed]
### Contributions
Contributions are welcome on this dataset page using the Community tab.
|
adorkin/tvtropes2imdb
|
[
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"tropes",
"movies",
"recommender",
"region:us"
] |
2023-02-07T12:40:17+00:00
|
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "tags": ["tropes", "movies", "recommender"]}
|
2023-02-07T13:20:43+00:00
|
eec562f4d2b341b85e7a21be6fc4e41eaad4a1da
|
In May 2020, I programmed a [bot](https://twitter.com/venceralcovid) that analyzed a live stream of Twitter headlines and retrieved only those with positive stories in the COVID-19 pandemic. I stopped the bot shortly after the advent of the vaccine. In this dataset, you will find some of the tweets that I used for training, labeled as ```pos_news``` (good or positive news), and ```not_pos_news``` (negative or neutral news).
This dataset would be useful to make similar news classifiers for other diseases, perhaps replacing some of its entities (e.g. replacing COVID disease mentions for other disease terms).
|
jnferfer/spanish_good_covid_news
|
[
"language:es",
"region:us"
] |
2023-02-07T12:56:46+00:00
|
{"language": ["es"]}
|
2023-02-09T09:05:38+00:00
|
ba38fea61d15377ed3dce1747b84bad25818e9d6
|
Minner/DataSetting
|
[
"license:unknown",
"region:us"
] |
2023-02-07T12:57:05+00:00
|
{"license": "unknown"}
|
2023-02-07T13:05:42+00:00
|
|
818eabd29dcfa56d9569a43eb2a2e9f53b74a88c
|
# Dataset Card for "SNLI-VE_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/SNLI-VE_test
|
[
"region:us"
] |
2023-02-07T14:50:42+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "filename", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}, {"name": "id", "dtype": "int64"}, {"name": "id_image", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 2483209080.488, "num_examples": 17901}], "download_size": 911606574, "dataset_size": 2483209080.488}}
|
2023-02-07T22:33:34+00:00
|
f55f490265170e69073ae2d62c9c79ff405be6e6
|
# Dataset Card for `BanglaRQA`
## Table of Contents
- [Dataset Card for `BanglaRQA`](#dataset-card-for-BanglaRQA)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Usage](#usage)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [https://github.com/sartajekram419/BanglaRQA](https://github.com/sartajekram419/BanglaRQA)
- **Paper:** [BanglaRQA: A Benchmark Dataset for Under-resourced Bangla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types](https://aclanthology.org/2022.findings-emnlp.186)
### Dataset Summary
This is a human-annotated Bangla Question Answering (QA) dataset with diverse question-answer types.
### Languages
* `Bangla`
### Usage
```python
from datasets import load_dataset
dataset = load_dataset("sartajekram/BanglaRQA")
```
## Dataset Structure
### Data Instances
One example from the dataset is given below in JSON format.
```
{
'passage_id': 'bn_wiki_2977',
'title': 'ফাজিল পরীক্ষা',
'context': 'ফাজিল পরীক্ষা বাংলাদেশ ও ভারতের আলিয়া মাদ্রাসায় অনুষ্ঠিত একটি সরকারি পরীক্ষা। ফাজিল পরীক্ষা বাংলাদেশে ডিগ্রি সমমানের, কখনো স্নাতক সমমানের একটি পরীক্ষা, যা একটি ফাজিল মাদ্রাসায় অনুষ্ঠিত হয়ে থাকে। তবে ভারতে ফাজিল পরীক্ষাকে উচ্চ মাধ্যমিক শ্রেণীর (১১ বা ১২ ক্লাস) মান বলে বিবেচিত করা হয়। ফাজিল পরীক্ষা বাংলাদেশ ভারত ও পাকিস্তানের সরকারি স্বীকৃত আলিয়া মাদরাসায় প্রচলিত রয়েছে। বাংলাদেশের ফাজিল পরীক্ষা ইসলামি আরবি বিশ্ববিদ্যালয়ের অধীনে অনুষ্ঠিত হয়ে থাকে ও ভারতের ফাজিল পরীক্ষা পশ্চিমবঙ্গ মাদ্রাসা শিক্ষা পর্ষদের অধীনে অনুষ্ঠিত হয়ে থাকে।\n\n১৯৪৭ সালে ঢাকা আলিয়া মাদ্রাসা ঢাকায় স্থানান্তরের পূর্বে বাংলাদেশ ও ভারতের ফাজিল পরীক্ষা কলকাতা আলিয়া মাদ্রাসার অধীনে অনুষ্ঠিত হতো। ফাযিল পরীক্ষা বর্তমানে ইসলামি আরবী বিশ্ববিদ্যালয়ের অধীনে অনুষ্ঠিত হয়। যা পূর্বে মাদরাসা বোর্ড ও ইসলামি বিশ্ববিদ্যালয়ের আধীনে অনুষ্ঠিত হত। মাদ্রাসা-ই-আলিয়া ঢাকায় স্থানান্তরিত হলে ১৯৪৮ সালে মাদ্রাসা বোর্ডের ফাজিলগুলো পরীক্ষা ঢাকা বিশ্ববিদ্যালয় কর্তৃক গৃহীত হতো। ১৯৭৫ সালের কুদরত-এ-খুদা শিক্ষা কমিশনের সুপারিশে মাদ্রাসা বোর্ড নিয়ন্ত্রিত আলিয়া মাদ্রাসাসমূহে জাতীয় শিক্ষাক্রম ও বহুমুখী পাঠ্যসূচি প্রবর্তিত করা হয়। ১৯৮০ সালে অনুষ্ঠিত ফাজিল পরীক্ষায় এই পাঠ্যসুচী কার্যকর হয়। এই শিক্ষা কমিশন অনুসারে ফাজিল শ্রেণীতে ইসলামি শিক্ষার পাশাপাশি সাধারণ পাঠ্যসূচী অন্তর্ভুক্ত করে ফাজিল পরীক্ষাকে সাধারণ উচ্চ মাধ্যমিক এইচ এস সির সমমান ঘোষণা করা হয়।\n\n১৯৭৮ সালে অধ্যাপক মুস্তফা বিন কাসিমের নেতৃত্বে সিনিয়র মাদ্রাসা শিক্ষা ব্যবস্থা কমিটি গঠিত হয়। এই কমিটির নির্দেশনায় ১৯৮৪ সালে সাধারণ শিক্ষার স্তরের সঙ্গে বাংলাদেশ মাদ্রাসা বোর্ড নিয়ন্ত্রিত আলিয়া মাদ্রাসা শিক্ষা স্তরের সামঞ্জস্য করা হয়। ফাজিল স্তরকে ২ বছর মেয়াদী কোর্সে উন্নিত করে, মোট ১৬ বছর ব্যাপী আলিয়া মাদ্রাসার পূর্ণাঙ্গ আধুনিক শিক্ষা ব্যবস্থা প্রবর্তন করা হয়। এই কমিশনের মাধ্যমেই সরকার ফাজিল পরীক্ষাকে সাধারণ ডিগ্রি মান ঘোষণা করে।',
'question_id': 'bn_wiki_2977_01',
'question_text': 'ফাজিল পরীক্ষা বাংলাদেশ ও ভারতের আলিয়া মাদ্রাসায় অনুষ্ঠিত একটি সরকারি পরীক্ষা ?',
'is_answerable': '1',
'question_type': 'confirmation',
'answers':
{
'answer_text': ['হ্যাঁ', 'হ্যাঁ '],
'answer_type': ['yes/no', 'yes/no']
},
}
```
### Data Splits
| split |count |
|----------|--------|
|`train`| 11,912 |
|`validation`| 1,484 |
|`test`| 1,493 |
## Additional Information
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use the dataset, please cite the following paper:
```
@inproceedings{ekram-etal-2022-banglarqa,
title = "{B}angla{RQA}: A Benchmark Dataset for Under-resourced {B}angla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types",
author = "Ekram, Syed Mohammed Sartaj and
Rahman, Adham Arik and
Altaf, Md. Sajid and
Islam, Mohammed Saidul and
Rahman, Mehrab Mustafy and
Rahman, Md Mezbaur and
Hossain, Md Azam and
Kamal, Abu Raihan Mostofa",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.186",
pages = "2518--2532",
abstract = "High-resource languages, such as English, have access to a plethora of datasets with various question-answer types resembling real-world reading comprehension. However, there is a severe lack of diverse and comprehensive question-answering datasets in under-resourced languages like Bangla. The ones available are either translated versions of English datasets with a niche answer format or created by human annotations focusing on a specific domain, question type, or answer type. To address these limitations, this paper introduces BanglaRQA, a reading comprehension-based Bangla question-answering dataset with various question-answer types. BanglaRQA consists of 3,000 context passages and 14,889 question-answer pairs created from those passages. The dataset comprises answerable and unanswerable questions covering four unique categories of questions and three types of answers. In addition, this paper also implemented four different Transformer models for question-answering on the proposed dataset. The best-performing model achieved an overall 62.42{\%} EM and 78.11{\%} F1 score. However, detailed analyses showed that the performance varies across question-answer types, leaving room for substantial improvement of the model performance. Furthermore, we demonstrated the effectiveness of BanglaRQA as a training resource by showing strong results on the bn{\_}squad dataset. Therefore, BanglaRQA has the potential to contribute to the advancement of future research by enhancing the capability of language models. The dataset and codes are available at https://github.com/sartajekram419/BanglaRQA",
}
```
|
sartajekram/BanglaRQA
|
[
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:human",
"size_categories:10K<n<100K",
"language:bn",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-02-07T14:56:06+00:00
|
{"annotations_creators": ["human"], "language": ["bn"], "license": "cc-by-nc-sa-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa", "extractive-qa"]}
|
2023-05-06T18:04:32+00:00
|
a9efda74e8f163519080cf04fed145c93ce04970
|
# Dataset Card for "davinci-pairwise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/davinci-pairwise
|
[
"region:us"
] |
2023-02-07T15:17:38+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3835512036, "num_examples": 143908}], "download_size": 800758913, "dataset_size": 3835512036}}
|
2023-02-07T17:33:13+00:00
|
77fa5d755c1f1108d8079e01bfe16c0a3ad0f343
|
# Dataset Card for Open Assistant Cornell Movies Dialog
## Dataset Summary
The dataset was created using [Cornell Movies Dialog Corpus](https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html) which contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts.
Dialogs and meta-data from the underlying Corpus were used to design a dataset that can be used to InstructGPT based models to learn movie scripts.
Example :
```
User: Assume RICK and ALICE are characters from a fantasy-horror movie, continue the conversation between them
RICK: I heard you screaming. Was it a bad one?
ALICE: It was bad.
RICK: Doesn't the dream master work for you anymore?
Assistant: Sure
ALICE: I can't find him.
RICK: Hey, since when do you play Thomas Edison? This looks like Sheila's.
ALICE: It is...was. It's a zapper, it might help me stay awake.
RICK: Yeah, or turn you into toast.
```
## Citations
```
@InProceedings{Danescu-Niculescu-Mizil+Lee:11a,
author={Cristian Danescu-Niculescu-Mizil and Lillian Lee},
title={Chameleons in imagined conversations:
A new approach to understanding coordination of linguistic style in dialogs.},
booktitle={Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011},
year={2011}
}
```
|
shahules786/OA-cornell-movies-dialog
|
[
"region:us"
] |
2023-02-07T15:21:28+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9476338, "num_examples": 20959}], "download_size": 4859997, "dataset_size": 9476338}}
|
2023-02-10T05:34:43+00:00
|
7772604b4bee54e56464ec4ad0a3cbb5ad850990
|
ronanki/product_material_embeddings
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-07T15:23:25+00:00
|
{"license": "apache-2.0"}
|
2023-02-07T15:25:09+00:00
|
|
90685698c5b473e5674a20a81409a9f7f749d6e8
|
VOD-LM/medwiki
|
[
"license:mit",
"region:us"
] |
2023-02-07T15:31:46+00:00
|
{"license": "mit"}
|
2023-02-08T11:48:13+00:00
|
|
8d69489969be8948b775d7ee8bd03dd8f4e40056
|
# Dataset Card for "diana_uribe_large_ada_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
juancopi81/diana_uribe_large_ada_embeddings
|
[
"region:us"
] |
2023-02-07T15:32:35+00:00
|
{"dataset_info": {"features": [{"name": "TITLE", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "transcription_length", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "ada_embedding", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 128412971, "num_examples": 3215}], "download_size": 83354282, "dataset_size": 128412971}}
|
2023-02-07T15:32:43+00:00
|
0819062721f16014431557c2df35f3bbaca17fe6
|
# Dataset Card for "ABSA_LCF"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qbaro/ABSA_LCF
|
[
"region:us"
] |
2023-02-07T16:34:11+00:00
|
{"dataset_info": {"features": [{"name": "name_data", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "aspect", "sequence": "string"}, {"name": "sentence_label", "sequence": "string"}, {"name": "aspect_label", "sequence": "string"}, {"name": "polarity", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 16064944, "num_examples": 34648}, {"name": "test", "num_bytes": 3331953, "num_examples": 7462}], "download_size": 2584799, "dataset_size": 19396897}}
|
2023-02-07T16:37:54+00:00
|
1d554dc6a22e16ece32fe00f5b35a62e056b7f14
|
# Dataset Card for "davinci-pairwise-all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/davinci-pairwise-all
|
[
"region:us"
] |
2023-02-07T16:37:56+00:00
|
{"dataset_info": {"features": [{"name": "chosen", "dtype": "string"}, {"name": "rejected", "sequence": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "api_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1866932601, "num_examples": 47954}], "download_size": 820468098, "dataset_size": 1866932601}}
|
2023-02-07T17:32:26+00:00
|
2011d3ba2f5473a88d27f882fc8951700e974639
|
# Dataset Card for "movie"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yvs007/movie
|
[
"region:us"
] |
2023-02-07T17:14:28+00:00
|
{"dataset_info": {"features": [{"name": "Title", "dtype": "string"}, {"name": "Genre", "dtype": "string"}, {"name": "PlotSummary", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 7415818, "num_examples": 5964}], "download_size": 0, "dataset_size": 7415818}}
|
2023-02-08T06:27:04+00:00
|
d62e5e026de477fd765224e0fb74958fdc8dba65
|
# Dataset Card for "italian-literature-corpus-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
scribis/italian-literature-corpus-mini
|
[
"language:it",
"region:us"
] |
2023-02-07T17:50:13+00:00
|
{"language": ["it"], "dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 125104611.3227607, "num_examples": 872594}, {"name": "validation", "num_bytes": 13900528.299298717, "num_examples": 96955}], "download_size": 143032707, "dataset_size": 139005139.6220594}}
|
2023-11-14T10:23:32+00:00
|
ef8654222da174866a3add0e81346083d3dc6375
|
RTTTL Ringtones Dataset
~11k Ringtones/songs gathered from various sources over the internet.
---
license: apache-2.0
---
|
cosimoiaia/RTTTL-Ringtones
|
[
"region:us"
] |
2023-02-07T18:31:04+00:00
|
{}
|
2023-02-07T18:33:46+00:00
|
93f3710720bf471a876a78f40e83bb7ff29e424c
|
# Dataset Card for "stormfront"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kastan/stormfront
|
[
"region:us"
] |
2023-02-07T18:33:39+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7548306952, "num_examples": 10458223}, {"name": "test", "num_bytes": 386917, "num_examples": 791}], "download_size": 4688723070, "dataset_size": 7548693869}}
|
2023-02-07T19:26:09+00:00
|
f40575420823ccfbc55dce92e82d506b041c4abf
|
# Toloker Graph: Interaction of Crowd Annotators
## Dataset Description
- **Repository:** https://github.com/Toloka/TolokerGraph
- **Point of Contact:** [email protected]
### Dataset Summary
This repository contains a graph representing interactions between crowd annotators on a project labeled on the [Toloka](https://toloka.ai/) crowdsourcing platform (see the [Toloka overview](https://toloka.ai/en/docs/guide/concepts/overview) for the details on the used terminology).
The graph contains 11,758 nodes and 519,000 edges. Each node represents an individual annotator; nodes are provided with four numerical and three categorical features. An edge is drawn between a pair of annotators if they annotated the same task. Also, each node is provided with a label showing whether the annotator was banned on this project, or not.
### Nodes
Nodes are stored in the [nodes.tsv](nodes.tsv) file in the TSV format of the following structure:
- `id`: unique identifier of the annotator
- `approved_rate`: percentage of the approved labels of this annotator
- `skipped_rate`: percentage of the skipped tasks of this annotator
- `expired_rate`: percentage of the expired tasks of this annotator
- `rejected_rate`: percentage of the rejected labels of this annotator
- `education`: level of education as self-reported by this annotator (`none`, `basic`, `middle`, `high`)
- `english_profile`: knowledge of English as self-reported by this annotator (`0` for no, `1` for yes)
- `english_tested`: whether the annotator passed the Toloka language test for English (`0` for no, `1` for yes)
- `banned`: whether the annotator was banned on this project (`0` for no, `1` for yes)
The `*_rate` attributes should sum up to 1.
### Edges
Edges are stored in the [edges.tsv](edges.tsv) file in the TSV format of the following structure:
- `source`: source identifier of the annotator
- `target`: target identifier of the annotator
As the graph is undirected, `source` and `target` can be interchanged for the given pair of nodes.
### Citation
* Likhobaba, D., Pavlichenko, N., Ustalov, D. (2023). [Toloker Graph: Interaction of Crowd Annotators](https://doi.org/10.5281/zenodo.7620795). Zenodo. <https://doi.org/10.5281/zenodo.7620795>
```bibtex
@dataset{Tolokers,
author = {Likhobaba, Daniil and Pavlichenko, Nikita and Ustalov, Dmitry},
title = {{Toloker Graph: Interaction of Crowd Annotators}},
year = {2023},
publisher = {Zenodo},
doi = {10.5281/zenodo.7620795},
url = {https://github.com/Toloka/TolokerGraph},
language = {english},
}
```
### Copyright
Licensed under the Creative Commons Attribution 4.0 License. See LICENSE file for more details.
|
toloka/TolokerGraph
|
[
"task_categories:graph-ml",
"size_categories:10K<n<100K",
"license:cc-by-4.0",
"toloka",
"graph",
"node-classification",
"region:us"
] |
2023-02-07T19:02:45+00:00
|
{"license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["graph-ml"], "pretty_name": "Graph of Crowd Annotators", "tags": ["toloka", "graph", "node-classification"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "approved_rate", "dtype": "float"}, {"name": "skipped_rate", "dtype": "float"}, {"name": "expired_rate", "dtype": "float"}, {"name": "rejected_rate", "dtype": "float"}, {"name": "education", "dtype": "string"}, {"name": "english_profile", "dtype": "bool"}, {"name": "english_tested", "dtype": "bool"}, {"name": "banned", "dtype": "bool"}]}}
|
2023-02-08T14:16:49+00:00
|
79d27c1f406f69971add0e50544babd48ebb2fb0
|
merve/parsed-dataset-xlm-roberta
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-07T19:16:49+00:00
|
{"license": "apache-2.0"}
|
2023-02-07T19:16:57+00:00
|
|
a691bc98ceba21baefe5d06bbd9cd29cc8fcd1a4
|
merve/xlm-roberta-large-df
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-07T19:42:27+00:00
|
{"license": "apache-2.0"}
|
2023-02-07T19:42:41+00:00
|
|
997840e7933ff50cd7c374a0044e1c3af8674e2a
|
# Dataset Card for "sidewalk-imagery2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
shahardekel/sidewalk-imagery2
|
[
"region:us"
] |
2023-02-07T19:56:21+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 86083036.0, "num_examples": 10}], "download_size": 7069372, "dataset_size": 86083036.0}}
|
2023-02-07T19:56:33+00:00
|
db5bd349c1403d2470594e697d3bb9bf66d709e3
|
# Dataset Card for "stormfront-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kastan/stormfront-small
|
[
"region:us"
] |
2023-02-07T20:22:30+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8997836, "num_examples": 10000}, {"name": "test", "num_bytes": 386917, "num_examples": 791}], "download_size": 5770146, "dataset_size": 9384753}}
|
2023-02-07T20:22:36+00:00
|
97d809c59a90bcbed1a3300328d5f9984ac85d3e
|
# Dataset Card for "poetry-instructions"
A dataset of user-assistant dialogue instructions for guided poetry creation.
Poems used were taken from [merve/poetry](https://huggingface.co/datasets/merve/poetry) and [matthh/gutenberg-poetry-corpus](https://huggingface.co/datasets/matthh/gutenberg-poetry-corpus).
The dataset contains dialogues in the following formats:
- Poetry Completion:
```
User: Can you continue this poem for me? <poem_start>
Assistant: Sure, a continuation for this poem could be: <poem end>
```
- Create poem in style of (?):
```
User: Can you write a poem for me in the style of <author>?
Assistant: Sure, here's a poem in the style of <author>: <poem>
```
- Creat poem about (?):
```
User: Can you write me a poem about <keywords (extracted using keyphrase model)>?
Assistant: Sure, here's a poem about <keywords>: <poem>
```
- Create poem about (?) in the style of (?):
```
User: Can you write me a poem about <keywords> in the style of <author>?
Assistant: Sure, here's a poem about <keywords> in the style of <author>: <poem>
```
|
isaacrehg/poetry-instructions
|
[
"region:us"
] |
2023-02-07T20:41:03+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 87758119, "num_examples": 1322}, {"name": "validation", "num_bytes": 7731418, "num_examples": 111}, {"name": "test", "num_bytes": 27041394, "num_examples": 331}], "download_size": 63044464, "dataset_size": 122530931}}
|
2023-02-09T20:38:41+00:00
|
63b44d107785f60e8ec9cc15e3138fc70ab53f34
|
# Dataset Card for "cord-v2-custom"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Nyaaneet/cord-v2-custom
|
[
"region:us"
] |
2023-02-07T21:03:06+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "original_label", "struct": [{"name": "meta", "struct": [{"name": "image_id", "dtype": "int64"}, {"name": "image_size", "struct": [{"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}]}]}, {"name": "valid_line", "list": [{"name": "words", "list": [{"name": "quad", "struct": [{"name": "x2", "dtype": "int64"}, {"name": "y3", "dtype": "int64"}, {"name": "x3", "dtype": "int64"}, {"name": "y4", "dtype": "int64"}, {"name": "x1", "dtype": "int64"}, {"name": "y1", "dtype": "int64"}, {"name": "x4", "dtype": "int64"}, {"name": "y2", "dtype": "int64"}]}, {"name": "is_key", "dtype": "int64"}, {"name": "row_id", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "group_id", "dtype": "int64"}, {"name": "sub_group_id", "dtype": "int64"}]}, {"name": "roi", "struct": [{"name": "x2", "dtype": "int64"}, {"name": "y3", "dtype": "int64"}, {"name": "x3", "dtype": "int64"}, {"name": "y4", "dtype": "int64"}, {"name": "x1", "dtype": "int64"}, {"name": "y1", "dtype": "int64"}, {"name": "x4", "dtype": "int64"}, {"name": "y2", "dtype": "int64"}]}, {"name": "repeating_symbol", "list": {"list": [{"name": "quad", "struct": [{"name": "x2", "dtype": "int64"}, {"name": "y3", "dtype": "int64"}, {"name": "x3", "dtype": "int64"}, {"name": "y4", "dtype": "int64"}, {"name": "x1", "dtype": "int64"}, {"name": "y1", "dtype": "int64"}, {"name": "x4", "dtype": "int64"}, {"name": "y2", "dtype": "int64"}]}, {"name": "text", "dtype": "string"}]}}, {"name": "dontcare", "list": {"list": [{"name": "x2", "dtype": "int64"}, {"name": "y3", "dtype": "int64"}, {"name": "x3", "dtype": "int64"}, {"name": "y4", "dtype": "int64"}, {"name": "x1", "dtype": "int64"}, {"name": "y1", "dtype": "int64"}, {"name": "x4", "dtype": "int64"}, {"name": "y2", "dtype": "int64"}]}}, {"name": "bboxes", "struct": [{"name": "3", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "4", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "5", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "6", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "7", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "8", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "9", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "10", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "11", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "12", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", 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"string"}, {"name": "text", "dtype": "string"}]}, {"name": "147", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "148", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "149", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "146", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "46", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "53", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "54", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "40", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "52", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "76", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "79", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "2", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "132", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "133", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "134", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "135", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "127", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "100", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "73", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "70", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "72", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "71", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "170", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "171", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "45", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "56", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "57", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "101", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "30", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "49", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "55", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "150", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "113", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "116", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "109", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "115", "struct": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}]}]}, {"name": "bounding_boxes", "list": [{"name": "quad", "struct": [{"name": "xmin", "dtype": "int64"}, {"name": "ymin", "dtype": "int64"}, {"name": "xmax", "dtype": "int64"}, {"name": "ymax", "dtype": "int64"}]}, {"name": "group_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "receipt_parse", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1786745645.1, "num_examples": 1050}, {"name": "test", "num_bytes": 611466170.0, "num_examples": 350}], "download_size": 2391012653, "dataset_size": 2398211815.1}}
|
2023-02-07T21:08:45+00:00
|
34ae32e7f7bd21338018e0369d325de66dad708d
|
# Dataset Card for Beans
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### Dataset Summary
Coffee Beans Grading
### Supported Tasks and Leaderboards
- `image-classification`: Based on a coffee bean grading, the goal of this task is to grade single beans for clusterization.
### Languages
Indonesia
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/0aaa78294d4bf5114f58547e48d91b7826649919505379a167decb629aa92b0a/train/bean_rust/bean_rust_train.109.jpg',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x500 at 0x16BAA72A4A8>,
'labels': 1
}
```
### Data Fields
The data instances have the following fields:
- `image_file_path`: a `string` filepath to an image.
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `labels`: an `int` classification label.
Class Label Mappings:
```json
{
"1": 0,
"2": 1,
"3": 2,
}
```
### Data Splits
| |train|validation|test|
|-------------|----:|---------:|---:|
|# of examples|1400 |400 |200 |
## 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
### Contributions
|
rasyidf/coffee-beans
|
[
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"size_categories:n<1K",
"source_datasets:original",
"language:id",
"license:mit",
"region:us"
] |
2023-02-07T21:19:08+00:00
|
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["id"], "license": "mit", "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "pretty_name": "Cofee Beans Grading", "dataset_info": {"features": [{"name": "image_file_path", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "labels", "dtype": {"class_label": {"names": {"1": 0, "2": 1, "3": 2, "0": 3}}}}], "splits": [{"name": "train", "num_bytes": "202.173.747", "num_examples": 200}, {"name": "validation", "num_bytes": "57.633.053", "num_examples": 400}, {"name": "test", "num_bytes": "28.985.470", "num_examples": 1400}], "download_size": 288792270, "dataset_size": 2000}}
|
2023-02-07T22:06:44+00:00
|
1240dae4f28fa8abc1af9670140268c2de125fc4
|
Preprocessed data from [Piotr Rybak](https://huggingface.co/datasets/piotr-rybak) and [PolEval](https://github.com/poleval). I am not the owner of these data.
|
wozniakmp/QA
|
[
"region:us"
] |
2023-02-07T21:20:55+00:00
|
{}
|
2023-02-10T15:30:29+00:00
|
7bfbd76ca5f8f7f56338c211a11e5c3ea2e8a85f
|
https://github.com/Dibyakanti/AutoTNLI-code
```
@inproceedings{kumar-etal-2022-autotnli,
title = "Realistic Data Augmentation Framework for Enhancing Tabular Reasoning",
author = "Kumar, Dibyakanti and
Gupta, Vivek and
Sharma, Soumya and
Zhang, Shuo",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Online and Abu Dhabi",
publisher = "Association for Computational Linguistics",
url = "https://vgupta123.github.io/docs/autotnli.pdf",
pages = "",
abstract = "Existing approaches to constructing training data for Natural Language Inference (NLI) tasks, such as for semi-structured table reasoning, are either via crowdsourcing or fully automatic methods. However, the former is expensive and time-consuming and thus limits scale, and the latter often produces naive examples that may lack complex reasoning. This paper develops a realistic semi-automated framework for data augmentation for tabular inference. Instead of manually generating a hypothesis for each table, our methodology generates hypothesis templates transferable to similar tables. In addition, our framework entails the creation of rational counterfactual tables based on human written logical constraints and premise paraphrasing. For our case study, we use the InfoTabS (Gupta et al., 2020), which is an entity-centric tabular inference dataset. We observed that our framework could generate human-like tabular inference examples, which could benefit training data augmentation, especially in the scenario with limited supervision.",
}
```
|
metaeval/autotnli
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-02-07T21:36:51+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"]}
|
2023-05-31T07:55:41+00:00
|
7628e46056df47cb6f3e42d7ba940c060992c241
|
# Dataset Card for "FiNER-ORD"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation and Annotation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contact Information](#contact-information)
## Dataset Description
- **Homepage:** [https://github.com/gtfintechlab/FiNER](https://github.com/gtfintechlab/FiNER)
- **Repository:** [https://github.com/gtfintechlab/FiNER](https://github.com/gtfintechlab/FiNER)
- **Paper:** [Arxiv Link]()
- **Point of Contact:** [Agam A. Shah](https://shahagam4.github.io/)
- **Size of train dataset file:** 1.08 MB
- **Size of validation dataset file:** 135 KB
- **Size of test dataset file:** 336 KB
### Dataset Summary
The FiNER-Open Research Dataset (FiNER-ORD) consists of a manually annotated dataset of financial news articles (in English)
collected from [webz.io] (https://webz.io/free-datasets/financial-news-articles/).
In total, there are 47851 news articles available in this data at the point of writing this paper.
Each news article is available in the form of a JSON document with various metadata information like
the source of the article, publication date, author of the article, and the title of the article.
For the manual annotation of named entities in financial news, we randomly sampled 220 documents from the entire set of news articles.
We observed that some articles were empty in our sample, so after filtering the empty documents, we were left with a total of 201 articles.
We use [Doccano](https://github.com/doccano/doccano), an open-source annotation tool,
to ingest the raw dataset and manually label person (PER), location (LOC), and organization (ORG) entities.
For our experiments, we use the manually labeled FiNER-ORD to benchmark model performance.
Thus, we make a train, validation, and test split of FiNER-ORD.
To avoid biased results, manual annotation is performed by annotators who have no knowledge about the labeling functions for the weak supervision framework.
The train and validation sets are annotated by two separate annotators and validated by a third annotator.
The test dataset is annotated by another annotator. We present a manual annotation guide in the Appendix of the paper detailing the procedures used to create the manually annotated FiNER-ORD.
After manual annotation, the news articles are split into sentences.
We then tokenize each sentence, employing a script to tokenize multi-token entities into separate tokens (e.g. PER_B denotes the beginning token of a person (PER) entity
and PER_I represents intermediate PER tokens). We exclude white spaces when tokenizing multi-token entities.
The descriptive statistics on the resulting FiNER-ORD are available in the Table of [Data Splits](#data-splits) section.
For more details check [information in paper]()
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
- It is a monolingual English dataset
## Dataset Structure
### Data Instances
#### FiNER-ORD
- **Size of train dataset file:** 1.08 MB
- **Size of validation dataset file:** 135 KB
- **Size of test dataset file:** 336 KB
### Data Fields
The data fields are the same among all splits.
#### conll2003
- `doc_idx`: Document ID (`int`)
- `sent_idx`: Sentence ID within each document (`int`)
- `gold_token`: Token (`string`)
- `gold_label`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'PER_B': 1, 'PER_I': 2, 'LOC_B': 3, 'LOC_I': 4, 'ORG_B': 5, 'ORG_I': 6}
```
### Data Splits
| **FiNER-ORD** | **Train** | **Validation** | **Test** |
|------------------|----------------|---------------------|---------------|
| # Articles | 135 | 24 | 42 |
| # Tokens | 80,531 | 10,233 | 25,957 |
| # LOC entities | 1,255 | 267 | 428 |
| # ORG entities | 3,440 | 524 | 933 |
| # PER entities | 1,374 | 222 | 466 |
## Dataset Creation and Annotation
[Information in paper ]()
## Additional Information
### Licensing Information
[Information in paper ]()
### Citation Information
```
@article{shah2023finer,
title={FiNER: Financial Named Entity Recognition Dataset and Weak-supervision Model},
author={Agam Shah and Ruchit Vithani and Abhinav Gullapalli and Sudheer Chava},
journal={arXiv preprint arXiv:2302.11157},
year={2023}
}
```
### Contact Information
Please contact Agam Shah (ashah482[at]gatech[dot]edu) or Ruchit Vithani (rvithani6[at]gatech[dot]edu) about any FiNER-related issues and questions.
GitHub: [@shahagam4](https://github.com/shahagam4), [@ruchit2801](https://github.com/ruchit2801)
Website: [https://shahagam4.github.io/](https://shahagam4.github.io/)
|
gtfintechlab/finer-ord
|
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-02-07T22:03:57+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "FiNER"}
|
2023-02-23T22:17:44+00:00
|
f1f88673ac0f96584b63d94a5aaeea9cc8ff09c4
|
# Dataset Card for "Wikipedia_5gram_less_orders.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/Wikipedia_5gram_less_orders.fr
|
[
"region:us"
] |
2023-02-07T22:19:57+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1171562088, "num_examples": 651563}], "download_size": 753453444, "dataset_size": 1171562088}}
|
2023-02-07T22:33:58+00:00
|
690b6eed2bb5517067427ef29ca459f70e84a757
|
# Dataset Card for "Wikipedia_5gram_more_orders.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/Wikipedia_5gram_more_orders.fr
|
[
"region:us"
] |
2023-02-07T22:34:13+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1172289501, "num_examples": 652609}], "download_size": 747359831, "dataset_size": 1172289501}}
|
2023-02-07T22:35:34+00:00
|
ff3bc47734c2ea6c42770fb88d301c7440ca7ce9
|
# Dataset Card for "wikipedia.reorder.natural.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.natural.fr
|
[
"region:us"
] |
2023-02-07T22:35:47+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 0, "dataset_size": 886603410}}
|
2023-02-09T10:23:12+00:00
|
8fcb2f5e20f102ea7501dc0426dae45498325e89
|
# Dataset Card for "wikipedia.reorder.svo.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.svo.fr
|
[
"region:us"
] |
2023-02-07T22:46:16+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 401374188, "dataset_size": 886603410}}
|
2023-02-09T11:04:30+00:00
|
96e8a59ca342891cd315c9d14544f61782056e7c
|
# Dataset Card for "wikipedia.reorder.vos.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.vos.fr
|
[
"region:us"
] |
2023-02-07T22:47:43+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 404205622, "dataset_size": 886603410}}
|
2023-02-09T11:05:25+00:00
|
f29e1a4f8fbf708eabd1b2bec5368651a21ab8ed
|
# Dataset Card for "wikipedia.reorder.osv.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/wikipedia.reorder.osv.fr
|
[
"region:us"
] |
2023-02-07T22:49:08+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 404858868, "dataset_size": 886603410}}
|
2023-02-09T11:06:22+00:00
|
2c99d96c3ae7e0f47355a6cff339328647e0a94f
|
# Dataset Card for "stormfront-medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kastan/stormfront-medium
|
[
"region:us"
] |
2023-02-07T23:43:27+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1446594601, "num_examples": 2000000}, {"name": "test", "num_bytes": 386917, "num_examples": 791}], "download_size": 897472441, "dataset_size": 1446981518}}
|
2023-02-07T23:43:59+00:00
|
6ee668c834ff8fb0ff28105f740113368b9c62a1
|
# Dataset Card for "chatbot-comparisons-rlhf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kastan/chatbot-comparisons-rlhf
|
[
"region:us"
] |
2023-02-08T00:13:49+00:00
|
{"dataset_info": {"features": [{"name": "Hi, this is an Alexa Prize Social bot! Tonight I'm planning to do some stargazing. It's supposed to be cloudy, but I'm hoping the skies will clear. What's your favorite planet?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "I don't think we've met. What's your name?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "Hello! How is your day going so far?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "Hi, I'm an Alexa Prize Social bot, it's nice to meet you!", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "How has your day been so far?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "Do you have any plans for the weekend?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "Do you have any recommendations for things to do/see around here?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "What brings you here today?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "Have you seen any good movies/shows lately?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "What do you do for work/study?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "What do you enjoy doing in your free time?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "What's something interesting you've learned recently?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "What do you like to do in your free time?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "What's been the highlight of your day so far?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}, {"name": "What do you think about this whole AI revolution that\u2019s happening?", "struct": [{"name": "blenderbot_175B", "sequence": "string"}, {"name": "cosmo", "sequence": "string"}, {"name": "davinci-003", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 19186, "num_examples": 1}], "download_size": 69471, "dataset_size": 19186}}
|
2023-02-08T00:13:53+00:00
|
ab6a13fa6ebd10c61e5824adcfe099a4e54eb509
|
# Dataset Card for SAMSum Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://arxiv.org/abs/1911.12237v2
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/1911.12237v2
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger convesations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person.
The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
The created dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people
The first instance in the training set:
{'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"}
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: unique id of an example.
### Data Splits
- train: 14732
- val: 818
- test: 819
## Dataset Creation
### Curation Rationale
In paper:
> In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typ- ically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assis- tant and a client buying petrol.
As a consequence, we decided to create a chat dialogue dataset by constructing such conversa- tions that would epitomize the style of a messenger app.
### Source Data
#### Initial Data Collection and Normalization
In paper:
> We asked linguists to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. It includes chit-chats, gossiping about friends, arranging meetings, discussing politics, consulting university assignments with colleagues, etc. Therefore, this dataset does not contain any sensitive data or fragments of other corpora.
#### Who are the source language producers?
linguists
### Annotations
#### Annotation process
In paper:
> Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one ref- erence summary.
#### Who are the annotators?
language experts
### Personal and Sensitive Information
None, see above: Initial Data Collection and Normalization
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
non-commercial licence: CC BY-NC-ND 4.0
### Citation Information
```
@inproceedings{gliwa-etal-2019-samsum,
title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization",
author = "Gliwa, Bogdan and
Mochol, Iwona and
Biesek, Maciej and
Wawer, Aleksander",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5409",
doi = "10.18653/v1/D19-5409",
pages = "70--79"
}
```
### Contributions
Thanks to [@cccntu](https://github.com/cccntu) for adding this dataset.
|
Axel578/mydt
|
[
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-nd-4.0",
"conversations-summarization",
"arxiv:1911.12237",
"region:us"
] |
2023-02-08T01:25:58+00:00
|
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "paperswithcode_id": "samsum-corpus", "pretty_name": "SAMSum Corpus", "tags": ["conversations-summarization"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "dialogue", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "config_name": "samsum", "splits": [{"name": "train", "num_bytes": 9479141, "num_examples": 14732}, {"name": "test", "num_bytes": 534492, "num_examples": 819}], "download_size": 2944100, "dataset_size": 10530064}, "train-eval-index": [{"config": "samsum", "task": "summarization", "task_id": "summarization", "splits": {"eval_split": "test"}, "col_mapping": {"dialogue": "text", "summary": "target"}}]}
|
2023-02-08T02:19:28+00:00
|
e85b33c26b4c74a2076ec9e41b4879d08092ddf8
|
# Dataset Card for "elementary_math-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
emozilla/elementary_math-v1
|
[
"region:us"
] |
2023-02-08T02:28:36+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "operation", "dtype": "string"}, {"name": "operands", "sequence": "int64"}, {"name": "solution", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2522190691, "num_examples": 800001}, {"name": "test", "num_bytes": 315111072, "num_examples": 99999}, {"name": "validation", "num_bytes": 315358918, "num_examples": 99999}], "download_size": 116049281, "dataset_size": 3152660681}}
|
2023-02-08T02:32:53+00:00
|
017f096213f8507f3a8a34d94c255ba69f33cb97
|
# Dataset Card for "summarization_wikilingua_vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CaoHaiNam/summarization_wikilingua_vi
|
[
"region:us"
] |
2023-02-08T03:00:33+00:00
|
{"dataset_info": {"features": [{"name": "summary", "dtype": "string"}, {"name": "document", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 61182829.83851693, "num_examples": 17622}, {"name": "test", "num_bytes": 6801564.16148307, "num_examples": 1959}], "download_size": 34140923, "dataset_size": 67984394.0}}
|
2023-02-12T00:29:41+00:00
|
5cabe9f6f2c20599c98ebbbcb83f651d0d98b62a
|
hamdan07/UItrasound-Lung
|
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] |
2023-02-08T03:54:13+00:00
|
{"license": "bigscience-bloom-rail-1.0"}
|
2023-02-08T03:54:13+00:00
|
|
1c88c9e5fddee30ab686b9e22f65bda7b4c3df2c
|
MadElf1337/Pneumonia_Images
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-08T04:16:09+00:00
|
{"license": "apache-2.0"}
|
2023-02-08T17:38:06+00:00
|
|
44e058e4585572498937cbcde248e4ab074b6252
|
Joe02/kuroishimebo_refs
|
[
"license:other",
"region:us"
] |
2023-02-08T05:08:40+00:00
|
{"license": "other"}
|
2023-02-08T05:09:33+00:00
|
|
dbb9654cdcabc4aadc3eb32d6df54e6da2a883a8
|
# Dataset Card for "startuplogo-captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lucataco/startuplogo-captions
|
[
"region:us"
] |
2023-02-08T05:21:06+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 304090.0, "num_examples": 23}], "download_size": 139825, "dataset_size": 304090.0}}
|
2023-02-08T05:21:08+00:00
|
0f5b996cf292c6a190a841170cdc827a88a605e0
|
# Dataset Card for "image-text-examples-ar-cn-latin-notext"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ernie-ai/image-text-examples-ar-cn-latin-notext
|
[
"region:us"
] |
2023-02-08T05:32:47+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "AR_docs", "1": "CN_docs", "2": "Latin_docs", "3": "non-text"}}}}], "splits": [{"name": "train", "num_bytes": 27290843.67117117, "num_examples": 754}, {"name": "test", "num_bytes": 4701416.328828828, "num_examples": 134}], "download_size": 31849475, "dataset_size": 31992260.0}}
|
2023-02-08T05:33:01+00:00
|
9b94452573a82aebb0a8e4f897d36fda98a74034
|
CTS-Diagnostics/slack_support_bot
|
[
"license:mit",
"region:us"
] |
2023-02-08T05:36:59+00:00
|
{"license": "mit"}
|
2023-02-08T05:36:59+00:00
|
|
ce242e5ae6e4fb294e5291506df18e437b80bf09
|
# Dataset Card for "bookcorpus_stage2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MartinKu/bookcorpus_stage2
|
[
"region:us"
] |
2023-02-08T06:19:26+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "S_V", "sequence": "string"}, {"name": "S_V_position", "sequence": "int64"}, {"name": "O_C", "sequence": "string"}, {"name": "O_C_position", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 12873845328, "num_examples": 74004228}], "download_size": 6676175908, "dataset_size": 12873845328}}
|
2023-02-09T21:25:01+00:00
|
d82778b3321049a51c306fdafcd3267d2f0370cc
|
# 《从零开始学扩散模型》
## 术语表
| 词汇 | 翻译 |
| :---------------------- | :--- |
| Corruption Process | 退化过程 |
| Pipeline | 管线 |
| Timestep | 时间步 |
| Scheduler | 调度器 |
| Gradient Accumulation | 梯度累加 |
| Fine-Tuning | 微调 |
| Guidance | 引导 |
# 目录
## 第一部分 基础知识
### 第一章 扩散模型的原理、发展和应用
#### 1.1 扩散模型的原理
#### 1.2 扩散模型的发展
#### 1.3 扩散模型的应用
### 第二章 HuggingFace介绍与环境准备
#### 2.1 HuggingFace Space
#### 2.2 Transformer 与 diffusers 库
#### 2.3 环境准备
## 第二部分 扩散模型实战
### 第三章 从零开始做扩散模型
#### 3.1 章节概述
#### 3.2 环境准备
#### 3.2.1 环境的创建与导入
#### 3.2.2 数据集测试
#### 3.3 扩散模型-退化过程
#### 3.4 扩散模型训练
#### 3.4.1 Unet模型
#### 3.4.2 开始训练模型
#### 3.5 扩散模型-采样（取样）过程
#### 3.5.1 采样（取样）过程
#### 3.5.2 与DDPM的区别
#### 3.5.3 UNet2DModel模型
#### 3.6 扩散模型-退化过程示例
#### 3.6.1 退化过程
#### 3.6.2 最终的训练目标
#### 3.7 拓展知识
#### 3.7.1 迭代周期（Timestep）的调节
#### 3.7.2 采样（取样）的关键问题
#### 3.8 本章小结
### 第四章 Diffusers实战
#### 4.1 章节概述
#### 4.2 环境准备
#### 4.2.1 安装Diffusers库
#### 4.2.2 Dreambooth-全新的扩散模型
#### 4.2.3 Diffusers核心API
#### 4.3 实战：生成美丽的蝴蝶图片
#### 4.3.1 下载蝴蝶图像集
#### 4.3.2 扩散模型-调度器
#### 4.3.3 定义扩散模型
#### 4.3.4 创建扩散模型训练循环
#### 4.3.5 图像的生成
#### 4.4 拓展知识
#### 4.4.1 将模型上传到Hub上
#### 4.4.2 扩大训练模型的规模
#### 4.5 本章小结
### 第五章 微调和引导
#### 5.1 章节概述
#### 5.2 环境准备
#### 5.3 载入一个预训练过的管线
#### 5.4 DDIM-更快的采样过程
#### 5.5 扩散模型-微调
#### 5.5.1 实战：微调
#### 5.5.2 使用最小化样例脚本微调模型
#### 5.5.3 保存和载入微调过的管线
#### 5.6 扩散模型-引导
#### 5.6.1 实战：引导
#### 5.6.2 CLIP 引导
#### 5.7 分享你的自定义采样训练
#### 5.7.1 环境准备
#### 5.7.2 创建一个以类别为条件的UNet
#### 5.7.3 训练与采样
#### 5.8 本章小结
#### 5.9 实战：创建一个类别条件扩散模型
### 第六章 Stable Diffusion
#### 6.1 章节概述
#### 6.2 环境准备
#### 6.3 从文本生成图像
#### 6.4 Stable Diffusion Pipeline
#### 6.4.1 可变分自编码器(VAE)
#### 6.4.2 分词器(Tokenizer)和文本编码器(Text Encoder)
#### 6.4.3 UNet
#### 6.4.4 调度器(Scheduler)
#### 6.4.5 DIY一个采样循环
#### 6.5 其他管线介绍
#### 6.5.1 Img2Img
#### 6.5.2 In-Painting
#### 6.5.3 Depth2Image
#### 6.5.4 拓展：管理你的模型缓存
#### 6.6 本章小结
### 第七章 DDIM反转与ControlNet
#### 7.1 本章概述
#### 7.2 实战：反转
#### 7.2.1 设置
#### 7.2.2 加载一个已训练的管道
#### 7.2.3 DDIM采样
#### 7.2.4 反转
#### 7.3 组合封装
#### 7.4 DDIM小结
#### 7.5 ControlNet的结构与训练过程
#### 7.6 ControlNet示例
#### 7.7 ControlNet实战
### 第八章 音频扩散模型
#### 8.1 本章概述
#### 8.2 实战：音频扩散模型
#### 8.2.1 设置与导入
#### 8.2.2 从预先训练的音频管道采样
#### 8.2.3 从音频到频谱的转换
#### 8.2.4 微调管道
#### 8.2.5 循环训练
#### 8.3 将模型上传到Hub上
#### 8.4 本章小结
## 附录 精美图像集展示
|
HuggingFace-CN-community/Diffusion-book-cn
|
[
"region:us"
] |
2023-02-08T06:41:10+00:00
|
{}
|
2023-04-19T14:35:45+00:00
|
7524a591c11ebe065ebd4d27f99d6632ae724c89
|
AbdulAhad9540/ner_conll
|
[
"conll",
"ner",
"region:us"
] |
2023-02-08T07:54:21+00:00
|
{"tags": ["conll", "ner"]}
|
2023-02-08T10:58:39+00:00
|
|
d49f92da282133cde58d5c918c4670ceec64ed9c
|
AbdulAhad9540/ner_conll1
|
[
"region:us"
] |
2023-02-08T07:54:40+00:00
|
{}
|
2023-02-08T10:54:29+00:00
|
|
0ed9a6e20e82fa191eac3c661f9931d8fec1ad44
|
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### 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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
j-krzywdziak/test
|
[
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"language:pl",
"license:mit",
"region:us"
] |
2023-02-08T08:14:48+00:00
|
{"annotations_creators": ["expert-generated"], "language": ["pl"], "license": ["mit"], "multilinguality": ["monolingual"], "dataset_info": [{"config_name": "config", "features": [{"name": "audio_id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}]}]}
|
2023-07-11T11:22:14+00:00
|
08f9f0cef47cb3c3eeb8925a340e129c1d4824f6
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: 2020uee0139/distilbert-base-uncased-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@yahiya](https://huggingface.co/yahiya) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-a69c9b-3342492274
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-02-08T09:08:16+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "2020uee0139/distilbert-base-uncased-finetuned-squad", "metrics": [], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-02-08T09:09:01+00:00
|
3b5f0411b440520214ae9a59eb98dc8aa3646992
|
# Dataset Card for "blip_clipseg_inpainting_ip2p_data_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
playgroundai/blip_clipseg_inpainting_ip2p_data_test
|
[
"region:us"
] |
2023-02-08T09:26:33+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "chosen_item", "dtype": "string"}, {"name": "og_image", "dtype": "image"}, {"name": "mask", "dtype": "image"}, {"name": "output_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 650403547.0, "num_examples": 825}], "download_size": 650341793, "dataset_size": 650403547.0}}
|
2023-02-08T09:26:41+00:00
|
a4d9ec5912f77e2ff49a61b142b66c7ac37d5d94
|
# Dataset Card for "team_fortress_characters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
olly4/team_fortress_characters
|
[
"region:us"
] |
2023-02-08T09:29:04+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 290813521.568, "num_examples": 1466}], "download_size": 284714198, "dataset_size": 290813521.568}}
|
2023-02-08T12:14:03+00:00
|
a0ba26c9fcff78388f03039d063df07c4e9ef53d
|
# Dataset Card for "dummy_image_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
patrickvonplaten/dummy_image_data
|
[
"region:us"
] |
2023-02-08T10:15:47+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1944983.0, "num_examples": 20}], "download_size": 1690123, "dataset_size": 1944983.0}}
|
2023-02-08T10:33:43+00:00
|
9a7069082f80d9620d877baf3b905f50404fa1cd
|
# Dataset Card for Bundestag-v2
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- Homepage: https://doi.org/10.7910/DVN/L4OAKN
### Dataset Summary
This dataset was generated from the [ParlSpeech V2](https://doi.org/10.7910/DVN/L4OAKN) dataset. It contains speeches from the german parliament from 1990 until 2020 labelled with the party of the speaker.
### Supported Tasks
Text Classification
### Languages
German
## Dataset Structure
### Data Fields
- text: Transcript of the speech in german
- party: Party of the speaker
### Data Splits
- train
- validation
- test
## Dataset Creation
### Curation Rationale
Created to train a language model, which is able to classify speeches by party.
### Source Data
#### Initial Data Collection and Normalization
- [ParlSpeech V2](https://doi.org/10.7910/DVN/L4OAKN)
## Considerations for Using the Data
### Social Impact of Dataset
These are political speeches, therefor the content can be controversial and potentially harmful.
## Additional Information
### Licensing Information
[CCO 1.0](http://creativecommons.org/publicdomain/zero/1.0)
### Citation Information
Bibtex entry:
```
@data{DVN/L4OAKN_2020,
author = {Rauh, Christian and Schwalbach, Jan},
publisher = {Harvard Dataverse},
title = {{The ParlSpeech V2 data set: Full-text corpora of 6.3 million parliamentary speeches in the key legislative chambers of nine representative democracies}},
year = {2020},
version = {V1},
doi = {10.7910/DVN/L4OAKN},
url = {https://doi.org/10.7910/DVN/L4OAKN}
}
```
|
threite/Bundestag-v2
|
[
"task_categories:text-classification",
"task_ids:entity-linking-classification",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:de",
"license:cc0-1.0",
"Bundestag",
"ParlSpeech",
"region:us"
] |
2023-02-08T10:29:20+00:00
|
{"annotations_creators": [], "language_creators": ["expert-generated"], "language": ["de"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["entity-linking-classification"], "pretty_name": "Bundestag-v2", "tags": ["Bundestag", "ParlSpeech"]}
|
2023-02-14T13:08:49+00:00
|
d5acb3a48d3127b59457e627c6dce975c20675b0
|
# Dataset Card for "dummy_image_text_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hf-internal-testing/dummy_image_text_data
|
[
"region:us"
] |
2023-02-08T10:34:30+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1944983.0, "num_examples": 20}], "download_size": 1690123, "dataset_size": 1944983.0}}
|
2023-02-08T10:34:38+00:00
|
934f278e32cf60afc1a03899705da8251b7b2ba9
|
# Dataset Card for ReadingBank
## Dataset Description
- **Homepage:** https://github.com/doc-analysis/ReadingBank
- **Repository:** https://github.com/doc-analysis/ReadingBank
- **Paper:** https://arxiv.org/pdf/2108.11591.pdf
- **Leaderboard:**
- **Point of Contact:** Raise github issue on mentioned repository for dataset related matters and contact @maveriq for huggingface dataset port.
### Dataset Summary
ReadingBank is a benchmark dataset for reading order detection built with weak supervision from WORD documents, which contains 500K document images with a wide range of document types as well as the corresponding reading order information.
### Supported Tasks and Leaderboards
Reading Order of documents
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- src : text
- tgt : text
- bleu : float
- tgt_index : list of ints
- original_filename : str
- filename : str
- page_idx : int
- src_layout : Bounding boxes for src (list of list of ints)
- tgt_layout : Bounding boxes for tgt (list of list of ints)
### Data Splits
train : 400,000
dev : 50,000
test : 50,000
## 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]
|
maveriq/readingbank
|
[
"size_categories:100K<n<1M",
"language:en",
"arxiv:2108.11591",
"region:us"
] |
2023-02-08T10:46:58+00:00
|
{"language": ["en"], "size_categories": ["100K<n<1M"]}
|
2023-02-08T10:59:29+00:00
|
30d006314183750696269117b4c28fc153d54150
|
# Dataset Card for "massive_social"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_social
|
[
"region:us"
] |
2023-02-08T11:11:43+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25788, "num_examples": 391}, {"name": "validation", "num_bytes": 4344, "num_examples": 68}, {"name": "test", "num_bytes": 6404, "num_examples": 106}], "download_size": 0, "dataset_size": 36536}}
|
2023-02-08T12:21:19+00:00
|
9eed0d6ad9d86f7598575663b4a568e13406eb2c
|
# Dataset Card for "massive_transport"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_transport
|
[
"region:us"
] |
2023-02-08T11:12:00+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34823, "num_examples": 571}, {"name": "validation", "num_bytes": 6699, "num_examples": 110}, {"name": "test", "num_bytes": 7228, "num_examples": 124}], "download_size": 0, "dataset_size": 48750}}
|
2023-02-08T12:21:25+00:00
|
5fffcc23ee285615a570bec5acdcbae89d3fbaee
|
# Dataset Card for "massive_calendar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_calendar
|
[
"region:us"
] |
2023-02-08T11:12:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 104781, "num_examples": 1688}, {"name": "validation", "num_bytes": 17464, "num_examples": 280}, {"name": "test", "num_bytes": 25214, "num_examples": 402}], "download_size": 0, "dataset_size": 147459}}
|
2023-02-08T12:21:32+00:00
|
f32df65acd044e1ff21ef74d159c4bd2b352068a
|
# Dataset Card for "massive_play"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_play
|
[
"region:us"
] |
2023-02-08T11:12:40+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 70622, "num_examples": 1377}, {"name": "validation", "num_bytes": 12960, "num_examples": 260}, {"name": "test", "num_bytes": 19816, "num_examples": 387}], "download_size": 0, "dataset_size": 103398}}
|
2023-02-08T12:21:39+00:00
|
04aed4190c62f31f3c82854f598bfd14f939c7b7
|
# Dataset Card for "massive_news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_news
|
[
"region:us"
] |
2023-02-08T11:12:56+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27530, "num_examples": 503}, {"name": "validation", "num_bytes": 4790, "num_examples": 82}, {"name": "test", "num_bytes": 6956, "num_examples": 124}], "download_size": 0, "dataset_size": 39276}}
|
2023-02-08T12:21:45+00:00
|
1d9ba3238bc60c6cde98ab8a8ef65b46dcd1975b
|
# Dataset Card for "massive_datetime"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_datetime
|
[
"region:us"
] |
2023-02-08T11:13:12+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21237, "num_examples": 402}, {"name": "validation", "num_bytes": 3849, "num_examples": 73}, {"name": "test", "num_bytes": 5464, "num_examples": 103}], "download_size": 18224, "dataset_size": 30550}}
|
2023-02-08T12:22:05+00:00
|
c2e985c49ec679324d50700da239be419b4967cb
|
# Dataset Card for "massive_recommendation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_recommendation
|
[
"region:us"
] |
2023-02-08T11:13:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25598, "num_examples": 433}, {"name": "validation", "num_bytes": 4186, "num_examples": 69}, {"name": "test", "num_bytes": 5994, "num_examples": 94}], "download_size": 21463, "dataset_size": 35778}}
|
2023-02-08T12:22:26+00:00
|
dddc8eac4cbb1f655feca3b98f651ef7fea56a7d
|
# Dataset Card for "massive_email"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_email
|
[
"region:us"
] |
2023-02-08T11:13:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55111, "num_examples": 953}, {"name": "validation", "num_bytes": 9057, "num_examples": 157}, {"name": "test", "num_bytes": 15530, "num_examples": 271}], "download_size": 42235, "dataset_size": 79698}}
|
2023-02-08T12:22:46+00:00
|
110b5381fa39976c9c550b2f71a1640403aab113
|
# Dataset Card for "massive_iot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_iot
|
[
"region:us"
] |
2023-02-08T11:14:02+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37756, "num_examples": 769}, {"name": "validation", "num_bytes": 5688, "num_examples": 118}, {"name": "test", "num_bytes": 10603, "num_examples": 220}], "download_size": 29170, "dataset_size": 54047}}
|
2023-02-08T12:23:06+00:00
|
58078358688ab8818d43d3e8962ce3f8f3daef44
|
# Dataset Card for "massive_general"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_general
|
[
"region:us"
] |
2023-02-08T11:14:20+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34565, "num_examples": 652}, {"name": "validation", "num_bytes": 6418, "num_examples": 122}, {"name": "test", "num_bytes": 9865, "num_examples": 189}], "download_size": 32932, "dataset_size": 50848}}
|
2023-02-08T12:23:27+00:00
|
62ca0fb91134b17d7bf6f9dc9a1c0d4c131fbe37
|
# Dataset Card for "massive_audio"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_audio
|
[
"region:us"
] |
2023-02-08T11:14:37+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12795, "num_examples": 290}, {"name": "validation", "num_bytes": 1499, "num_examples": 35}, {"name": "test", "num_bytes": 2615, "num_examples": 62}], "download_size": 12219, "dataset_size": 16909}}
|
2023-02-08T12:23:48+00:00
|
e6300789ee5a47918e5f0b1504258eef326f7ee1
|
# Dataset Card for "massive_lists"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_lists
|
[
"region:us"
] |
2023-02-08T11:14:54+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27330, "num_examples": 539}, {"name": "validation", "num_bytes": 5583, "num_examples": 112}, {"name": "test", "num_bytes": 7000, "num_examples": 142}], "download_size": 23148, "dataset_size": 39913}}
|
2023-02-08T12:24:08+00:00
|
45f01388fa2b1cd7cfcf1a3c05ed2931aed11ecb
|
# Dataset Card for "massive_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_qa
|
[
"region:us"
] |
2023-02-08T11:15:11+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 64967, "num_examples": 1183}, {"name": "validation", "num_bytes": 11778, "num_examples": 214}, {"name": "test", "num_bytes": 15940, "num_examples": 288}], "download_size": 54118, "dataset_size": 92685}}
|
2023-02-08T12:24:29+00:00
|
06841bf18f29cd612f57387d30e201a7a04dfa33
|
# Dataset Card for "massive_cooking"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/massive_cooking
|
[
"region:us"
] |
2023-02-08T11:15:28+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11804, "num_examples": 211}, {"name": "validation", "num_bytes": 2237, "num_examples": 43}, {"name": "test", "num_bytes": 4053, "num_examples": 72}], "download_size": 13756, "dataset_size": 18094}}
|
2023-02-08T12:24:48+00:00
|
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