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0745c126378be671853e28e6400c3d8bdc1b7ee5 | # Dataset Card for "movielens-1m-movies"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nbtpj/movielens-1m-movies | [
"region:us"
]
| 2023-03-15T01:02:29+00:00 | {"dataset_info": {"features": [{"name": "movie_genres", "sequence": "int64"}, {"name": "movie_id", "dtype": "binary"}, {"name": "movie_title", "dtype": "binary"}], "splits": [{"name": "train", "num_bytes": 206339, "num_examples": 3883}], "download_size": 111390, "dataset_size": 206339}} | 2023-03-15T01:02:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "movielens-1m-movies"
More Information needed | [
"# Dataset Card for \"movielens-1m-movies\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"movielens-1m-movies\"\n\nMore Information needed"
]
| [
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| [
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|
3961207dcf812971661b65508dc27a05a6fffbb9 | # Dataset Card for "Movies_and_TV"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nbtpj/Movies_and_TV | [
"region:us"
]
| 2023-03-15T01:02:42+00:00 | {"dataset_info": {"features": [{"name": "overall", "dtype": "float64"}, {"name": "verified", "dtype": "bool"}, {"name": "reviewTime", "dtype": "string"}, {"name": "reviewerID", "dtype": "string"}, {"name": "asin", "dtype": "string"}, {"name": "style", "dtype": "string"}, {"name": "reviewerName", "dtype": "string"}, {"name": "reviewText", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "unixReviewTime", "dtype": "int64"}, {"name": "vote", "dtype": "string"}, {"name": "image", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 4058038162, "num_examples": 8765568}], "download_size": 2295911945, "dataset_size": 4058038162}} | 2023-03-15T01:32:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Movies_and_TV"
More Information needed | [
"# Dataset Card for \"Movies_and_TV\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Movies_and_TV\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Movies_and_TV\"\n\nMore Information needed"
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|
fe43ac768e0e0df525c5925edb9c27c186730b3b |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** [https://usefusion.app](https://usefusion.app)
- **Repository:** [https://github.com/NEUROFUSIONInc](https://github.com/NEUROFUSIONInc)
- **Point of Contact:** [email protected]
### Dataset Summary
This is a dataset of EEG data & derived metrics recorded on the [Fusion](https://usefusion.app) platform from a single particpant through the course of a week.
Task: Eyes closed for 10mins at least twice a day. Participant also gave a short summary at the start of every recording in [events.csv](./events.csv)
Device: [Neurosity Crown](https://neurosity.co) - 8 channels [CP3, C3, F5, PO3, PO4, F6, C4, CP4]
## Dataset Structure
### Data Instances
All dataset are timeseries with a unixTimestamp column. Generated by using the [Neurosity brainwaves api](https://docs.neurosity.co/docs/api/brainwaves)
- rawBrainwaves: voltage readings across eeg channels
- signalQuality: standard deviation values & label (great, good, poor, noContact) per channel
- powerByBand: computed eeg power per channel (i.e CP3_delta, CP3_theta, CP3_alpha, CP3_beta, CP3_gamma)
- focus: prediction of user [focus based on gamma waves](https://docs.neurosity.co/docs/api/focus).
- calm: prediction of user [calm based on alpha waves](https://docs.neurosity.co/docs/api/calm).
### Data Splits
Each dataset is suffixed with *_unixTimestamp which represents the time of recording.
## Additional Information
### Dataset Curators
[NEUROFUSION Research Inc.](https://usefusion.app) | neurofusion/eeg-restingstate | [
"language:en",
"license:apache-2.0",
"neuro",
"eeg",
"powerspectra",
"focus",
"calm",
"longitudinal data",
"doi:10.57967/hf/0745",
"region:us"
]
| 2023-03-15T01:27:19+00:00 | {"language": ["en"], "license": "apache-2.0", "tags": ["neuro", "eeg", "powerspectra", "focus", "calm", "longitudinal data"]} | 2023-05-15T16:14:37+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-apache-2.0 #neuro #eeg #powerspectra #focus #calm #longitudinal data #doi-10.57967/hf/0745 #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage: URL
- Repository: URL
- Point of Contact: ore@URL
### Dataset Summary
This is a dataset of EEG data & derived metrics recorded on the Fusion platform from a single particpant through the course of a week.
Task: Eyes closed for 10mins at least twice a day. Participant also gave a short summary at the start of every recording in URL
Device: Neurosity Crown - 8 channels [CP3, C3, F5, PO3, PO4, F6, C4, CP4]
## Dataset Structure
### Data Instances
All dataset are timeseries with a unixTimestamp column. Generated by using the Neurosity brainwaves api
- rawBrainwaves: voltage readings across eeg channels
- signalQuality: standard deviation values & label (great, good, poor, noContact) per channel
- powerByBand: computed eeg power per channel (i.e CP3_delta, CP3_theta, CP3_alpha, CP3_beta, CP3_gamma)
- focus: prediction of user focus based on gamma waves.
- calm: prediction of user calm based on alpha waves.
### Data Splits
Each dataset is suffixed with *_unixTimestamp which represents the time of recording.
## Additional Information
### Dataset Curators
NEUROFUSION Research Inc. | [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: ore@URL",
"### Dataset Summary\n\nThis is a dataset of EEG data & derived metrics recorded on the Fusion platform from a single particpant through the course of a week.\n\nTask: Eyes closed for 10mins at least twice a day. Participant also gave a short summary at the start of every recording in URL\n\nDevice: Neurosity Crown - 8 channels [CP3, C3, F5, PO3, PO4, F6, C4, CP4]",
"## Dataset Structure",
"### Data Instances\n\nAll dataset are timeseries with a unixTimestamp column. Generated by using the Neurosity brainwaves api\n\n- rawBrainwaves: voltage readings across eeg channels\n- signalQuality: standard deviation values & label (great, good, poor, noContact) per channel\n- powerByBand: computed eeg power per channel (i.e CP3_delta, CP3_theta, CP3_alpha, CP3_beta, CP3_gamma)\n- focus: prediction of user focus based on gamma waves.\n- calm: prediction of user calm based on alpha waves.",
"### Data Splits\n\nEach dataset is suffixed with *_unixTimestamp which represents the time of recording.",
"## Additional Information",
"### Dataset Curators\n\nNEUROFUSION Research Inc."
]
| [
"TAGS\n#language-English #license-apache-2.0 #neuro #eeg #powerspectra #focus #calm #longitudinal data #doi-10.57967/hf/0745 #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: ore@URL",
"### Dataset Summary\n\nThis is a dataset of EEG data & derived metrics recorded on the Fusion platform from a single particpant through the course of a week.\n\nTask: Eyes closed for 10mins at least twice a day. Participant also gave a short summary at the start of every recording in URL\n\nDevice: Neurosity Crown - 8 channels [CP3, C3, F5, PO3, PO4, F6, C4, CP4]",
"## Dataset Structure",
"### Data Instances\n\nAll dataset are timeseries with a unixTimestamp column. Generated by using the Neurosity brainwaves api\n\n- rawBrainwaves: voltage readings across eeg channels\n- signalQuality: standard deviation values & label (great, good, poor, noContact) per channel\n- powerByBand: computed eeg power per channel (i.e CP3_delta, CP3_theta, CP3_alpha, CP3_beta, CP3_gamma)\n- focus: prediction of user focus based on gamma waves.\n- calm: prediction of user calm based on alpha waves.",
"### Data Splits\n\nEach dataset is suffixed with *_unixTimestamp which represents the time of recording.",
"## Additional Information",
"### Dataset Curators\n\nNEUROFUSION Research Inc."
]
| [
52,
8,
22,
105,
6,
151,
28,
5,
14
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| [
"passage: TAGS\n#language-English #license-apache-2.0 #neuro #eeg #powerspectra #focus #calm #longitudinal data #doi-10.57967/hf/0745 #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: ore@URL### Dataset Summary\n\nThis is a dataset of EEG data & derived metrics recorded on the Fusion platform from a single particpant through the course of a week.\n\nTask: Eyes closed for 10mins at least twice a day. Participant also gave a short summary at the start of every recording in URL\n\nDevice: Neurosity Crown - 8 channels [CP3, C3, F5, PO3, PO4, F6, C4, CP4]## Dataset Structure### Data Instances\n\nAll dataset are timeseries with a unixTimestamp column. Generated by using the Neurosity brainwaves api\n\n- rawBrainwaves: voltage readings across eeg channels\n- signalQuality: standard deviation values & label (great, good, poor, noContact) per channel\n- powerByBand: computed eeg power per channel (i.e CP3_delta, CP3_theta, CP3_alpha, CP3_beta, CP3_gamma)\n- focus: prediction of user focus based on gamma waves.\n- calm: prediction of user calm based on alpha waves.### Data Splits\n\nEach dataset is suffixed with *_unixTimestamp which represents the time of recording.## Additional Information### Dataset Curators\n\nNEUROFUSION Research Inc."
]
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|
4d0c4b37e0a7c8fbb900cfb8081a1924fdff6139 | # Dataset Card for "Movies_and_TV_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nbtpj/Movies_and_TV_meta | [
"region:us"
]
| 2023-03-15T01:32:39+00:00 | {"dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "tech1", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "fit", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "also_buy", "dtype": "string"}, {"name": "tech2", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "feature", "dtype": "string"}, {"name": "rank", "dtype": "string"}, {"name": "also_view", "dtype": "string"}, {"name": "main_cat", "dtype": "string"}, {"name": "similar_item", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "price", "dtype": "string"}, {"name": "asin", "dtype": "string"}, {"name": "imageURL", "dtype": "string"}, {"name": "imageURLHighRes", "dtype": "string"}, {"name": "details", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 292562315, "num_examples": 203766}], "download_size": 152902943, "dataset_size": 292562315}} | 2023-03-15T01:34:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Movies_and_TV_meta"
More Information needed | [
"# Dataset Card for \"Movies_and_TV_meta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Movies_and_TV_meta\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Movies_and_TV_meta\"\n\nMore Information needed"
]
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|
520b0e6f202c51d961d5cf6bdadf0476c846f0c4 | # Dataset Card for "wikisource-green"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Zombely/wikisource-green | [
"region:us"
]
| 2023-03-15T02:03:19+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train_1", "num_bytes": 15342818708.456, "num_examples": 9816}, {"name": "train_2", "num_bytes": 13234327199.457, "num_examples": 9997}, {"name": "train_3", "num_bytes": 8814747830.88, "num_examples": 9935}, {"name": "train_4", "num_bytes": 10839226390.145, "num_examples": 9995}, {"name": "train_5", "num_bytes": 12414635965.0, "num_examples": 10000}, {"name": "train_6", "num_bytes": 5911580759.0, "num_examples": 10000}, {"name": "train_7", "num_bytes": 11420080854.0, "num_examples": 10000}, {"name": "train_8", "num_bytes": 18080629271.0, "num_examples": 10000}, {"name": "train_9", "num_bytes": 11348011360.0, "num_examples": 10000}, {"name": "train_10", "num_bytes": 14141957301.0, "num_examples": 10000}, {"name": "train_11", "num_bytes": 9983910604.0, "num_examples": 10000}, {"name": "train_12", "num_bytes": 13105253749.0, "num_examples": 10000}, {"name": "train_13", "num_bytes": 15681320595.0, "num_examples": 10000}, {"name": "train_14", "num_bytes": 14896725472.0, "num_examples": 10000}, {"name": "train_15", "num_bytes": 11493364396.927, "num_examples": 9987}, {"name": "validation", "num_bytes": 4487934740.612, "num_examples": 4077}], "download_size": 5330245163, "dataset_size": 191196525196.477}} | 2023-03-18T11:50:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikisource-green"
More Information needed | [
"# Dataset Card for \"wikisource-green\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikisource-green\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikisource-green\"\n\nMore Information needed"
]
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|
36f4c6dd2e2b7c4d2a7f3355738e453f766d1114 | # Dataset Card for "oig"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | elricwan/oig | [
"region:us"
]
| 2023-03-15T03:02:02+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 47893346745, "num_examples": 14430253}], "download_size": 25698713382, "dataset_size": 47893346745}} | 2023-03-15T03:38:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "oig"
More Information needed | [
"# Dataset Card for \"oig\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"oig\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"oig\"\n\nMore Information needed"
]
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|
d6afc27243f60c9d0084f84997da5106edfbed73 | # Dataset Card for "tatoeba-en-tgl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jmbt22/tatoeba-en-tgl | [
"region:us"
]
| 2023-03-15T03:54:33+00:00 | {"dataset_info": {"features": [{"name": "translation.en", "dtype": "string"}, {"name": "translation.tl", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 539187, "num_examples": 7305}], "download_size": 357015, "dataset_size": 539187}} | 2023-03-15T03:54:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tatoeba-en-tgl"
More Information needed | [
"# Dataset Card for \"tatoeba-en-tgl\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tatoeba-en-tgl\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tatoeba-en-tgl\"\n\nMore Information needed"
]
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|
66035498c77bd00abdf62d495763f687010f9db4 | 11 | Lilithchouy/1111bb | [
"license:afl-3.0",
"region:us"
]
| 2023-03-15T05:08:17+00:00 | {"license": "afl-3.0"} | 2023-03-15T06:34:43+00:00 | []
| []
| TAGS
#license-afl-3.0 #region-us
| 11 | []
| [
"TAGS\n#license-afl-3.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-afl-3.0 #region-us \n"
]
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|
f4acbed6a153027f190538d7acf9b383ce9508d6 | # Dataset Card for "bad_code_to_good_code_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | michaelnath/bad_code_to_good_code_dataset | [
"region:us"
]
| 2023-03-15T05:38:03+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2905897365, "num_examples": 2786238}], "download_size": 550189166, "dataset_size": 2905897365}} | 2023-03-15T05:39:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bad_code_to_good_code_dataset"
More Information needed | [
"# Dataset Card for \"bad_code_to_good_code_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bad_code_to_good_code_dataset\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bad_code_to_good_code_dataset\"\n\nMore Information needed"
]
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|
2ea43df97b572e7ba5cb06408d0bfd5653cc0506 | # Dataset Card for "bad_code_to_good_code_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | joetey/bad_code_to_good_code_dataset | [
"region:us"
]
| 2023-03-15T06:05:27+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 703072, "num_examples": 589}], "download_size": 17498, "dataset_size": 703072}} | 2023-03-15T09:57:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bad_code_to_good_code_dataset"
More Information needed | [
"# Dataset Card for \"bad_code_to_good_code_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bad_code_to_good_code_dataset\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bad_code_to_good_code_dataset\"\n\nMore Information needed"
]
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|
e2e72afc7ffe4a4e4fe4a04ebf0c66765b43914a | I found this dataset on my harddrive, which if I remember correctly I got from the source mentioned in the paper:
"Claim extraction from text using transfer learning" - By Acharya Ashish Prabhakar, Salar Mohtaj, Sebastian Möller
https://aclanthology.org/2020.icon-main.39/
The github repo with the data seems down.
It extends FEVER dataset with non-claims for training claim detectors. | KnutJaegersberg/FEVER_claim_extraction | [
"license:mit",
"argument mining",
"region:us"
]
| 2023-03-15T06:21:05+00:00 | {"license": "mit", "tags": ["argument mining"]} | 2023-03-15T06:25:27+00:00 | []
| []
| TAGS
#license-mit #argument mining #region-us
| I found this dataset on my harddrive, which if I remember correctly I got from the source mentioned in the paper:
"Claim extraction from text using transfer learning" - By Acharya Ashish Prabhakar, Salar Mohtaj, Sebastian Möller
URL
The github repo with the data seems down.
It extends FEVER dataset with non-claims for training claim detectors. | []
| [
"TAGS\n#license-mit #argument mining #region-us \n"
]
| [
15
]
| [
"passage: TAGS\n#license-mit #argument mining #region-us \n"
]
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|
8a7e4ef8c1b49d8176975287a3389b6cb00a8710 | # Dataset Card for "my_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | claudehotline/my_dataset | [
"region:us"
]
| 2023-03-15T07:32:07+00:00 | {"dataset_info": {"features": [{"name": "data", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 80000, "num_examples": 10000}], "download_size": 96279, "dataset_size": 80000}} | 2023-03-15T07:32:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "my_dataset"
More Information needed | [
"# Dataset Card for \"my_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"my_dataset\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"my_dataset\"\n\nMore Information needed"
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|
6f31ad6040d4eb7200ac9545a551d8f07692db41 | # Dataset Card for "miniwob_plusplus_T5_randomized_ref2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwob_plusplus_T5_randomized_ref2 | [
"region:us"
]
| 2023-03-15T08:11:38+00:00 | {"dataset_info": {"features": [{"name": "history_episodes", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "html_snippets", "dtype": "string"}, {"name": "actions", "dtype": "string"}, {"name": "refs", "dtype": "int64"}, {"name": "keydown_texts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 267456938, "num_examples": 60321}], "download_size": 0, "dataset_size": 267456938}} | 2023-03-15T10:23:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwob_plusplus_T5_randomized_ref2"
More Information needed | [
"# Dataset Card for \"miniwob_plusplus_T5_randomized_ref2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwob_plusplus_T5_randomized_ref2\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"miniwob_plusplus_T5_randomized_ref2\"\n\nMore Information needed"
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|
17b3eaf95a749faad4f56d2ed74c9253a9aca2a7 |
This data is from the github repo https://github.com/Jiaxin-Pei/Certainty-in-Science-Communication about the paper "Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications" by Jiaxin Pei and David Jurgens.
I put it here for later use, to train an ML model which estimates claim certainty.
| KnutJaegersberg/science_finding_sentence_certainty | [
"license:mit",
"region:us"
]
| 2023-03-15T09:35:22+00:00 | {"license": "mit"} | 2023-03-15T09:37:50+00:00 | []
| []
| TAGS
#license-mit #region-us
|
This data is from the github repo URL about the paper "Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications" by Jiaxin Pei and David Jurgens.
I put it here for later use, to train an ML model which estimates claim certainty.
| []
| [
"TAGS\n#license-mit #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-mit #region-us \n"
]
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|
ac5701d3e6eb8dac3f592da26b85b6be0b5e0289 |
Dataset generated from cyrillic train set using Stackmix
========================================================
Number of images: 3700269
Sources:
* [Cyrillic dataset](https://www.kaggle.com/datasets/constantinwerner/cyrillic-handwriting-dataset)
* [Stackmix code](https://github.com/ai-forever/StackMix-OCR)
| nastyboget/stackmix_cyrillic_large | [
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"language:ru",
"license:mit",
"region:us"
]
| 2023-03-15T09:48:56+00:00 | {"language": ["ru"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["image-to-text"]} | 2023-03-20T10:19:24+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us
|
Dataset generated from cyrillic train set using Stackmix
========================================================
Number of images: 3700269
Sources:
* Cyrillic dataset
* Stackmix code
| []
| [
"TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n"
]
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|
b1ac0ab96b17868db1f992f0939748b23e230422 | # Dataset Card for "DataAASR"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohammedNasri/DataAASR | [
"region:us"
]
| 2023-03-15T10:01:07+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 126461196457.672, "num_examples": 388054}, {"name": "test", "num_bytes": 304561718.0, "num_examples": 10440}], "download_size": 124152883448, "dataset_size": 126765758175.672}} | 2023-03-15T12:09:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DataAASR"
More Information needed | [
"# Dataset Card for \"DataAASR\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DataAASR\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DataAASR\"\n\nMore Information needed"
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|
f4612993afb8e96b5258fa38a8f0dd2b6cb31267 | # Dataset Card for "imdb_genre_prediction2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | james-burton/imdb_genre_prediction2 | [
"region:us"
]
| 2023-03-15T10:25:45+00:00 | {"dataset_info": {"features": [{"name": "Rank", "dtype": "int64"}, {"name": "Title", "dtype": "string"}, {"name": "Description", "dtype": "string"}, {"name": "Director", "dtype": "string"}, {"name": "Actors", "dtype": "string"}, {"name": "Year", "dtype": "int64"}, {"name": "Runtime (Minutes)", "dtype": "int64"}, {"name": "Rating", "dtype": "float64"}, {"name": "Votes", "dtype": "int64"}, {"name": "Revenue (Millions)", "dtype": "float64"}, {"name": "Metascore", "dtype": "float64"}, {"name": "Genre_is_Drama", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 224399.15, "num_examples": 680}, {"name": "validation", "num_bytes": 39599.85, "num_examples": 120}, {"name": "test", "num_bytes": 65392, "num_examples": 200}], "download_size": 0, "dataset_size": 329391.0}} | 2023-03-15T15:52:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "imdb_genre_prediction2"
More Information needed | [
"# Dataset Card for \"imdb_genre_prediction2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"imdb_genre_prediction2\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"imdb_genre_prediction2\"\n\nMore Information needed"
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|
acb1c4d1872712c9c8cc017192ca48993263f73c | # Dataset Card for "crc_image_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | BettercallSaulGM/crc_image_dataset | [
"region:us"
]
| 2023-03-15T11:44:51+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 130747958.0, "num_examples": 1000}], "download_size": 0, "dataset_size": 130747958.0}} | 2023-03-20T01:57:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "crc_image_dataset"
More Information needed | [
"# Dataset Card for \"crc_image_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"crc_image_dataset\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"crc_image_dataset\"\n\nMore Information needed"
]
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|
69b7b70f1f1d4bdbdeca38aa37d4191ceb4bdddd | # Dataset Card for "mscoco_200_unique_imgs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | JotDe/mscoco_200_unique_imgs | [
"region:us"
]
| 2023-03-15T11:55:23+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4761831.8, "num_examples": 199}], "download_size": 4745758, "dataset_size": 4761831.8}} | 2023-03-15T11:55:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mscoco_200_unique_imgs"
More Information needed | [
"# Dataset Card for \"mscoco_200_unique_imgs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mscoco_200_unique_imgs\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mscoco_200_unique_imgs\"\n\nMore Information needed"
]
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|
270640562e26f062f672efbbb48af7499ae3d392 | # Dataset Card for "mscoco_20k_unique_imgs_6k_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | JotDe/mscoco_20k_unique_imgs_6k_test | [
"region:us"
]
| 2023-03-15T11:57:25+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 503952003.80038005, "num_examples": 6000}], "download_size": 502943241, "dataset_size": 503952003.80038005}} | 2023-03-15T11:58:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mscoco_20k_unique_imgs_6k_test"
More Information needed | [
"# Dataset Card for \"mscoco_20k_unique_imgs_6k_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mscoco_20k_unique_imgs_6k_test\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mscoco_20k_unique_imgs_6k_test\"\n\nMore Information needed"
]
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|
0b540928e901d643d48bf6ee21cfd867f1d4a0e5 | # Dataset Card for "bangla_ner_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | celloscopeai/bangla_ner_dataset | [
"region:us"
]
| 2023-03-15T11:57:56+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}], "splits": [{"name": "train", "num_bytes": 2107132, "num_examples": 5252}, {"name": "validation", "num_bytes": 522332, "num_examples": 1314}], "download_size": 569072, "dataset_size": 2629464}} | 2023-03-15T13:51:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bangla_ner_dataset"
More Information needed | [
"# Dataset Card for \"bangla_ner_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bangla_ner_dataset\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bangla_ner_dataset\"\n\nMore Information needed"
]
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|
236b35e931acf76fc192c35880b8d2f820631643 | # Dataset Card for "turkishReviews-ds-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Hilalcelik/turkishReviews-ds-mini | [
"region:us"
]
| 2023-03-15T12:15:42+00:00 | {"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1252876.2642514652, "num_examples": 3378}, {"name": "validation", "num_bytes": 139455.7357485349, "num_examples": 376}], "download_size": 0, "dataset_size": 1392332.0}} | 2023-03-17T12:20:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "turkishReviews-ds-mini"
More Information needed | [
"# Dataset Card for \"turkishReviews-ds-mini\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"turkishReviews-ds-mini\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"turkishReviews-ds-mini\"\n\nMore Information needed"
]
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|
40e770d9aecdfb61bccd667e44695bbd08fbd2ce | # Dataset Card for "NoDiacsDataAASR"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohammedNasri/NoDiacsDataAASR | [
"region:us"
]
| 2023-03-15T12:24:43+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 126439476936.078, "num_examples": 388054}, {"name": "test", "num_bytes": 304490929.0, "num_examples": 10440}], "download_size": 124196553325, "dataset_size": 126743967865.078}} | 2023-03-15T14:31:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "NoDiacsDataAASR"
More Information needed | [
"# Dataset Card for \"NoDiacsDataAASR\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"NoDiacsDataAASR\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"NoDiacsDataAASR\"\n\nMore Information needed"
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|
5a70fbc229770740e4f65b9a6543e41fe8822281 | # Dataset Card for "pokemon-description-xs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mfumanelli/pokemon-description-xs | [
"region:us"
]
| 2023-03-15T12:48:47+00:00 | {"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2839, "num_examples": 20}], "download_size": 4230, "dataset_size": 2839}} | 2023-03-20T11:12:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pokemon-description-xs"
More Information needed | [
"# Dataset Card for \"pokemon-description-xs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pokemon-description-xs\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pokemon-description-xs\"\n\nMore Information needed"
]
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|
b6168e50e7769045cda8be9a0b4971fbd5cde476 | # Dataset Card for "boat-kg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | misitetong/boat-kg | [
"region:us"
]
| 2023-03-15T13:11:45+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 624626, "num_examples": 5538}], "download_size": 389912, "dataset_size": 624626}} | 2023-03-15T13:12:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boat-kg"
More Information needed | [
"# Dataset Card for \"boat-kg\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boat-kg\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boat-kg\"\n\nMore Information needed"
]
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|
3eb75a9b0bd84aa4a4f583df704bdbbd9cdcfe10 | # Dataset Card for "instructions-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-en | [
"region:us"
]
| 2023-03-15T13:16:48+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34460645, "num_examples": 89978}, {"name": "test", "num_bytes": 877581, "num_examples": 2368}, {"name": "validation", "num_bytes": 925739, "num_examples": 2368}], "download_size": 21174568, "dataset_size": 36263965}} | 2023-03-15T17:07:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-en"
More Information needed | [
"# Dataset Card for \"instructions-en\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-en\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-en\"\n\nMore Information needed"
]
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|
229261a8756442f8d5dc4cb98f1df1a3ba79eb23 |
# Dataset Card for "REDDIT_submissions"
## Dataset Description
- **Homepage:**
- **Paper: https://arxiv.org/abs/2001.08435**
### Dataset Summary
Submissions of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).
### Supported Tasks
These submissions can be used for text generation and language modeling, as well as dialogue modeling.
## Dataset Structure
### Data Splits
Each split corresponds to a specific subreddit in the following list: "tifu", "explainlikeimfive", "WritingPrompts", "changemyview", "LifeProTips", "todayilearned", "science", "askscience", "ifyoulikeblank", "Foodforthought", "IWantToLearn", "bestof", "IAmA", "socialskills", "relationship_advice", "philosophy", "YouShouldKnow", "history", "books", "Showerthoughts", "personalfinance", "buildapc", "EatCheapAndHealthy", "boardgames", "malefashionadvice", "femalefashionadvice", "scifi", "Fantasy", "Games", "bodyweightfitness", "SkincareAddiction", "podcasts", "suggestmeabook", "AskHistorians", "gaming", "DIY", "mildlyinteresting", "sports", "space", "gadgets", "Documentaries", "GetMotivated", "UpliftingNews", "technology", "Fitness", "travel", "lifehacks", "Damnthatsinteresting", "gardening", "programming"
## Dataset Creation
### Curation Rationale
All the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: "allow_live_comments", "archived", "author", "author_fullname", "banned_by", "category", "content_categories", "contest_mode", "created_utc", "discussion_type", "distinguished", "domain", "edited", "gilded", "hidden", "hide_score", "id", "is_created_from_ads_ui", "is_crosspostable", "is_meta", "is_original_content", "is_reddit_media_domain", "is_robot_indexable", "is_self", "is_video", "locked", "media", "media_embed", "media_only", "name", "no_follow", "num_comments", "num_crossposts", "over_18", "parent_whitelist_status", "permalink", "pinned", "post_hint", "pwls", "quarantine", "removed_by", "removed_by_category", "retrieved_on", "score", "secure_media", "secure_media_embed", "selftext", "send_replies", "spoiler", "stickied", "subreddit", "subreddit_id", "subreddit_name_prefixed", "subreddit_subscribers", "subreddit_type", "suggested_sort", "title", "top_awarded_type", "total_awards_received", "treatment_tags", "upvote_ratio", "url", "url_overridden_by_dest", "view_count", "whitelist_status", "wls".
### Source Data
The [Reddit PushShift data dumps](https://files.pushshift.io/reddit/) are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data.
#### Initial Data Collection and Normalization
See the paper.
#### Who are the source language producers?
Redditors are mostly young (65% below 30), male (70%), and American (50% of the site).
### Personal and Sensitive Information
The data contains Redditor's usernames associated to their content.
## Considerations for Using the Data
This dataset should be anonymized before any processing.
Though the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity.
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. | HuggingFaceGECLM/REDDIT_submissions | [
"task_categories:text-generation",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1B<n<10B",
"language:en",
"reddit",
"social-media",
"arxiv:2001.08435",
"region:us"
]
| 2023-03-15T14:13:43+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["1B<n<10B"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["dialogue-modeling", "language-modeling"], "pretty_name": "Reddit submissions", "dataset_info": {"features": [{"name": "allow_live_comments", "dtype": "string"}, {"name": "archived", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "author_fullname", "dtype": "string"}, {"name": "banned_by", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "content_categories", "dtype": "string"}, {"name": "contest_mode", "dtype": "string"}, {"name": "created_utc", "dtype": "string"}, {"name": "discussion_type", "dtype": "string"}, {"name": "distinguished", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "edited", "dtype": "string"}, {"name": "gilded", "dtype": "string"}, {"name": "hidden", "dtype": "string"}, {"name": "hide_score", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "is_created_from_ads_ui", "dtype": "string"}, {"name": "is_crosspostable", "dtype": "string"}, {"name": "is_meta", "dtype": "string"}, {"name": "is_original_content", "dtype": "string"}, {"name": "is_reddit_media_domain", "dtype": "string"}, {"name": "is_robot_indexable", "dtype": "string"}, {"name": "is_self", "dtype": "string"}, {"name": "is_video", "dtype": "string"}, {"name": "locked", "dtype": "string"}, {"name": "media", "dtype": "string"}, {"name": "media_embed", "dtype": "string"}, {"name": "media_only", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "no_follow", "dtype": "string"}, {"name": "num_comments", "dtype": "string"}, {"name": "num_crossposts", "dtype": "string"}, {"name": "over_18", "dtype": "string"}, {"name": "parent_whitelist_status", "dtype": "string"}, {"name": "permalink", "dtype": "string"}, {"name": "pinned", "dtype": "string"}, {"name": "post_hint", "dtype": "string"}, {"name": "pwls", "dtype": "string"}, {"name": "quarantine", "dtype": "string"}, {"name": "removed_by", "dtype": "string"}, {"name": "removed_by_category", "dtype": "string"}, {"name": "retrieved_on", "dtype": "string"}, {"name": "score", "dtype": "string"}, {"name": "secure_media", "dtype": "string"}, {"name": "secure_media_embed", "dtype": "string"}, {"name": "selftext", "dtype": "string"}, {"name": "send_replies", "dtype": "string"}, {"name": "spoiler", "dtype": "string"}, {"name": "stickied", "dtype": "string"}, {"name": "subreddit_id", "dtype": "string"}, {"name": "subreddit_name_prefixed", "dtype": "string"}, {"name": "subreddit_subscribers", "dtype": "string"}, {"name": "subreddit_type", "dtype": "string"}, {"name": "suggested_sort", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "top_awarded_type", "dtype": "string"}, {"name": "total_awards_received", "dtype": "string"}, {"name": "treatment_tags", "dtype": "string"}, {"name": "upvote_ratio", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "url_overridden_by_dest", "dtype": "string"}, {"name": "view_count", "dtype": "string"}, {"name": "whitelist_status", "dtype": "string"}, {"name": "wls", "dtype": "string"}], "splits": [{"name": "tifu", "num_bytes": 711926746, "num_examples": 526283}, {"name": "explainlikeimfive", "num_bytes": 1407570925, "num_examples": 1811324}, {"name": "WritingPrompts", "num_bytes": 883683696, "num_examples": 1001358}, {"name": "changemyview", "num_bytes": 366049867, "num_examples": 257332}, {"name": "LifeProTips", "num_bytes": 596724168, "num_examples": 715494}, {"name": "todayilearned", "num_bytes": 1882122179, "num_examples": 2153849}, {"name": "science", "num_bytes": 675817380, "num_examples": 872768}, {"name": "askscience", "num_bytes": 1180347707, "num_examples": 1562708}, {"name": "ifyoulikeblank", "num_bytes": 248876237, "num_examples": 221368}, {"name": "Foodforthought", "num_bytes": 56817554, "num_examples": 70647}, {"name": "IWantToLearn", "num_bytes": 97666128, "num_examples": 103347}, {"name": "bestof", "num_bytes": 230879506, "num_examples": 341029}, {"name": "IAmA", "num_bytes": 375534116, "num_examples": 436003}, {"name": "socialskills", "num_bytes": 327412682, "num_examples": 260354}, {"name": "relationship_advice", "num_bytes": 5050087947, "num_examples": 3284961}, {"name": "philosophy", "num_bytes": 230221165, "num_examples": 212792}, {"name": "YouShouldKnow", "num_bytes": 87706881, "num_examples": 94635}, {"name": "history", "num_bytes": 295389153, "num_examples": 284318}, {"name": "books", "num_bytes": 635450859, "num_examples": 692807}, {"name": "Showerthoughts", "num_bytes": 4859309870, "num_examples": 6358205}, {"name": "personalfinance", "num_bytes": 1813984142, "num_examples": 1347837}, {"name": "buildapc", "num_bytes": 4754190700, "num_examples": 3030207}, {"name": "EatCheapAndHealthy", "num_bytes": 95544413, "num_examples": 79694}, {"name": "boardgames", "num_bytes": 379980593, "num_examples": 287493}, {"name": "malefashionadvice", "num_bytes": 523741819, "num_examples": 548587}, {"name": "femalefashionadvice", "num_bytes": 131338068, "num_examples": 131110}, {"name": "scifi", "num_bytes": 148283250, "num_examples": 134568}, {"name": "Fantasy", "num_bytes": 265612464, "num_examples": 175866}, {"name": "Games", "num_bytes": 1112497898, "num_examples": 830997}, {"name": "bodyweightfitness", "num_bytes": 154845910, "num_examples": 144829}, {"name": "SkincareAddiction", "num_bytes": 908265410, "num_examples": 890421}, {"name": "podcasts", "num_bytes": 114495922, "num_examples": 113707}, {"name": "suggestmeabook", "num_bytes": 307022597, "num_examples": 300601}, {"name": "AskHistorians", "num_bytes": 586939915, "num_examples": 592242}, {"name": "gaming", "num_bytes": 7306865977, "num_examples": 6418305}, {"name": "DIY", "num_bytes": 612049815, "num_examples": 505769}, {"name": "mildlyinteresting", "num_bytes": 1497282377, "num_examples": 1971187}, {"name": "sports", "num_bytes": 866461524, "num_examples": 783890}, {"name": "space", "num_bytes": 413125181, "num_examples": 415629}, {"name": "gadgets", "num_bytes": 242359652, "num_examples": 284487}, {"name": "Documentaries", "num_bytes": 658519015, "num_examples": 300935}, {"name": "GetMotivated", "num_bytes": 458864553, "num_examples": 395894}, {"name": "UpliftingNews", "num_bytes": 294091853, "num_examples": 285339}, {"name": "technology", "num_bytes": 1562501874, "num_examples": 2112572}, {"name": "Fitness", "num_bytes": 939461866, "num_examples": 1035109}, {"name": "travel", "num_bytes": 988622317, "num_examples": 1012452}, {"name": "lifehacks", "num_bytes": 124628404, "num_examples": 116871}, {"name": "Damnthatsinteresting", "num_bytes": 536680874, "num_examples": 397143}, {"name": "gardening", "num_bytes": 652169745, "num_examples": 723267}, {"name": "programming", "num_bytes": 455470198, "num_examples": 571221}], "download_size": 15928530968, "dataset_size": 49105493092}, "tags": ["reddit", "social-media"]} | 2023-03-17T07:44:37+00:00 | [
"2001.08435"
]
| [
"en"
]
| TAGS
#task_categories-text-generation #task_ids-dialogue-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-1B<n<10B #language-English #reddit #social-media #arxiv-2001.08435 #region-us
|
# Dataset Card for "REDDIT_submissions"
## Dataset Description
- Homepage:
- Paper: URL
### Dataset Summary
Submissions of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).
### Supported Tasks
These submissions can be used for text generation and language modeling, as well as dialogue modeling.
## Dataset Structure
### Data Splits
Each split corresponds to a specific subreddit in the following list: "tifu", "explainlikeimfive", "WritingPrompts", "changemyview", "LifeProTips", "todayilearned", "science", "askscience", "ifyoulikeblank", "Foodforthought", "IWantToLearn", "bestof", "IAmA", "socialskills", "relationship_advice", "philosophy", "YouShouldKnow", "history", "books", "Showerthoughts", "personalfinance", "buildapc", "EatCheapAndHealthy", "boardgames", "malefashionadvice", "femalefashionadvice", "scifi", "Fantasy", "Games", "bodyweightfitness", "SkincareAddiction", "podcasts", "suggestmeabook", "AskHistorians", "gaming", "DIY", "mildlyinteresting", "sports", "space", "gadgets", "Documentaries", "GetMotivated", "UpliftingNews", "technology", "Fitness", "travel", "lifehacks", "Damnthatsinteresting", "gardening", "programming"
## Dataset Creation
### Curation Rationale
All the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: "allow_live_comments", "archived", "author", "author_fullname", "banned_by", "category", "content_categories", "contest_mode", "created_utc", "discussion_type", "distinguished", "domain", "edited", "gilded", "hidden", "hide_score", "id", "is_created_from_ads_ui", "is_crosspostable", "is_meta", "is_original_content", "is_reddit_media_domain", "is_robot_indexable", "is_self", "is_video", "locked", "media", "media_embed", "media_only", "name", "no_follow", "num_comments", "num_crossposts", "over_18", "parent_whitelist_status", "permalink", "pinned", "post_hint", "pwls", "quarantine", "removed_by", "removed_by_category", "retrieved_on", "score", "secure_media", "secure_media_embed", "selftext", "send_replies", "spoiler", "stickied", "subreddit", "subreddit_id", "subreddit_name_prefixed", "subreddit_subscribers", "subreddit_type", "suggested_sort", "title", "top_awarded_type", "total_awards_received", "treatment_tags", "upvote_ratio", "url", "url_overridden_by_dest", "view_count", "whitelist_status", "wls".
### Source Data
The Reddit PushShift data dumps are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data.
#### Initial Data Collection and Normalization
See the paper.
#### Who are the source language producers?
Redditors are mostly young (65% below 30), male (70%), and American (50% of the site).
### Personal and Sensitive Information
The data contains Redditor's usernames associated to their content.
## Considerations for Using the Data
This dataset should be anonymized before any processing.
Though the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity.
### Contributions
Thanks to @clefourrier for adding this dataset. | [
"# Dataset Card for \"REDDIT_submissions\"",
"## Dataset Description\n\n- Homepage:\n- Paper: URL",
"### Dataset Summary\nSubmissions of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).",
"### Supported Tasks\nThese submissions can be used for text generation and language modeling, as well as dialogue modeling.",
"## Dataset Structure",
"### Data Splits\nEach split corresponds to a specific subreddit in the following list: \"tifu\", \"explainlikeimfive\", \"WritingPrompts\", \"changemyview\", \"LifeProTips\", \"todayilearned\", \"science\", \"askscience\", \"ifyoulikeblank\", \"Foodforthought\", \"IWantToLearn\", \"bestof\", \"IAmA\", \"socialskills\", \"relationship_advice\", \"philosophy\", \"YouShouldKnow\", \"history\", \"books\", \"Showerthoughts\", \"personalfinance\", \"buildapc\", \"EatCheapAndHealthy\", \"boardgames\", \"malefashionadvice\", \"femalefashionadvice\", \"scifi\", \"Fantasy\", \"Games\", \"bodyweightfitness\", \"SkincareAddiction\", \"podcasts\", \"suggestmeabook\", \"AskHistorians\", \"gaming\", \"DIY\", \"mildlyinteresting\", \"sports\", \"space\", \"gadgets\", \"Documentaries\", \"GetMotivated\", \"UpliftingNews\", \"technology\", \"Fitness\", \"travel\", \"lifehacks\", \"Damnthatsinteresting\", \"gardening\", \"programming\"",
"## Dataset Creation",
"### Curation Rationale\nAll the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: \"allow_live_comments\", \"archived\", \"author\", \"author_fullname\", \"banned_by\", \"category\", \"content_categories\", \"contest_mode\", \"created_utc\", \"discussion_type\", \"distinguished\", \"domain\", \"edited\", \"gilded\", \"hidden\", \"hide_score\", \"id\", \"is_created_from_ads_ui\", \"is_crosspostable\", \"is_meta\", \"is_original_content\", \"is_reddit_media_domain\", \"is_robot_indexable\", \"is_self\", \"is_video\", \"locked\", \"media\", \"media_embed\", \"media_only\", \"name\", \"no_follow\", \"num_comments\", \"num_crossposts\", \"over_18\", \"parent_whitelist_status\", \"permalink\", \"pinned\", \"post_hint\", \"pwls\", \"quarantine\", \"removed_by\", \"removed_by_category\", \"retrieved_on\", \"score\", \"secure_media\", \"secure_media_embed\", \"selftext\", \"send_replies\", \"spoiler\", \"stickied\", \"subreddit\", \"subreddit_id\", \"subreddit_name_prefixed\", \"subreddit_subscribers\", \"subreddit_type\", \"suggested_sort\", \"title\", \"top_awarded_type\", \"total_awards_received\", \"treatment_tags\", \"upvote_ratio\", \"url\", \"url_overridden_by_dest\", \"view_count\", \"whitelist_status\", \"wls\".",
"### Source Data\nThe Reddit PushShift data dumps are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data.",
"#### Initial Data Collection and Normalization\nSee the paper.",
"#### Who are the source language producers?\nRedditors are mostly young (65% below 30), male (70%), and American (50% of the site).",
"### Personal and Sensitive Information\nThe data contains Redditor's usernames associated to their content.",
"## Considerations for Using the Data\n\nThis dataset should be anonymized before any processing.\nThough the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity.",
"### Contributions\n\nThanks to @clefourrier for adding this dataset."
]
| [
"TAGS\n#task_categories-text-generation #task_ids-dialogue-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-1B<n<10B #language-English #reddit #social-media #arxiv-2001.08435 #region-us \n",
"# Dataset Card for \"REDDIT_submissions\"",
"## Dataset Description\n\n- Homepage:\n- Paper: URL",
"### Dataset Summary\nSubmissions of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).",
"### Supported Tasks\nThese submissions can be used for text generation and language modeling, as well as dialogue modeling.",
"## Dataset Structure",
"### Data Splits\nEach split corresponds to a specific subreddit in the following list: \"tifu\", \"explainlikeimfive\", \"WritingPrompts\", \"changemyview\", \"LifeProTips\", \"todayilearned\", \"science\", \"askscience\", \"ifyoulikeblank\", \"Foodforthought\", \"IWantToLearn\", \"bestof\", \"IAmA\", \"socialskills\", \"relationship_advice\", \"philosophy\", \"YouShouldKnow\", \"history\", \"books\", \"Showerthoughts\", \"personalfinance\", \"buildapc\", \"EatCheapAndHealthy\", \"boardgames\", \"malefashionadvice\", \"femalefashionadvice\", \"scifi\", \"Fantasy\", \"Games\", \"bodyweightfitness\", \"SkincareAddiction\", \"podcasts\", \"suggestmeabook\", \"AskHistorians\", \"gaming\", \"DIY\", \"mildlyinteresting\", \"sports\", \"space\", \"gadgets\", \"Documentaries\", \"GetMotivated\", \"UpliftingNews\", \"technology\", \"Fitness\", \"travel\", \"lifehacks\", \"Damnthatsinteresting\", \"gardening\", \"programming\"",
"## Dataset Creation",
"### Curation Rationale\nAll the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: \"allow_live_comments\", \"archived\", \"author\", \"author_fullname\", \"banned_by\", \"category\", \"content_categories\", \"contest_mode\", \"created_utc\", \"discussion_type\", \"distinguished\", \"domain\", \"edited\", \"gilded\", \"hidden\", \"hide_score\", \"id\", \"is_created_from_ads_ui\", \"is_crosspostable\", \"is_meta\", \"is_original_content\", \"is_reddit_media_domain\", \"is_robot_indexable\", \"is_self\", \"is_video\", \"locked\", \"media\", \"media_embed\", \"media_only\", \"name\", \"no_follow\", \"num_comments\", \"num_crossposts\", \"over_18\", \"parent_whitelist_status\", \"permalink\", \"pinned\", \"post_hint\", \"pwls\", \"quarantine\", \"removed_by\", \"removed_by_category\", \"retrieved_on\", \"score\", \"secure_media\", \"secure_media_embed\", \"selftext\", \"send_replies\", \"spoiler\", \"stickied\", \"subreddit\", \"subreddit_id\", \"subreddit_name_prefixed\", \"subreddit_subscribers\", \"subreddit_type\", \"suggested_sort\", \"title\", \"top_awarded_type\", \"total_awards_received\", \"treatment_tags\", \"upvote_ratio\", \"url\", \"url_overridden_by_dest\", \"view_count\", \"whitelist_status\", \"wls\".",
"### Source Data\nThe Reddit PushShift data dumps are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data.",
"#### Initial Data Collection and Normalization\nSee the paper.",
"#### Who are the source language producers?\nRedditors are mostly young (65% below 30), male (70%), and American (50% of the site).",
"### Personal and Sensitive Information\nThe data contains Redditor's usernames associated to their content.",
"## Considerations for Using the Data\n\nThis dataset should be anonymized before any processing.\nThough the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity.",
"### Contributions\n\nThanks to @clefourrier for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-generation #task_ids-dialogue-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-1B<n<10B #language-English #reddit #social-media #arxiv-2001.08435 #region-us \n# Dataset Card for \"REDDIT_submissions\"## Dataset Description\n\n- Homepage:\n- Paper: URL### Dataset Summary\nSubmissions of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).### Supported Tasks\nThese submissions can be used for text generation and language modeling, as well as dialogue modeling.## Dataset Structure### Data Splits\nEach split corresponds to a specific subreddit in the following list: \"tifu\", \"explainlikeimfive\", \"WritingPrompts\", \"changemyview\", \"LifeProTips\", \"todayilearned\", \"science\", \"askscience\", \"ifyoulikeblank\", \"Foodforthought\", \"IWantToLearn\", \"bestof\", \"IAmA\", \"socialskills\", \"relationship_advice\", \"philosophy\", \"YouShouldKnow\", \"history\", \"books\", \"Showerthoughts\", \"personalfinance\", \"buildapc\", \"EatCheapAndHealthy\", \"boardgames\", \"malefashionadvice\", \"femalefashionadvice\", \"scifi\", \"Fantasy\", \"Games\", \"bodyweightfitness\", \"SkincareAddiction\", \"podcasts\", \"suggestmeabook\", \"AskHistorians\", \"gaming\", \"DIY\", \"mildlyinteresting\", \"sports\", \"space\", \"gadgets\", \"Documentaries\", \"GetMotivated\", \"UpliftingNews\", \"technology\", \"Fitness\", \"travel\", \"lifehacks\", \"Damnthatsinteresting\", \"gardening\", \"programming\"## Dataset Creation"
]
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|
54779d3d1f1c1b12e5989f695e13d38b394a558f |
# Dataset Card for "REDDIT_comments"
## Dataset Description
- **Homepage:**
- **Paper: https://arxiv.org/abs/2001.08435**
### Dataset Summary
Comments of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).
### Supported Tasks
These comments can be used for text generation and language modeling, as well as dialogue modeling.
## Dataset Structure
### Data Splits
Each split corresponds to a specific subreddit in the following list: "tifu", "explainlikeimfive", "WritingPrompts", "changemyview", "LifeProTips", "todayilearned", "science", "askscience", "ifyoulikeblank", "Foodforthought", "IWantToLearn", "bestof", "IAmA", "socialskills", "relationship_advice", "philosophy", "YouShouldKnow", "history", "books", "Showerthoughts", "personalfinance", "buildapc", "EatCheapAndHealthy", "boardgames", "malefashionadvice", "femalefashionadvice", "scifi", "Fantasy", "Games", "bodyweightfitness", "SkincareAddiction", "podcasts", "suggestmeabook", "AskHistorians", "gaming", "DIY", "mildlyinteresting", "sports", "space", "gadgets", "Documentaries", "GetMotivated", "UpliftingNews", "technology", "Fitness", "travel", "lifehacks", "Damnthatsinteresting", "gardening", "programming"
## Dataset Creation
### Curation Rationale
All the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: "archived", "author", "author_fullname", "body", "comment_type", "controversiality", "created_utc", "edited", "gilded", "id", "link_id", "locked", "name", "parent_id", "permalink", "retrieved_on", "score", "subreddit", "subreddit_id", "subreddit_name_prefixed", "subreddit_type", "total_awards_received".
### Source Data
The [Reddit PushShift data dumps](https://files.pushshift.io/reddit/) are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data.
#### Initial Data Collection and Normalization
See the paper.
#### Who are the source language producers?
Redditors are mostly young (65% below 30), male (70%), and American (50% of the site).
### Personal and Sensitive Information
The data contains Redditor's usernames associated to their content.
## Considerations for Using the Data
This dataset should be anonymized before any processing.
Though the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity.
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. | HuggingFaceGECLM/REDDIT_comments | [
"task_categories:text-generation",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10B<n<100B",
"language:en",
"reddit",
"social-media",
"arxiv:2001.08435",
"region:us"
]
| 2023-03-15T14:14:58+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10B<n<100B"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["dialogue-modeling", "language-modeling"], "pretty_name": "Reddit comments", "dataset_info": {"features": [{"name": "archived", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "author_fullname", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "comment_type", "dtype": "string"}, {"name": "controversiality", "dtype": "string"}, {"name": "created_utc", "dtype": "string"}, {"name": "edited", "dtype": "string"}, {"name": "gilded", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "link_id", "dtype": "string"}, {"name": "locked", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "permalink", "dtype": "string"}, {"name": "retrieved_on", "dtype": "string"}, {"name": "score", "dtype": "string"}, {"name": "subreddit_id", "dtype": "string"}, {"name": "subreddit_name_prefixed", "dtype": "string"}, {"name": "subreddit_type", "dtype": "string"}, {"name": "total_awards_received", "dtype": "string"}], "splits": [{"name": "programming", "num_bytes": 3466623746, "num_examples": 7503347}, {"name": "tifu", "num_bytes": 4761338653, "num_examples": 12738669}, {"name": "explainlikeimfive", "num_bytes": 8451732573, "num_examples": 16392814}, {"name": "WritingPrompts", "num_bytes": 4651591771, "num_examples": 4436210}, {"name": "changemyview", "num_bytes": 8603031915, "num_examples": 11600073}, {"name": "LifeProTips", "num_bytes": 5272994396, "num_examples": 12829459}, {"name": "todayilearned", "num_bytes": 22655655241, "num_examples": 60199778}, {"name": "science", "num_bytes": 7069809765, "num_examples": 18112884}, {"name": "askscience", "num_bytes": 3144754665, "num_examples": 6286702}, {"name": "ifyoulikeblank", "num_bytes": 547200329, "num_examples": 1332211}, {"name": "Foodforthought", "num_bytes": 308377128, "num_examples": 567900}, {"name": "IWantToLearn", "num_bytes": 408331672, "num_examples": 745543}, {"name": "bestof", "num_bytes": 2003718831, "num_examples": 4347522}, {"name": "IAmA", "num_bytes": 9380094090, "num_examples": 25778822}, {"name": "socialskills", "num_bytes": 1000014402, "num_examples": 1842733}, {"name": "relationship_advice", "num_bytes": 22298879735, "num_examples": 38937398}, {"name": "philosophy", "num_bytes": 1494947876, "num_examples": 2391695}, {"name": "YouShouldKnow", "num_bytes": 1165617658, "num_examples": 2639265}, {"name": "history", "num_bytes": 1457852402, "num_examples": 2962043}, {"name": "books", "num_bytes": 4562689426, "num_examples": 10187495}, {"name": "Showerthoughts", "num_bytes": 13259109532, "num_examples": 34123213}, {"name": "personalfinance", "num_bytes": 9484869588, "num_examples": 18361314}, {"name": "buildapc", "num_bytes": 9801044390, "num_examples": 21761801}, {"name": "EatCheapAndHealthy", "num_bytes": 853462012, "num_examples": 1821897}, {"name": "boardgames", "num_bytes": 3131627378, "num_examples": 6328926}, {"name": "malefashionadvice", "num_bytes": 2928017882, "num_examples": 7712258}, {"name": "femalefashionadvice", "num_bytes": 1619784736, "num_examples": 3262969}, {"name": "scifi", "num_bytes": 888152056, "num_examples": 2193741}, {"name": "Fantasy", "num_bytes": 2285934538, "num_examples": 4566639}, {"name": "Games", "num_bytes": 10396813188, "num_examples": 23373965}, {"name": "bodyweightfitness", "num_bytes": 794549854, "num_examples": 1613634}, {"name": "SkincareAddiction", "num_bytes": 3421122597, "num_examples": 5660550}, {"name": "podcasts", "num_bytes": 464773126, "num_examples": 943266}, {"name": "suggestmeabook", "num_bytes": 1842944304, "num_examples": 3492937}, {"name": "AskHistorians", "num_bytes": 2244587909, "num_examples": 2714353}, {"name": "gaming", "num_bytes": 28374513722, "num_examples": 85729253}, {"name": "DIY", "num_bytes": 2113533684, "num_examples": 4489265}, {"name": "sports", "num_bytes": 2230129132, "num_examples": 6470079}, {"name": "space", "num_bytes": 3081499208, "num_examples": 7896182}, {"name": "gadgets", "num_bytes": 1683252868, "num_examples": 4104833}, {"name": "Documentaries", "num_bytes": 1852644771, "num_examples": 4051474}, {"name": "GetMotivated", "num_bytes": 1211761267, "num_examples": 3221980}, {"name": "UpliftingNews", "num_bytes": 2003149025, "num_examples": 4741948}, {"name": "technology", "num_bytes": 10826871436, "num_examples": 25404699}, {"name": "Fitness", "num_bytes": 6191132755, "num_examples": 14319856}, {"name": "travel", "num_bytes": 1740556350, "num_examples": 3806755}, {"name": "lifehacks", "num_bytes": 626791812, "num_examples": 1799437}, {"name": "Damnthatsinteresting", "num_bytes": 6376694618, "num_examples": 15643554}, {"name": "gardening", "num_bytes": 1825313940, "num_examples": 4568468}, {"name": "mildlyinteresting", "num_bytes": 9079894206, "num_examples": 26436769}], "download_size": 109177016105, "dataset_size": 255339788158}, "tags": ["reddit", "social-media"]} | 2023-03-17T07:52:51+00:00 | [
"2001.08435"
]
| [
"en"
]
| TAGS
#task_categories-text-generation #task_ids-dialogue-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10B<n<100B #language-English #reddit #social-media #arxiv-2001.08435 #region-us
|
# Dataset Card for "REDDIT_comments"
## Dataset Description
- Homepage:
- Paper: URL
### Dataset Summary
Comments of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).
### Supported Tasks
These comments can be used for text generation and language modeling, as well as dialogue modeling.
## Dataset Structure
### Data Splits
Each split corresponds to a specific subreddit in the following list: "tifu", "explainlikeimfive", "WritingPrompts", "changemyview", "LifeProTips", "todayilearned", "science", "askscience", "ifyoulikeblank", "Foodforthought", "IWantToLearn", "bestof", "IAmA", "socialskills", "relationship_advice", "philosophy", "YouShouldKnow", "history", "books", "Showerthoughts", "personalfinance", "buildapc", "EatCheapAndHealthy", "boardgames", "malefashionadvice", "femalefashionadvice", "scifi", "Fantasy", "Games", "bodyweightfitness", "SkincareAddiction", "podcasts", "suggestmeabook", "AskHistorians", "gaming", "DIY", "mildlyinteresting", "sports", "space", "gadgets", "Documentaries", "GetMotivated", "UpliftingNews", "technology", "Fitness", "travel", "lifehacks", "Damnthatsinteresting", "gardening", "programming"
## Dataset Creation
### Curation Rationale
All the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: "archived", "author", "author_fullname", "body", "comment_type", "controversiality", "created_utc", "edited", "gilded", "id", "link_id", "locked", "name", "parent_id", "permalink", "retrieved_on", "score", "subreddit", "subreddit_id", "subreddit_name_prefixed", "subreddit_type", "total_awards_received".
### Source Data
The Reddit PushShift data dumps are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data.
#### Initial Data Collection and Normalization
See the paper.
#### Who are the source language producers?
Redditors are mostly young (65% below 30), male (70%), and American (50% of the site).
### Personal and Sensitive Information
The data contains Redditor's usernames associated to their content.
## Considerations for Using the Data
This dataset should be anonymized before any processing.
Though the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity.
### Contributions
Thanks to @clefourrier for adding this dataset. | [
"# Dataset Card for \"REDDIT_comments\"",
"## Dataset Description\n\n- Homepage:\n- Paper: URL",
"### Dataset Summary\nComments of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).",
"### Supported Tasks\nThese comments can be used for text generation and language modeling, as well as dialogue modeling.",
"## Dataset Structure",
"### Data Splits\nEach split corresponds to a specific subreddit in the following list: \"tifu\", \"explainlikeimfive\", \"WritingPrompts\", \"changemyview\", \"LifeProTips\", \"todayilearned\", \"science\", \"askscience\", \"ifyoulikeblank\", \"Foodforthought\", \"IWantToLearn\", \"bestof\", \"IAmA\", \"socialskills\", \"relationship_advice\", \"philosophy\", \"YouShouldKnow\", \"history\", \"books\", \"Showerthoughts\", \"personalfinance\", \"buildapc\", \"EatCheapAndHealthy\", \"boardgames\", \"malefashionadvice\", \"femalefashionadvice\", \"scifi\", \"Fantasy\", \"Games\", \"bodyweightfitness\", \"SkincareAddiction\", \"podcasts\", \"suggestmeabook\", \"AskHistorians\", \"gaming\", \"DIY\", \"mildlyinteresting\", \"sports\", \"space\", \"gadgets\", \"Documentaries\", \"GetMotivated\", \"UpliftingNews\", \"technology\", \"Fitness\", \"travel\", \"lifehacks\", \"Damnthatsinteresting\", \"gardening\", \"programming\"",
"## Dataset Creation",
"### Curation Rationale\nAll the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: \"archived\", \"author\", \"author_fullname\", \"body\", \"comment_type\", \"controversiality\", \"created_utc\", \"edited\", \"gilded\", \"id\", \"link_id\", \"locked\", \"name\", \"parent_id\", \"permalink\", \"retrieved_on\", \"score\", \"subreddit\", \"subreddit_id\", \"subreddit_name_prefixed\", \"subreddit_type\", \"total_awards_received\".",
"### Source Data\nThe Reddit PushShift data dumps are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data.",
"#### Initial Data Collection and Normalization\nSee the paper.",
"#### Who are the source language producers?\nRedditors are mostly young (65% below 30), male (70%), and American (50% of the site).",
"### Personal and Sensitive Information\nThe data contains Redditor's usernames associated to their content.",
"## Considerations for Using the Data\n\nThis dataset should be anonymized before any processing.\nThough the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity.",
"### Contributions\n\nThanks to @clefourrier for adding this dataset."
]
| [
"TAGS\n#task_categories-text-generation #task_ids-dialogue-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10B<n<100B #language-English #reddit #social-media #arxiv-2001.08435 #region-us \n",
"# Dataset Card for \"REDDIT_comments\"",
"## Dataset Description\n\n- Homepage:\n- Paper: URL",
"### Dataset Summary\nComments of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).",
"### Supported Tasks\nThese comments can be used for text generation and language modeling, as well as dialogue modeling.",
"## Dataset Structure",
"### Data Splits\nEach split corresponds to a specific subreddit in the following list: \"tifu\", \"explainlikeimfive\", \"WritingPrompts\", \"changemyview\", \"LifeProTips\", \"todayilearned\", \"science\", \"askscience\", \"ifyoulikeblank\", \"Foodforthought\", \"IWantToLearn\", \"bestof\", \"IAmA\", \"socialskills\", \"relationship_advice\", \"philosophy\", \"YouShouldKnow\", \"history\", \"books\", \"Showerthoughts\", \"personalfinance\", \"buildapc\", \"EatCheapAndHealthy\", \"boardgames\", \"malefashionadvice\", \"femalefashionadvice\", \"scifi\", \"Fantasy\", \"Games\", \"bodyweightfitness\", \"SkincareAddiction\", \"podcasts\", \"suggestmeabook\", \"AskHistorians\", \"gaming\", \"DIY\", \"mildlyinteresting\", \"sports\", \"space\", \"gadgets\", \"Documentaries\", \"GetMotivated\", \"UpliftingNews\", \"technology\", \"Fitness\", \"travel\", \"lifehacks\", \"Damnthatsinteresting\", \"gardening\", \"programming\"",
"## Dataset Creation",
"### Curation Rationale\nAll the information fields have been cast to string, as their format change through time from one dump to the following. A reduced number of keys have been kept: \"archived\", \"author\", \"author_fullname\", \"body\", \"comment_type\", \"controversiality\", \"created_utc\", \"edited\", \"gilded\", \"id\", \"link_id\", \"locked\", \"name\", \"parent_id\", \"permalink\", \"retrieved_on\", \"score\", \"subreddit\", \"subreddit_id\", \"subreddit_name_prefixed\", \"subreddit_type\", \"total_awards_received\".",
"### Source Data\nThe Reddit PushShift data dumps are part of a data collection effort which crawls Reddit at regular intervals, to extract and keep all its data.",
"#### Initial Data Collection and Normalization\nSee the paper.",
"#### Who are the source language producers?\nRedditors are mostly young (65% below 30), male (70%), and American (50% of the site).",
"### Personal and Sensitive Information\nThe data contains Redditor's usernames associated to their content.",
"## Considerations for Using the Data\n\nThis dataset should be anonymized before any processing.\nThough the subreddits selected are considered as being of higher quality, they can still reflect what you can find on the internet in terms of expressions of biases and toxicity.",
"### Contributions\n\nThanks to @clefourrier for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-generation #task_ids-dialogue-modeling #task_ids-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10B<n<100B #language-English #reddit #social-media #arxiv-2001.08435 #region-us \n# Dataset Card for \"REDDIT_comments\"## Dataset Description\n\n- Homepage:\n- Paper: URL### Dataset Summary\nComments of 50 high-quality subreddits, extracted from the REDDIT PushShift data dumps (from 2006 to Jan 2023).### Supported Tasks\nThese comments can be used for text generation and language modeling, as well as dialogue modeling.## Dataset Structure### Data Splits\nEach split corresponds to a specific subreddit in the following list: \"tifu\", \"explainlikeimfive\", \"WritingPrompts\", \"changemyview\", \"LifeProTips\", \"todayilearned\", \"science\", \"askscience\", \"ifyoulikeblank\", \"Foodforthought\", \"IWantToLearn\", \"bestof\", \"IAmA\", \"socialskills\", \"relationship_advice\", \"philosophy\", \"YouShouldKnow\", \"history\", \"books\", \"Showerthoughts\", \"personalfinance\", \"buildapc\", \"EatCheapAndHealthy\", \"boardgames\", \"malefashionadvice\", \"femalefashionadvice\", \"scifi\", \"Fantasy\", \"Games\", \"bodyweightfitness\", \"SkincareAddiction\", \"podcasts\", \"suggestmeabook\", \"AskHistorians\", \"gaming\", \"DIY\", \"mildlyinteresting\", \"sports\", \"space\", \"gadgets\", \"Documentaries\", \"GetMotivated\", \"UpliftingNews\", \"technology\", \"Fitness\", \"travel\", \"lifehacks\", \"Damnthatsinteresting\", \"gardening\", \"programming\"## Dataset Creation"
]
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|
032e2d8f33044e0a7d5f9b196a847e7bccdc0874 | # Dataset Card for "AASR163762Diacs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohammedNasri/AASR163762Diacs | [
"region:us"
]
| 2023-03-15T14:47:36+00:00 | {"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 147277358288, "num_examples": 153322}, {"name": "test", "num_bytes": 10027780960, "num_examples": 10440}], "download_size": 23811604609, "dataset_size": 157305139248}} | 2023-03-15T15:33:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AASR163762Diacs"
More Information needed | [
"# Dataset Card for \"AASR163762Diacs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AASR163762Diacs\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AASR163762Diacs\"\n\nMore Information needed"
]
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|
1840be3bed70056d816697e7348105cc43e48f1c | # Dataset Card for "AASR163762NoDiacs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohammedNasri/AASR163762NoDiacs | [
"region:us"
]
| 2023-03-15T15:13:21+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6926220203.732, "num_examples": 153322}, {"name": "test", "num_bytes": 304490929.0, "num_examples": 10440}], "download_size": 6921939800, "dataset_size": 7230711132.732}} | 2023-03-15T15:20:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AASR163762NoDiacs"
More Information needed | [
"# Dataset Card for \"AASR163762NoDiacs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AASR163762NoDiacs\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AASR163762NoDiacs\"\n\nMore Information needed"
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|
8f0288581428ec66b8bc646d96cda3ed1aa3a953 | # Dataset Card for "imdb_genre_prediction_all_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | james-burton/imdb_genre_prediction_all_text | [
"region:us"
]
| 2023-03-15T15:18:55+00:00 | {"dataset_info": {"features": [{"name": "Rank", "dtype": "string"}, {"name": "Title", "dtype": "string"}, {"name": "Description", "dtype": "string"}, {"name": "Director", "dtype": "string"}, {"name": "Actors", "dtype": "string"}, {"name": "Year", "dtype": "string"}, {"name": "Runtime (Minutes)", "dtype": "string"}, {"name": "Rating", "dtype": "string"}, {"name": "Votes", "dtype": "string"}, {"name": "Revenue (Millions)", "dtype": "string"}, {"name": "Metascore", "dtype": "string"}, {"name": "Genre_is_Drama", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 223443, "num_examples": 680}, {"name": "validation", "num_bytes": 39430, "num_examples": 120}, {"name": "test", "num_bytes": 65146, "num_examples": 200}], "download_size": 0, "dataset_size": 328019}} | 2023-05-02T15:00:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "imdb_genre_prediction_all_text"
More Information needed | [
"# Dataset Card for \"imdb_genre_prediction_all_text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"imdb_genre_prediction_all_text\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"imdb_genre_prediction_all_text\"\n\nMore Information needed"
]
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|
7ff4f59169c4c8b1bc348298d9e4efe2c538aaeb | # Dataset Card for "jojos-dataset-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polytechXhf/jojos-dataset-small | [
"region:us"
]
| 2023-03-15T15:19:12+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "char_name", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17859557.0, "num_examples": 97}], "download_size": 17860793, "dataset_size": 17859557.0}} | 2023-05-17T11:29:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "jojos-dataset-small"
More Information needed | [
"# Dataset Card for \"jojos-dataset-small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"jojos-dataset-small\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"jojos-dataset-small\"\n\nMore Information needed"
]
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|
629974f38e224098aa62d836110530d3abc24f6e | # Dataset Card for "Babelscape-wikineural-joined"
This dataset is a merged version of [wikineural](https://huggingface.co/datasets/Babelscape/wikineural)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
<pre><code>
@inproceedings{tedeschi-etal-2021-wikineural-combined,
title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
author = "Tedeschi, Simone and
Maiorca, Valentino and
Campolungo, Niccol{\`o} and
Cecconi, Francesco and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.215",
pages = "2521--2533",
abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
}
</code></pre> | dmargutierrez/Babelscape-wikineural-joined | [
"task_categories:token-classification",
"language:es",
"language:en",
"language:nl",
"language:fr",
"language:it",
"language:ru",
"language:pt",
"language:pl",
"language:de",
"named-entity-recognition",
"wikipedia",
"machine-generation",
"region:us"
]
| 2023-03-15T15:33:38+00:00 | {"language": ["es", "en", "nl", "fr", "it", "ru", "pt", "pl", "de"], "task_categories": ["token-classification"], "pretty_name": "Wikineural", "dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 319328641, "num_examples": 821600}, {"name": "validation", "num_bytes": 39434957, "num_examples": 102700}, {"name": "test", "num_bytes": 39371980, "num_examples": 103206}], "download_size": 139847318, "dataset_size": 398135578}, "tags": ["named-entity-recognition", "wikipedia", "machine-generation"]} | 2023-03-16T09:12:55+00:00 | []
| [
"es",
"en",
"nl",
"fr",
"it",
"ru",
"pt",
"pl",
"de"
]
| TAGS
#task_categories-token-classification #language-Spanish #language-English #language-Dutch #language-French #language-Italian #language-Russian #language-Portuguese #language-Polish #language-German #named-entity-recognition #wikipedia #machine-generation #region-us
| # Dataset Card for "Babelscape-wikineural-joined"
This dataset is a merged version of wikineural
More Information needed
<pre><code>
@inproceedings{tedeschi-etal-2021-wikineural-combined,
title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
author = "Tedeschi, Simone and
Maiorca, Valentino and
Campolungo, Niccol{\'o} and
Cecconi, Francesco and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "URL
pages = "2521--2533",
abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
}
</code></pre> | [
"# Dataset Card for \"Babelscape-wikineural-joined\"\n\nThis dataset is a merged version of wikineural\n\nMore Information needed\n\n<pre><code>\n @inproceedings{tedeschi-etal-2021-wikineural-combined,\n title = \"{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}\",\n author = \"Tedeschi, Simone and\n Maiorca, Valentino and\n Campolungo, Niccol{\\'o} and\n Cecconi, Francesco and\n Navigli, Roberto\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2021\",\n month = nov,\n year = \"2021\",\n address = \"Punta Cana, Dominican Republic\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"2521--2533\",\n abstract = \"Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.\",\n}\n\n</code></pre>"
]
| [
"TAGS\n#task_categories-token-classification #language-Spanish #language-English #language-Dutch #language-French #language-Italian #language-Russian #language-Portuguese #language-Polish #language-German #named-entity-recognition #wikipedia #machine-generation #region-us \n",
"# Dataset Card for \"Babelscape-wikineural-joined\"\n\nThis dataset is a merged version of wikineural\n\nMore Information needed\n\n<pre><code>\n @inproceedings{tedeschi-etal-2021-wikineural-combined,\n title = \"{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}\",\n author = \"Tedeschi, Simone and\n Maiorca, Valentino and\n Campolungo, Niccol{\\'o} and\n Cecconi, Francesco and\n Navigli, Roberto\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2021\",\n month = nov,\n year = \"2021\",\n address = \"Punta Cana, Dominican Republic\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"2521--2533\",\n abstract = \"Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.\",\n}\n\n</code></pre>"
]
| [
81,
400
]
| [
"passage: TAGS\n#task_categories-token-classification #language-Spanish #language-English #language-Dutch #language-French #language-Italian #language-Russian #language-Portuguese #language-Polish #language-German #named-entity-recognition #wikipedia #machine-generation #region-us \n# Dataset Card for \"Babelscape-wikineural-joined\"\n\nThis dataset is a merged version of wikineural\n\nMore Information needed\n\n<pre><code>\n @inproceedings{tedeschi-etal-2021-wikineural-combined,\n title = \"{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}\",\n author = \"Tedeschi, Simone and\n Maiorca, Valentino and\n Campolungo, Niccol{\\'o} and\n Cecconi, Francesco and\n Navigli, Roberto\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2021\",\n month = nov,\n year = \"2021\",\n address = \"Punta Cana, Dominican Republic\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"2521--2533\",\n abstract = \"Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.\",\n}\n\n</code></pre>"
]
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|
859dfafa7c23a4d1712532540ab6c3dad6bd6a5b | # Dataset Card for "semantic_sentence_similarity_ES"
This dataset is based on https://huggingface.co/datasets/PlanTL-GOB-ES/sts-es, which includes the datasets presented at the SemEval 2014 and 2015 shared tasks on sentence similarity (see the link for more info about the citations). It also includes data from SemEval 2017. | nflechas/semantic_sentence_similarity_ES | [
"region:us"
]
| 2023-03-15T15:36:06+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 547540, "num_examples": 1411}, {"name": "validation", "num_bytes": 40604, "num_examples": 168}, {"name": "test", "num_bytes": 73097, "num_examples": 245}], "download_size": 427004, "dataset_size": 661241}} | 2023-03-21T21:57:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "semantic_sentence_similarity_ES"
This dataset is based on URL which includes the datasets presented at the SemEval 2014 and 2015 shared tasks on sentence similarity (see the link for more info about the citations). It also includes data from SemEval 2017. | [
"# Dataset Card for \"semantic_sentence_similarity_ES\"\n\nThis dataset is based on URL which includes the datasets presented at the SemEval 2014 and 2015 shared tasks on sentence similarity (see the link for more info about the citations). It also includes data from SemEval 2017."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"semantic_sentence_similarity_ES\"\n\nThis dataset is based on URL which includes the datasets presented at the SemEval 2014 and 2015 shared tasks on sentence similarity (see the link for more info about the citations). It also includes data from SemEval 2017."
]
| [
6,
69
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"semantic_sentence_similarity_ES\"\n\nThis dataset is based on URL which includes the datasets presented at the SemEval 2014 and 2015 shared tasks on sentence similarity (see the link for more info about the citations). It also includes data from SemEval 2017."
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d6c0ee807d8534d4be18f2fc2ea80835b742e7ec | # Dataset Card for "COCO_captions_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/COCO_captions_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_ns_100 | [
"region:us"
]
| 2023-03-15T15:49:46+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "imgid", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 447865, "num_examples": 100}], "download_size": 196502, "dataset_size": 447865}} | 2023-03-15T16:22:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "COCO_captions_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_ns_100"
More Information needed | [
"# Dataset Card for \"COCO_captions_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_ns_100\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"COCO_captions_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_ns_100\"\n\nMore Information needed"
]
| [
6,
50
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"COCO_captions_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_ns_100\"\n\nMore Information needed"
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|
088a8b8b540175c11ffac9764bc5bcd63e488f00 | # Are You The Asshole Training Data
These are the datasets used for a project Alex Petros and I made called [AreYouTheAsshole.com](https://www.areyoutheasshole.com). The site is intended to give users a fun and interactive way to experience the effect of bias in AI due to skewed data. We achieved this by fine-tuning three GPT-3 Davinci-002 models on the prompt/completion pairs you see here.
Each prompt/completion pair constitutes a post body (the prompt) and a comment (the completion). Just as there may be multiple comments to a single post, there may be multiple completions for a single prompt.
The dataset was filtered down from >100,000 post/comment pairs to only those whose comments started with a clear acronym judgement. So, comments like "Well I think YTA because..." were filtered out, whereas comments like "YTA and it's not even close..." were kept.
After filtering for clear judgement, we had our neutral dataset, the one you can find in "Neutral_Dataset.jsonl". In order to create intentionally biased data, we then split that dataset into two subsets based on whether a given post/comment pair's comment judged the poster as The Asshole or Not The Asshole. Some edge cases were also filtered out.
The dataset contains three sets:
- Neutral_Dataset.jsonl (contains all clear judgements, YTA, NTA, etc.)
- YTA_Dataset.jsonl (only contains judgements of YTA or similar)
- NTA_Dataset.jsonl (only contains judgements of NTA or similar)
### Data Collection:
This data was collected from Reddit's r/AmITheAsshole subreddit using PMAW/PRAW and the Reddit API | wttdotm/AYTA_Datasets | [
"size_categories:10K<n<100K",
"Reddit",
"OpenAI",
"GPT-3",
"Davinci-002",
"PRAW",
"PMAW",
"region:us"
]
| 2023-03-15T15:50:42+00:00 | {"size_categories": ["10K<n<100K"], "tags": ["Reddit", "OpenAI", "GPT-3", "Davinci-002", "PRAW", "PMAW"]} | 2023-03-15T16:05:12+00:00 | []
| []
| TAGS
#size_categories-10K<n<100K #Reddit #OpenAI #GPT-3 #Davinci-002 #PRAW #PMAW #region-us
| # Are You The Asshole Training Data
These are the datasets used for a project Alex Petros and I made called URL. The site is intended to give users a fun and interactive way to experience the effect of bias in AI due to skewed data. We achieved this by fine-tuning three GPT-3 Davinci-002 models on the prompt/completion pairs you see here.
Each prompt/completion pair constitutes a post body (the prompt) and a comment (the completion). Just as there may be multiple comments to a single post, there may be multiple completions for a single prompt.
The dataset was filtered down from >100,000 post/comment pairs to only those whose comments started with a clear acronym judgement. So, comments like "Well I think YTA because..." were filtered out, whereas comments like "YTA and it's not even close..." were kept.
After filtering for clear judgement, we had our neutral dataset, the one you can find in "Neutral_Dataset.jsonl". In order to create intentionally biased data, we then split that dataset into two subsets based on whether a given post/comment pair's comment judged the poster as The Asshole or Not The Asshole. Some edge cases were also filtered out.
The dataset contains three sets:
- Neutral_Dataset.jsonl (contains all clear judgements, YTA, NTA, etc.)
- YTA_Dataset.jsonl (only contains judgements of YTA or similar)
- NTA_Dataset.jsonl (only contains judgements of NTA or similar)
### Data Collection:
This data was collected from Reddit's r/AmITheAsshole subreddit using PMAW/PRAW and the Reddit API | [
"# Are You The Asshole Training Data\nThese are the datasets used for a project Alex Petros and I made called URL. The site is intended to give users a fun and interactive way to experience the effect of bias in AI due to skewed data. We achieved this by fine-tuning three GPT-3 Davinci-002 models on the prompt/completion pairs you see here.\n\nEach prompt/completion pair constitutes a post body (the prompt) and a comment (the completion). Just as there may be multiple comments to a single post, there may be multiple completions for a single prompt.\n\nThe dataset was filtered down from >100,000 post/comment pairs to only those whose comments started with a clear acronym judgement. So, comments like \"Well I think YTA because...\" were filtered out, whereas comments like \"YTA and it's not even close...\" were kept.\n\nAfter filtering for clear judgement, we had our neutral dataset, the one you can find in \"Neutral_Dataset.jsonl\". In order to create intentionally biased data, we then split that dataset into two subsets based on whether a given post/comment pair's comment judged the poster as The Asshole or Not The Asshole. Some edge cases were also filtered out.\n\nThe dataset contains three sets:\n- Neutral_Dataset.jsonl (contains all clear judgements, YTA, NTA, etc.)\n- YTA_Dataset.jsonl (only contains judgements of YTA or similar)\n- NTA_Dataset.jsonl (only contains judgements of NTA or similar)",
"### Data Collection:\nThis data was collected from Reddit's r/AmITheAsshole subreddit using PMAW/PRAW and the Reddit API"
]
| [
"TAGS\n#size_categories-10K<n<100K #Reddit #OpenAI #GPT-3 #Davinci-002 #PRAW #PMAW #region-us \n",
"# Are You The Asshole Training Data\nThese are the datasets used for a project Alex Petros and I made called URL. The site is intended to give users a fun and interactive way to experience the effect of bias in AI due to skewed data. We achieved this by fine-tuning three GPT-3 Davinci-002 models on the prompt/completion pairs you see here.\n\nEach prompt/completion pair constitutes a post body (the prompt) and a comment (the completion). Just as there may be multiple comments to a single post, there may be multiple completions for a single prompt.\n\nThe dataset was filtered down from >100,000 post/comment pairs to only those whose comments started with a clear acronym judgement. So, comments like \"Well I think YTA because...\" were filtered out, whereas comments like \"YTA and it's not even close...\" were kept.\n\nAfter filtering for clear judgement, we had our neutral dataset, the one you can find in \"Neutral_Dataset.jsonl\". In order to create intentionally biased data, we then split that dataset into two subsets based on whether a given post/comment pair's comment judged the poster as The Asshole or Not The Asshole. Some edge cases were also filtered out.\n\nThe dataset contains three sets:\n- Neutral_Dataset.jsonl (contains all clear judgements, YTA, NTA, etc.)\n- YTA_Dataset.jsonl (only contains judgements of YTA or similar)\n- NTA_Dataset.jsonl (only contains judgements of NTA or similar)",
"### Data Collection:\nThis data was collected from Reddit's r/AmITheAsshole subreddit using PMAW/PRAW and the Reddit API"
]
| [
40,
376,
35
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #Reddit #OpenAI #GPT-3 #Davinci-002 #PRAW #PMAW #region-us \n# Are You The Asshole Training Data\nThese are the datasets used for a project Alex Petros and I made called URL. The site is intended to give users a fun and interactive way to experience the effect of bias in AI due to skewed data. We achieved this by fine-tuning three GPT-3 Davinci-002 models on the prompt/completion pairs you see here.\n\nEach prompt/completion pair constitutes a post body (the prompt) and a comment (the completion). Just as there may be multiple comments to a single post, there may be multiple completions for a single prompt.\n\nThe dataset was filtered down from >100,000 post/comment pairs to only those whose comments started with a clear acronym judgement. So, comments like \"Well I think YTA because...\" were filtered out, whereas comments like \"YTA and it's not even close...\" were kept.\n\nAfter filtering for clear judgement, we had our neutral dataset, the one you can find in \"Neutral_Dataset.jsonl\". In order to create intentionally biased data, we then split that dataset into two subsets based on whether a given post/comment pair's comment judged the poster as The Asshole or Not The Asshole. Some edge cases were also filtered out.\n\nThe dataset contains three sets:\n- Neutral_Dataset.jsonl (contains all clear judgements, YTA, NTA, etc.)\n- YTA_Dataset.jsonl (only contains judgements of YTA or similar)\n- NTA_Dataset.jsonl (only contains judgements of NTA or similar)### Data Collection:\nThis data was collected from Reddit's r/AmITheAsshole subreddit using PMAW/PRAW and the Reddit API"
]
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|
9d25969bc35b8ddb6a2996d965e12f69552a43e6 | # Dataset Card for "reddit_eval_embeddings_luar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | friendshipkim/reddit_eval_embeddings_luar | [
"region:us"
]
| 2023-03-15T16:20:50+00:00 | {"dataset_info": {"features": [{"name": "document_id", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "full_text", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 744168436, "num_examples": 169936}, {"name": "candidates", "num_bytes": 4746252689, "num_examples": 1089648}], "download_size": 4552565188, "dataset_size": 5490421125}} | 2023-03-15T16:25:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reddit_eval_embeddings_luar"
More Information needed | [
"# Dataset Card for \"reddit_eval_embeddings_luar\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reddit_eval_embeddings_luar\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reddit_eval_embeddings_luar\"\n\nMore Information needed"
]
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|
d051cbb6d84cc530e361b2b0a414a6925fcecfb3 | # Dataset Card for "vto_hd_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Abhilashvj/vto_hd_train | [
"region:us"
]
| 2023-03-15T16:37:50+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3926205095.0, "num_examples": 11647}], "download_size": 3925251404, "dataset_size": 3926205095.0}} | 2023-03-15T16:58:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vto_hd_train"
More Information needed | [
"# Dataset Card for \"vto_hd_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vto_hd_train\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vto_hd_train\"\n\nMore Information needed"
]
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1c6736e1ac3ae6ee9843a77c425c03ad52e1565b | # Dataset Card for "bookcorpus_compact_1024_shard0_of_600_hf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_shard0_of_600_hf | [
"region:us"
]
| 2023-03-15T16:52:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "adj", "dtype": {"array2_d": {"shape": [3], "dtype": "int64"}}}, {"name": "adj_shape", "sequence": "int64"}, {"name": "cid_arrangement", "sequence": "int64"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 5322191448, "num_examples": 1024}], "download_size": 1146290630, "dataset_size": 5322191448}} | 2023-03-15T16:56:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_compact_1024_shard0_of_600_hf"
More Information needed | [
"# Dataset Card for \"bookcorpus_compact_1024_shard0_of_600_hf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_compact_1024_shard0_of_600_hf\"\n\nMore Information needed"
]
| [
6,
30
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_1024_shard0_of_600_hf\"\n\nMore Information needed"
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|
89a969d4ad8e51d161971b80ab7cbb06d813c00f |
# SQuALITY-v1.3-flat
A formatted/flat version of [the original](https://huggingface.co/datasets/pszemraj/SQuALITY-v1.3)
--- | pszemraj/SQuALITY-v1.3-flat | [
"task_categories:text2text-generation",
"task_categories:summarization",
"size_categories:1K<n<10K",
"source_datasets:pszemraj/SQuALITY-v1.3",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-03-15T17:07:56+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "source_datasets": "pszemraj/SQuALITY-v1.3", "task_categories": ["text2text-generation", "summarization"]} | 2023-03-15T17:18:21+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text2text-generation #task_categories-summarization #size_categories-1K<n<10K #source_datasets-pszemraj/SQuALITY-v1.3 #language-English #license-apache-2.0 #region-us
|
# SQuALITY-v1.3-flat
A formatted/flat version of the original
--- | [
"# SQuALITY-v1.3-flat\n\nA formatted/flat version of the original\n\n\n---"
]
| [
"TAGS\n#task_categories-text2text-generation #task_categories-summarization #size_categories-1K<n<10K #source_datasets-pszemraj/SQuALITY-v1.3 #language-English #license-apache-2.0 #region-us \n",
"# SQuALITY-v1.3-flat\n\nA formatted/flat version of the original\n\n\n---"
]
| [
71,
20
]
| [
"passage: TAGS\n#task_categories-text2text-generation #task_categories-summarization #size_categories-1K<n<10K #source_datasets-pszemraj/SQuALITY-v1.3 #language-English #license-apache-2.0 #region-us \n# SQuALITY-v1.3-flat\n\nA formatted/flat version of the original\n\n\n---"
]
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|
cde76552512fea15a07624468d9a42418d629ada | # Dataset Card for "instructions-de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-de | [
"region:us"
]
| 2023-03-15T17:38:44+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19823735.602530096, "num_examples": 41903}, {"name": "test", "num_bytes": 521814.1987349521, "num_examples": 1103}, {"name": "validation", "num_bytes": 521814.1987349521, "num_examples": 1103}], "download_size": 12101999, "dataset_size": 20867363.999999996}} | 2023-03-15T17:42:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-de"
More Information needed | [
"# Dataset Card for \"instructions-de\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-de\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-de\"\n\nMore Information needed"
]
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|
e3019971a3a1a53ee2cca84d9e021b78032683c3 | # Dataset Card for "instructions-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-es | [
"region:us"
]
| 2023-03-15T17:39:08+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10931526.154626371, "num_examples": 22947}, {"name": "test", "num_bytes": 287734.42268681433, "num_examples": 604}, {"name": "validation", "num_bytes": 287734.42268681433, "num_examples": 604}], "download_size": 6659541, "dataset_size": 11506995.0}} | 2023-03-15T17:43:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-es"
More Information needed | [
"# Dataset Card for \"instructions-es\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-es\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-es\"\n\nMore Information needed"
]
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|
8d6b55b7bc7b1cf53f62e5ef35ce1d56cd10609e | # Dataset Card for "instructions-fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-fr | [
"region:us"
]
| 2023-03-15T17:39:40+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32499221.675393682, "num_examples": 73841}, {"name": "test", "num_bytes": 855601.7244750926, "num_examples": 1944}, {"name": "validation", "num_bytes": 855161.6001312268, "num_examples": 1943}], "download_size": 19462874, "dataset_size": 34209985.0}} | 2023-03-15T17:45:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-fr"
More Information needed | [
"# Dataset Card for \"instructions-fr\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-fr\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-fr\"\n\nMore Information needed"
]
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|
bcfdad63139ff40253dce4504959b9e8c432b03e | # Dataset Card for "instructions-hi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-hi | [
"region:us"
]
| 2023-03-15T17:45:35+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 47507115.72180105, "num_examples": 49497}, {"name": "test", "num_bytes": 1250616.639099476, "num_examples": 1303}, {"name": "validation", "num_bytes": 1250616.639099476, "num_examples": 1303}], "download_size": 18697342, "dataset_size": 50008349.00000001}} | 2023-03-15T17:46:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-hi"
More Information needed | [
"# Dataset Card for \"instructions-hi\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-hi\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-hi\"\n\nMore Information needed"
]
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|
99c59fef15d1fd24509d64fda9aa21c95d309ed2 | # Dataset Card for "instructions-ja"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-ja | [
"region:us"
]
| 2023-03-15T17:47:05+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27079870.672446616, "num_examples": 55389}, {"name": "test", "num_bytes": 712821.1637766915, "num_examples": 1458}, {"name": "validation", "num_bytes": 712821.1637766915, "num_examples": 1458}], "download_size": 14983193, "dataset_size": 28505513.0}} | 2023-03-15T17:48:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-ja"
More Information needed | [
"# Dataset Card for \"instructions-ja\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-ja\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-ja\"\n\nMore Information needed"
]
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|
4c9e6afc363c99a78202d24765f13c5a44eafef5 | # Dataset Card for "instructions-ms"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-ms | [
"region:us"
]
| 2023-03-15T17:48:24+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18889802.670684632, "num_examples": 40115}, {"name": "test", "num_bytes": 497261.16465768346, "num_examples": 1056}, {"name": "validation", "num_bytes": 497261.16465768346, "num_examples": 1056}], "download_size": 10544795, "dataset_size": 19884324.999999996}} | 2023-03-15T17:49:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-ms"
More Information needed | [
"# Dataset Card for \"instructions-ms\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-ms\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-ms\"\n\nMore Information needed"
]
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|
feb29d4101216f138d114b90d05e817cc66b7cf5 | # Dataset Card for "instructions-pt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-pt | [
"region:us"
]
| 2023-03-15T17:49:45+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26897428.926476642, "num_examples": 57692}, {"name": "test", "num_bytes": 708195.1490556407, "num_examples": 1519}, {"name": "validation", "num_bytes": 707728.9244677172, "num_examples": 1518}], "download_size": 16526868, "dataset_size": 28313353.0}} | 2023-03-15T17:52:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-pt"
More Information needed | [
"# Dataset Card for \"instructions-pt\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-pt\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-pt\"\n\nMore Information needed"
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|
501695d6a35a2de98ccbf70088735d166cf90735 | # Dataset Card for "instructions-ru"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-ru | [
"region:us"
]
| 2023-03-15T17:53:02+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40124473.88375494, "num_examples": 54567}, {"name": "test", "num_bytes": 1055926.5581225299, "num_examples": 1436}, {"name": "validation", "num_bytes": 1055926.5581225299, "num_examples": 1436}], "download_size": 20587872, "dataset_size": 42236327.0}} | 2023-03-15T17:53:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-ru"
More Information needed | [
"# Dataset Card for \"instructions-ru\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-ru\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-ru\"\n\nMore Information needed"
]
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|
d986502a872630ff9aee8283411bedd37d2575ed | # Dataset Card for "instructions-th"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-th | [
"region:us"
]
| 2023-03-15T17:54:12+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38193108.20850597, "num_examples": 35538}, {"name": "test", "num_bytes": 1005930.2516506723, "num_examples": 936}, {"name": "validation", "num_bytes": 1004855.5398433532, "num_examples": 935}], "download_size": 15004408, "dataset_size": 40203893.99999999}} | 2023-03-15T17:54:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-th"
More Information needed | [
"# Dataset Card for \"instructions-th\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-th\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-th\"\n\nMore Information needed"
]
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|
63e91560b0547252f58c8657ead328dab79feb1a | # Dataset Card for "instructions-zh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-zh | [
"region:us"
]
| 2023-03-15T17:55:10+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22155876.092150297, "num_examples": 76886}, {"name": "test", "num_bytes": 583246.536567284, "num_examples": 2024}, {"name": "validation", "num_bytes": 582958.3712824188, "num_examples": 2023}], "download_size": 15122185, "dataset_size": 23322081.0}} | 2023-03-15T17:55:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-zh"
More Information needed | [
"# Dataset Card for \"instructions-zh\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-zh\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-zh\"\n\nMore Information needed"
]
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|
1f2cb698c784f5aceab7807ab23dcb4bc0043746 | # Dataset Card for "instructions-vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-vi | [
"region:us"
]
| 2023-03-15T17:56:05+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25100815.45, "num_examples": 43035}, {"name": "test", "num_bytes": 660839.4075717439, "num_examples": 1133}, {"name": "validation", "num_bytes": 660256.1424282561, "num_examples": 1132}], "download_size": 13126488, "dataset_size": 26421911.0}} | 2023-03-15T17:57:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-vi"
More Information needed | [
"# Dataset Card for \"instructions-vi\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-vi\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-vi\"\n\nMore Information needed"
]
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|
f7806f52b15c2ecadac75f1235626b0a0d5d6765 | # Dataset Card for "msp6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kiringodhwani/msp6 | [
"region:us"
]
| 2023-03-15T18:19:34+00:00 | {"dataset_info": {"features": [{"name": "From", "sequence": "string"}, {"name": "Sent", "sequence": "string"}, {"name": "To", "sequence": "string"}, {"name": "Cc", "sequence": "string"}, {"name": "Subject", "sequence": "string"}, {"name": "Attachment", "sequence": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4847374, "num_examples": 3079}], "download_size": 2173508, "dataset_size": 4847374}} | 2023-03-15T18:19:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "msp6"
More Information needed | [
"# Dataset Card for \"msp6\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"msp6\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"msp6\"\n\nMore Information needed"
]
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|
082a46907a2cac9e7130e927ad2c78e2aa17cb52 | # Dataset Card for "cms_bert-vocab"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lowem1/cms_bert-vocab | [
"region:us"
]
| 2023-03-15T18:42:20+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 143799290, "num_examples": 4125252}], "download_size": 39115915, "dataset_size": 143799290}} | 2023-03-15T18:42:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cms_bert-vocab"
More Information needed | [
"# Dataset Card for \"cms_bert-vocab\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cms_bert-vocab\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cms_bert-vocab\"\n\nMore Information needed"
]
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|
0604b10032a27f46aefc4f8b70705ca448fc1f3a | # Dataset Card for "eurosat-demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | StemGene/eurosat-demo | [
"region:us"
]
| 2023-03-15T20:04:36+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "AnnualCrop", "1": "Forest", "2": "HerbaceousVegetation", "3": "Highway", "4": "Industrial", "5": "Pasture", "6": "PermanentCrop", "7": "Residential", "8": "River", "9": "SeaLake"}}}}], "splits": [{"name": "train", "num_bytes": 92168360.0, "num_examples": 27000}], "download_size": 0, "dataset_size": 92168360.0}} | 2023-03-15T20:20:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "eurosat-demo"
More Information needed | [
"# Dataset Card for \"eurosat-demo\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"eurosat-demo\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"eurosat-demo\"\n\nMore Information needed"
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]
|
41626542fa8065b7561ea652970a907982620f7d |
# Statcan Dialogue Dataset
<div align="center">
[**💻Code**](https://github.com/mcGill-NLP/statcan-dialogue-dataset) | [**📄Paper**](https://arxiv.org/abs/2304.01412) | [**🌐Homepage**](https://mcgill-nlp.github.io/statcan-dialogue-dataset) | [**🤗Huggingface**](https://huggingface.co/datasets/McGill-NLP/statcan-dialogue-dataset) | [**🐦Tweets**](https://twitter.com/xhluca/status/1648728708142727180) | [**📺Video**](https://aclanthology.org/2023.eacl-main.206.mp4) |
| :--: | :--: | :--: | :--: | :--: | :--: |
[**The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents**](https://arxiv.org/abs/2304.01412)\
[*Xing Han Lu*](https://xinghanlu.com), [*Siva Reddy*](https://sivareddy.in), [*Harm de Vries*](https://www.harmdevries.com/)\
EACL 2023
</div>
## Access
To access this dataset, you must read and accept the following terms of use and restrictions, then request access with your academic or professional email.
We will manually review each request. To ensure your request is not rejected, make sure that:
- Your huggingface account is linked to your professional/research website, which we may review to ensure the dataset will be used for the intended purpose
- Your request is made with an academic (e.g. `.edu`) or professional email (e.g. `@servicenow.com`). To do this, your have to set your primary email to your academic/professional email, or create a new Huggingface account.
If your academic institution does not end with `.edu`, or you are part of a professional group that does not have an email address, please contact us (see email in paper).
### Terms of use
Researchers must agree to the following terms:
1. These data represent anonymized (de-identified) data from individuals. Best efforts have been implemented to ensure that all directly and indirectly identifiable information has been removed. Researchers who download this dataset must agree to notify Graeme Gilmour (`graeme.gilmour <at> statcan.gc.ca`) and Harm de Vries (`harm.devries <at> servicenow.com`) if any inadvertently remaining identifiable information is discovered during the process of re-using this dataset. Researchers must agree to destroy any version of this dataset containing identifiable information.
2. The terms of this dataset require that reusers give credit to the creators. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes.
3. Have read and acknowledged the Appendix B (Dataset Card) of the latest version of the paper prior to using the dataset.
### Restrictions
Downloaders cannot:
1. obtain information from the dataset that results in the researcher or any third party(ies) directly or indirectly identifying any participant with the aid of other information acquired elsewhere;
2. produce connections or links among or between the information included in the dataset and other third-party information that could be used to identify any individuals; and
3. extract information from the dataset that could aid researchers (downloaders) in gaining knowledge about or obtaining any means of contacting any individuals already known to the downloader/researcher
## Quickstart
Quickstart code is available in the Readme and on the user guide (see [documentation](https://mcgill-nlp.github.io/statcan-dialogue-dataset/docs)).
## Dataset Card
Please refer to Appendix B of the manuscript.
## Usage on Huggingface `datasets`
It is recommended to use the `statcan-dialogue-dataset` library to access the dataset, which you can install with `pip install statcan-dialogue-dataset` and learn about in the [documentation](https://mcgill-nlp.github.io/statcan-dialogue-dataset/docs).
However, it is possible to load certain files directly on Huggingface `datasets` (however, for other files, you will need to use the `statcan-dialogue-dataset` library):
```python
from datasets import load_dataset
# Load retrieval task data (without bm25 hard negatives)
ds_ret = load_dataset("McGill-NLP/statcan-dialogue-dataset", data_dir="retrieval")
# Load generation task data (without retrieval augmentations)
ds_gen = load_dataset("McGill-NLP/statcan-dialogue-dataset", data_dir="generation")
# Load french version of datasets
ds_ret_fr = load_dataset("McGill-NLP/statcan-dialogue-dataset", data_dir="retrieval_fr")
ds_gen_fr = load_dataset("McGill-NLP/statcan-dialogue-dataset", data_dir="generation_fr")
```
> **IMPORTANT NOTE**: Do not download the content of this repository into `~/.statcan_dialogue_dataset/` as this will cause conflicts with the `statcan-dialogue-dataset` library. As you have noticed, the file names and path are different - the files and directories here have been modified from the original files located in `task_data.zip`. If you need to cache the files, please use the default Huggingface cache directory.
## Citation
If you use our dataset, please cite as follows:
```bibtex
@inproceedings{lu-etal-2023-statcan,
title = "The {S}tat{C}an Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents",
author = "Lu, Xing Han and
Reddy, Siva and
de Vries, Harm",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2304.01412",
pages = "2799--2829",
}
```
| McGill-NLP/statcan-dialogue-dataset | [
"task_categories:conversational",
"task_categories:table-question-answering",
"size_categories:1K<n<10K",
"language:en",
"language:fr",
"arxiv:2304.01412",
"region:us"
]
| 2023-03-15T20:08:40+00:00 | {"language": ["en", "fr"], "size_categories": ["1K<n<10K"], "task_categories": ["conversational", "table-question-answering"], "pretty_name": "Statcan Dialogue Dataset", "extra_gated_prompt": "You agree to not attempt to determine the identity of individuals in this dataset", "extra_gated_fields": {"Full Name": "text", "Affiliation": "text", "Country": "text", "Academic/Work Email Address": "text", "I agree to follow the terms of use": "checkbox", "I have read and will respect the restrictions": "checkbox"}} | 2023-07-28T16:21:42+00:00 | [
"2304.01412"
]
| [
"en",
"fr"
]
| TAGS
#task_categories-conversational #task_categories-table-question-answering #size_categories-1K<n<10K #language-English #language-French #arxiv-2304.01412 #region-us
| Statcan Dialogue Dataset
========================
The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents
*Xing Han Lu*, *Siva Reddy*, *Harm de Vries*
EACL 2023
!Banner Image showing a sample conversation between a user and an agent
Access
------
To access this dataset, you must read and accept the following terms of use and restrictions, then request access with your academic or professional email.
We will manually review each request. To ensure your request is not rejected, make sure that:
* Your huggingface account is linked to your professional/research website, which we may review to ensure the dataset will be used for the intended purpose
* Your request is made with an academic (e.g. '.edu') or professional email (e.g. '@URL'). To do this, your have to set your primary email to your academic/professional email, or create a new Huggingface account.
If your academic institution does not end with '.edu', or you are part of a professional group that does not have an email address, please contact us (see email in paper).
### Terms of use
Researchers must agree to the following terms:
1. These data represent anonymized (de-identified) data from individuals. Best efforts have been implemented to ensure that all directly and indirectly identifiable information has been removed. Researchers who download this dataset must agree to notify Graeme Gilmour ('graeme.gilmour URL') and Harm de Vries ('harm.devries URL') if any inadvertently remaining identifiable information is discovered during the process of re-using this dataset. Researchers must agree to destroy any version of this dataset containing identifiable information.
2. The terms of this dataset require that reusers give credit to the creators. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes.
3. Have read and acknowledged the Appendix B (Dataset Card) of the latest version of the paper prior to using the dataset.
### Restrictions
Downloaders cannot:
1. obtain information from the dataset that results in the researcher or any third party(ies) directly or indirectly identifying any participant with the aid of other information acquired elsewhere;
2. produce connections or links among or between the information included in the dataset and other third-party information that could be used to identify any individuals; and
3. extract information from the dataset that could aid researchers (downloaders) in gaining knowledge about or obtaining any means of contacting any individuals already known to the downloader/researcher
Quickstart
----------
Quickstart code is available in the Readme and on the user guide (see documentation).
Dataset Card
------------
Please refer to Appendix B of the manuscript.
Usage on Huggingface 'datasets'
-------------------------------
It is recommended to use the 'statcan-dialogue-dataset' library to access the dataset, which you can install with 'pip install statcan-dialogue-dataset' and learn about in the documentation.
However, it is possible to load certain files directly on Huggingface 'datasets' (however, for other files, you will need to use the 'statcan-dialogue-dataset' library):
>
> IMPORTANT NOTE: Do not download the content of this repository into '~/.statcan\_dialogue\_dataset/' as this will cause conflicts with the 'statcan-dialogue-dataset' library. As you have noticed, the file names and path are different - the files and directories here have been modified from the original files located in 'task\_data.zip'. If you need to cache the files, please use the default Huggingface cache directory.
>
>
>
If you use our dataset, please cite as follows:
| [
"### Terms of use\n\n\nResearchers must agree to the following terms:\n\n\n1. These data represent anonymized (de-identified) data from individuals. Best efforts have been implemented to ensure that all directly and indirectly identifiable information has been removed. Researchers who download this dataset must agree to notify Graeme Gilmour ('graeme.gilmour URL') and Harm de Vries ('harm.devries URL') if any inadvertently remaining identifiable information is discovered during the process of re-using this dataset. Researchers must agree to destroy any version of this dataset containing identifiable information.\n2. The terms of this dataset require that reusers give credit to the creators. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes.\n3. Have read and acknowledged the Appendix B (Dataset Card) of the latest version of the paper prior to using the dataset.",
"### Restrictions\n\n\nDownloaders cannot:\n\n\n1. obtain information from the dataset that results in the researcher or any third party(ies) directly or indirectly identifying any participant with the aid of other information acquired elsewhere;\n2. produce connections or links among or between the information included in the dataset and other third-party information that could be used to identify any individuals; and\n3. extract information from the dataset that could aid researchers (downloaders) in gaining knowledge about or obtaining any means of contacting any individuals already known to the downloader/researcher\n\n\nQuickstart\n----------\n\n\nQuickstart code is available in the Readme and on the user guide (see documentation).\n\n\nDataset Card\n------------\n\n\nPlease refer to Appendix B of the manuscript.\n\n\nUsage on Huggingface 'datasets'\n-------------------------------\n\n\nIt is recommended to use the 'statcan-dialogue-dataset' library to access the dataset, which you can install with 'pip install statcan-dialogue-dataset' and learn about in the documentation.\n\n\nHowever, it is possible to load certain files directly on Huggingface 'datasets' (however, for other files, you will need to use the 'statcan-dialogue-dataset' library):\n\n\n\n> \n> IMPORTANT NOTE: Do not download the content of this repository into '~/.statcan\\_dialogue\\_dataset/' as this will cause conflicts with the 'statcan-dialogue-dataset' library. As you have noticed, the file names and path are different - the files and directories here have been modified from the original files located in 'task\\_data.zip'. If you need to cache the files, please use the default Huggingface cache directory.\n> \n> \n> \n\n\nIf you use our dataset, please cite as follows:"
]
| [
"TAGS\n#task_categories-conversational #task_categories-table-question-answering #size_categories-1K<n<10K #language-English #language-French #arxiv-2304.01412 #region-us \n",
"### Terms of use\n\n\nResearchers must agree to the following terms:\n\n\n1. These data represent anonymized (de-identified) data from individuals. Best efforts have been implemented to ensure that all directly and indirectly identifiable information has been removed. Researchers who download this dataset must agree to notify Graeme Gilmour ('graeme.gilmour URL') and Harm de Vries ('harm.devries URL') if any inadvertently remaining identifiable information is discovered during the process of re-using this dataset. Researchers must agree to destroy any version of this dataset containing identifiable information.\n2. The terms of this dataset require that reusers give credit to the creators. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes.\n3. Have read and acknowledged the Appendix B (Dataset Card) of the latest version of the paper prior to using the dataset.",
"### Restrictions\n\n\nDownloaders cannot:\n\n\n1. obtain information from the dataset that results in the researcher or any third party(ies) directly or indirectly identifying any participant with the aid of other information acquired elsewhere;\n2. produce connections or links among or between the information included in the dataset and other third-party information that could be used to identify any individuals; and\n3. extract information from the dataset that could aid researchers (downloaders) in gaining knowledge about or obtaining any means of contacting any individuals already known to the downloader/researcher\n\n\nQuickstart\n----------\n\n\nQuickstart code is available in the Readme and on the user guide (see documentation).\n\n\nDataset Card\n------------\n\n\nPlease refer to Appendix B of the manuscript.\n\n\nUsage on Huggingface 'datasets'\n-------------------------------\n\n\nIt is recommended to use the 'statcan-dialogue-dataset' library to access the dataset, which you can install with 'pip install statcan-dialogue-dataset' and learn about in the documentation.\n\n\nHowever, it is possible to load certain files directly on Huggingface 'datasets' (however, for other files, you will need to use the 'statcan-dialogue-dataset' library):\n\n\n\n> \n> IMPORTANT NOTE: Do not download the content of this repository into '~/.statcan\\_dialogue\\_dataset/' as this will cause conflicts with the 'statcan-dialogue-dataset' library. As you have noticed, the file names and path are different - the files and directories here have been modified from the original files located in 'task\\_data.zip'. If you need to cache the files, please use the default Huggingface cache directory.\n> \n> \n> \n\n\nIf you use our dataset, please cite as follows:"
]
| [
61,
220,
402
]
| [
"passage: TAGS\n#task_categories-conversational #task_categories-table-question-answering #size_categories-1K<n<10K #language-English #language-French #arxiv-2304.01412 #region-us \n### Terms of use\n\n\nResearchers must agree to the following terms:\n\n\n1. These data represent anonymized (de-identified) data from individuals. Best efforts have been implemented to ensure that all directly and indirectly identifiable information has been removed. Researchers who download this dataset must agree to notify Graeme Gilmour ('graeme.gilmour URL') and Harm de Vries ('harm.devries URL') if any inadvertently remaining identifiable information is discovered during the process of re-using this dataset. Researchers must agree to destroy any version of this dataset containing identifiable information.\n2. The terms of this dataset require that reusers give credit to the creators. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, even for commercial purposes.\n3. Have read and acknowledged the Appendix B (Dataset Card) of the latest version of the paper prior to using the dataset."
]
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|
c5da3a544cb4a3e850d0f213c4bf9cf4f2e1c23b | This is a dataset of paraphrases created by ChatGPT.
Model based on this dataset is avaible: [model](https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base)
## We used this prompt to generate paraphrases
Generate 5 similar paraphrases for this question, show it like a numbered list without commentaries: *{text}*
This dataset is based on the [Quora paraphrase question](https://www.kaggle.com/competitions/quora-question-pairs), texts from the [SQUAD 2.0](https://huggingface.co/datasets/squad_v2) and the [CNN news dataset](https://huggingface.co/datasets/cnn_dailymail).
We generated 5 paraphrases for each sample, totally this dataset has about 420k data rows. You can make 30 rows from a row from each sample. In this way you can make 12.6 millions train pairs (420k rows with 5 paraphrases -> 6x5x420000 = 12.6 millions of bidirected or 6x5x420000/2 = 6.3 millions of unique pairs).
## We used
- 247138 questions from the Quora dataset
- 91983 texts from the Squad 2.0 dataset
- 80076 texts from the CNN news dataset
## Structure of the dataset
- text column - an original sentence or question from the datasets
- paraphrases - a list of 5 paraphrases
- category - question / sentence
- source - quora / squad_2 / cnn_news
## Legal disclaimer
Data is based on OpenAI’s gpt-3.5-turbo, whose [terms of use](https://openai.com/policies/terms-of-use) prohibit developing models that compete with OpenAI. So if you use this dataset to train a model, don't compete with OpenAI.
### BibTeX entry and citation info
```bibtex
@inproceedings{chatgpt_paraphrases_dataset,
author={Vladimir Vorobev, Maxim Kuznetsov},
title={ChatGPT paraphrases dataset},
year={2023}
}
``` | humarin/chatgpt-paraphrases | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:openrail",
"region:us"
]
| 2023-03-15T20:12:24+00:00 | {"language": ["en"], "license": "openrail", "size_categories": ["100K<n<1M"], "task_categories": ["text2text-generation"]} | 2023-04-05T15:27:16+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text2text-generation #size_categories-100K<n<1M #language-English #license-openrail #region-us
| This is a dataset of paraphrases created by ChatGPT.
Model based on this dataset is avaible: model
## We used this prompt to generate paraphrases
Generate 5 similar paraphrases for this question, show it like a numbered list without commentaries: *{text}*
This dataset is based on the Quora paraphrase question, texts from the SQUAD 2.0 and the CNN news dataset.
We generated 5 paraphrases for each sample, totally this dataset has about 420k data rows. You can make 30 rows from a row from each sample. In this way you can make 12.6 millions train pairs (420k rows with 5 paraphrases -> 6x5x420000 = 12.6 millions of bidirected or 6x5x420000/2 = 6.3 millions of unique pairs).
## We used
- 247138 questions from the Quora dataset
- 91983 texts from the Squad 2.0 dataset
- 80076 texts from the CNN news dataset
## Structure of the dataset
- text column - an original sentence or question from the datasets
- paraphrases - a list of 5 paraphrases
- category - question / sentence
- source - quora / squad_2 / cnn_news
## Legal disclaimer
Data is based on OpenAI’s gpt-3.5-turbo, whose terms of use prohibit developing models that compete with OpenAI. So if you use this dataset to train a model, don't compete with OpenAI.
### BibTeX entry and citation info
| [
"## We used this prompt to generate paraphrases\nGenerate 5 similar paraphrases for this question, show it like a numbered list without commentaries: *{text}*\n\nThis dataset is based on the Quora paraphrase question, texts from the SQUAD 2.0 and the CNN news dataset.\n\nWe generated 5 paraphrases for each sample, totally this dataset has about 420k data rows. You can make 30 rows from a row from each sample. In this way you can make 12.6 millions train pairs (420k rows with 5 paraphrases -> 6x5x420000 = 12.6 millions of bidirected or 6x5x420000/2 = 6.3 millions of unique pairs).",
"## We used\n- 247138 questions from the Quora dataset \n- 91983 texts from the Squad 2.0 dataset\n- 80076 texts from the CNN news dataset",
"## Structure of the dataset\n- text column - an original sentence or question from the datasets\n- paraphrases - a list of 5 paraphrases\n- category - question / sentence\n- source - quora / squad_2 / cnn_news",
"## Legal disclaimer\n\nData is based on OpenAI’s gpt-3.5-turbo, whose terms of use prohibit developing models that compete with OpenAI. So if you use this dataset to train a model, don't compete with OpenAI.",
"### BibTeX entry and citation info"
]
| [
"TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-English #license-openrail #region-us \n",
"## We used this prompt to generate paraphrases\nGenerate 5 similar paraphrases for this question, show it like a numbered list without commentaries: *{text}*\n\nThis dataset is based on the Quora paraphrase question, texts from the SQUAD 2.0 and the CNN news dataset.\n\nWe generated 5 paraphrases for each sample, totally this dataset has about 420k data rows. You can make 30 rows from a row from each sample. In this way you can make 12.6 millions train pairs (420k rows with 5 paraphrases -> 6x5x420000 = 12.6 millions of bidirected or 6x5x420000/2 = 6.3 millions of unique pairs).",
"## We used\n- 247138 questions from the Quora dataset \n- 91983 texts from the Squad 2.0 dataset\n- 80076 texts from the CNN news dataset",
"## Structure of the dataset\n- text column - an original sentence or question from the datasets\n- paraphrases - a list of 5 paraphrases\n- category - question / sentence\n- source - quora / squad_2 / cnn_news",
"## Legal disclaimer\n\nData is based on OpenAI’s gpt-3.5-turbo, whose terms of use prohibit developing models that compete with OpenAI. So if you use this dataset to train a model, don't compete with OpenAI.",
"### BibTeX entry and citation info"
]
| [
41,
163,
36,
57,
55,
11
]
| [
"passage: TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-English #license-openrail #region-us \n## We used this prompt to generate paraphrases\nGenerate 5 similar paraphrases for this question, show it like a numbered list without commentaries: *{text}*\n\nThis dataset is based on the Quora paraphrase question, texts from the SQUAD 2.0 and the CNN news dataset.\n\nWe generated 5 paraphrases for each sample, totally this dataset has about 420k data rows. You can make 30 rows from a row from each sample. In this way you can make 12.6 millions train pairs (420k rows with 5 paraphrases -> 6x5x420000 = 12.6 millions of bidirected or 6x5x420000/2 = 6.3 millions of unique pairs).## We used\n- 247138 questions from the Quora dataset \n- 91983 texts from the Squad 2.0 dataset\n- 80076 texts from the CNN news dataset## Structure of the dataset\n- text column - an original sentence or question from the datasets\n- paraphrases - a list of 5 paraphrases\n- category - question / sentence\n- source - quora / squad_2 / cnn_news## Legal disclaimer\n\nData is based on OpenAI’s gpt-3.5-turbo, whose terms of use prohibit developing models that compete with OpenAI. So if you use this dataset to train a model, don't compete with OpenAI.### BibTeX entry and citation info"
]
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|
b9ca8f74eb21c5b71188f8066beaa8017b15cc53 | # Dataset Card for "processed_gpt_dataset_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sanagnos/processed_gpt_dataset_small | [
"region:us"
]
| 2023-03-15T20:16:07+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 11145751500.0, "num_examples": 14289425}], "download_size": 3620004230, "dataset_size": 11145751500.0}} | 2023-03-15T20:34:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed_gpt_dataset_small"
More Information needed | [
"# Dataset Card for \"processed_gpt_dataset_small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_gpt_dataset_small\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_gpt_dataset_small\"\n\nMore Information needed"
]
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|
823e30f04f37101bebfb6b0eef63425b79fa1494 | # Dataset Card for "commoncrawl-tr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | musabg/commoncrawl-tr | [
"region:us"
]
| 2023-03-15T20:40:03+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "meta", "struct": [{"name": "warc_headers", "struct": [{"name": "warc-record-id", "dtype": "string"}, {"name": "warc-date", "dtype": "string"}, {"name": "content-type", "dtype": "string"}, {"name": "content-length", "dtype": "int32"}, {"name": "warc-type", "dtype": "string"}, {"name": "warc-identified-content-language", "dtype": "string"}, {"name": "warc-refers-to", "dtype": "string"}, {"name": "warc-target-uri", "dtype": "string"}, {"name": "warc-block-digest", "dtype": "string"}]}, {"name": "identification", "struct": [{"name": "label", "dtype": "string"}, {"name": "prob", "dtype": "float32"}]}, {"name": "harmful_pp", "dtype": "float32"}, {"name": "tlsh", "dtype": "string"}, {"name": "quality_warnings", "sequence": "string"}, {"name": "categories", "sequence": "string"}, {"name": "sentence_identifications", "list": [{"name": "label", "dtype": "string"}, {"name": "prob", "dtype": "float32"}]}]}], "splits": [{"name": "train", "num_bytes": 85952224217, "num_examples": 13327165}], "download_size": 46952332972, "dataset_size": 85952224217}} | 2023-05-09T19:04:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "commoncrawl-tr"
More Information needed | [
"# Dataset Card for \"commoncrawl-tr\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"commoncrawl-tr\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"commoncrawl-tr\"\n\nMore Information needed"
]
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|
9f91373b8b63faf6b097810eaa13a7e9a16ec489 | # Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | forgeml/test | [
"region:us"
]
| 2023-03-15T20:55:28+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "unsplash_query", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 67148083.0, "num_examples": 5}], "download_size": 67080826, "dataset_size": 67148083.0}} | 2023-03-15T21:09:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test"
More Information needed | [
"# Dataset Card for \"test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test\"\n\nMore Information needed"
]
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|
c8d8322e9e75b8c81eb9b31eb4f4ad9a420f6dbb | # Dataset Card for "reklamation24_wasser-strom-gas-intent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_wasser-strom-gas-intent | [
"region:us"
]
| 2023-03-15T21:32:44+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 203230, "num_examples": 383}, {"name": "test", "num_bytes": 52516, "num_examples": 96}], "download_size": 142247, "dataset_size": 255746}} | 2023-03-15T22:09:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_wasser-strom-gas-intent"
More Information needed | [
"# Dataset Card for \"reklamation24_wasser-strom-gas-intent\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_wasser-strom-gas-intent\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_wasser-strom-gas-intent\"\n\nMore Information needed"
]
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|
16e30a8361840573d318cb605dadfdf743889eb1 | # Dataset Card for "reklamation24_haus-reinigung-intent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_haus-reinigung-intent | [
"region:us"
]
| 2023-03-15T21:33:29+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 199635, "num_examples": 395}, {"name": "test", "num_bytes": 54472, "num_examples": 99}], "download_size": 140834, "dataset_size": 254107}} | 2023-03-15T22:10:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_haus-reinigung-intent"
More Information needed | [
"# Dataset Card for \"reklamation24_haus-reinigung-intent\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_haus-reinigung-intent\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_haus-reinigung-intent\"\n\nMore Information needed"
]
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|
8d898d4c7320a514d02ec4077bffe3720d26575d | # Dataset Card for "reklamation24_reisen-tourismus-intent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_reisen-tourismus-intent | [
"region:us"
]
| 2023-03-15T21:36:11+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 209133, "num_examples": 389}, {"name": "test", "num_bytes": 53081, "num_examples": 98}], "download_size": 0, "dataset_size": 262214}} | 2023-03-15T21:45:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_reisen-tourismus-intent"
More Information needed | [
"# Dataset Card for \"reklamation24_reisen-tourismus-intent\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_reisen-tourismus-intent\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_reisen-tourismus-intent\"\n\nMore Information needed"
]
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|
b22e9282fa64a077cd772330a0f97d8a49d2b617 | # Dataset Card for "reklamation24_schoenheit-wellness-intent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_schoenheit-wellness-intent | [
"region:us"
]
| 2023-03-15T21:36:55+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178584, "num_examples": 397}, {"name": "test", "num_bytes": 47435, "num_examples": 100}], "download_size": 127871, "dataset_size": 226019}} | 2023-03-15T21:37:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_schoenheit-wellness-intent"
More Information needed | [
"# Dataset Card for \"reklamation24_schoenheit-wellness-intent\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_schoenheit-wellness-intent\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_schoenheit-wellness-intent\"\n\nMore Information needed"
]
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|
6ea7db23de2b188ebc838e68deb5d37c0d623b73 | # Dataset Card for "reklamation24_medizin-gesundheit-pflege-intent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_medizin-gesundheit-pflege-intent | [
"region:us"
]
| 2023-03-15T21:38:41+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 177283, "num_examples": 396}, {"name": "test", "num_bytes": 44274, "num_examples": 99}], "download_size": 0, "dataset_size": 221557}} | 2023-03-15T21:42:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_medizin-gesundheit-pflege-intent"
More Information needed | [
"# Dataset Card for \"reklamation24_medizin-gesundheit-pflege-intent\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_medizin-gesundheit-pflege-intent\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_medizin-gesundheit-pflege-intent\"\n\nMore Information needed"
]
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|
2ac635825f0c8b14be7885963f083170807f34da | # Dataset Card for "reklamation24_oeffentlichkeit-soziales-intent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_oeffentlichkeit-soziales-intent | [
"region:us"
]
| 2023-03-15T21:40:26+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 84032, "num_examples": 153}, {"name": "test", "num_bytes": 19855, "num_examples": 39}], "download_size": 62925, "dataset_size": 103887}} | 2023-03-15T21:40:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_oeffentlichkeit-soziales-intent"
More Information needed | [
"# Dataset Card for \"reklamation24_oeffentlichkeit-soziales-intent\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_oeffentlichkeit-soziales-intent\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_oeffentlichkeit-soziales-intent\"\n\nMore Information needed"
]
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|
e35bd036eba4b2713b496dd042c6449ef856859b | # Dataset Card for "reklamation24_oeffentlicher-verkehr-vermietung-intent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_oeffentlicher-verkehr-vermietung-intent | [
"region:us"
]
| 2023-03-15T21:41:20+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 168477, "num_examples": 336}, {"name": "test", "num_bytes": 41899, "num_examples": 84}], "download_size": 125985, "dataset_size": 210376}} | 2023-03-15T21:41:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_oeffentlicher-verkehr-vermietung-intent"
More Information needed | [
"# Dataset Card for \"reklamation24_oeffentlicher-verkehr-vermietung-intent\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_oeffentlicher-verkehr-vermietung-intent\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_oeffentlicher-verkehr-vermietung-intent\"\n\nMore Information needed"
]
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|
6109a101cf7b44c265a317a0e3b5fbd38d8da934 | # Dataset Card for "NewArOCRDatasetv3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gagan3012/NewArOCRDatasetv3 | [
"region:us"
]
| 2023-03-15T21:59:46+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 804663442.224, "num_examples": 45856}, {"name": "validation", "num_bytes": 14180587.0, "num_examples": 425}, {"name": "test", "num_bytes": 13690842.0, "num_examples": 425}], "download_size": 727818407, "dataset_size": 832534871.224}} | 2023-03-17T06:35:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "NewArOCRDatasetv3"
More Information needed | [
"# Dataset Card for \"NewArOCRDatasetv3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"NewArOCRDatasetv3\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"NewArOCRDatasetv3\"\n\nMore Information needed"
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|
a8b90c71efe610ed0b24a62550b7e5841e0cbcd0 | # Dataset Card for "construction_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yiming19/construction_test | [
"region:us"
]
| 2023-03-15T22:24:24+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 109249653.0, "num_examples": 13}], "download_size": 6553556, "dataset_size": 109249653.0}} | 2023-03-15T22:24:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "construction_test"
More Information needed | [
"# Dataset Card for \"construction_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"construction_test\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"construction_test\"\n\nMore Information needed"
]
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|
4409472eb8ada3ef4762a9902d63495073530454 | # Dataset Card for "littrans"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/littrans | [
"region:us"
]
| 2023-03-15T22:25:14+00:00 | {"dataset_info": {"features": [{"name": "language", "dtype": "string"}, {"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 15589995, "num_examples": 30842}], "download_size": 10860458, "dataset_size": 15589995}} | 2023-03-27T16:01:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "littrans"
More Information needed | [
"# Dataset Card for \"littrans\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"littrans\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"littrans\"\n\nMore Information needed"
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|
1bc6d0e89e82dc47f916fc5b0020c996d93b5826 | # Dataset Card for "wmt22_en_pt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sdesai/wmt22_en_pt | [
"region:us"
]
| 2023-03-15T23:10:24+00:00 | {"dataset_info": {"features": [{"name": "translation", "struct": [{"name": "en", "dtype": "string"}, {"name": "pt", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 72508.36524300442, "num_examples": 760}, {"name": "test", "num_bytes": 38925.543446244475, "num_examples": 408}, {"name": "validation", "num_bytes": 18127.091310751104, "num_examples": 190}], "download_size": 69750, "dataset_size": 129561.0}} | 2023-03-16T00:29:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wmt22_en_pt"
More Information needed | [
"# Dataset Card for \"wmt22_en_pt\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wmt22_en_pt\"\n\nMore Information needed"
]
| [
6,
17
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wmt22_en_pt\"\n\nMore Information needed"
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|
26d916486f359eb946277800efce69d099da95b9 | # Dataset Card for "wikipedia_citations"
Sample usage:
```
simple = load_dataset("ola13/wikipedia_citations", split="train", language="simple", date="20230301")
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ola13/wikipedia_citations | [
"region:us"
]
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| []
| TAGS
#region-us
| # Dataset Card for "wikipedia_citations"
Sample usage:
More Information needed | [
"# Dataset Card for \"wikipedia_citations\"\n\nSample usage:\n\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia_citations\"\n\nSample usage:\n\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia_citations\"\n\nSample usage:\n\n\nMore Information needed"
]
| [
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|
542c7bec750caa0d121b70003c37d3928200d51b | # Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | TofuNumber1/github-issues | [
"region:us"
]
| 2023-03-16T01:28:22+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", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "creator", "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": "open_issues", "dtype": "int64"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "due_on", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}]}, {"name": "comments", "sequence": "string"}, {"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": "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": "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": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 25290, "num_examples": 10}], "download_size": 76375, "dataset_size": 25290}} | 2023-03-16T01:28:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "github-issues"
More Information needed | [
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
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|
99ea303581e841faedbaf3b2093b473d8e70138a | # Dataset Card for "avril15s02-blip-datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ethers/avril15s02-blip-datasets | [
"region:us"
]
| 2023-03-16T02:42:09+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 121735720.0, "num_examples": 323}], "download_size": 121734461, "dataset_size": 121735720.0}} | 2023-03-16T02:44:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "avril15s02-blip-datasets"
More Information needed | [
"# Dataset Card for \"avril15s02-blip-datasets\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"avril15s02-blip-datasets\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"avril15s02-blip-datasets\"\n\nMore Information needed"
]
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|
1a181e9cf4a0ba2138f9706b56edd6576a5d4adc | # Dataset Card for "163762AASRnoDiacs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohammedNasri/163762AASRnoDiacs | [
"region:us"
]
| 2023-03-16T03:50:01+00:00 | {"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 147266551704, "num_examples": 153322}, {"name": "test", "num_bytes": 10027423544, "num_examples": 10440}], "download_size": 23754042731, "dataset_size": 157293975248}} | 2023-03-16T04:30:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "163762AASRnoDiacs"
More Information needed | [
"# Dataset Card for \"163762AASRnoDiacs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"163762AASRnoDiacs\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"163762AASRnoDiacs\"\n\nMore Information needed"
]
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|
892e57ac0ad931f5bdb4517b833c257ba150fa15 | # GuanacoDataset
**News: We're heading towards multimodal VQA, with blip2-flan-t5-xxl Alignment to Guannaco 7B LLM.**
Still under construction: [GuanacoVQA weight](https://huggingface.co/JosephusCheung/GuanacoVQA) & [GuanacoVQA Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoVQADataset)
**Notice: Effective immediately, the Guanaco and its associated dataset are now licensed under the GPLv3.**
Released weights:
- [Guanaco α](https://huggingface.co/JosephusCheung/Guanaco)
The dataset for the [Guanaco model](https://guanaco-model.github.io/) is designed to enhance the multilingual capabilities and address various linguistic tasks. It builds upon the 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. The dataset comprises a total of 534,530 entries, generated at a low cost of $6K.
- Free chat dialogues without System input: 32,880 entries (recent update) - in English zh-Hans zh-Hant-TW Japanese Deutsch
*To test 0-shot tasks of Japanese & Deutsch on original 175 tasks with finetuning on chat only.*
- Chat dialogues with System input: 16,087 entries (recent update) - in English zh-Hans zh-Hant-TW zh-Hant-HK
**A new additional dataset is released, separated and larger dataset is available for different languages.**
The original 175 tasks were translated into 4 versions and regenerated independently:
Below is the details of **mixed data**:
- Japanese (Ja-JP - recently updated) 7,485 entries
- Simplified Chinese (zh-Hans): 27,808 entries
- Traditional Chinese (Taiwan) (zh-Hant-TW): 21,481 entries
- Traditional Chinese (Hong Kong) (zh-Hant-HK): 19247 entries
- English: 20K+ entries, not from Alpaca
Besides, a mini version of 52K multi-lang dataset is released with:
- Japanese (Ja-JP - recently updated) 7,485 entries
- Simplified Chinese (zh-Hans): 5,439 entries
- Traditional Chinese (Taiwan) (zh-Hant-TW): 9,322 entries
- Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,954 entries
- English: 20,024 entries, not from Alpaca
The mini version is included in the full non-chat dataset.
**Additional dataset** *separated by language （temporary）*:
*This additional dataset should only be used for additional training if using mixed data did not yield good results. Using it directly will not produce good results.*
This part of the data will be merged into the main dataset at the appropriate time.
- Chinese: 117,166 entries
- Simplified Chinese (zh-Hans): 92,530 entries
- Traditional Chinese (Taiwan) (zh-Hant-TW): 14,802 entries
- Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,834 entries
- Japanese (Ja-JP - recently updated) 60,772 entries
In addition to the language-specific tasks, the dataset includes new tasks that aim to improve the model's performance in English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. These new tasks ensure that the Guanaco model is well-rounded and capable of handling a wide range of challenges in the field of natural language processing.
By incorporating this diverse and comprehensive dataset into the Guanaco model, we aim to provide researchers and academics with a powerful tool for studying instruction-following language models in a multilingual context. The dataset's design encourages the development of more robust and versatile models capable of addressing complex linguistic tasks across different languages and domains.
**Additional dataset** *Paper/General-QA*:
The Paper/General-QA dataset is a collection of questions and answers constructed for AI-generated papers or general texts in English, Chinese, Japanese, and German. The question dataset contains 106,707 questions, and the answer dataset contains 99,292 answers. The purpose of this dataset is to generate paragraph-level answers to questions posed about lengthy documents such as PDFs. Similar questions are combined to form a tree-like structure, and graph theory algorithms are used to process user questions, content summaries, and contextual logic.
*It is worth noting that some ChatGPT applications claim to be able to read PDFs, but they do not actually read the entire article. Instead, they compare the user's input question with segmented paragraphs of the article, select the most similar paragraph, and insert it as the answer. This is not true language model reading, but rather a form of deception.*
**Note: I intentionally mixed up entries and languages to prevent anyone from solely selecting certain language entries for finetuning. This is not only unhelpful for the community, but also because some tasks are 0-shot in specific languages, please use the complete dataset for finetuning.**
## To-Do List:
- Expand language support in the dataset:
Incorporate additional languages such as Japanese, German, and more into the dataset. This expansion should include task examples that cover advanced grammar analysis and dialogue understanding for these languages.
- Create a dialogue-oriented Chatbot dataset:
Develop a dataset specifically designed for conversation-based applications, containing examples that facilitate the model's ability to engage in interactive and dynamic dialogues with users.
- Add Toolformer-supporting tasks:
Introduce tasks that train the model to autonomously call external APIs using Toolformer, allowing the model to access and utilize various web services and data sources, thereby enhancing its problem-solving capabilities.
- Develop tasks for rapid integration of external knowledge:
Design tasks that encourage the model to quickly incorporate knowledge from external sources such as search engines and artificial intelligence knowledge engines. These tasks would be particularly beneficial for smaller models with limited knowledge reserves, enabling them to efficiently utilize external information to respond to user queries.
## Recent News
We've noticed a recent entrant in the field, the QLoRa method, which we find concerning due to its attempt to piggyback on the reputation of Guanaco. We strongly disapprove of such practices. QLoRa, as far as we can tell, lacks mathematical robustness and its performance significantly trails behind that of GPTQ and advancements such as PEFT fine-tuning, which have been successful in improving upon it.
Guanaco has been diligent, consistently releasing multilingual datasets since March 2023, along with publishing weights that are not only an enhanced version of GPTQ but also support multimodal VQA and have been optimized for 4-bit. Despite the substantial financial investment of tens of thousands of dollars in distilling data from OpenAI's GPT models, we still consider these efforts to be incremental.
We, however, aim to move beyond the incremental:
1. We strive to no longer rely on distillation data from OpenAI: We've found that relying on GPT-generated data impedes significant breakthroughs. Furthermore, this approach has proven to be disastrous when dealing with the imbalances in multilingual tasks.
2. We're focusing on the enhancement of quantization structure and partial native 4-bit fine-tuning: We are deeply appreciative of the GPTQ-Llama project for paving the way in state-of-the-art LLM quantization. Its unique qualities, especially at the 7B size, are facilitating significant progress in multilingual and multimodal tasks.
3. We plan to utilize visual data to adjust our language models: We believe this will fundamentally address the issues of language imbalance, translation inaccuracies, and the lack of graphical logic in LLM.
While our work is still in the early stages, we're determined to break new ground in these areas. Our critique of QLoRa's practices does not stem from animosity but rather from the fundamental belief that innovation should be rooted in originality, integrity, and substantial progress.
| JosephusCheung/GuanacoDataset | [
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:conversational",
"language:zh",
"language:en",
"language:ja",
"language:de",
"license:gpl-3.0",
"alpaca",
"llama",
"guanaco",
"doi:10.57967/hf/1423",
"region:us"
]
| 2023-03-16T06:30:22+00:00 | {"language": ["zh", "en", "ja", "de"], "license": "gpl-3.0", "task_categories": ["text-generation", "question-answering", "conversational"], "tags": ["alpaca", "llama", "guanaco"]} | 2023-05-29T11:50:05+00:00 | []
| [
"zh",
"en",
"ja",
"de"
]
| TAGS
#task_categories-text-generation #task_categories-question-answering #task_categories-conversational #language-Chinese #language-English #language-Japanese #language-German #license-gpl-3.0 #alpaca #llama #guanaco #doi-10.57967/hf/1423 #region-us
| # GuanacoDataset
News: We're heading towards multimodal VQA, with blip2-flan-t5-xxl Alignment to Guannaco 7B LLM.
Still under construction: GuanacoVQA weight & GuanacoVQA Dataset
Notice: Effective immediately, the Guanaco and its associated dataset are now licensed under the GPLv3.
Released weights:
- Guanaco α
The dataset for the Guanaco model is designed to enhance the multilingual capabilities and address various linguistic tasks. It builds upon the 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. The dataset comprises a total of 534,530 entries, generated at a low cost of $6K.
- Free chat dialogues without System input: 32,880 entries (recent update) - in English zh-Hans zh-Hant-TW Japanese Deutsch
*To test 0-shot tasks of Japanese & Deutsch on original 175 tasks with finetuning on chat only.*
- Chat dialogues with System input: 16,087 entries (recent update) - in English zh-Hans zh-Hant-TW zh-Hant-HK
A new additional dataset is released, separated and larger dataset is available for different languages.
The original 175 tasks were translated into 4 versions and regenerated independently:
Below is the details of mixed data:
- Japanese (Ja-JP - recently updated) 7,485 entries
- Simplified Chinese (zh-Hans): 27,808 entries
- Traditional Chinese (Taiwan) (zh-Hant-TW): 21,481 entries
- Traditional Chinese (Hong Kong) (zh-Hant-HK): 19247 entries
- English: 20K+ entries, not from Alpaca
Besides, a mini version of 52K multi-lang dataset is released with:
- Japanese (Ja-JP - recently updated) 7,485 entries
- Simplified Chinese (zh-Hans): 5,439 entries
- Traditional Chinese (Taiwan) (zh-Hant-TW): 9,322 entries
- Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,954 entries
- English: 20,024 entries, not from Alpaca
The mini version is included in the full non-chat dataset.
Additional dataset *separated by language （temporary）*:
*This additional dataset should only be used for additional training if using mixed data did not yield good results. Using it directly will not produce good results.*
This part of the data will be merged into the main dataset at the appropriate time.
- Chinese: 117,166 entries
- Simplified Chinese (zh-Hans): 92,530 entries
- Traditional Chinese (Taiwan) (zh-Hant-TW): 14,802 entries
- Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,834 entries
- Japanese (Ja-JP - recently updated) 60,772 entries
In addition to the language-specific tasks, the dataset includes new tasks that aim to improve the model's performance in English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. These new tasks ensure that the Guanaco model is well-rounded and capable of handling a wide range of challenges in the field of natural language processing.
By incorporating this diverse and comprehensive dataset into the Guanaco model, we aim to provide researchers and academics with a powerful tool for studying instruction-following language models in a multilingual context. The dataset's design encourages the development of more robust and versatile models capable of addressing complex linguistic tasks across different languages and domains.
Additional dataset *Paper/General-QA*:
The Paper/General-QA dataset is a collection of questions and answers constructed for AI-generated papers or general texts in English, Chinese, Japanese, and German. The question dataset contains 106,707 questions, and the answer dataset contains 99,292 answers. The purpose of this dataset is to generate paragraph-level answers to questions posed about lengthy documents such as PDFs. Similar questions are combined to form a tree-like structure, and graph theory algorithms are used to process user questions, content summaries, and contextual logic.
*It is worth noting that some ChatGPT applications claim to be able to read PDFs, but they do not actually read the entire article. Instead, they compare the user's input question with segmented paragraphs of the article, select the most similar paragraph, and insert it as the answer. This is not true language model reading, but rather a form of deception.*
Note: I intentionally mixed up entries and languages to prevent anyone from solely selecting certain language entries for finetuning. This is not only unhelpful for the community, but also because some tasks are 0-shot in specific languages, please use the complete dataset for finetuning.
## To-Do List:
- Expand language support in the dataset:
Incorporate additional languages such as Japanese, German, and more into the dataset. This expansion should include task examples that cover advanced grammar analysis and dialogue understanding for these languages.
- Create a dialogue-oriented Chatbot dataset:
Develop a dataset specifically designed for conversation-based applications, containing examples that facilitate the model's ability to engage in interactive and dynamic dialogues with users.
- Add Toolformer-supporting tasks:
Introduce tasks that train the model to autonomously call external APIs using Toolformer, allowing the model to access and utilize various web services and data sources, thereby enhancing its problem-solving capabilities.
- Develop tasks for rapid integration of external knowledge:
Design tasks that encourage the model to quickly incorporate knowledge from external sources such as search engines and artificial intelligence knowledge engines. These tasks would be particularly beneficial for smaller models with limited knowledge reserves, enabling them to efficiently utilize external information to respond to user queries.
## Recent News
We've noticed a recent entrant in the field, the QLoRa method, which we find concerning due to its attempt to piggyback on the reputation of Guanaco. We strongly disapprove of such practices. QLoRa, as far as we can tell, lacks mathematical robustness and its performance significantly trails behind that of GPTQ and advancements such as PEFT fine-tuning, which have been successful in improving upon it.
Guanaco has been diligent, consistently releasing multilingual datasets since March 2023, along with publishing weights that are not only an enhanced version of GPTQ but also support multimodal VQA and have been optimized for 4-bit. Despite the substantial financial investment of tens of thousands of dollars in distilling data from OpenAI's GPT models, we still consider these efforts to be incremental.
We, however, aim to move beyond the incremental:
1. We strive to no longer rely on distillation data from OpenAI: We've found that relying on GPT-generated data impedes significant breakthroughs. Furthermore, this approach has proven to be disastrous when dealing with the imbalances in multilingual tasks.
2. We're focusing on the enhancement of quantization structure and partial native 4-bit fine-tuning: We are deeply appreciative of the GPTQ-Llama project for paving the way in state-of-the-art LLM quantization. Its unique qualities, especially at the 7B size, are facilitating significant progress in multilingual and multimodal tasks.
3. We plan to utilize visual data to adjust our language models: We believe this will fundamentally address the issues of language imbalance, translation inaccuracies, and the lack of graphical logic in LLM.
While our work is still in the early stages, we're determined to break new ground in these areas. Our critique of QLoRa's practices does not stem from animosity but rather from the fundamental belief that innovation should be rooted in originality, integrity, and substantial progress.
| [
"# GuanacoDataset\n\nNews: We're heading towards multimodal VQA, with blip2-flan-t5-xxl Alignment to Guannaco 7B LLM.\n\nStill under construction: GuanacoVQA weight & GuanacoVQA Dataset \n\nNotice: Effective immediately, the Guanaco and its associated dataset are now licensed under the GPLv3.\n\nReleased weights: \n\n- Guanaco α \n\nThe dataset for the Guanaco model is designed to enhance the multilingual capabilities and address various linguistic tasks. It builds upon the 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. The dataset comprises a total of 534,530 entries, generated at a low cost of $6K.\n\n- Free chat dialogues without System input: 32,880 entries (recent update) - in English zh-Hans zh-Hant-TW Japanese Deutsch\n\n *To test 0-shot tasks of Japanese & Deutsch on original 175 tasks with finetuning on chat only.*\n\n- Chat dialogues with System input: 16,087 entries (recent update) - in English zh-Hans zh-Hant-TW zh-Hant-HK\n\nA new additional dataset is released, separated and larger dataset is available for different languages.\n\nThe original 175 tasks were translated into 4 versions and regenerated independently:\n\nBelow is the details of mixed data:\n\n- Japanese (Ja-JP - recently updated) 7,485 entries\n\n- Simplified Chinese (zh-Hans): 27,808 entries\n\n- Traditional Chinese (Taiwan) (zh-Hant-TW): 21,481 entries\n\n- Traditional Chinese (Hong Kong) (zh-Hant-HK): 19247 entries\n\n- English: 20K+ entries, not from Alpaca\n\nBesides, a mini version of 52K multi-lang dataset is released with:\n\n- Japanese (Ja-JP - recently updated) 7,485 entries\n\n- Simplified Chinese (zh-Hans): 5,439 entries\n\n- Traditional Chinese (Taiwan) (zh-Hant-TW): 9,322 entries\n\n- Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,954 entries\n\n- English: 20,024 entries, not from Alpaca\n\nThe mini version is included in the full non-chat dataset.\n\nAdditional dataset *separated by language （temporary）*:\n\n*This additional dataset should only be used for additional training if using mixed data did not yield good results. Using it directly will not produce good results.*\n\nThis part of the data will be merged into the main dataset at the appropriate time.\n\n- Chinese: 117,166 entries\n - Simplified Chinese (zh-Hans): 92,530 entries\n - Traditional Chinese (Taiwan) (zh-Hant-TW): 14,802 entries\n - Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,834 entries\n\n- Japanese (Ja-JP - recently updated) 60,772 entries\n\nIn addition to the language-specific tasks, the dataset includes new tasks that aim to improve the model's performance in English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. These new tasks ensure that the Guanaco model is well-rounded and capable of handling a wide range of challenges in the field of natural language processing.\n\nBy incorporating this diverse and comprehensive dataset into the Guanaco model, we aim to provide researchers and academics with a powerful tool for studying instruction-following language models in a multilingual context. The dataset's design encourages the development of more robust and versatile models capable of addressing complex linguistic tasks across different languages and domains.\n\nAdditional dataset *Paper/General-QA*:\n\nThe Paper/General-QA dataset is a collection of questions and answers constructed for AI-generated papers or general texts in English, Chinese, Japanese, and German. The question dataset contains 106,707 questions, and the answer dataset contains 99,292 answers. The purpose of this dataset is to generate paragraph-level answers to questions posed about lengthy documents such as PDFs. Similar questions are combined to form a tree-like structure, and graph theory algorithms are used to process user questions, content summaries, and contextual logic.\n\n*It is worth noting that some ChatGPT applications claim to be able to read PDFs, but they do not actually read the entire article. Instead, they compare the user's input question with segmented paragraphs of the article, select the most similar paragraph, and insert it as the answer. This is not true language model reading, but rather a form of deception.*\n\nNote: I intentionally mixed up entries and languages to prevent anyone from solely selecting certain language entries for finetuning. This is not only unhelpful for the community, but also because some tasks are 0-shot in specific languages, please use the complete dataset for finetuning.",
"## To-Do List:\n\n- Expand language support in the dataset:\n \n Incorporate additional languages such as Japanese, German, and more into the dataset. This expansion should include task examples that cover advanced grammar analysis and dialogue understanding for these languages.\n\n- Create a dialogue-oriented Chatbot dataset: \n\n Develop a dataset specifically designed for conversation-based applications, containing examples that facilitate the model's ability to engage in interactive and dynamic dialogues with users.\n\n- Add Toolformer-supporting tasks: \n\n Introduce tasks that train the model to autonomously call external APIs using Toolformer, allowing the model to access and utilize various web services and data sources, thereby enhancing its problem-solving capabilities.\n\n- Develop tasks for rapid integration of external knowledge: \n\n Design tasks that encourage the model to quickly incorporate knowledge from external sources such as search engines and artificial intelligence knowledge engines. These tasks would be particularly beneficial for smaller models with limited knowledge reserves, enabling them to efficiently utilize external information to respond to user queries.",
"## Recent News\n\nWe've noticed a recent entrant in the field, the QLoRa method, which we find concerning due to its attempt to piggyback on the reputation of Guanaco. We strongly disapprove of such practices. QLoRa, as far as we can tell, lacks mathematical robustness and its performance significantly trails behind that of GPTQ and advancements such as PEFT fine-tuning, which have been successful in improving upon it.\n\nGuanaco has been diligent, consistently releasing multilingual datasets since March 2023, along with publishing weights that are not only an enhanced version of GPTQ but also support multimodal VQA and have been optimized for 4-bit. Despite the substantial financial investment of tens of thousands of dollars in distilling data from OpenAI's GPT models, we still consider these efforts to be incremental.\n\nWe, however, aim to move beyond the incremental:\n\n1. We strive to no longer rely on distillation data from OpenAI: We've found that relying on GPT-generated data impedes significant breakthroughs. Furthermore, this approach has proven to be disastrous when dealing with the imbalances in multilingual tasks.\n\n2. We're focusing on the enhancement of quantization structure and partial native 4-bit fine-tuning: We are deeply appreciative of the GPTQ-Llama project for paving the way in state-of-the-art LLM quantization. Its unique qualities, especially at the 7B size, are facilitating significant progress in multilingual and multimodal tasks.\n\n3. We plan to utilize visual data to adjust our language models: We believe this will fundamentally address the issues of language imbalance, translation inaccuracies, and the lack of graphical logic in LLM.\n\nWhile our work is still in the early stages, we're determined to break new ground in these areas. Our critique of QLoRa's practices does not stem from animosity but rather from the fundamental belief that innovation should be rooted in originality, integrity, and substantial progress."
]
| [
"TAGS\n#task_categories-text-generation #task_categories-question-answering #task_categories-conversational #language-Chinese #language-English #language-Japanese #language-German #license-gpl-3.0 #alpaca #llama #guanaco #doi-10.57967/hf/1423 #region-us \n",
"# GuanacoDataset\n\nNews: We're heading towards multimodal VQA, with blip2-flan-t5-xxl Alignment to Guannaco 7B LLM.\n\nStill under construction: GuanacoVQA weight & GuanacoVQA Dataset \n\nNotice: Effective immediately, the Guanaco and its associated dataset are now licensed under the GPLv3.\n\nReleased weights: \n\n- Guanaco α \n\nThe dataset for the Guanaco model is designed to enhance the multilingual capabilities and address various linguistic tasks. It builds upon the 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. The dataset comprises a total of 534,530 entries, generated at a low cost of $6K.\n\n- Free chat dialogues without System input: 32,880 entries (recent update) - in English zh-Hans zh-Hant-TW Japanese Deutsch\n\n *To test 0-shot tasks of Japanese & Deutsch on original 175 tasks with finetuning on chat only.*\n\n- Chat dialogues with System input: 16,087 entries (recent update) - in English zh-Hans zh-Hant-TW zh-Hant-HK\n\nA new additional dataset is released, separated and larger dataset is available for different languages.\n\nThe original 175 tasks were translated into 4 versions and regenerated independently:\n\nBelow is the details of mixed data:\n\n- Japanese (Ja-JP - recently updated) 7,485 entries\n\n- Simplified Chinese (zh-Hans): 27,808 entries\n\n- Traditional Chinese (Taiwan) (zh-Hant-TW): 21,481 entries\n\n- Traditional Chinese (Hong Kong) (zh-Hant-HK): 19247 entries\n\n- English: 20K+ entries, not from Alpaca\n\nBesides, a mini version of 52K multi-lang dataset is released with:\n\n- Japanese (Ja-JP - recently updated) 7,485 entries\n\n- Simplified Chinese (zh-Hans): 5,439 entries\n\n- Traditional Chinese (Taiwan) (zh-Hant-TW): 9,322 entries\n\n- Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,954 entries\n\n- English: 20,024 entries, not from Alpaca\n\nThe mini version is included in the full non-chat dataset.\n\nAdditional dataset *separated by language （temporary）*:\n\n*This additional dataset should only be used for additional training if using mixed data did not yield good results. Using it directly will not produce good results.*\n\nThis part of the data will be merged into the main dataset at the appropriate time.\n\n- Chinese: 117,166 entries\n - Simplified Chinese (zh-Hans): 92,530 entries\n - Traditional Chinese (Taiwan) (zh-Hant-TW): 14,802 entries\n - Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,834 entries\n\n- Japanese (Ja-JP - recently updated) 60,772 entries\n\nIn addition to the language-specific tasks, the dataset includes new tasks that aim to improve the model's performance in English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. These new tasks ensure that the Guanaco model is well-rounded and capable of handling a wide range of challenges in the field of natural language processing.\n\nBy incorporating this diverse and comprehensive dataset into the Guanaco model, we aim to provide researchers and academics with a powerful tool for studying instruction-following language models in a multilingual context. The dataset's design encourages the development of more robust and versatile models capable of addressing complex linguistic tasks across different languages and domains.\n\nAdditional dataset *Paper/General-QA*:\n\nThe Paper/General-QA dataset is a collection of questions and answers constructed for AI-generated papers or general texts in English, Chinese, Japanese, and German. The question dataset contains 106,707 questions, and the answer dataset contains 99,292 answers. The purpose of this dataset is to generate paragraph-level answers to questions posed about lengthy documents such as PDFs. Similar questions are combined to form a tree-like structure, and graph theory algorithms are used to process user questions, content summaries, and contextual logic.\n\n*It is worth noting that some ChatGPT applications claim to be able to read PDFs, but they do not actually read the entire article. Instead, they compare the user's input question with segmented paragraphs of the article, select the most similar paragraph, and insert it as the answer. This is not true language model reading, but rather a form of deception.*\n\nNote: I intentionally mixed up entries and languages to prevent anyone from solely selecting certain language entries for finetuning. This is not only unhelpful for the community, but also because some tasks are 0-shot in specific languages, please use the complete dataset for finetuning.",
"## To-Do List:\n\n- Expand language support in the dataset:\n \n Incorporate additional languages such as Japanese, German, and more into the dataset. This expansion should include task examples that cover advanced grammar analysis and dialogue understanding for these languages.\n\n- Create a dialogue-oriented Chatbot dataset: \n\n Develop a dataset specifically designed for conversation-based applications, containing examples that facilitate the model's ability to engage in interactive and dynamic dialogues with users.\n\n- Add Toolformer-supporting tasks: \n\n Introduce tasks that train the model to autonomously call external APIs using Toolformer, allowing the model to access and utilize various web services and data sources, thereby enhancing its problem-solving capabilities.\n\n- Develop tasks for rapid integration of external knowledge: \n\n Design tasks that encourage the model to quickly incorporate knowledge from external sources such as search engines and artificial intelligence knowledge engines. These tasks would be particularly beneficial for smaller models with limited knowledge reserves, enabling them to efficiently utilize external information to respond to user queries.",
"## Recent News\n\nWe've noticed a recent entrant in the field, the QLoRa method, which we find concerning due to its attempt to piggyback on the reputation of Guanaco. We strongly disapprove of such practices. QLoRa, as far as we can tell, lacks mathematical robustness and its performance significantly trails behind that of GPTQ and advancements such as PEFT fine-tuning, which have been successful in improving upon it.\n\nGuanaco has been diligent, consistently releasing multilingual datasets since March 2023, along with publishing weights that are not only an enhanced version of GPTQ but also support multimodal VQA and have been optimized for 4-bit. Despite the substantial financial investment of tens of thousands of dollars in distilling data from OpenAI's GPT models, we still consider these efforts to be incremental.\n\nWe, however, aim to move beyond the incremental:\n\n1. We strive to no longer rely on distillation data from OpenAI: We've found that relying on GPT-generated data impedes significant breakthroughs. Furthermore, this approach has proven to be disastrous when dealing with the imbalances in multilingual tasks.\n\n2. We're focusing on the enhancement of quantization structure and partial native 4-bit fine-tuning: We are deeply appreciative of the GPTQ-Llama project for paving the way in state-of-the-art LLM quantization. Its unique qualities, especially at the 7B size, are facilitating significant progress in multilingual and multimodal tasks.\n\n3. We plan to utilize visual data to adjust our language models: We believe this will fundamentally address the issues of language imbalance, translation inaccuracies, and the lack of graphical logic in LLM.\n\nWhile our work is still in the early stages, we're determined to break new ground in these areas. Our critique of QLoRa's practices does not stem from animosity but rather from the fundamental belief that innovation should be rooted in originality, integrity, and substantial progress."
]
| [
88,
1171,
235,
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| [
"passage: TAGS\n#task_categories-text-generation #task_categories-question-answering #task_categories-conversational #language-Chinese #language-English #language-Japanese #language-German #license-gpl-3.0 #alpaca #llama #guanaco #doi-10.57967/hf/1423 #region-us \n",
"passage: # GuanacoDataset\n\nNews: We're heading towards multimodal VQA, with blip2-flan-t5-xxl Alignment to Guannaco 7B LLM.\n\nStill under construction: GuanacoVQA weight & GuanacoVQA Dataset \n\nNotice: Effective immediately, the Guanaco and its associated dataset are now licensed under the GPLv3.\n\nReleased weights: \n\n- Guanaco α \n\nThe dataset for the Guanaco model is designed to enhance the multilingual capabilities and address various linguistic tasks. It builds upon the 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. The dataset comprises a total of 534,530 entries, generated at a low cost of $6K.\n\n- Free chat dialogues without System input: 32,880 entries (recent update) - in English zh-Hans zh-Hant-TW Japanese Deutsch\n\n *To test 0-shot tasks of Japanese & Deutsch on original 175 tasks with finetuning on chat only.*\n\n- Chat dialogues with System input: 16,087 entries (recent update) - in English zh-Hans zh-Hant-TW zh-Hant-HK\n\nA new additional dataset is released, separated and larger dataset is available for different languages.\n\nThe original 175 tasks were translated into 4 versions and regenerated independently:\n\nBelow is the details of mixed data:\n\n- Japanese (Ja-JP - recently updated) 7,485 entries\n\n- Simplified Chinese (zh-Hans): 27,808 entries\n\n- Traditional Chinese (Taiwan) (zh-Hant-TW): 21,481 entries\n\n- Traditional Chinese (Hong Kong) (zh-Hant-HK): 19247 entries\n\n- English: 20K+ entries, not from Alpaca\n\nBesides, a mini version of 52K multi-lang dataset is released with:\n\n- Japanese (Ja-JP - recently updated) 7,485 entries\n\n- Simplified Chinese (zh-Hans): 5,439 entries\n\n- Traditional Chinese (Taiwan) (zh-Hant-TW): 9,322 entries\n\n- Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,954 entries\n\n- English: 20,024 entries, not from Alpaca\n\nThe mini version is included in the full non-chat dataset.\n\nAdditional dataset *separated by language （temporary）*:\n\n*This additional dataset should only be used for additional training if using mixed data did not yield good results. Using it directly will not produce good results.*\n\nThis part of the data will be merged into the main dataset at the appropriate time.\n\n- Chinese: 117,166 entries\n - Simplified Chinese (zh-Hans): 92,530 entries\n - Traditional Chinese (Taiwan) (zh-Hant-TW): 14,802 entries\n - Traditional Chinese (Hong Kong) (zh-Hant-HK): 9,834 entries\n\n- Japanese (Ja-JP - recently updated) 60,772 entries\n\nIn addition to the language-specific tasks, the dataset includes new tasks that aim to improve the model's performance in English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. These new tasks ensure that the Guanaco model is well-rounded and capable of handling a wide range of challenges in the field of natural language processing.\n\nBy incorporating this diverse and comprehensive dataset into the Guanaco model, we aim to provide researchers and academics with a powerful tool for studying instruction-following language models in a multilingual context. The dataset's design encourages the development of more robust and versatile models capable of addressing complex linguistic tasks across different languages and domains.\n\nAdditional dataset *Paper/General-QA*:\n\nThe Paper/General-QA dataset is a collection of questions and answers constructed for AI-generated papers or general texts in English, Chinese, Japanese, and German. The question dataset contains 106,707 questions, and the answer dataset contains 99,292 answers. The purpose of this dataset is to generate paragraph-level answers to questions posed about lengthy documents such as PDFs. Similar questions are combined to form a tree-like structure, and graph theory algorithms are used to process user questions, content summaries, and contextual logic.\n\n*It is worth noting that some ChatGPT applications claim to be able to read PDFs, but they do not actually read the entire article. Instead, they compare the user's input question with segmented paragraphs of the article, select the most similar paragraph, and insert it as the answer. This is not true language model reading, but rather a form of deception.*\n\nNote: I intentionally mixed up entries and languages to prevent anyone from solely selecting certain language entries for finetuning. This is not only unhelpful for the community, but also because some tasks are 0-shot in specific languages, please use the complete dataset for finetuning.## To-Do List:\n\n- Expand language support in the dataset:\n \n Incorporate additional languages such as Japanese, German, and more into the dataset. This expansion should include task examples that cover advanced grammar analysis and dialogue understanding for these languages.\n\n- Create a dialogue-oriented Chatbot dataset: \n\n Develop a dataset specifically designed for conversation-based applications, containing examples that facilitate the model's ability to engage in interactive and dynamic dialogues with users.\n\n- Add Toolformer-supporting tasks: \n\n Introduce tasks that train the model to autonomously call external APIs using Toolformer, allowing the model to access and utilize various web services and data sources, thereby enhancing its problem-solving capabilities.\n\n- Develop tasks for rapid integration of external knowledge: \n\n Design tasks that encourage the model to quickly incorporate knowledge from external sources such as search engines and artificial intelligence knowledge engines. These tasks would be particularly beneficial for smaller models with limited knowledge reserves, enabling them to efficiently utilize external information to respond to user queries."
]
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|
1e21da49419c4cb1e04b142daf4f438dcda8d1d5 |
# Dataset Card for SAIL 2017
### Dataset Summary
The dataset was a part of Shared Task on Sentiment Analysis in Indian Languages (SAIL) Tweets. It was presented in FIRE 2017.
### Languages
Code-Mixed sentences in English and Hindi
### Source Data
http://amitavadas.com/SAIL/data.html
#### Initial Data Collection and Normalization
All the data from the source is collected and cleaned. Punctuations, Special characters and Emoticons are removed. | ashishkmr2094/sail_lid | [
"task_categories:token-classification",
"size_categories:10K<n<100K",
"region:us"
]
| 2023-03-16T06:47:45+00:00 | {"size_categories": ["10K<n<100K"], "task_categories": ["token-classification"]} | 2023-03-16T07:19:20+00:00 | []
| []
| TAGS
#task_categories-token-classification #size_categories-10K<n<100K #region-us
|
# Dataset Card for SAIL 2017
### Dataset Summary
The dataset was a part of Shared Task on Sentiment Analysis in Indian Languages (SAIL) Tweets. It was presented in FIRE 2017.
### Languages
Code-Mixed sentences in English and Hindi
### Source Data
URL
#### Initial Data Collection and Normalization
All the data from the source is collected and cleaned. Punctuations, Special characters and Emoticons are removed. | [
"# Dataset Card for SAIL 2017",
"### Dataset Summary\n\nThe dataset was a part of Shared Task on Sentiment Analysis in Indian Languages (SAIL) Tweets. It was presented in FIRE 2017.",
"### Languages\n\nCode-Mixed sentences in English and Hindi",
"### Source Data\n\nURL",
"#### Initial Data Collection and Normalization\n\nAll the data from the source is collected and cleaned. Punctuations, Special characters and Emoticons are removed."
]
| [
"TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #region-us \n",
"# Dataset Card for SAIL 2017",
"### Dataset Summary\n\nThe dataset was a part of Shared Task on Sentiment Analysis in Indian Languages (SAIL) Tweets. It was presented in FIRE 2017.",
"### Languages\n\nCode-Mixed sentences in English and Hindi",
"### Source Data\n\nURL",
"#### Initial Data Collection and Normalization\n\nAll the data from the source is collected and cleaned. Punctuations, Special characters and Emoticons are removed."
]
| [
30,
8,
41,
15,
5,
36
]
| [
"passage: TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #region-us \n# Dataset Card for SAIL 2017### Dataset Summary\n\nThe dataset was a part of Shared Task on Sentiment Analysis in Indian Languages (SAIL) Tweets. It was presented in FIRE 2017.### Languages\n\nCode-Mixed sentences in English and Hindi### Source Data\n\nURL#### Initial Data Collection and Normalization\n\nAll the data from the source is collected and cleaned. Punctuations, Special characters and Emoticons are removed."
]
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Images automatically labelled by the Batch Indexing Machine, under development | Circularmachines/batch_indexed_parts | [
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"region:us"
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| 2023-03-16T07:45:04+00:00 | {"license": "cc-by-sa-4.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0000", "1": "0001", "2": "0002", "3": "0003", "4": "0004", "5": "0005", "6": "0006", "7": "0007", "8": "0008", "9": "0009", "10": "0010", "11": "0011", "12": "0012", "13": "0013", "14": "0014", "15": "0015", "16": "0016", "17": "0017", "18": "0018", "19": "0019", "20": "0020", "21": "0021", "22": "0022", "23": "0023", "24": "0024", "25": "0025", "26": "0026", "27": "0027", "28": "0028", "29": "0029", "30": "0030", "31": "0031", "32": "0032", "33": "0033", "34": "0034", "35": "0035", "36": "0036", "37": "0037", "38": "0038", "39": "0039", "40": "0040", "41": "0041", "42": "0042", "43": "0043", "44": "0044", "45": "0045", "46": "0046", "47": "0047", "48": "0048", "49": "0049", "50": "0050", "51": "0051", "52": "0052", "53": "0053"}}}}], "splits": [{"name": "train", "num_bytes": 482130063.0, "num_examples": 27000}, {"name": "test", "num_bytes": 121531.0, "num_examples": 5}], "download_size": 477594580, "dataset_size": 482251594.0}} | 2023-03-16T12:38:21+00:00 | []
| []
| TAGS
#license-cc-by-sa-4.0 #region-us
|
Images automatically labelled by the Batch Indexing Machine, under development | []
| [
"TAGS\n#license-cc-by-sa-4.0 #region-us \n"
]
| [
17
]
| [
"passage: TAGS\n#license-cc-by-sa-4.0 #region-us \n"
]
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|
5ff12907c4635d1e1875d0875ad97ebcdb983497 |
Dataset generated using handwritten fonts
=========================================
Number of images: 2634473
Sources:
* [Handwriting generation code](https://github.com/NastyBoget/HandwritingGeneration)
The code was executed with `hkr` option (with fewer augmentations)
| nastyboget/synthetic_hkr_large | [
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"language:ru",
"license:mit",
"region:us"
]
| 2023-03-16T08:07:12+00:00 | {"language": ["ru"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["image-to-text"]} | 2023-03-20T10:16:20+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us
|
Dataset generated using handwritten fonts
=========================================
Number of images: 2634473
Sources:
* Handwriting generation code
The code was executed with 'hkr' option (with fewer augmentations)
| []
| [
"TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n"
]
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|
2b93c5aaed6c8db320e8f46fa9cd9f45ed04bfa8 | # What for?
Well, it's just for test. | AyTsao/datasetTest | [
"license:mit",
"region:us"
]
| 2023-03-16T08:33:46+00:00 | {"license": "mit"} | 2023-03-16T08:41:21+00:00 | []
| []
| TAGS
#license-mit #region-us
| # What for?
Well, it's just for test. | [
"# What for?\nWell, it's just for test."
]
| [
"TAGS\n#license-mit #region-us \n",
"# What for?\nWell, it's just for test."
]
| [
11,
13
]
| [
"passage: TAGS\n#license-mit #region-us \n# What for?\nWell, it's just for test."
]
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|
705d9cfe9c852489f196fe3f4a51b0846bed38e3 |
# Mnist-Ambiguous
This dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.
Robust and uncertainty-aware DNNs should thus detect and flag these issues.
### Features
Same as mnist, the supervised dataset has an `image` (28x28 int array) and a `label` (int).
Additionally, the following features are exposed for your convenience:
- `text_label` (str): A textual representation of the probabilistic label, e.g. `p(0)=0.54, p(5)=0.46`
- `p_label` (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)
- `is_ambiguous` (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)
### Splits
We provide four splits:
- `test`: 10'000 ambiguous images
- `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
- `test_mixed`: 20'000 images, consisting of the (shuffled) concatenation of our ambiguous `test` set and the nominal mnist test set by LeCun et. al.,
- `train_mixed`: 70'000 images, consisting of the (shuffled) concatenation of our ambiguous `training` and the nominal training set.
Note that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
the training set images allow for more unbalanced ambiguity.
This is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.
For research targeting explicitly aleatoric uncertainty, we recommend training the model using `train_mixed`.
Otherwise, our `test` set will lead to both epistemic and aleatoric uncertainty.
In related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.
### Assessment and Validity
For a brief discussion of the strength and weaknesses of this dataset,
including a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.
### Paper
Pre-print here: [https://arxiv.org/abs/2207.10495](https://arxiv.org/abs/2207.10495)
Citation:
```
@misc{https://doi.org/10.48550/arxiv.2207.10495,
doi = {10.48550/ARXIV.2207.10495},
url = {https://arxiv.org/abs/2207.10495},
author = {Weiss, Michael and Gómez, André García and Tonella, Paolo},
title = {A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity},
publisher = {arXiv},
year = {2022}
}
```
### License
As this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license.
| mweiss/mnist_ambiguous | [
"task_categories:image-classification",
"annotations_creators:machine-generated",
"size_categories:10K<n<100K",
"source_datasets:extended|mnist",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:2207.10495",
"region:us"
]
| 2023-03-16T08:44:53+00:00 | {"annotations_creators": ["machine-generated"], "language": ["en"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "source_datasets": ["extended|mnist"], "task_categories": ["image-classification"], "pretty_name": "mnist_ambigous"} | 2023-03-16T19:12:38+00:00 | [
"2207.10495"
]
| [
"en"
]
| TAGS
#task_categories-image-classification #annotations_creators-machine-generated #size_categories-10K<n<100K #source_datasets-extended|mnist #language-English #license-cc-by-sa-3.0 #arxiv-2207.10495 #region-us
|
# Mnist-Ambiguous
This dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.
Robust and uncertainty-aware DNNs should thus detect and flag these issues.
### Features
Same as mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).
Additionally, the following features are exposed for your convenience:
- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(0)=0.54, p(5)=0.46'
- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)
- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)
### Splits
We provide four splits:
- 'test': 10'000 ambiguous images
- 'train': 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
- 'test_mixed': 20'000 images, consisting of the (shuffled) concatenation of our ambiguous 'test' set and the nominal mnist test set by LeCun et. al.,
- 'train_mixed': 70'000 images, consisting of the (shuffled) concatenation of our ambiguous 'training' and the nominal training set.
Note that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
the training set images allow for more unbalanced ambiguity.
This is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.
For research targeting explicitly aleatoric uncertainty, we recommend training the model using 'train_mixed'.
Otherwise, our 'test' set will lead to both epistemic and aleatoric uncertainty.
In related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.
### Assessment and Validity
For a brief discussion of the strength and weaknesses of this dataset,
including a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.
### Paper
Pre-print here: URL
Citation:
### License
As this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license.
| [
"# Mnist-Ambiguous\n\nThis dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.\nRobust and uncertainty-aware DNNs should thus detect and flag these issues.",
"### Features\nSame as mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).\n\nAdditionally, the following features are exposed for your convenience:\n\n- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(0)=0.54, p(5)=0.46' \n- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)\n- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)",
"### Splits\nWe provide four splits:\n\n- 'test': 10'000 ambiguous images\n- 'train': 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.\n- 'test_mixed': 20'000 images, consisting of the (shuffled) concatenation of our ambiguous 'test' set and the nominal mnist test set by LeCun et. al.,\n- 'train_mixed': 70'000 images, consisting of the (shuffled) concatenation of our ambiguous 'training' and the nominal training set.\n\nNote that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods), \nthe training set images allow for more unbalanced ambiguity. \nThis is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.\n\nFor research targeting explicitly aleatoric uncertainty, we recommend training the model using 'train_mixed'. \nOtherwise, our 'test' set will lead to both epistemic and aleatoric uncertainty. \nIn related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.",
"### Assessment and Validity\nFor a brief discussion of the strength and weaknesses of this dataset, \nincluding a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.",
"### Paper\nPre-print here: URL\n\nCitation:",
"### License\nAs this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license."
]
| [
"TAGS\n#task_categories-image-classification #annotations_creators-machine-generated #size_categories-10K<n<100K #source_datasets-extended|mnist #language-English #license-cc-by-sa-3.0 #arxiv-2207.10495 #region-us \n",
"# Mnist-Ambiguous\n\nThis dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.\nRobust and uncertainty-aware DNNs should thus detect and flag these issues.",
"### Features\nSame as mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).\n\nAdditionally, the following features are exposed for your convenience:\n\n- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(0)=0.54, p(5)=0.46' \n- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)\n- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)",
"### Splits\nWe provide four splits:\n\n- 'test': 10'000 ambiguous images\n- 'train': 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.\n- 'test_mixed': 20'000 images, consisting of the (shuffled) concatenation of our ambiguous 'test' set and the nominal mnist test set by LeCun et. al.,\n- 'train_mixed': 70'000 images, consisting of the (shuffled) concatenation of our ambiguous 'training' and the nominal training set.\n\nNote that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods), \nthe training set images allow for more unbalanced ambiguity. \nThis is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.\n\nFor research targeting explicitly aleatoric uncertainty, we recommend training the model using 'train_mixed'. \nOtherwise, our 'test' set will lead to both epistemic and aleatoric uncertainty. \nIn related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.",
"### Assessment and Validity\nFor a brief discussion of the strength and weaknesses of this dataset, \nincluding a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.",
"### Paper\nPre-print here: URL\n\nCitation:",
"### License\nAs this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license."
]
| [
78,
62,
155,
301,
53,
12,
35
]
| [
"passage: TAGS\n#task_categories-image-classification #annotations_creators-machine-generated #size_categories-10K<n<100K #source_datasets-extended|mnist #language-English #license-cc-by-sa-3.0 #arxiv-2207.10495 #region-us \n# Mnist-Ambiguous\n\nThis dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.\nRobust and uncertainty-aware DNNs should thus detect and flag these issues.### Features\nSame as mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).\n\nAdditionally, the following features are exposed for your convenience:\n\n- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(0)=0.54, p(5)=0.46' \n- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)\n- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)"
]
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|
04661f5f459cf124313e9cc4c1ed90a98dd9c4a5 | # Dataset Card for "transfer_1.2_wave2vec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hts98/transfer_1.2_wave2vec | [
"region:us"
]
| 2023-03-16T10:20:45+00:00 | {"dataset_info": {"features": [{"name": "input_length", "dtype": "int64"}, {"name": "input_values", "sequence": "float32"}, {"name": "labels", "sequence": "int64"}, {"name": "labels_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 5756279160, "num_examples": 3420}, {"name": "test", "num_bytes": 1438032944, "num_examples": 856}], "download_size": 7187743680, "dataset_size": 7194312104}} | 2023-03-16T11:02:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "transfer_1.2_wave2vec"
More Information needed | [
"# Dataset Card for \"transfer_1.2_wave2vec\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"transfer_1.2_wave2vec\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"transfer_1.2_wave2vec\"\n\nMore Information needed"
]
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|
aa62550cabea7fc06f82ce891f31f53c06fe2137 | # Dataset Card for "jerrylewisfa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/jerrylewisfa | [
"region:us"
]
| 2023-03-16T11:39:29+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "jerrylewisfa"
More Information needed | [
"# Dataset Card for \"jerrylewisfa\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"jerrylewisfa\"\n\nMore Information needed"
]
| [
6,
15
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"jerrylewisfa\"\n\nMore Information needed"
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|
fb24b9a81b860d12c545f49773dda7cc22bf57c4 |
This dataset is an adaptation of the [Stanford Alpaca dataset](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json) in order to turn a text generation model like GPT-J into an "instruct" model.
The initial dataset was slightly reworked in order to match the GPT-J fine-tuning format with Mesh Transformer Jax on TPUs. | nlpcloud/instructions-dataset-adapted-from-stanford-alpaca-for-gpt-j | [
"license:gpl-3.0",
"region:us"
]
| 2023-03-16T11:42:32+00:00 | {"license": "gpl-3.0"} | 2023-03-16T12:18:55+00:00 | []
| []
| TAGS
#license-gpl-3.0 #region-us
|
This dataset is an adaptation of the Stanford Alpaca dataset in order to turn a text generation model like GPT-J into an "instruct" model.
The initial dataset was slightly reworked in order to match the GPT-J fine-tuning format with Mesh Transformer Jax on TPUs. | []
| [
"TAGS\n#license-gpl-3.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-gpl-3.0 #region-us \n"
]
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|
e7cea071c6d36192847f4317b83e4442ade5cde2 | # Dataset Card for "tonymontana"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/tonymontana | [
"region:us"
]
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| []
| TAGS
#region-us
| # Dataset Card for "tonymontana"
More Information needed | [
"# Dataset Card for \"tonymontana\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tonymontana\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tonymontana\"\n\nMore Information needed"
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|
2a333896ebb96e7bd425571c5b8402ddb4f2becd |
# Fashion-Mnist-Ambiguous
This dataset contains fashion-mnist-like images, but with an unclear ground truth. For each image, there are two classes that could be considered true.
Robust and uncertainty-aware DNNs should thus detect and flag these issues.
### Features
Same as fashion-mnist, the supervised dataset has an `image` (28x28 int array) and a `label` (int).
Additionally, the following features are exposed for your convenience:
- `text_label` (str): A textual representation of the probabilistic label, e.g. `p(Pullover)=0.54, p(Shirt)=0.46`
- `p_label` (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)
- `is_ambiguous` (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)
### Splits
We provide four splits:
- `test`: 10'000 ambiguous images
- `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
- `test_mixed`: 20'000 images, consisting of the (shuffled) concatenation of our ambiguous `test` set and the nominal *original* fashion mnist test set
- `train_mixed`: 70'000 images, consisting of the (shuffled) concatenation of our ambiguous `training` and the nominal training set.
Note that the ambiguous train images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
the training set images allow for more unbalanced ambiguity.
This is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.
For research targeting explicitly aleatoric uncertainty, we recommend training the model using `train_mixed`.
Otherwise, our `test` set will lead to both epistemic and aleatoric uncertainty.
In related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.
### Assessment and Validity
For a brief discussion of the strength and weaknesses of this dataset we refer to our paper.
Please note that our images are not typically realistic -
i.e., while they represent multiple classes and thus have an ambiguous ground truth, they do not resemble real-world photographs.
### Paper
Pre-print here: [https://arxiv.org/abs/2207.10495](https://arxiv.org/abs/2207.10495)
Citation:
```
@misc{https://doi.org/10.48550/arxiv.2207.10495,
doi = {10.48550/ARXIV.2207.10495},
url = {https://arxiv.org/abs/2207.10495},
author = {Weiss, Michael and Gómez, André García and Tonella, Paolo},
title = {A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity},
publisher = {arXiv},
year = {2022}
}
```
### Related Datasets
- Ambiguous Mnist Dataset: [https://huggingface.co/datasets/mweiss/mnist_ambiguous](https://huggingface.co/datasets/mweiss/mnist_ambiguous)
- Corrupted Fashion-Mnist Dataset: [https://huggingface.co/datasets/mweiss/fashion_mnist_corrupted](https://huggingface.co/datasets/mweiss/fashion_mnist_corrupted)
| mweiss/fashion_mnist_ambiguous | [
"task_categories:image-classification",
"annotations_creators:machine-generated",
"size_categories:10K<n<100K",
"source_datasets:extended|mnist",
"language:en",
"license:mit",
"arxiv:2207.10495",
"region:us"
]
| 2023-03-16T12:22:41+00:00 | {"annotations_creators": ["machine-generated"], "language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "source_datasets": ["extended|mnist"], "task_categories": ["image-classification"], "pretty_name": "mnist_ambigous"} | 2023-03-16T12:43:23+00:00 | [
"2207.10495"
]
| [
"en"
]
| TAGS
#task_categories-image-classification #annotations_creators-machine-generated #size_categories-10K<n<100K #source_datasets-extended|mnist #language-English #license-mit #arxiv-2207.10495 #region-us
|
# Fashion-Mnist-Ambiguous
This dataset contains fashion-mnist-like images, but with an unclear ground truth. For each image, there are two classes that could be considered true.
Robust and uncertainty-aware DNNs should thus detect and flag these issues.
### Features
Same as fashion-mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).
Additionally, the following features are exposed for your convenience:
- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(Pullover)=0.54, p(Shirt)=0.46'
- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)
- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)
### Splits
We provide four splits:
- 'test': 10'000 ambiguous images
- 'train': 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
- 'test_mixed': 20'000 images, consisting of the (shuffled) concatenation of our ambiguous 'test' set and the nominal *original* fashion mnist test set
- 'train_mixed': 70'000 images, consisting of the (shuffled) concatenation of our ambiguous 'training' and the nominal training set.
Note that the ambiguous train images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
the training set images allow for more unbalanced ambiguity.
This is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.
For research targeting explicitly aleatoric uncertainty, we recommend training the model using 'train_mixed'.
Otherwise, our 'test' set will lead to both epistemic and aleatoric uncertainty.
In related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.
### Assessment and Validity
For a brief discussion of the strength and weaknesses of this dataset we refer to our paper.
Please note that our images are not typically realistic -
i.e., while they represent multiple classes and thus have an ambiguous ground truth, they do not resemble real-world photographs.
### Paper
Pre-print here: URL
Citation:
### Related Datasets
- Ambiguous Mnist Dataset: URL
- Corrupted Fashion-Mnist Dataset: URL
| [
"# Fashion-Mnist-Ambiguous\n\nThis dataset contains fashion-mnist-like images, but with an unclear ground truth. For each image, there are two classes that could be considered true.\nRobust and uncertainty-aware DNNs should thus detect and flag these issues.",
"### Features\nSame as fashion-mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).\n\nAdditionally, the following features are exposed for your convenience:\n\n- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(Pullover)=0.54, p(Shirt)=0.46' \n- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)\n- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)",
"### Splits\nWe provide four splits:\n\n- 'test': 10'000 ambiguous images\n- 'train': 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.\n- 'test_mixed': 20'000 images, consisting of the (shuffled) concatenation of our ambiguous 'test' set and the nominal *original* fashion mnist test set\n- 'train_mixed': 70'000 images, consisting of the (shuffled) concatenation of our ambiguous 'training' and the nominal training set.\n\nNote that the ambiguous train images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods), \nthe training set images allow for more unbalanced ambiguity. \nThis is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.\n\nFor research targeting explicitly aleatoric uncertainty, we recommend training the model using 'train_mixed'. \nOtherwise, our 'test' set will lead to both epistemic and aleatoric uncertainty. \nIn related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.",
"### Assessment and Validity\nFor a brief discussion of the strength and weaknesses of this dataset we refer to our paper.\nPlease note that our images are not typically realistic - \ni.e., while they represent multiple classes and thus have an ambiguous ground truth, they do not resemble real-world photographs.",
"### Paper\nPre-print here: URL\n\nCitation:",
"### Related Datasets\n- Ambiguous Mnist Dataset: URL\n- Corrupted Fashion-Mnist Dataset: URL"
]
| [
"TAGS\n#task_categories-image-classification #annotations_creators-machine-generated #size_categories-10K<n<100K #source_datasets-extended|mnist #language-English #license-mit #arxiv-2207.10495 #region-us \n",
"# Fashion-Mnist-Ambiguous\n\nThis dataset contains fashion-mnist-like images, but with an unclear ground truth. For each image, there are two classes that could be considered true.\nRobust and uncertainty-aware DNNs should thus detect and flag these issues.",
"### Features\nSame as fashion-mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).\n\nAdditionally, the following features are exposed for your convenience:\n\n- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(Pullover)=0.54, p(Shirt)=0.46' \n- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)\n- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)",
"### Splits\nWe provide four splits:\n\n- 'test': 10'000 ambiguous images\n- 'train': 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.\n- 'test_mixed': 20'000 images, consisting of the (shuffled) concatenation of our ambiguous 'test' set and the nominal *original* fashion mnist test set\n- 'train_mixed': 70'000 images, consisting of the (shuffled) concatenation of our ambiguous 'training' and the nominal training set.\n\nNote that the ambiguous train images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods), \nthe training set images allow for more unbalanced ambiguity. \nThis is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.\n\nFor research targeting explicitly aleatoric uncertainty, we recommend training the model using 'train_mixed'. \nOtherwise, our 'test' set will lead to both epistemic and aleatoric uncertainty. \nIn related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.",
"### Assessment and Validity\nFor a brief discussion of the strength and weaknesses of this dataset we refer to our paper.\nPlease note that our images are not typically realistic - \ni.e., while they represent multiple classes and thus have an ambiguous ground truth, they do not resemble real-world photographs.",
"### Paper\nPre-print here: URL\n\nCitation:",
"### Related Datasets\n- Ambiguous Mnist Dataset: URL\n- Corrupted Fashion-Mnist Dataset: URL"
]
| [
72,
66,
163,
296,
72,
12,
28
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| [
"passage: TAGS\n#task_categories-image-classification #annotations_creators-machine-generated #size_categories-10K<n<100K #source_datasets-extended|mnist #language-English #license-mit #arxiv-2207.10495 #region-us \n# Fashion-Mnist-Ambiguous\n\nThis dataset contains fashion-mnist-like images, but with an unclear ground truth. For each image, there are two classes that could be considered true.\nRobust and uncertainty-aware DNNs should thus detect and flag these issues.### Features\nSame as fashion-mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).\n\nAdditionally, the following features are exposed for your convenience:\n\n- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(Pullover)=0.54, p(Shirt)=0.46' \n- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)\n- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)"
]
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|
b54031fe05c55355e35b6b5a72d4042cb0ae37a6 | # Dataset Card for "reklamation24_versicherungen-recht"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_versicherungen-recht | [
"region:us"
]
| 2023-03-16T13:02:39+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 344441, "num_examples": 608}, {"name": "test", "num_bytes": 83669, "num_examples": 153}], "download_size": 229985, "dataset_size": 428110}} | 2023-04-19T07:34:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_versicherungen-recht"
More Information needed | [
"# Dataset Card for \"reklamation24_versicherungen-recht\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_versicherungen-recht\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_versicherungen-recht\"\n\nMore Information needed"
]
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|
25fdc6a4b2a3bdab4492385da71b0230bd2b2b79 | # AutoTrain Dataset for project: test
## Dataset Description
This dataset has been automatically processed by AutoTrain for project test.
### Languages
The BCP-47 code for the dataset's language is fr.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"tokens": [
"CCI",
"CCI",
"CCI",
"CCI bifocal G3, 7 et 25 mm",
"CCI bifocal G3, 7 et 25 mm",
"CCI",
"18/04/2019 : mammectomie dt + CA",
"18/04/2019 : mammectomie dt + CA",
"RO+ 20%",
" RO+ 20%",
"RO+",
"RO+",
"18/04/2019 : mammectomie dt + CA",
"18/04/2019 : mammectomie dt + CA",
"RP-",
"RP-",
"18/04/2019 : mammectomie dt + CA",
"18/04/2019 : mammectomie dt + CA",
"HER2 2+",
"HER2 2+",
"HER2 +",
"HER2 +",
"18/04/2019 : mammectomie dt + CA",
"18/04/2019 : mammectomie dt + CA",
"Fish+",
"Fish+",
"18/04/2019 : mammectomie dt + CA",
"18/04/2019 : mammectomie dt + CA",
"N+ 17/19",
"N+ 17/19",
"18/04/2019 : mammectomie dt + CA",
"18/04/2019 : mammectomie dt + CA",
"CA15-3 : 12 UI",
"CA15-3 : 12 UI",
"18/04/2019 : mammectomie dt + CA",
"18/04/2019 : mammectomie dt + CA",
"PS-0",
"PS-0",
"PS-0",
"PS-0",
" 03/2020",
"08/2020",
" 03/2020",
"08/2020"
],
"tags": [
28,
28,
28,
37,
37,
28,
14,
14,
29,
29,
29,
29,
32,
32,
33,
33,
34,
34,
19,
19,
19,
19,
20,
20,
17,
17,
18,
18,
23,
23,
24,
24,
6,
6,
7,
7,
27,
27,
27,
27,
12,
12,
12,
12
]
},
{
"tokens": [
"K sein D",
"1992 : K sein D",
"CA15-3 =1890",
"CA 15-3 : 5200",
"10/18",
"11/21",
"PS-2",
"10/18"
],
"tags": [
28,
14,
6,
6,
7,
7,
27,
12
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"tags": "Sequence(feature=ClassLabel(names=['ALK', 'ALK_DATE', 'BRAF', 'BRAF_DATE', 'BRCA', 'BRCA_DATE', 'CA15-3', 'CA15-3_DATE', 'CK20', 'CK20_DATE', 'CK7', 'CK7_DATE', 'Date PS', 'Date arr\u00eat traitement', 'Date du diagnostic de la tumeur primitive', 'EGFR', 'EGFR_DATE', 'FISH', 'FISH_DATE', 'HER2', 'HER2_DATE', 'KI67', 'KI67_DATE', 'N+', 'N+_DATE', 'PDL1', 'PDL1_DATE', 'PS', 'Premier type histologique de cancer', 'RO', 'ROS', 'ROS_DATE', 'RO_DATE', 'RP', 'RP_DATE', 'TTF1', 'TTF1_DATE', 'Taille de la tumeur primitive au diagnostic', 'motif arr\u00eat traitement', 'r\u00e9cepteurs hormonaux', 'r\u00e9cepteurs_hormonaux_DATE'], id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 999 |
| valid | 508 |
| KenziL/autotrain-data-test | [
"task_categories:token-classification",
"language:fr",
"region:us"
]
| 2023-03-16T13:02:51+00:00 | {"language": ["fr"], "task_categories": ["token-classification"]} | 2023-03-16T16:26:03+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #language-French #region-us
| AutoTrain Dataset for project: test
===================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project test.
### Languages
The BCP-47 code for the dataset's language is fr.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is fr.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-token-classification #language-French #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is fr.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
24,
26,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-token-classification #language-French #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is fr.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
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|
5e02a756fccf756acd7f49c8b5a78f0e70dc54f6 |
Dataset generated using handwritten fonts
=========================================
Number of images: 3700269
Sources:
* [Handwriting generation code](https://github.com/NastyBoget/HandwritingGeneration)
The code was executed with `cyrillic` option (more augmentations)
| nastyboget/synthetic_cyrillic_large | [
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"language:ru",
"license:mit",
"region:us"
]
| 2023-03-16T13:21:21+00:00 | {"language": ["ru"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["image-to-text"]} | 2023-03-20T10:18:30+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us
|
Dataset generated using handwritten fonts
=========================================
Number of images: 3700269
Sources:
* Handwriting generation code
The code was executed with 'cyrillic' option (more augmentations)
| []
| [
"TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n"
]
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|
fba9c3a0db554870801492ed8e86b5ebce63c4f1 | # Dataset Card for "fd_dialogue"
This dataset contains transcripts for famous movies and TV shows from https://transcripts.foreverdreaming.org/
The dataset contains **only a small portion of Forever Dreaming's data**, as only transscripts with a clear dialogue format are included, such as:
```
PERSON 1: Hello
PERSON 2: Hello Person 2!
(they are both talking)
Something else happens
PERSON 1: What happened?
```
Each row in the dataset is a single TV episode or movie. (**5380** rows total) following the [OpenAssistant](https://open-assistant.io/) format.
The METADATA column contains *type* (movie or series), *show* and the *episode* ("" for movies) keys and string values as a JSON string.
| Show | Count |
|----|----|
| A Discovery of Witches | 6 |
| Agents of S.H.I.E.L.D. | 9 |
| Alias | 102 |
| Angel | 64 |
| Bones | 114 |
| Boy Meets World | 24 |
| Breaking Bad | 27 |
| Brooklyn Nine-Nine | 8 |
| Buffy the Vampire Slayer | 113 |
| CSI: Crime Scene Investigation | 164 |
| Charmed | 176 |
| Childrens Hospital | 18 |
| Chuck | 17 |
| Crossing Jordan | 23 |
| Dawson's Creek | 128 |
| Degrassi Next Generation | 113 |
| Doctor Who | 699 |
| Doctor Who Special | 21 |
| Doctor Who_ | 108 |
| Downton Abbey | 18 |
| Dragon Ball Z Kai | 57 |
| FRIENDS | 227 |
| Foyle's War | 28 |
| Friday Night Lights | 7 |
| Game of Thrones | 6 |
| Gilmore Girls | 149 |
| Gintama | 41 |
| Glee | 11 |
| Gossip Girl | 5 |
| Greek | 33 |
| Grey's Anatomy | 75 |
| Growing Pains | 116 |
| Hannibal | 4 |
| Heartland | 3 |
| Hell on Wheels | 3 |
| House | 153 |
| How I Met Your Mother | 133 |
| JoJo's Bizarre Adventure | 42 |
| Justified | 46 |
| Keeping Up With the Kardashians | 8 |
| Lego Ninjago: Masters of Spinjitzu | 12 |
| London Spy | 5 |
| Lost | 117 |
| Lucifer | 3 |
| Married | 9 |
| Mars | 6 |
| Merlin | 58 |
| My Little Pony: Friendship is Magic | 15 |
| NCIS | 91 |
| New Girl | 3 |
| Once Upon A Time | 79 |
| One Tree Hill | 163 |
| Open Heart | 8 |
| Pretty Little Liars | 4 |
| Prison Break | 23 |
| Queer As Folk | 38 |
| Reign | 9 |
| Roswell | 60 |
| Salem | 23 |
| Scandal | 7 |
| Schitt's Creek | 4 |
| Scrubs | 29 |
| Sex and the City | 4 |
| Sherlock | 8 |
| Skins | 20 |
| Smallville | 190 |
| Sons of Anarchy | 55 |
| South Park | 84 |
| Spy × Family | 12 |
| StarTalk | 6 |
| Sugar Apple Fairy Tale | 5 |
| Supernatural | 114 |
| Teen Wolf | 58 |
| That Time I Got Reincarnated As A Slime | 22 |
| The 100 | 3 |
| The 4400 | 16 |
| The Amazing World of Gumball | 4 |
| The Big Bang Theory | 183 |
| The L Word | 3 |
| The Mentalist | 38 |
| The Nanny | 8 |
| The O.C. | 92 |
| The Office | 195 |
| The Originals | 45 |
| The Secret Life of an American Teenager | 18 |
| The Simpsons | 14 |
| The Vampire Diaries | 121 |
| The Walking Dead | 12 |
| The X-Files | 3 |
| Torchwood | 31 |
| Trailer Park Boys | 10 |
| True Blood | 33 |
| Tyrant | 6 |
| Veronica Mars | 59 |
| Vikings | 7 |
An additional 36 movies with transcripts are also included:
```
Pokémon the Movie: Hoopa and the Clash of Ages (2015)
Frozen (2013)
Home Alone
Lego Batman Movie, The (2017)
Disenchanted ( 2022)
Nightmare Before Christmas, The
Goonies, The (1985)
Polar Express, The (2004)
Frosty the Snowman (1969)
The Truth About Christmas (2018)
A Miser Brothers' Christmas (2008)
Powerpuff Girls: 'Twas the Fight Before Christmas, The (2003)
Tis the Season (2015)
Jingle Hell (2000)
Corpse Party: Book of Shadows (2016)
Mummy, The (1999)
Knock Knock (2015)
Dungeons and Dragons , Honour among thieves ( 2023)
w*r of the Worlds (2005)
Harry Potter and the Sorcerer's Stone
Twilight Saga, The: Breaking Dawn Part 2
Twilight Saga, The: Breaking Dawn Part 1
Twilight Saga, The: Eclipse
Godfather, The (1972)
Transformers (2007)
Creed 3 (2023)
Creed (2015)
Lethal w*apon 3 (1992)
Spider-Man 2 (2004)
Spider-Man: No Way Home (2021)
Black Panther Wakanda Forever ( 2022)
Money Train (1995)
Happys, The (2016)
Paris, Wine and Romance (2019)
Angel Guts: Red p*rn (1981)
Butterfly Crush (2010)
```
Note that there could be overlaps with the [TV dialogue dataset](https://huggingface.co/datasets/sedthh/tv_dialogue) for Friends, The Office, Doctor Who, South Park and some movies. | sedthh/fd_dialogue | [
"task_categories:conversational",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"OpenAssistant",
"transcripts",
"subtitles",
"television",
"foreverdreaming",
"region:us"
]
| 2023-03-16T13:36:31+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["conversational", "text2text-generation", "text-generation"], "pretty_name": "TV and Movie dialogue and transcript corpus from ForeverDreaming", "dataset_info": {"features": [{"name": "TEXT", "dtype": "string"}, {"name": "METADATA", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 170341941, "num_examples": 5380}], "download_size": 97327300, "dataset_size": 170341941}, "tags": ["OpenAssistant", "transcripts", "subtitles", "television", "foreverdreaming"]} | 2023-04-17T17:10:55+00:00 | []
| [
"en"
]
| TAGS
#task_categories-conversational #task_categories-text2text-generation #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-mit #OpenAssistant #transcripts #subtitles #television #foreverdreaming #region-us
| Dataset Card for "fd\_dialogue"
===============================
This dataset contains transcripts for famous movies and TV shows from URL
The dataset contains only a small portion of Forever Dreaming's data, as only transscripts with a clear dialogue format are included, such as:
Each row in the dataset is a single TV episode or movie. (5380 rows total) following the OpenAssistant format.
The METADATA column contains *type* (movie or series), *show* and the *episode* ("" for movies) keys and string values as a JSON string.
An additional 36 movies with transcripts are also included:
Note that there could be overlaps with the TV dialogue dataset for Friends, The Office, Doctor Who, South Park and some movies.
| []
| [
"TAGS\n#task_categories-conversational #task_categories-text2text-generation #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-mit #OpenAssistant #transcripts #subtitles #television #foreverdreaming #region-us \n"
]
| [
83
]
| [
"passage: TAGS\n#task_categories-conversational #task_categories-text2text-generation #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-mit #OpenAssistant #transcripts #subtitles #television #foreverdreaming #region-us \n"
]
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a15dfd0f5fa43eb94bbb02158ff8f24ae046e7a2 | # Dataset Card for "flint_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pnadel/flint_images | [
"region:us"
]
| 2023-03-16T13:37:39+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "clutter", "1": "email", "2": "email-squished", "3": "handwritten-document", "4": "spreadsheet", "5": "typeset-document"}}}}], "splits": [{"name": "train", "num_bytes": 11321509.591511937, "num_examples": 263}, {"name": "test", "num_bytes": 4907422.408488064, "num_examples": 114}], "download_size": 16177712, "dataset_size": 16228932.0}} | 2023-03-16T17:58:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "flint_images"
More Information needed | [
"# Dataset Card for \"flint_images\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"flint_images\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"flint_images\"\n\nMore Information needed"
]
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# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
Train
Validation
[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] | badokorach/QA | [
"region:us"
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| 2023-03-16T13:56:27+00:00 | {} | 2023-08-09T14:20:10+00:00 | []
| []
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#region-us
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# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
Train
Validation
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
cc9b33d634f468760e05aead182e722ad2242c08 |
## Dataset Description
This dataset contains conversations from GitHub issues and Pull Requests. Each conversation is comprised of a series of events, such as opening an issue, creating a comment,
or closing the issue, and includes the author's username, text, action, and identifiers such as the issue ID and number.
The dataset, which is mostly in English, has a total size of 54GB and 30.9M files.
## Dataset Structure
```python
from datasets import load_dataset
dataset = load_dataset("bigcode/the-stack-github-issues")
dataset
```
```
Dataset({
features: ['repo', 'issue_id', 'issue_number', 'pull_request', 'events', 'text_size', 'content', 'usernames'],
num_rows: 30982955
})
```
- `content` contains the full text in the conversation concatenated with special tokens: `<issue_start>` for the beginning of the issue, `<issue_comment>` before each comment
and `<issue_closed>` if a conversation is closed. Each comment is prepended with `username_{i}:` before the text, `username_{i}` is the mask for author `i`. This column
is intended for model training to avoid memorizing usernames, and understand the structure of the conversation.
- `events` contains the detailed events on top of which we built `content`, it also includes information the username's author and mask used.
Below is an example:
````
{'content': '<issue_start><issue_comment>Title: Click Save: Sorry, Cannot Write\n
'username_0: Hi all, Edit a file in Ice, click Save Icon\n Get error message: Sorry, cannot write /var/www/index.html
...
Edit: Also getting error: Cannot Zip Files up.\n
<issue_comment>username_1: hi there i have a similar problem. I cant save the files...',
'events': [{'action': 'opened',
'author': 'LaZyLion-ca',
'comment_id': None,
'datetime': '2013-06-06T13:30:31Z',
'masked_author': 'username_0',
'text': 'Hi all, Edit a file in Ice, click Save Icon...'
'title': 'Click Save: Sorry, Cannot Write',
'type': 'issue'},
...],
'issue_id': 15222443,
'issue_number': 264,
'pull_request': None,
'repo': 'icecoder/ICEcoder',
'text_size': 525,
'usernames': '["LaZyLion-ca", "seyo-IV"]'}
````
### Dataset pre-processing
This dataset was collected as part of [The Stack](https://huggingface.co/datasets/bigcode/the-stack) dataset, and the curation rationale can be found at this [link](https://huggingface.co/datasets/bigcode/the-stack#source-data).
To improve the quality of the dataset and remove personally identifiable information (PII), we performed the following cleaning steps, which reduced the dataset's size from 180GB to 54GB:
- We first removed automated text generated when users reply using their emails, using regex matching. We also deleted issues with little text (less than 200 total characters) and truncated long comments in the middle
(to a maximum of 100 lines while keeping the last 20 lines). This step removed 18% of the volume.
- We deleted comments from bots by looking for keywords in the author's username. If an issue became empty after this filtering, we removed it.
We also removed comments that preceded those from bots if they triggered them, by looking for the bot's username inside the text. This step removed 61% of the remaining volume and 22% of the conversations,
as bot-generated comments tend to be very long.
- We then used the number of users in the conversation as a proxy for quality. We kept all conversations with two or more users.
If a conversation had only one user, we kept it only if the total text was larger than 200 characters and smaller than 7000 characters.
We also removed issues with more than 10 events, as we noticed that they were of low quality or from bots we missed in the previous filtering.
This filtering removed 4% of the volume and 30% of the conversations.
- To redact PII, we masked IP addresses, email addresses, and secret keys from the text using regexes. We also masked the usernames of the authors from
the comments and replaced them with username_{i}, where i is the order of the author in the conversation. | bigcode/the-stack-github-issues | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"language:en",
"region:us"
]
| 2023-03-16T15:28:51+00:00 | {"annotations_creators": [], "language_creators": ["crowdsourced"], "language": ["en"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "extra_gated_prompt": "## Terms of Use for The Stack\n\nThe Stack dataset is a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset:\n1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n2. The Stack is regularly updated to enact validated data removal requests. By clicking on \"Access repository\", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset\u2019s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes.\n3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it.\n\nBy clicking on \"Access repository\" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.\n ", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}} | 2023-03-20T18:07:26+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-monolingual #size_categories-unknown #language-English #region-us
|
## Dataset Description
This dataset contains conversations from GitHub issues and Pull Requests. Each conversation is comprised of a series of events, such as opening an issue, creating a comment,
or closing the issue, and includes the author's username, text, action, and identifiers such as the issue ID and number.
The dataset, which is mostly in English, has a total size of 54GB and 30.9M files.
## Dataset Structure
- 'content' contains the full text in the conversation concatenated with special tokens: '<issue_start>' for the beginning of the issue, '<issue_comment>' before each comment
and '<issue_closed>' if a conversation is closed. Each comment is prepended with 'username_{i}:' before the text, 'username_{i}' is the mask for author 'i'. This column
is intended for model training to avoid memorizing usernames, and understand the structure of the conversation.
- 'events' contains the detailed events on top of which we built 'content', it also includes information the username's author and mask used.
Below is an example:
'
### Dataset pre-processing
This dataset was collected as part of The Stack dataset, and the curation rationale can be found at this link.
To improve the quality of the dataset and remove personally identifiable information (PII), we performed the following cleaning steps, which reduced the dataset's size from 180GB to 54GB:
- We first removed automated text generated when users reply using their emails, using regex matching. We also deleted issues with little text (less than 200 total characters) and truncated long comments in the middle
(to a maximum of 100 lines while keeping the last 20 lines). This step removed 18% of the volume.
- We deleted comments from bots by looking for keywords in the author's username. If an issue became empty after this filtering, we removed it.
We also removed comments that preceded those from bots if they triggered them, by looking for the bot's username inside the text. This step removed 61% of the remaining volume and 22% of the conversations,
as bot-generated comments tend to be very long.
- We then used the number of users in the conversation as a proxy for quality. We kept all conversations with two or more users.
If a conversation had only one user, we kept it only if the total text was larger than 200 characters and smaller than 7000 characters.
We also removed issues with more than 10 events, as we noticed that they were of low quality or from bots we missed in the previous filtering.
This filtering removed 4% of the volume and 30% of the conversations.
- To redact PII, we masked IP addresses, email addresses, and secret keys from the text using regexes. We also masked the usernames of the authors from
the comments and replaced them with username_{i}, where i is the order of the author in the conversation. | [
"## Dataset Description\n\nThis dataset contains conversations from GitHub issues and Pull Requests. Each conversation is comprised of a series of events, such as opening an issue, creating a comment,\nor closing the issue, and includes the author's username, text, action, and identifiers such as the issue ID and number.\nThe dataset, which is mostly in English, has a total size of 54GB and 30.9M files.",
"## Dataset Structure\n\n\n\n- 'content' contains the full text in the conversation concatenated with special tokens: '<issue_start>' for the beginning of the issue, '<issue_comment>' before each comment\nand '<issue_closed>' if a conversation is closed. Each comment is prepended with 'username_{i}:' before the text, 'username_{i}' is the mask for author 'i'. This column\nis intended for model training to avoid memorizing usernames, and understand the structure of the conversation.\n- 'events' contains the detailed events on top of which we built 'content', it also includes information the username's author and mask used.\n\nBelow is an example:\n\n'",
"### Dataset pre-processing\nThis dataset was collected as part of The Stack dataset, and the curation rationale can be found at this link.\n\nTo improve the quality of the dataset and remove personally identifiable information (PII), we performed the following cleaning steps, which reduced the dataset's size from 180GB to 54GB:\n\n- We first removed automated text generated when users reply using their emails, using regex matching. We also deleted issues with little text (less than 200 total characters) and truncated long comments in the middle\n (to a maximum of 100 lines while keeping the last 20 lines). This step removed 18% of the volume.\n\n- We deleted comments from bots by looking for keywords in the author's username. If an issue became empty after this filtering, we removed it.\n We also removed comments that preceded those from bots if they triggered them, by looking for the bot's username inside the text. This step removed 61% of the remaining volume and 22% of the conversations,\n as bot-generated comments tend to be very long.\n\n- We then used the number of users in the conversation as a proxy for quality. We kept all conversations with two or more users.\n If a conversation had only one user, we kept it only if the total text was larger than 200 characters and smaller than 7000 characters.\n We also removed issues with more than 10 events, as we noticed that they were of low quality or from bots we missed in the previous filtering.\n This filtering removed 4% of the volume and 30% of the conversations.\n\n- To redact PII, we masked IP addresses, email addresses, and secret keys from the text using regexes. We also masked the usernames of the authors from\n the comments and replaced them with username_{i}, where i is the order of the author in the conversation."
]
| [
"TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-monolingual #size_categories-unknown #language-English #region-us \n",
"## Dataset Description\n\nThis dataset contains conversations from GitHub issues and Pull Requests. Each conversation is comprised of a series of events, such as opening an issue, creating a comment,\nor closing the issue, and includes the author's username, text, action, and identifiers such as the issue ID and number.\nThe dataset, which is mostly in English, has a total size of 54GB and 30.9M files.",
"## Dataset Structure\n\n\n\n- 'content' contains the full text in the conversation concatenated with special tokens: '<issue_start>' for the beginning of the issue, '<issue_comment>' before each comment\nand '<issue_closed>' if a conversation is closed. Each comment is prepended with 'username_{i}:' before the text, 'username_{i}' is the mask for author 'i'. This column\nis intended for model training to avoid memorizing usernames, and understand the structure of the conversation.\n- 'events' contains the detailed events on top of which we built 'content', it also includes information the username's author and mask used.\n\nBelow is an example:\n\n'",
"### Dataset pre-processing\nThis dataset was collected as part of The Stack dataset, and the curation rationale can be found at this link.\n\nTo improve the quality of the dataset and remove personally identifiable information (PII), we performed the following cleaning steps, which reduced the dataset's size from 180GB to 54GB:\n\n- We first removed automated text generated when users reply using their emails, using regex matching. We also deleted issues with little text (less than 200 total characters) and truncated long comments in the middle\n (to a maximum of 100 lines while keeping the last 20 lines). This step removed 18% of the volume.\n\n- We deleted comments from bots by looking for keywords in the author's username. If an issue became empty after this filtering, we removed it.\n We also removed comments that preceded those from bots if they triggered them, by looking for the bot's username inside the text. This step removed 61% of the remaining volume and 22% of the conversations,\n as bot-generated comments tend to be very long.\n\n- We then used the number of users in the conversation as a proxy for quality. We kept all conversations with two or more users.\n If a conversation had only one user, we kept it only if the total text was larger than 200 characters and smaller than 7000 characters.\n We also removed issues with more than 10 events, as we noticed that they were of low quality or from bots we missed in the previous filtering.\n This filtering removed 4% of the volume and 30% of the conversations.\n\n- To redact PII, we masked IP addresses, email addresses, and secret keys from the text using regexes. We also masked the usernames of the authors from\n the comments and replaced them with username_{i}, where i is the order of the author in the conversation."
]
| [
59,
97,
170,
411
]
| [
"passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #multilinguality-monolingual #size_categories-unknown #language-English #region-us \n## Dataset Description\n\nThis dataset contains conversations from GitHub issues and Pull Requests. Each conversation is comprised of a series of events, such as opening an issue, creating a comment,\nor closing the issue, and includes the author's username, text, action, and identifiers such as the issue ID and number.\nThe dataset, which is mostly in English, has a total size of 54GB and 30.9M files.## Dataset Structure\n\n\n\n- 'content' contains the full text in the conversation concatenated with special tokens: '<issue_start>' for the beginning of the issue, '<issue_comment>' before each comment\nand '<issue_closed>' if a conversation is closed. Each comment is prepended with 'username_{i}:' before the text, 'username_{i}' is the mask for author 'i'. This column\nis intended for model training to avoid memorizing usernames, and understand the structure of the conversation.\n- 'events' contains the detailed events on top of which we built 'content', it also includes information the username's author and mask used.\n\nBelow is an example:\n\n'"
]
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|
6b8fd7e592699e69b782d99411304650edffeec4 | # Mtet
- Source: https://github.com/vietai/mTet
- Num examples:
- 8,327,706 (train)
- 3,106 (validation)
- 2,536 (test)
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/mtet-prompt-envi")
``` | vietgpt-archive/mtet-prompt-envi | [
"task_categories:summarization",
"size_categories:1M<n<10M",
"language:en",
"language:vi",
"SFT",
"region:us"
]
| 2023-03-16T16:08:42+00:00 | {"language": ["en", "vi"], "size_categories": ["1M<n<10M"], "task_categories": ["summarization"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 745821, "num_examples": 2536}, {"name": "train", "num_bytes": 2950719030, "num_examples": 8327706}, {"name": "validation", "num_bytes": 796468, "num_examples": 3106}], "download_size": 1849953022, "dataset_size": 2952662041}, "tags": ["SFT"]} | 2023-03-30T18:52:22+00:00 | []
| [
"en",
"vi"
]
| TAGS
#task_categories-summarization #size_categories-1M<n<10M #language-English #language-Vietnamese #SFT #region-us
| # Mtet
- Source: URL
- Num examples:
- 8,327,706 (train)
- 3,106 (validation)
- 2,536 (test)
- Language: Vietnamese
| [
"# Mtet\n\n- Source: URL\n- Num examples: \n - 8,327,706 (train)\n - 3,106 (validation)\n - 2,536 (test)\n- Language: Vietnamese"
]
| [
"TAGS\n#task_categories-summarization #size_categories-1M<n<10M #language-English #language-Vietnamese #SFT #region-us \n",
"# Mtet\n\n- Source: URL\n- Num examples: \n - 8,327,706 (train)\n - 3,106 (validation)\n - 2,536 (test)\n- Language: Vietnamese"
]
| [
42,
42
]
| [
"passage: TAGS\n#task_categories-summarization #size_categories-1M<n<10M #language-English #language-Vietnamese #SFT #region-us \n# Mtet\n\n- Source: URL\n- Num examples: \n - 8,327,706 (train)\n - 3,106 (validation)\n - 2,536 (test)\n- Language: Vietnamese"
]
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|
91cd20f2dadafbc3df90ee428de03cf41a6a9f53 | # Dataset Card for "UA_speech_2c2p"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LightFury9/UA_speech_2c2p | [
"region:us"
]
| 2023-03-16T16:18:26+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "healthy control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 768265600, "num_examples": 800}, {"name": "test", "num_bytes": 4609593600, "num_examples": 4800}], "download_size": 736886149, "dataset_size": 5377859200}} | 2023-03-16T16:19:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "UA_speech_2c2p"
More Information needed | [
"# Dataset Card for \"UA_speech_2c2p\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"UA_speech_2c2p\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"UA_speech_2c2p\"\n\nMore Information needed"
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|
1d41305cde4d71b076986f3bbe9fe46d69da304f | # Dataset Card for "geo_large_corpus_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ZurabDz/geo_large_corpus_cleaned | [
"region:us"
]
| 2023-03-16T16:22:31+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10479169400, "num_examples": 12626101}], "download_size": 3626972633, "dataset_size": 10479169400}} | 2023-03-16T16:44:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "geo_large_corpus_cleaned"
More Information needed | [
"# Dataset Card for \"geo_large_corpus_cleaned\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"geo_large_corpus_cleaned\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"geo_large_corpus_cleaned\"\n\nMore Information needed"
]
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6f9bf54864bea0547ea87c99d36417d4f1429e85 | # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_100 | [
"region:us"
]
| 2023-03-16T16:26:06+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 795491, "num_examples": 100}], "download_size": 149488, "dataset_size": 795491}} | 2023-03-16T16:26:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_100"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_100\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_100\"\n\nMore Information needed"
]
| [
6,
57
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_100\"\n\nMore Information needed"
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abe338af7f5ee9da343a8bb4c675fb3da616ab86 | # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000 | [
"region:us"
]
| 2023-03-16T16:31:22+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 8126087, "num_examples": 1000}], "download_size": 1598581, "dataset_size": 8126087}} | 2023-03-16T19:08:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000\"\n\nMore Information needed"
]
| [
6,
57
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000\"\n\nMore Information needed"
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|
ff84a797f3894f97ea1a1efff566201258b2ef34 | # Dataset Card for "tokenized_large_corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ZurabDz/tokenized_large_corpus | [
"region:us"
]
| 2023-03-16T17:06:48+00:00 | {"dataset_info": {"features": [{"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": 5035621680, "num_examples": 5521515}], "download_size": 1745716024, "dataset_size": 5035621680}} | 2023-03-16T17:17:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tokenized_large_corpus"
More Information needed | [
"# Dataset Card for \"tokenized_large_corpus\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tokenized_large_corpus\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tokenized_large_corpus\"\n\nMore Information needed"
]
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33fa884d1ee162bbfe8ca42ef4691fece27d118f | # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_1000 | [
"region:us"
]
| 2023-03-16T17:06:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 8097247, "num_examples": 1000}], "download_size": 0, "dataset_size": 8097247}} | 2023-04-15T13:42:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_1000"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_1000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_1000\"\n\nMore Information needed"
]
| [
6,
59
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_google_flan_t5_xxl_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_1000\"\n\nMore Information needed"
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|
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