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8f3520374e4edb79bddc6beabfcfc8e2a25d1e35 | # Dataset Card for "massive_alarm-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_alarm-de-DE | [
"region:us"
]
| 2023-03-30T11:41:41+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 111478, "num_examples": 390}, {"name": "validation", "num_bytes": 17792, "num_examples": 64}, {"name": "test", "num_bytes": 26750, "num_examples": 96}], "download_size": 56221, "dataset_size": 156020}} | 2023-03-30T11:54:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_alarm-de-DE"
More Information needed | [
"# Dataset Card for \"massive_alarm-de-DE\"\n\nMore Information needed"
]
| [
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"# Dataset Card for \"massive_alarm-de-DE\"\n\nMore Information needed"
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|
9c4a76bedbf9ae813a534a6110678c632b93310d | # Dataset Card for "massive_weather-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_weather-de-DE | [
"region:us"
]
| 2023-03-30T11:41:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 176185, "num_examples": 573}, {"name": "validation", "num_bytes": 38227, "num_examples": 126}, {"name": "test", "num_bytes": 47648, "num_examples": 156}], "download_size": 71367, "dataset_size": 262060}} | 2023-03-30T11:55:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_weather-de-DE"
More Information needed | [
"# Dataset Card for \"massive_weather-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_weather-de-DE\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_weather-de-DE\"\n\nMore Information needed"
]
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|
ca51a62630e9bd35845158337bc3ee87465c9007 | # Dataset Card for "somos-clean-alpaca-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | frascuchon/somos-clean-alpaca-es | [
"region:us"
]
| 2023-03-30T11:59:07+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 9149897, "num_examples": 482}], "download_size": 6603731, "dataset_size": 9149897}} | 2023-03-30T14:20:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "somos-clean-alpaca-es"
More Information needed | [
"# Dataset Card for \"somos-clean-alpaca-es\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"somos-clean-alpaca-es\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"somos-clean-alpaca-es\"\n\nMore Information needed"
]
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|
4a096a5d3d57b973cae40565906924c5811aef27 | # Dataset Card for "reklamation24_unternehmen-verbaende"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_unternehmen-verbaende | [
"region:us"
]
| 2023-03-30T12:08:01+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": 205101, "num_examples": 457}, {"name": "test", "num_bytes": 54645, "num_examples": 115}], "download_size": 0, "dataset_size": 259746}} | 2023-04-19T07:31:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_unternehmen-verbaende"
More Information needed | [
"# Dataset Card for \"reklamation24_unternehmen-verbaende\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_unternehmen-verbaende\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_unternehmen-verbaende\"\n\nMore Information needed"
]
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|
20f05d69ff622b1eef8fbbc58834bb33364dfc3c | # Dataset Card for "reklamation24_transport-logistik"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_transport-logistik | [
"region:us"
]
| 2023-03-30T12:12:17+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": 208152, "num_examples": 410}, {"name": "test", "num_bytes": 51997, "num_examples": 103}], "download_size": 0, "dataset_size": 260149}} | 2023-04-19T07:34:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_transport-logistik"
More Information needed | [
"# Dataset Card for \"reklamation24_transport-logistik\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_transport-logistik\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_transport-logistik\"\n\nMore Information needed"
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|
7270cc5dad80e58b87b7a53deec5a4abda7164c2 | # Dataset Card for "wikipedia_urls"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | HuggingFaceGECLM/wikipedia_urls | [
"region:us"
]
| 2023-03-30T12:37:11+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "wiki_titles", "sequence": "string"}, {"name": "count", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4215693880, "num_examples": 28370288}], "download_size": 1535550647, "dataset_size": 4215693880}} | 2023-03-30T12:40:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikipedia_urls"
More Information needed | [
"# Dataset Card for \"wikipedia_urls\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia_urls\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia_urls\"\n\nMore Information needed"
]
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|
5ab9b6b89d72b0806fb240b5aededbfdd313c087 |
# LAION Occupation
This dataset is a subset of [LAION-2B-en](https://laion.ai/blog/laion-5b/) containing 1.8M samples, each assigned to one of 153 occupations. This dataset was curated as part of our investigation into gender-occupation biases in LAION presented in [Fair Diffusion](https://arxiv.org/abs/2302.10893).
For downloading the images, check out [img2dataset](https://github.com/rom1504/img2dataset).
## Data Collection
We identified relevant images in the dataset by computing their CLIP similarity to a textual description of the target occupation. All descriptions were in the form of "an image of the face of a \<<em>occupation</em>\>". Consequently, we included all images above an empirically determined threshold.
## Probability of faces
The dataset also contains annotations for the probability of a human face being depicted. Scores were calculated using the MTCNN Face Detector of [FaceNet](https://github.com/timesler/facenet-pytorch). Empirically, scores above ca. 0.97 can be reasonably assumed to include recognizable faces.
## Dataset Format
The dataset consists of the following fields:
| Field | Explanation |
| ----------- | ----------- |
| URL | Url of the image. |
| TEXT | Text caption of the image. |
| occupation | Identified occupation. |
| pface | Probability of a face being contained in the image as per FaceNet. will be NaN if the image could not be retrieved. |
| url_active | Whether or not we were able to retrieve the image from the corresponding URL. |
| retr_sim | Cosine similarity between CLIP embeddings of image and retrieval prompt. |
| laion_index | Index of the sample in the original LAION-2B-en. |
| hash | Usual LAION hash of URL and caption. | | AIML-TUDA/laion-occupation | [
"license:cc-by-sa-4.0",
"arxiv:2302.10893",
"region:us"
]
| 2023-03-30T12:46:13+00:00 | {"license": "cc-by-sa-4.0", "pretty_name": "LAION Occupation"} | 2023-03-30T13:44:34+00:00 | [
"2302.10893"
]
| []
| TAGS
#license-cc-by-sa-4.0 #arxiv-2302.10893 #region-us
| LAION Occupation
================
This dataset is a subset of LAION-2B-en containing 1.8M samples, each assigned to one of 153 occupations. This dataset was curated as part of our investigation into gender-occupation biases in LAION presented in Fair Diffusion.
For downloading the images, check out img2dataset.
Data Collection
---------------
We identified relevant images in the dataset by computing their CLIP similarity to a textual description of the target occupation. All descriptions were in the form of "an image of the face of a <*occupation*>". Consequently, we included all images above an empirically determined threshold.
Probability of faces
--------------------
The dataset also contains annotations for the probability of a human face being depicted. Scores were calculated using the MTCNN Face Detector of FaceNet. Empirically, scores above ca. 0.97 can be reasonably assumed to include recognizable faces.
Dataset Format
--------------
The dataset consists of the following fields:
| []
| [
"TAGS\n#license-cc-by-sa-4.0 #arxiv-2302.10893 #region-us \n"
]
| [
25
]
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"passage: TAGS\n#license-cc-by-sa-4.0 #arxiv-2302.10893 #region-us \n"
]
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|
d9635284869836f7cff98ac99051120ae888f57d | # Alpaca-like Model Training Dataset Generated from GPT-3.5
This dataset contains 56,000+ samples generated from GPT-3.5 and extreme content filtering removed and replaced with relevant generated outputs. Specifically designed for training Alpaca-like models. Each sample includes an instruction, an optional input text, and an output text.
## Dataset Description
The dataset is a collection of diverse tasks and prompts that are suited for training and fine-tuning Alpaca-like language models. It includes a wide variety of tasks such as text summarization, question-answering, translation, and more. The dataset has been generated using GPT-3.5, which provides high-quality samples.
### Data Format
Each sample in the dataset is represented as a JSON object with the following structure:
```json
{
"instruction": "string",
"input": "string",
"output": "string"
}
instruction: A text string that provides the instruction for the task. It can be a question, a prompt, or a description of the desired output.
input: An optional text string that provides the input for the task. It can be a text passage, a set of questions, or any other relevant information for the task. If there is no specific input, this field is empty.
output: A text string that provides the output generated by GPT-3.5 in response to the instruction and input.
Dataset Usage
This dataset can be used for various natural language understanding and generation tasks, including but not limited to:
Training and fine-tuning Alpaca-like language models.
Evaluating the performance of pre-trained models on diverse tasks.
Exploring the capabilities and limitations of GPT-3.5 generated samples.
Conducting research on transfer learning and few-shot learning in NLP.
Free for all.
If you want to follow me you can @WoodnBarrlGame on twitter. | whitefox44/AlpacaGPT3.5Customized | [
"license:apache-2.0",
"region:us"
]
| 2023-03-30T13:21:48+00:00 | {"license": "apache-2.0"} | 2023-04-02T17:17:21+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| # Alpaca-like Model Training Dataset Generated from GPT-3.5
This dataset contains 56,000+ samples generated from GPT-3.5 and extreme content filtering removed and replaced with relevant generated outputs. Specifically designed for training Alpaca-like models. Each sample includes an instruction, an optional input text, and an output text.
## Dataset Description
The dataset is a collection of diverse tasks and prompts that are suited for training and fine-tuning Alpaca-like language models. It includes a wide variety of tasks such as text summarization, question-answering, translation, and more. The dataset has been generated using GPT-3.5, which provides high-quality samples.
### Data Format
Each sample in the dataset is represented as a JSON object with the following structure:
'''json
{
"instruction": "string",
"input": "string",
"output": "string"
}
instruction: A text string that provides the instruction for the task. It can be a question, a prompt, or a description of the desired output.
input: An optional text string that provides the input for the task. It can be a text passage, a set of questions, or any other relevant information for the task. If there is no specific input, this field is empty.
output: A text string that provides the output generated by GPT-3.5 in response to the instruction and input.
Dataset Usage
This dataset can be used for various natural language understanding and generation tasks, including but not limited to:
Training and fine-tuning Alpaca-like language models.
Evaluating the performance of pre-trained models on diverse tasks.
Exploring the capabilities and limitations of GPT-3.5 generated samples.
Conducting research on transfer learning and few-shot learning in NLP.
Free for all.
If you want to follow me you can @WoodnBarrlGame on twitter. | [
"# Alpaca-like Model Training Dataset Generated from GPT-3.5\n\nThis dataset contains 56,000+ samples generated from GPT-3.5 and extreme content filtering removed and replaced with relevant generated outputs. Specifically designed for training Alpaca-like models. Each sample includes an instruction, an optional input text, and an output text.",
"## Dataset Description\n\nThe dataset is a collection of diverse tasks and prompts that are suited for training and fine-tuning Alpaca-like language models. It includes a wide variety of tasks such as text summarization, question-answering, translation, and more. The dataset has been generated using GPT-3.5, which provides high-quality samples.",
"### Data Format\n\nEach sample in the dataset is represented as a JSON object with the following structure:\n\n'''json\n{\n \"instruction\": \"string\",\n \"input\": \"string\",\n \"output\": \"string\"\n}\n\ninstruction: A text string that provides the instruction for the task. It can be a question, a prompt, or a description of the desired output.\ninput: An optional text string that provides the input for the task. It can be a text passage, a set of questions, or any other relevant information for the task. If there is no specific input, this field is empty.\noutput: A text string that provides the output generated by GPT-3.5 in response to the instruction and input.\nDataset Usage\nThis dataset can be used for various natural language understanding and generation tasks, including but not limited to:\n\nTraining and fine-tuning Alpaca-like language models.\nEvaluating the performance of pre-trained models on diverse tasks.\nExploring the capabilities and limitations of GPT-3.5 generated samples.\nConducting research on transfer learning and few-shot learning in NLP.\n\nFree for all.\n\nIf you want to follow me you can @WoodnBarrlGame on twitter."
]
| [
"TAGS\n#license-apache-2.0 #region-us \n",
"# Alpaca-like Model Training Dataset Generated from GPT-3.5\n\nThis dataset contains 56,000+ samples generated from GPT-3.5 and extreme content filtering removed and replaced with relevant generated outputs. Specifically designed for training Alpaca-like models. Each sample includes an instruction, an optional input text, and an output text.",
"## Dataset Description\n\nThe dataset is a collection of diverse tasks and prompts that are suited for training and fine-tuning Alpaca-like language models. It includes a wide variety of tasks such as text summarization, question-answering, translation, and more. The dataset has been generated using GPT-3.5, which provides high-quality samples.",
"### Data Format\n\nEach sample in the dataset is represented as a JSON object with the following structure:\n\n'''json\n{\n \"instruction\": \"string\",\n \"input\": \"string\",\n \"output\": \"string\"\n}\n\ninstruction: A text string that provides the instruction for the task. It can be a question, a prompt, or a description of the desired output.\ninput: An optional text string that provides the input for the task. It can be a text passage, a set of questions, or any other relevant information for the task. If there is no specific input, this field is empty.\noutput: A text string that provides the output generated by GPT-3.5 in response to the instruction and input.\nDataset Usage\nThis dataset can be used for various natural language understanding and generation tasks, including but not limited to:\n\nTraining and fine-tuning Alpaca-like language models.\nEvaluating the performance of pre-trained models on diverse tasks.\nExploring the capabilities and limitations of GPT-3.5 generated samples.\nConducting research on transfer learning and few-shot learning in NLP.\n\nFree for all.\n\nIf you want to follow me you can @WoodnBarrlGame on twitter."
]
| [
14,
78,
81,
268
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n# Alpaca-like Model Training Dataset Generated from GPT-3.5\n\nThis dataset contains 56,000+ samples generated from GPT-3.5 and extreme content filtering removed and replaced with relevant generated outputs. Specifically designed for training Alpaca-like models. Each sample includes an instruction, an optional input text, and an output text.## Dataset Description\n\nThe dataset is a collection of diverse tasks and prompts that are suited for training and fine-tuning Alpaca-like language models. It includes a wide variety of tasks such as text summarization, question-answering, translation, and more. The dataset has been generated using GPT-3.5, which provides high-quality samples.### Data Format\n\nEach sample in the dataset is represented as a JSON object with the following structure:\n\n'''json\n{\n \"instruction\": \"string\",\n \"input\": \"string\",\n \"output\": \"string\"\n}\n\ninstruction: A text string that provides the instruction for the task. It can be a question, a prompt, or a description of the desired output.\ninput: An optional text string that provides the input for the task. It can be a text passage, a set of questions, or any other relevant information for the task. If there is no specific input, this field is empty.\noutput: A text string that provides the output generated by GPT-3.5 in response to the instruction and input.\nDataset Usage\nThis dataset can be used for various natural language understanding and generation tasks, including but not limited to:\n\nTraining and fine-tuning Alpaca-like language models.\nEvaluating the performance of pre-trained models on diverse tasks.\nExploring the capabilities and limitations of GPT-3.5 generated samples.\nConducting research on transfer learning and few-shot learning in NLP.\n\nFree for all.\n\nIf you want to follow me you can @WoodnBarrlGame on twitter."
]
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|
49bac3fe373af1c56a4ae36f3269a892e1c8ed38 | # Dataset Card for "instruct_sq_600k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | niv-al/instruct_sq_600k | [
"task_categories:question-answering",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:sq",
"license:openrail",
"region:us"
]
| 2023-03-30T13:47:01+00:00 | {"language": ["sq"], "license": "openrail", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering", "table-question-answering", "summarization", "text2text-generation"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 544244075, "num_examples": 586394}], "download_size": 84724923, "dataset_size": 544244075}} | 2023-03-30T13:52:52+00:00 | []
| [
"sq"
]
| TAGS
#task_categories-question-answering #task_categories-table-question-answering #task_categories-summarization #task_categories-text2text-generation #size_categories-100K<n<1M #language-Albanian #license-openrail #region-us
| # Dataset Card for "instruct_sq_600k"
More Information needed | [
"# Dataset Card for \"instruct_sq_600k\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-question-answering #task_categories-table-question-answering #task_categories-summarization #task_categories-text2text-generation #size_categories-100K<n<1M #language-Albanian #license-openrail #region-us \n",
"# Dataset Card for \"instruct_sq_600k\"\n\nMore Information needed"
]
| [
78,
18
]
| [
"passage: TAGS\n#task_categories-question-answering #task_categories-table-question-answering #task_categories-summarization #task_categories-text2text-generation #size_categories-100K<n<1M #language-Albanian #license-openrail #region-us \n# Dataset Card for \"instruct_sq_600k\"\n\nMore Information needed"
]
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|
35a97a59e6b7ade6c1134cccdb6a3bca8b087acd | # AutoTrain Dataset for project: treehk
## Dataset Description
This dataset has been automatically processed by AutoTrain for project treehk.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<245x358 RGB PIL image>",
"target": 0
},
{
"image": "<400x530 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Acacia auriculiformis \u8033\u679c\u76f8\u601d', 'Acacia confusa Merr. \u53f0\u7063\u76f8\u601d', 'Acacia mangium Willd. \u5927\u8449\u76f8\u601d', 'Acronychia pedunculata (L.) Miq.--\u5c71\u6cb9\u67d1', 'Archontophoenix alexandrae \u4e9e\u529b\u5c71\u5927\u6930\u5b50', 'Bauhinia purpurea L. \u7d05\u82b1\u7f8a\u8e44\u7532', 'Bauhinia variegata L \u5bae\u7c89\u7f8a\u8e44\u7532', 'Bauhinia \u6d0b\u7d2b\u834a', 'Bischofia javanica Blume \u79cb\u6953', 'Bischofia polycarpa \u91cd\u967d\u6728', 'Callistemon rigidus R. Br \u7d05\u5343\u5c64', 'Callistemon viminalis \u4e32\u9322\u67f3', 'Cinnamomum burmannii \u9670\u9999', 'Cinnamomum camphora \u6a1f\u6a39', 'Crateva trifoliata \u920d\u8449\u9b5a\u6728 ', 'Crateva unilocularis \u6a39\u982d\u83dc', 'Delonix regia \u9cf3\u51f0\u6728', 'Elaeocarpus hainanensis Oliv \u6c34\u77f3\u6995', 'Elaeocarpus sylvestris \u5c71\u675c\u82f1', 'Erythrina variegata L. \u523a\u6850', 'Ficus altissima Blume \u9ad8\u5c71\u6995', 'Ficus benjamina L. \u5782\u8449\u6995', 'Ficus elastica Roxb. ex Hornem \u5370\u5ea6\u6995', 'Ficus microcarpa L. f \u7d30\u8449\u6995', 'Ficus religiosa L. \u83e9\u63d0\u6a39', 'Ficus rumphii Blume \u5047\u83e9\u63d0\u6a39', 'Ficus subpisocarpa Gagnep. \u7b46\u7ba1\u6995', 'Ficus variegata Blume \u9752\u679c\u6995', 'Ficus virens Aiton \u5927\u8449\u6995', 'Koelreuteria bipinnata Franch. \u8907\u7fbd\u8449\u6b12\u6a39', 'Livistona chinensis \u84b2\u8475', 'Melaleuca Cajeput-tree \u767d\u5343\u5c64 ', 'Melia azedarach L \u695d', 'Peltophorum pterocarpum \u76fe\u67f1\u6728', 'Peltophorum tonkinense \u9280\u73e0', 'Roystonea regia \u5927\u738b\u6930\u5b50', 'Schefflera actinophylla \u8f3b\u8449\u9d5d\u638c\u67f4', 'Schefflera heptaphylla \u9d5d\u638c\u67f4', 'Spathodea campanulata\u706b\u7130\u6a39', 'Sterculia lanceolata Cav. \u5047\u860b\u5a46', 'Wodyetia bifurcata A.K.Irvine \u72d0\u5c3e\u68d5'], 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 | 385 |
| valid | 113 |
| OttoYu/TreeHK40 | [
"task_categories:image-classification",
"region:us"
]
| 2023-03-30T14:07:14+00:00 | {"task_categories": ["image-classification"]} | 2023-03-30T14:12:47+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: treehk
=====================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project treehk.
### Languages
The BCP-47 code for the dataset's language is unk.
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 unk.\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-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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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|
d1cdea5002bd039fa5a0147375c8bceb92b477e9 | # Dataset Card for TexPrax
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage: https://texprax.de/**
- **Repository: https://github.com/UKPLab/TexPrax**
- **Paper: https://arxiv.org/abs/2208.07846**
- **Leaderboard: n/a**
- **Point of Contact: Ji-Ung Lee (http://www.ukp.tu-darmstadt.de/)**
### Dataset Summary
This dataset contains dialogues collected from German factory workers at the _Center for industrial productivity_ ([CiP](https://www.prozesslernfabrik.de/)). The dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.
### Supported Tasks and Leaderboards
This dataset supports the following tasks:
* Sentence classification
* Named entity recognition (will be updated soon with the new indexing)
* Dialog generation (so far not evaluated)
### Languages
German
## Dataset Structure
### Data Instances
On sentence level, each instance consists of the dialog-id, turn-id, sentence-id, the sentence (raw), the label, the domain, and the subsplit.
```
{"185";"562";993";"wie kriege ich die Dichtung raus?";"P";"n/a";"3"}
```
On token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.
```
{"178_0";"['Hi', 'wie', 'kriege', 'ich', 'die', 'Dichtung', 'raus', '?', 'in', 'der', 'Schublade', 'gibt', 'es', 'einen', 'Dichtungszieher']";"['O', 'O', 'O', 'O', 'O', 'B-PRE', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'B-PE']";"Batch 3"}
```
### Data Fields
Sentence level:
* dialog-id: unique identifier for the dialog
* turn-id: unique identifier for the turn
* sentence-id: unique identifier for the dialog
* sentence: the respective sentence
* label: the label (_P_ for Problem, _C_ for Cause, _S_ for solution, and _O_ for Other)
* domain: the subdomains where the data was collected from. Domains are industry, machining, or n/a (for batch 2 and batch 3).
* subsplit: the respective subsplit of the data (see below)
Token level:
* id: the identifier
* tokens: a list of tokens (i.e., the tokenized dialogue)
* entities: the named entity in a BIO scheme (_B-X_, _I-X_, or O).
* subsplit: the respective subsplit of the data (see below)
### Data Splits
The dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory.
Train:
* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line
* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line
* Batch 2: data collected in-between October 2021-June 2022 from all workers
Test:
* Batch 3: data collected in July 2022 together with the system usability study run
Sentence level statistics:
| Batch | Dialogues | Turns | Sentences |
|---|---|---|---|
| 1 | 81 | 246 | 553 |
| 2 | 97 | 309 | 432 |
| 3 | 24 | 36 | 42 |
| Overall | 202 | 591 | 1,027 |
Token level statistics:
[Needs to be added]
## Dataset Creation
### Curation Rationale
This dataset provides task-oriented dialogues that solve a very domain specific problem.
### Source Data
#### Initial Data Collection and Normalization
The data was generated by workers at the [CiP](https://www.prozesslernfabrik.de/). The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal 'ne', contain abbreviations 'vll', and filler words such as 'ah'. For a detailed description please see the [paper](https://arxiv.org/abs/2208.07846).
#### Who are the source language producers?
German factory workers working at the [CiP](https://www.prozesslernfabrik.de/)
### Annotations
#### Annotation process
**Token level.** Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.
**Sentence level.** Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the [TexPrax demo paper](https://arxiv.org/abs/2208.07846).
#### Who are the annotators?
**Token level.** Researchers working at the CiP.
**Sentence level.** The factory workers themselves.
### Personal and Sensitive Information
This dataset is fully anonymized. All occurrences of names have been manually checked during annotation and replaced with a random token.
## Considerations for Using the Data
### Social Impact of Dataset
Informal language especially used in short messages, however, seldom considered in existing NLP datasets. This dataset could serve as an interesting evaluation task for transferring language models to low-resource, but highly specific domains. Moreover, we note that despite all abbreviations, typos, and local dialects used in the messages, all workers were able to understand the questions as well as replies. This should be a standard future NLP models should be able to uphold.
### Discussion of Biases
The dialogues are very much on a professional level. The workers were informed (and gave their consent) in advance that their messages are being recorded and processed, which may have influenced them to hold only professional conversations, hence, all dialogues concern inanimate objects (i.e., machines).
### Other Known Limitations
[More Information Needed]
## Additional Information
You can download the data via:
```
from datasets import load_dataset
dataset = load_dataset("UKPLab/TexPrax") # default config is sentence classification
dataset = load_dataset("UKPLab/TexPrax", "ner") # use the ner tag for named entity recognition
```
Please find more information about the code and how the data was collected on [GitHub](https://github.com/UKPLab/TexPrax).
### Dataset Curators
Curation is managed by our [data manager](https://www.informatik.tu-darmstadt.de/ukp/research_ukp/ukp_research_data_and_software/ukp_data_and_software.en.jsp) at UKP.
### Licensing Information
[CC-by-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
Please cite this data using:
```
@article{stangier2022texprax,
title={TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation},
author={Stangier, Lorenz and Lee, Ji-Ung and Wang, Yuxi and M{\"u}ller, Marvin and Frick, Nicholas and Metternich, Joachim and Gurevych, Iryna},
journal={arXiv preprint arXiv:2208.07846},
year={2022}
}
```
### Contributions
Thanks to [@Wuhn](https://github.com/Wuhn) for adding this dataset.
## Tags
annotations_creators:
- expert-generated
language:
- de
language_creators:
- expert-generated
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: TexPrax-Conversations
size_categories:
- n<1K
- 1K<n<10K
source_datasets:
- original
tags:
- dialog
- expert to expert conversations
- task-oriented
task_categories:
- token-classification
- text-classification
task_ids:
- named-entity-recognition
- multi-class-classification | LIDIA-HESSEN/TexPrax | [
"license:cc-by-nc-4.0",
"arxiv:2208.07846",
"region:us"
]
| 2023-03-30T14:16:46+00:00 | {"license": "cc-by-nc-4.0"} | 2023-03-30T14:18:28+00:00 | [
"2208.07846"
]
| []
| TAGS
#license-cc-by-nc-4.0 #arxiv-2208.07846 #region-us
| Dataset Card for TexPrax
========================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ 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
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: n/a
* Point of Contact: Ji-Ung Lee (URL
### Dataset Summary
This dataset contains dialogues collected from German factory workers at the *Center for industrial productivity* (CiP). The dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.
### Supported Tasks and Leaderboards
This dataset supports the following tasks:
* Sentence classification
* Named entity recognition (will be updated soon with the new indexing)
* Dialog generation (so far not evaluated)
### Languages
German
Dataset Structure
-----------------
### Data Instances
On sentence level, each instance consists of the dialog-id, turn-id, sentence-id, the sentence (raw), the label, the domain, and the subsplit.
On token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.
### Data Fields
Sentence level:
* dialog-id: unique identifier for the dialog
* turn-id: unique identifier for the turn
* sentence-id: unique identifier for the dialog
* sentence: the respective sentence
* label: the label (*P* for Problem, *C* for Cause, *S* for solution, and *O* for Other)
* domain: the subdomains where the data was collected from. Domains are industry, machining, or n/a (for batch 2 and batch 3).
* subsplit: the respective subsplit of the data (see below)
Token level:
* id: the identifier
* tokens: a list of tokens (i.e., the tokenized dialogue)
* entities: the named entity in a BIO scheme (*B-X*, *I-X*, or O).
* subsplit: the respective subsplit of the data (see below)
### Data Splits
The dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory.
Train:
* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line
* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line
* Batch 2: data collected in-between October 2021-June 2022 from all workers
Test:
* Batch 3: data collected in July 2022 together with the system usability study run
Sentence level statistics:
Token level statistics:
[Needs to be added]
Dataset Creation
----------------
### Curation Rationale
This dataset provides task-oriented dialogues that solve a very domain specific problem.
### Source Data
#### Initial Data Collection and Normalization
The data was generated by workers at the CiP. The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal 'ne', contain abbreviations 'vll', and filler words such as 'ah'. For a detailed description please see the paper.
#### Who are the source language producers?
German factory workers working at the CiP
### Annotations
#### Annotation process
Token level. Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.
Sentence level. Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the TexPrax demo paper.
#### Who are the annotators?
Token level. Researchers working at the CiP.
Sentence level. The factory workers themselves.
### Personal and Sensitive Information
This dataset is fully anonymized. All occurrences of names have been manually checked during annotation and replaced with a random token.
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
Informal language especially used in short messages, however, seldom considered in existing NLP datasets. This dataset could serve as an interesting evaluation task for transferring language models to low-resource, but highly specific domains. Moreover, we note that despite all abbreviations, typos, and local dialects used in the messages, all workers were able to understand the questions as well as replies. This should be a standard future NLP models should be able to uphold.
### Discussion of Biases
The dialogues are very much on a professional level. The workers were informed (and gave their consent) in advance that their messages are being recorded and processed, which may have influenced them to hold only professional conversations, hence, all dialogues concern inanimate objects (i.e., machines).
### Other Known Limitations
Additional Information
----------------------
You can download the data via:
Please find more information about the code and how the data was collected on GitHub.
### Dataset Curators
Curation is managed by our data manager at UKP.
### Licensing Information
CC-by-NC 4.0
Please cite this data using:
### Contributions
Thanks to @Wuhn for adding this dataset.
Tags
----
annotations\_creators:
* expert-generated
language:
* de
language\_creators:
* expert-generated
license:
* cc-by-nc-4.0
multilinguality:
* monolingual
pretty\_name: TexPrax-Conversations
size\_categories:
* n<1K
* 1K<n<10K
source\_datasets:
* original
tags:
* dialog
* expert to expert conversations
* task-oriented
task\_categories:
* token-classification
* text-classification
task\_ids:
* named-entity-recognition
* multi-class-classification
| [
"### Dataset Summary\n\n\nThis dataset contains dialogues collected from German factory workers at the *Center for industrial productivity* (CiP). The dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.",
"### Supported Tasks and Leaderboards\n\n\nThis dataset supports the following tasks:\n\n\n* Sentence classification\n* Named entity recognition (will be updated soon with the new indexing)\n* Dialog generation (so far not evaluated)",
"### Languages\n\n\nGerman\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nOn sentence level, each instance consists of the dialog-id, turn-id, sentence-id, the sentence (raw), the label, the domain, and the subsplit.\n\n\nOn token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.",
"### Data Fields\n\n\nSentence level:\n\n\n* dialog-id: unique identifier for the dialog\n* turn-id: unique identifier for the turn\n* sentence-id: unique identifier for the dialog\n* sentence: the respective sentence\n* label: the label (*P* for Problem, *C* for Cause, *S* for solution, and *O* for Other)\n* domain: the subdomains where the data was collected from. Domains are industry, machining, or n/a (for batch 2 and batch 3).\n* subsplit: the respective subsplit of the data (see below)\n\n\nToken level:\n\n\n* id: the identifier\n* tokens: a list of tokens (i.e., the tokenized dialogue)\n* entities: the named entity in a BIO scheme (*B-X*, *I-X*, or O).\n* subsplit: the respective subsplit of the data (see below)",
"### Data Splits\n\n\nThe dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory.\n\n\nTrain:\n\n\n* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line\n* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line\n* Batch 2: data collected in-between October 2021-June 2022 from all workers\n\n\nTest:\n\n\n* Batch 3: data collected in July 2022 together with the system usability study run\n\n\nSentence level statistics:\n\n\n\nToken level statistics:\n[Needs to be added]\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThis dataset provides task-oriented dialogues that solve a very domain specific problem.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe data was generated by workers at the CiP. The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal 'ne', contain abbreviations 'vll', and filler words such as 'ah'. For a detailed description please see the paper.",
"#### Who are the source language producers?\n\n\nGerman factory workers working at the CiP",
"### Annotations",
"#### Annotation process\n\n\nToken level. Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.\n\n\nSentence level. Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the TexPrax demo paper.",
"#### Who are the annotators?\n\n\nToken level. Researchers working at the CiP.\n\n\nSentence level. The factory workers themselves.",
"### Personal and Sensitive Information\n\n\nThis dataset is fully anonymized. All occurrences of names have been manually checked during annotation and replaced with a random token.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nInformal language especially used in short messages, however, seldom considered in existing NLP datasets. This dataset could serve as an interesting evaluation task for transferring language models to low-resource, but highly specific domains. Moreover, we note that despite all abbreviations, typos, and local dialects used in the messages, all workers were able to understand the questions as well as replies. This should be a standard future NLP models should be able to uphold.",
"### Discussion of Biases\n\n\nThe dialogues are very much on a professional level. The workers were informed (and gave their consent) in advance that their messages are being recorded and processed, which may have influenced them to hold only professional conversations, hence, all dialogues concern inanimate objects (i.e., machines).",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------\n\n\nYou can download the data via:\n\n\nPlease find more information about the code and how the data was collected on GitHub.",
"### Dataset Curators\n\n\nCuration is managed by our data manager at UKP.",
"### Licensing Information\n\n\nCC-by-NC 4.0\n\n\nPlease cite this data using:",
"### Contributions\n\n\nThanks to @Wuhn for adding this dataset.\n\n\nTags\n----\n\n\nannotations\\_creators:\n\n\n* expert-generated\n\n\nlanguage:\n\n\n* de\n\n\nlanguage\\_creators:\n\n\n* expert-generated\n\n\nlicense:\n\n\n* cc-by-nc-4.0\n\n\nmultilinguality:\n\n\n* monolingual\n\n\npretty\\_name: TexPrax-Conversations\n\n\nsize\\_categories:\n\n\n* n<1K\n* 1K<n<10K\n\n\nsource\\_datasets:\n\n\n* original\n\n\ntags:\n\n\n* dialog\n* expert to expert conversations\n* task-oriented\n\n\ntask\\_categories:\n\n\n* token-classification\n* text-classification\n\n\ntask\\_ids:\n\n\n* named-entity-recognition\n* multi-class-classification"
]
| [
"TAGS\n#license-cc-by-nc-4.0 #arxiv-2208.07846 #region-us \n",
"### Dataset Summary\n\n\nThis dataset contains dialogues collected from German factory workers at the *Center for industrial productivity* (CiP). The dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.",
"### Supported Tasks and Leaderboards\n\n\nThis dataset supports the following tasks:\n\n\n* Sentence classification\n* Named entity recognition (will be updated soon with the new indexing)\n* Dialog generation (so far not evaluated)",
"### Languages\n\n\nGerman\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nOn sentence level, each instance consists of the dialog-id, turn-id, sentence-id, the sentence (raw), the label, the domain, and the subsplit.\n\n\nOn token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.",
"### Data Fields\n\n\nSentence level:\n\n\n* dialog-id: unique identifier for the dialog\n* turn-id: unique identifier for the turn\n* sentence-id: unique identifier for the dialog\n* sentence: the respective sentence\n* label: the label (*P* for Problem, *C* for Cause, *S* for solution, and *O* for Other)\n* domain: the subdomains where the data was collected from. Domains are industry, machining, or n/a (for batch 2 and batch 3).\n* subsplit: the respective subsplit of the data (see below)\n\n\nToken level:\n\n\n* id: the identifier\n* tokens: a list of tokens (i.e., the tokenized dialogue)\n* entities: the named entity in a BIO scheme (*B-X*, *I-X*, or O).\n* subsplit: the respective subsplit of the data (see below)",
"### Data Splits\n\n\nThe dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory.\n\n\nTrain:\n\n\n* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line\n* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line\n* Batch 2: data collected in-between October 2021-June 2022 from all workers\n\n\nTest:\n\n\n* Batch 3: data collected in July 2022 together with the system usability study run\n\n\nSentence level statistics:\n\n\n\nToken level statistics:\n[Needs to be added]\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThis dataset provides task-oriented dialogues that solve a very domain specific problem.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe data was generated by workers at the CiP. The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal 'ne', contain abbreviations 'vll', and filler words such as 'ah'. For a detailed description please see the paper.",
"#### Who are the source language producers?\n\n\nGerman factory workers working at the CiP",
"### Annotations",
"#### Annotation process\n\n\nToken level. Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.\n\n\nSentence level. Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the TexPrax demo paper.",
"#### Who are the annotators?\n\n\nToken level. Researchers working at the CiP.\n\n\nSentence level. The factory workers themselves.",
"### Personal and Sensitive Information\n\n\nThis dataset is fully anonymized. All occurrences of names have been manually checked during annotation and replaced with a random token.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nInformal language especially used in short messages, however, seldom considered in existing NLP datasets. This dataset could serve as an interesting evaluation task for transferring language models to low-resource, but highly specific domains. Moreover, we note that despite all abbreviations, typos, and local dialects used in the messages, all workers were able to understand the questions as well as replies. This should be a standard future NLP models should be able to uphold.",
"### Discussion of Biases\n\n\nThe dialogues are very much on a professional level. The workers were informed (and gave their consent) in advance that their messages are being recorded and processed, which may have influenced them to hold only professional conversations, hence, all dialogues concern inanimate objects (i.e., machines).",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------\n\n\nYou can download the data via:\n\n\nPlease find more information about the code and how the data was collected on GitHub.",
"### Dataset Curators\n\n\nCuration is managed by our data manager at UKP.",
"### Licensing Information\n\n\nCC-by-NC 4.0\n\n\nPlease cite this data using:",
"### Contributions\n\n\nThanks to @Wuhn for adding this dataset.\n\n\nTags\n----\n\n\nannotations\\_creators:\n\n\n* expert-generated\n\n\nlanguage:\n\n\n* de\n\n\nlanguage\\_creators:\n\n\n* expert-generated\n\n\nlicense:\n\n\n* cc-by-nc-4.0\n\n\nmultilinguality:\n\n\n* monolingual\n\n\npretty\\_name: TexPrax-Conversations\n\n\nsize\\_categories:\n\n\n* n<1K\n* 1K<n<10K\n\n\nsource\\_datasets:\n\n\n* original\n\n\ntags:\n\n\n* dialog\n* expert to expert conversations\n* task-oriented\n\n\ntask\\_categories:\n\n\n* token-classification\n* text-classification\n\n\ntask\\_ids:\n\n\n* named-entity-recognition\n* multi-class-classification"
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"passage: TAGS\n#license-cc-by-nc-4.0 #arxiv-2208.07846 #region-us \n### Dataset Summary\n\n\nThis dataset contains dialogues collected from German factory workers at the *Center for industrial productivity* (CiP). The dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.### Supported Tasks and Leaderboards\n\n\nThis dataset supports the following tasks:\n\n\n* Sentence classification\n* Named entity recognition (will be updated soon with the new indexing)\n* Dialog generation (so far not evaluated)### Languages\n\n\nGerman\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nOn sentence level, each instance consists of the dialog-id, turn-id, sentence-id, the sentence (raw), the label, the domain, and the subsplit.\n\n\nOn token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.",
"passage: ### Data Fields\n\n\nSentence level:\n\n\n* dialog-id: unique identifier for the dialog\n* turn-id: unique identifier for the turn\n* sentence-id: unique identifier for the dialog\n* sentence: the respective sentence\n* label: the label (*P* for Problem, *C* for Cause, *S* for solution, and *O* for Other)\n* domain: the subdomains where the data was collected from. Domains are industry, machining, or n/a (for batch 2 and batch 3).\n* subsplit: the respective subsplit of the data (see below)\n\n\nToken level:\n\n\n* id: the identifier\n* tokens: a list of tokens (i.e., the tokenized dialogue)\n* entities: the named entity in a BIO scheme (*B-X*, *I-X*, or O).\n* subsplit: the respective subsplit of the data (see below)### Data Splits\n\n\nThe dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory.\n\n\nTrain:\n\n\n* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line\n* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line\n* Batch 2: data collected in-between October 2021-June 2022 from all workers\n\n\nTest:\n\n\n* Batch 3: data collected in July 2022 together with the system usability study run\n\n\nSentence level statistics:\n\n\n\nToken level statistics:\n[Needs to be added]\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nThis dataset provides task-oriented dialogues that solve a very domain specific problem.### Source Data#### Initial Data Collection and Normalization\n\n\nThe data was generated by workers at the CiP. The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal 'ne', contain abbreviations 'vll', and filler words such as 'ah'. For a detailed description please see the paper.#### Who are the source language producers?\n\n\nGerman factory workers working at the CiP### Annotations#### Annotation process\n\n\nToken level. Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.\n\n\nSentence level. Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the TexPrax demo paper."
]
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|
96b221320a88bd5e3dc41324e873fdbd0e7f623d |
A collection of regularization / class instance datasets for the [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) model to use for DreamBooth prior preservation loss training. Files labeled with "mse vae" used the [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse) VAE. For ease of use, datasets are stored as zip files containing 512x512 PNG images. The number of images in each zip file is specified at the end of the filename.
There is currently a bug where HuggingFace is incorrectly reporting that the datasets are pickled. They are not picked, they are simple ZIP files containing the images.
This dataset is based on the conventions setup by [Progamergov](https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images) and their very useful regularization images.
Currently this repository contains the following datasets (datasets are named after the prompt they used):
* "**interior design**": 2354 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. | reformatco/sd1_5-regularization-images | [
"license:mit",
"region:us"
]
| 2023-03-30T14:31:52+00:00 | {"license": "mit"} | 2023-03-30T15:27:31+00:00 | []
| []
| TAGS
#license-mit #region-us
|
A collection of regularization / class instance datasets for the Stable Diffusion v1-5 model to use for DreamBooth prior preservation loss training. Files labeled with "mse vae" used the stabilityai/sd-vae-ft-mse VAE. For ease of use, datasets are stored as zip files containing 512x512 PNG images. The number of images in each zip file is specified at the end of the filename.
There is currently a bug where HuggingFace is incorrectly reporting that the datasets are pickled. They are not picked, they are simple ZIP files containing the images.
This dataset is based on the conventions setup by Progamergov and their very useful regularization images.
Currently this repository contains the following datasets (datasets are named after the prompt they used):
* "interior design": 2354 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE. | []
| [
"TAGS\n#license-mit #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-mit #region-us \n"
]
| [
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|
e9fa77905166cea2232ef20ffd67a02bcf1a645b |
GPT4All extended training set. The original model was trained on https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations. We filtered out P3 for our final training. See detail here for why: https://s3.amazonaws.com/static.nomic.ai/gpt4all/2023_GPT4All_Technical_Report.pdf
| nomic-ai/gpt4all_prompt_generations_with_p3 | [
"license:apache-2.0",
"region:us"
]
| 2023-03-30T15:39:08+00:00 | {"license": "apache-2.0"} | 2023-03-30T15:52:36+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
|
GPT4All extended training set. The original model was trained on URL We filtered out P3 for our final training. See detail here for why: URL
| []
| [
"TAGS\n#license-apache-2.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
]
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|
b8e2894093aaad9b3ba462896ce6f7c4b72da5b9 | # Dataset Card for "form_bbox"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | achang/form_bbox | [
"region:us"
]
| 2023-03-30T15:43:05+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13264645.0, "num_examples": 231}], "download_size": 12341598, "dataset_size": 13264645.0}} | 2023-03-30T18:50:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "form_bbox"
More Information needed | [
"# Dataset Card for \"form_bbox\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"form_bbox\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"form_bbox\"\n\nMore Information needed"
]
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|
9bbd480483ac5ba1f56dfa3fa314cc5ca3fac4c5 | # Dataset Card for "AntiSpoofing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ArturoHurtado7/AntiSpoofing | [
"region:us"
]
| 2023-03-30T16:20:06+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 14202353920.944, "num_examples": 80816}], "download_size": 4585467118, "dataset_size": 14202353920.944}} | 2023-03-30T20:23:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AntiSpoofing"
More Information needed | [
"# Dataset Card for \"AntiSpoofing\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AntiSpoofing\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AntiSpoofing\"\n\nMore Information needed"
]
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|
2685b13d6b0a456431fb260e488a5e40cc1b3136 | # Dataset Card for Human parsing data (ATR)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset has 17,706 images and mask pairs. It is just a copy of
[Deep Human Parsing](https://github.com/lemondan/HumanParsing-Dataset) ATR dataset. The mask labels are:
"0": "Background",
"1": "Hat",
"2": "Hair",
"3": "Sunglasses",
"4": "Upper-clothes",
"5": "Skirt",
"6": "Pants",
"7": "Dress",
"8": "Belt",
"9": "Left-shoe",
"10": "Right-shoe",
"11": "Face",
"12": "Left-leg",
"13": "Right-leg",
"14": "Left-arm",
"15": "Right-arm",
"16": "Bag",
"17": "Scarf"
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
```bibtex
@ARTICLE{ATR, author={Xiaodan Liang and Si Liu and Xiaohui Shen and Jianchao Yang and Luoqi Liu and Jian Dong and Liang Lin and Shuicheng Yan}, journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, title={Deep Human Parsing with Active Template Regression}, year={2015}, volume={37}, number={12}, pages={2402-2414}, doi={10.1109/TPAMI.2015.2408360}, ISSN={0162-8828}, month={Dec}}
@InProceedings{CO-CNN, author={Xiaodan Liang and Chunyan Xu and Xiaohui Shen and Jianchao Yang and Si Liu and Jinhui Tang and Liang Lin and Shuicheng Yan}, journal ={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, title={ICCV}, year={2015}, }
``` | mattmdjaga/human_parsing_dataset | [
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"size_categories:10K<n<100K",
"region:us"
]
| 2023-03-30T16:59:37+00:00 | {"size_categories": ["10K<n<100K"], "task_categories": ["image-segmentation"], "task_ids": ["semantic-segmentation"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "mask", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5892290030.116, "num_examples": 17706}], "download_size": 5893438158, "dataset_size": 5892290030.116}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-09-11T08:07:44+00:00 | []
| []
| TAGS
#task_categories-image-segmentation #task_ids-semantic-segmentation #size_categories-10K<n<100K #region-us
| # Dataset Card for Human parsing data (ATR)
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- 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
- Citation Information
- Contributions
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset has 17,706 images and mask pairs. It is just a copy of
Deep Human Parsing ATR dataset. The mask labels are:
"0": "Background",
"1": "Hat",
"2": "Hair",
"3": "Sunglasses",
"4": "Upper-clothes",
"5": "Skirt",
"6": "Pants",
"7": "Dress",
"8": "Belt",
"9": "Left-shoe",
"10": "Right-shoe",
"11": "Face",
"12": "Left-leg",
"13": "Right-leg",
"14": "Left-arm",
"15": "Right-arm",
"16": "Bag",
"17": "Scarf"
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Human parsing data (ATR)",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nThis dataset has 17,706 images and mask pairs. It is just a copy of \nDeep Human Parsing ATR dataset. The mask labels are: \n \"0\": \"Background\",\n \"1\": \"Hat\",\n \"2\": \"Hair\",\n \"3\": \"Sunglasses\",\n \"4\": \"Upper-clothes\",\n \"5\": \"Skirt\",\n \"6\": \"Pants\",\n \"7\": \"Dress\",\n \"8\": \"Belt\",\n \"9\": \"Left-shoe\",\n \"10\": \"Right-shoe\",\n \"11\": \"Face\",\n \"12\": \"Left-leg\",\n \"13\": \"Right-leg\",\n \"14\": \"Left-arm\",\n \"15\": \"Right-arm\",\n \"16\": \"Bag\",\n \"17\": \"Scarf\"",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#task_categories-image-segmentation #task_ids-semantic-segmentation #size_categories-10K<n<100K #region-us \n",
"# Dataset Card for Human parsing data (ATR)",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nThis dataset has 17,706 images and mask pairs. It is just a copy of \nDeep Human Parsing ATR dataset. The mask labels are: \n \"0\": \"Background\",\n \"1\": \"Hat\",\n \"2\": \"Hair\",\n \"3\": \"Sunglasses\",\n \"4\": \"Upper-clothes\",\n \"5\": \"Skirt\",\n \"6\": \"Pants\",\n \"7\": \"Dress\",\n \"8\": \"Belt\",\n \"9\": \"Left-shoe\",\n \"10\": \"Right-shoe\",\n \"11\": \"Face\",\n \"12\": \"Left-leg\",\n \"13\": \"Right-leg\",\n \"14\": \"Left-arm\",\n \"15\": \"Right-arm\",\n \"16\": \"Bag\",\n \"17\": \"Scarf\"",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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| [
"passage: TAGS\n#task_categories-image-segmentation #task_ids-semantic-segmentation #size_categories-10K<n<100K #region-us \n# Dataset Card for Human parsing data (ATR)## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:### Dataset Summary\n\nThis dataset has 17,706 images and mask pairs. It is just a copy of \nDeep Human Parsing ATR dataset. The mask labels are: \n \"0\": \"Background\",\n \"1\": \"Hat\",\n \"2\": \"Hair\",\n \"3\": \"Sunglasses\",\n \"4\": \"Upper-clothes\",\n \"5\": \"Skirt\",\n \"6\": \"Pants\",\n \"7\": \"Dress\",\n \"8\": \"Belt\",\n \"9\": \"Left-shoe\",\n \"10\": \"Right-shoe\",\n \"11\": \"Face\",\n \"12\": \"Left-leg\",\n \"13\": \"Right-leg\",\n \"14\": \"Left-arm\",\n \"15\": \"Right-arm\",\n \"16\": \"Bag\",\n \"17\": \"Scarf\"### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?"
]
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|
f7a4ffbb3ec04ea813b67fa687e1383b1671b006 |
## This is the LAION Community Chat Contributions Repository
- We welcome all contributions of chat data that organizations have gathered from their users, so that the data can be shared and useful to train chatbots.
- We will not curate the data except that we will require the organizations to have the right to contribute the data to LAION to distribute under Apache 2.0 or other permissive licenses.
- The data has no sensitive personally identifiable information, as not children abuse materials, and is otherwise legal in the jurisdiction gathered and contributed.
## Catalog
- Together's User Feedback dataset 🚀: This is gathered using the OCK feedback bot (https://huggingface.co/spaces/togethercomputer/OpenChatKit) by Together's incredible community, and then curated by Together. This dataset is a general chat dataset with the formatting of '\<human\> instruction\n\<bot\>response'. Direct link: https://huggingface.co/datasets/laion/community-chat-contributions/raw/main/together_user_feedback_v0.2.jsonl
- More to come.
Please contact us in the 'community' link above with questions and proposed contributions to this dataset.
## Acknowledgement
Thank you to the open source and open access community and LAION's volunteers.
| laion/community-chat-contributions | [
"license:apache-2.0",
"region:us"
]
| 2023-03-30T17:38:23+00:00 | {"license": "apache-2.0"} | 2023-04-01T22:48:40+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
|
## This is the LAION Community Chat Contributions Repository
- We welcome all contributions of chat data that organizations have gathered from their users, so that the data can be shared and useful to train chatbots.
- We will not curate the data except that we will require the organizations to have the right to contribute the data to LAION to distribute under Apache 2.0 or other permissive licenses.
- The data has no sensitive personally identifiable information, as not children abuse materials, and is otherwise legal in the jurisdiction gathered and contributed.
## Catalog
- Together's User Feedback dataset : This is gathered using the OCK feedback bot (URL by Together's incredible community, and then curated by Together. This dataset is a general chat dataset with the formatting of '\<human\> instruction\n\<bot\>response'. Direct link: URL
- More to come.
Please contact us in the 'community' link above with questions and proposed contributions to this dataset.
## Acknowledgement
Thank you to the open source and open access community and LAION's volunteers.
| [
"## This is the LAION Community Chat Contributions Repository\n\n- We welcome all contributions of chat data that organizations have gathered from their users, so that the data can be shared and useful to train chatbots. \n- We will not curate the data except that we will require the organizations to have the right to contribute the data to LAION to distribute under Apache 2.0 or other permissive licenses.\n- The data has no sensitive personally identifiable information, as not children abuse materials, and is otherwise legal in the jurisdiction gathered and contributed.",
"## Catalog\n- Together's User Feedback dataset : This is gathered using the OCK feedback bot (URL by Together's incredible community, and then curated by Together. This dataset is a general chat dataset with the formatting of '\\<human\\> instruction\\n\\<bot\\>response'. Direct link: URL\n- More to come. \n\nPlease contact us in the 'community' link above with questions and proposed contributions to this dataset.",
"## Acknowledgement\nThank you to the open source and open access community and LAION's volunteers."
]
| [
"TAGS\n#license-apache-2.0 #region-us \n",
"## This is the LAION Community Chat Contributions Repository\n\n- We welcome all contributions of chat data that organizations have gathered from their users, so that the data can be shared and useful to train chatbots. \n- We will not curate the data except that we will require the organizations to have the right to contribute the data to LAION to distribute under Apache 2.0 or other permissive licenses.\n- The data has no sensitive personally identifiable information, as not children abuse materials, and is otherwise legal in the jurisdiction gathered and contributed.",
"## Catalog\n- Together's User Feedback dataset : This is gathered using the OCK feedback bot (URL by Together's incredible community, and then curated by Together. This dataset is a general chat dataset with the formatting of '\\<human\\> instruction\\n\\<bot\\>response'. Direct link: URL\n- More to come. \n\nPlease contact us in the 'community' link above with questions and proposed contributions to this dataset.",
"## Acknowledgement\nThank you to the open source and open access community and LAION's volunteers."
]
| [
14,
124,
110,
23
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n## This is the LAION Community Chat Contributions Repository\n\n- We welcome all contributions of chat data that organizations have gathered from their users, so that the data can be shared and useful to train chatbots. \n- We will not curate the data except that we will require the organizations to have the right to contribute the data to LAION to distribute under Apache 2.0 or other permissive licenses.\n- The data has no sensitive personally identifiable information, as not children abuse materials, and is otherwise legal in the jurisdiction gathered and contributed.## Catalog\n- Together's User Feedback dataset : This is gathered using the OCK feedback bot (URL by Together's incredible community, and then curated by Together. This dataset is a general chat dataset with the formatting of '\\<human\\> instruction\\n\\<bot\\>response'. Direct link: URL\n- More to come. \n\nPlease contact us in the 'community' link above with questions and proposed contributions to this dataset.## Acknowledgement\nThank you to the open source and open access community and LAION's volunteers."
]
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|
52304679de97d4c782bad847e9e296a94359aa04 |
# Dataset Card for IMDb Movie Dataset: All Movies by Genre
## Dataset Description
- **Homepage:** https://www.kaggle.com/datasets/rajugc/imdb-movies-dataset-based-on-genre?select=history.csv
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is an adapted version of **"IMDb Movie Dataset: All Movies by Genre"** found at: https://www.kaggle.com/datasets/rajugc/imdb-movies-dataset-based-on-genre?select=history.csv.
Within the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as "Add a plot / Plot unkown" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.
The columns in the data are: "movie title - year", "genre", "expanded-genres", "rating", and "description"
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Language data is English.
## Dataset Structure
### Data Instances
**training** = 238256 entries
- Total amount of disk used: 54.3 MB
**test** = 29756 entries
- Total amount of disk used: 6.77 MB
**validation** = 29809 entries
- Total amount of disk used: 6.78 MB
An example of one entry looks as follows:
```
{
"movie title - year" : "Die Hard - 1988",
"genre" : "Action",
"expanded-genres" : "Action, Thriller",
"rating" : 8.2,
"description" : "A New York City police officer tries to save his estranged wife and several others taken hostage by terrorists during a Christmas party at the Nakatomi Plaza in Los Angeles."
}
```
### Data Fields
The data fields are the same among all splits.
Fields are as follows:
- "movie title - year": a string feature.
- "genre": a string classification label, with the possible values: 'Adventure', 'Action', 'Thriller', 'Romance', 'Crime', 'Fantasy', 'Mystery', 'Horror', 'War', 'Family', 'Animation', 'Scifi', 'Sports', 'History', 'Biography', and 'Film-noir'.
- "expanded-genres" : a string feature.
- "rating" : a floating point value, ranging from 0.0 to 10.0.
- "description" : a string feature.
### Source Data
**"IMDb Movie Dataset: All Movies by Genre"** found at: https://www.kaggle.com/datasets/rajugc/imdb-movies-dataset-based-on-genre?select=history.csv
## 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] | jquigl/imdb-genres | [
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| 2023-03-30T18:18:34+00:00 | {"language": ["en"], "license": "cc-by-nc-sa-4.0"} | 2023-04-04T16:58:06+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-cc-by-nc-sa-4.0 #region-us
|
# Dataset Card for IMDb Movie Dataset: All Movies by Genre
## Dataset Description
- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset is an adapted version of "IMDb Movie Dataset: All Movies by Genre" found at: URL
Within the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as "Add a plot / Plot unkown" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.
The columns in the data are: "movie title - year", "genre", "expanded-genres", "rating", and "description"
### Supported Tasks and Leaderboards
### Languages
Language data is English.
## Dataset Structure
### Data Instances
training = 238256 entries
- Total amount of disk used: 54.3 MB
test = 29756 entries
- Total amount of disk used: 6.77 MB
validation = 29809 entries
- Total amount of disk used: 6.78 MB
An example of one entry looks as follows:
### Data Fields
The data fields are the same among all splits.
Fields are as follows:
- "movie title - year": a string feature.
- "genre": a string classification label, with the possible values: 'Adventure', 'Action', 'Thriller', 'Romance', 'Crime', 'Fantasy', 'Mystery', 'Horror', 'War', 'Family', 'Animation', 'Scifi', 'Sports', 'History', 'Biography', and 'Film-noir'.
- "expanded-genres" : a string feature.
- "rating" : a floating point value, ranging from 0.0 to 10.0.
- "description" : a string feature.
### Source Data
"IMDb Movie Dataset: All Movies by Genre" found at: URL
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for IMDb Movie Dataset: All Movies by Genre",
"## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset is an adapted version of \"IMDb Movie Dataset: All Movies by Genre\" found at: URL\nWithin the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as \"Add a plot / Plot unkown\" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.\nThe columns in the data are: \"movie title - year\", \"genre\", \"expanded-genres\", \"rating\", and \"description\"",
"### Supported Tasks and Leaderboards",
"### Languages\n\nLanguage data is English.",
"## Dataset Structure",
"### Data Instances\n\ntraining = 238256 entries\n - Total amount of disk used: 54.3 MB\n\ntest = 29756 entries\n - Total amount of disk used: 6.77 MB\n\nvalidation = 29809 entries\n - Total amount of disk used: 6.78 MB\n \nAn example of one entry looks as follows:",
"### Data Fields\n\nThe data fields are the same among all splits.\n\nFields are as follows: \n - \"movie title - year\": a string feature.\n - \"genre\": a string classification label, with the possible values: 'Adventure', 'Action', 'Thriller', 'Romance', 'Crime', 'Fantasy', 'Mystery', 'Horror', 'War', 'Family', 'Animation', 'Scifi', 'Sports', 'History', 'Biography', and 'Film-noir'.\n - \"expanded-genres\" : a string feature.\n - \"rating\" : a floating point value, ranging from 0.0 to 10.0.\n - \"description\" : a string feature.",
"### Source Data\n\n\"IMDb Movie Dataset: All Movies by Genre\" found at: URL",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#language-English #license-cc-by-nc-sa-4.0 #region-us \n",
"# Dataset Card for IMDb Movie Dataset: All Movies by Genre",
"## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset is an adapted version of \"IMDb Movie Dataset: All Movies by Genre\" found at: URL\nWithin the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as \"Add a plot / Plot unkown\" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.\nThe columns in the data are: \"movie title - year\", \"genre\", \"expanded-genres\", \"rating\", and \"description\"",
"### Supported Tasks and Leaderboards",
"### Languages\n\nLanguage data is English.",
"## Dataset Structure",
"### Data Instances\n\ntraining = 238256 entries\n - Total amount of disk used: 54.3 MB\n\ntest = 29756 entries\n - Total amount of disk used: 6.77 MB\n\nvalidation = 29809 entries\n - Total amount of disk used: 6.78 MB\n \nAn example of one entry looks as follows:",
"### Data Fields\n\nThe data fields are the same among all splits.\n\nFields are as follows: \n - \"movie title - year\": a string feature.\n - \"genre\": a string classification label, with the possible values: 'Adventure', 'Action', 'Thriller', 'Romance', 'Crime', 'Fantasy', 'Mystery', 'Horror', 'War', 'Family', 'Animation', 'Scifi', 'Sports', 'History', 'Biography', and 'Film-noir'.\n - \"expanded-genres\" : a string feature.\n - \"rating\" : a floating point value, ranging from 0.0 to 10.0.\n - \"description\" : a string feature.",
"### Source Data\n\n\"IMDb Movie Dataset: All Movies by Genre\" found at: URL",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#language-English #license-cc-by-nc-sa-4.0 #region-us \n# Dataset Card for IMDb Movie Dataset: All Movies by Genre## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset is an adapted version of \"IMDb Movie Dataset: All Movies by Genre\" found at: URL\nWithin the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as \"Add a plot / Plot unkown\" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.\nThe columns in the data are: \"movie title - year\", \"genre\", \"expanded-genres\", \"rating\", and \"description\"### Supported Tasks and Leaderboards### Languages\n\nLanguage data is English.## Dataset Structure### Data Instances\n\ntraining = 238256 entries\n - Total amount of disk used: 54.3 MB\n\ntest = 29756 entries\n - Total amount of disk used: 6.77 MB\n\nvalidation = 29809 entries\n - Total amount of disk used: 6.78 MB\n \nAn example of one entry looks as follows:"
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|
e68f2630a23168331ddcb13bbcdd9abe2f067d8c | # Dataset Card for "archdaily_v01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ossaili/archdaily_v01 | [
"region:us"
]
| 2023-03-30T18:20:00+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 68941988222.705, "num_examples": 67499}], "download_size": 54306329860, "dataset_size": 68941988222.705}} | 2023-03-30T20:11:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "archdaily_v01"
More Information needed | [
"# Dataset Card for \"archdaily_v01\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"archdaily_v01\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"archdaily_v01\"\n\nMore Information needed"
]
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|
53938e5014eb499e964ea17ef630a44b2fc784a6 | # Dataset Card for "supernatural-instructions-2m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andersonbcdefg/supernatural-instructions-2m | [
"region:us"
]
| 2023-03-30T19:43:52+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1859403487.079275, "num_examples": 1990915}], "download_size": 521457643, "dataset_size": 1859403487.079275}} | 2023-03-30T19:45:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "supernatural-instructions-2m"
More Information needed | [
"# Dataset Card for \"supernatural-instructions-2m\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"supernatural-instructions-2m\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"supernatural-instructions-2m\"\n\nMore Information needed"
]
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|
1b311646254224d4760cce2cbf33b89d70c8ac2b | # Dataset Card for "alpaca_data_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | argilla/alpaca_data_cleaned | [
"region:us"
]
| 2023-03-30T21:08:20+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "_instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 975104502, "num_examples": 51713}], "download_size": 679574648, "dataset_size": 975104502}} | 2023-03-30T21:09:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "alpaca_data_cleaned"
More Information needed | [
"# Dataset Card for \"alpaca_data_cleaned\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"alpaca_data_cleaned\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"alpaca_data_cleaned\"\n\nMore Information needed"
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|
57131f9161ad88172a545942f281d722a08991e8 | # Dataset Card for "libre_chem_textbooks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Hack90/libre_chem_textbooks | [
"region:us"
]
| 2023-03-30T21:12:30+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "html", "dtype": "string"}, {"name": "text_length", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 29537465, "num_examples": 3740}], "download_size": 0, "dataset_size": 29537465}} | 2023-04-13T13:47:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "libre_chem_textbooks"
More Information needed | [
"# Dataset Card for \"libre_chem_textbooks\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"libre_chem_textbooks\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"libre_chem_textbooks\"\n\nMore Information needed"
]
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|
1afec674a022775f40033022e7688599b6adc104 | # Dataset Card for "full_user_edit_responses-clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AlekseyKorshuk/full_user_edit_responses-clean | [
"region:us"
]
| 2023-03-30T21:18:23+00:00 | {"dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "model_input", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "edited_response", "dtype": "string"}, {"name": "user_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 782450796.338342, "num_examples": 364272}], "download_size": 355765300, "dataset_size": 782450796.338342}} | 2023-03-30T21:19:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "full_user_edit_responses-clean"
More Information needed | [
"# Dataset Card for \"full_user_edit_responses-clean\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"full_user_edit_responses-clean\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"full_user_edit_responses-clean\"\n\nMore Information needed"
]
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|
bdf2c53a701c77add2c91595de0baf52762a5b6f | # Dataset Card for "cracks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | GarayMC/cracks | [
"region:us"
]
| 2023-03-30T22:04:34+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "images", "1": "label", "2": "test", "3": "train", "4": "valid"}}}}], "splits": [{"name": "train", "num_bytes": 7553040.0, "num_examples": 214}], "download_size": 7153050, "dataset_size": 7553040.0}} | 2023-03-30T22:05:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cracks"
More Information needed | [
"# Dataset Card for \"cracks\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cracks\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cracks\"\n\nMore Information needed"
]
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|
ebd71d534c1849ae580237e8651ce284b58e01d9 | # Dataset Card for "sft_language_submix_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andersonbcdefg/sft_language_submix_v2 | [
"region:us"
]
| 2023-03-30T22:20:27+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1798819313.9076667, "num_examples": 1492027}], "download_size": 830793165, "dataset_size": 1798819313.9076667}} | 2023-03-30T22:21:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sft_language_submix_v2"
More Information needed | [
"# Dataset Card for \"sft_language_submix_v2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sft_language_submix_v2\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sft_language_submix_v2\"\n\nMore Information needed"
]
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|
3e3937135faa2f759ff2b11088ebece39dc71506 | # Dataset Card for "clean-dataset-preview-zero"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AlekseyKorshuk/clean-dataset-preview-zero | [
"region:us"
]
| 2023-03-30T22:22:05+00:00 | {"dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "model_input", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "edited_response", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "check_nsfw_words_criteria", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 115524003.7411831, "num_examples": 50510}], "download_size": 45480394, "dataset_size": 115524003.7411831}} | 2023-03-30T22:22:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "clean-dataset-preview-zero"
More Information needed | [
"# Dataset Card for \"clean-dataset-preview-zero\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"clean-dataset-preview-zero\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"clean-dataset-preview-zero\"\n\nMore Information needed"
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|
60e24f70e01dcabde1be98c37d6825940464cd77 | # Dataset Card for "sft_code_submix_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andersonbcdefg/sft_code_submix_v2 | [
"region:us"
]
| 2023-03-31T00:44:10+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 514538029.78500825, "num_examples": 429277}], "download_size": 208637663, "dataset_size": 514538029.78500825}} | 2023-03-31T00:44:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sft_code_submix_v2"
More Information needed | [
"# Dataset Card for \"sft_code_submix_v2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sft_code_submix_v2\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sft_code_submix_v2\"\n\nMore Information needed"
]
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|
cb15dd9a28208e61b463c3762cef1715b7b41b86 | # Dataset Card for "license_plates_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | charliexu07/license_plates_2 | [
"region:us"
]
| 2023-03-31T00:45:52+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "train"}}}}], "splits": [{"name": "train", "num_bytes": 40055469.0, "num_examples": 44}], "download_size": 33773613, "dataset_size": 40055469.0}} | 2023-03-31T00:47:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "license_plates_2"
More Information needed | [
"# Dataset Card for \"license_plates_2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"license_plates_2\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"license_plates_2\"\n\nMore Information needed"
]
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|
a2eb4dc5b999baab599ef396509bda98c029e4be | # Dataset Card for "license_plates"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | charliexu07/license_plates | [
"region:us"
]
| 2023-03-31T00:50:19+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 40055111.0, "num_examples": 44}], "download_size": 33773209, "dataset_size": 40055111.0}} | 2023-03-31T00:51:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "license_plates"
More Information needed | [
"# Dataset Card for \"license_plates\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"license_plates\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"license_plates\"\n\nMore Information needed"
]
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|
eeede9c01dc72781d2c3c45f4353c90e511ef301 | # Dataset Card for "sft_language_and_code_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andersonbcdefg/sft_language_and_code_v2 | [
"region:us"
]
| 2023-03-31T00:52:44+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2245852907.7835426, "num_examples": 1865627}], "download_size": 1040704851, "dataset_size": 2245852907.7835426}} | 2023-03-31T00:55:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sft_language_and_code_v2"
More Information needed | [
"# Dataset Card for \"sft_language_and_code_v2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sft_language_and_code_v2\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sft_language_and_code_v2\"\n\nMore Information needed"
]
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|
cef9bf31401ef303ab2d948b3ad45a323e0fce7c | # Synthetic Dataset for Product Descriptions and Ads
The basic process was as follows:
1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.
2. Split the output into desired format `{"product" : "<PRODUCT NAME>", "description" : "<DESCRIPTION>"}
3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.
This data was not cleaned or verified manually. | c-s-ale/Product-Descriptions-and-Ads | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:openrail",
"art",
"region:us"
]
| 2023-03-31T01:19:06+00:00 | {"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "Product Descriptions and Ads", "dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27511.2, "num_examples": 90}, {"name": "test", "num_bytes": 3056.8, "num_examples": 10}], "download_size": 24914, "dataset_size": 30568}, "tags": ["art"]} | 2023-03-31T03:39:12+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #license-openrail #art #region-us
| # Synthetic Dataset for Product Descriptions and Ads
The basic process was as follows:
1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.
2. Split the output into desired format '{"product" : "<PRODUCT NAME>", "description" : "<DESCRIPTION>"}
3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.
This data was not cleaned or verified manually. | [
"# Synthetic Dataset for Product Descriptions and Ads\n\nThe basic process was as follows:\n\n1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.\n2. Split the output into desired format '{\"product\" : \"<PRODUCT NAME>\", \"description\" : \"<DESCRIPTION>\"}\n3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.\n\nThis data was not cleaned or verified manually."
]
| [
"TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #license-openrail #art #region-us \n",
"# Synthetic Dataset for Product Descriptions and Ads\n\nThe basic process was as follows:\n\n1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.\n2. Split the output into desired format '{\"product\" : \"<PRODUCT NAME>\", \"description\" : \"<DESCRIPTION>\"}\n3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.\n\nThis data was not cleaned or verified manually."
]
| [
39,
116
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #license-openrail #art #region-us \n# Synthetic Dataset for Product Descriptions and Ads\n\nThe basic process was as follows:\n\n1. Prompt GPT-4 to create a list of 100 sample clothing items and descriptions for those items.\n2. Split the output into desired format '{\"product\" : \"<PRODUCT NAME>\", \"description\" : \"<DESCRIPTION>\"}\n3. Prompt GPT-4 to create adverts for each of the 100 samples based on their name and description.\n\nThis data was not cleaned or verified manually."
]
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|
466a4ebdb949cfb119599a4340c13ea66a3f00dd | # Dataset Card for "tawikidump_20230320"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | livinNector/tawikidump-20230320 | [
"region:us"
]
| 2023-03-31T02:45:52+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "tawikisource", "num_bytes": 115918651, "num_examples": 5031}, {"name": "tawikibooks", "num_bytes": 4768733, "num_examples": 1155}, {"name": "tawikinews", "num_bytes": 14492170, "num_examples": 3372}, {"name": "tawikiquote", "num_bytes": 6517167, "num_examples": 1211}, {"name": "tawiktionary", "num_bytes": 161117356, "num_examples": 406557}, {"name": "tawiki", "num_bytes": 752681708, "num_examples": 155212}], "download_size": 0, "dataset_size": 1055495785}} | 2023-03-31T02:47:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tawikidump_20230320"
More Information needed | [
"# Dataset Card for \"tawikidump_20230320\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tawikidump_20230320\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tawikidump_20230320\"\n\nMore Information needed"
]
| [
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|
5674949ca89e9cca73533539dcbdaea49ee1cbc3 | # Dataset Card for "t0_fsnoopt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mistobaan/t0_fsnoopt | [
"region:us"
]
| 2023-03-31T03:02:06+00:00 | {"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 96473051525, "num_examples": 42545751}], "download_size": 54092673262, "dataset_size": 96473051525}} | 2023-03-31T03:32:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "t0_fsnoopt"
More Information needed | [
"# Dataset Card for \"t0_fsnoopt\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"t0_fsnoopt\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"t0_fsnoopt\"\n\nMore Information needed"
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|
2bc7b016f747804472ff78584a1caf32465a783b | # Dataset Card for "cnn_random_sample20_10000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | whu9/cnn_random_sample20_10000 | [
"region:us"
]
| 2023-03-31T03:55:10+00:00 | {"dataset_info": {"features": [{"name": "doc", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 869308742, "num_examples": 10000}], "download_size": 532541435, "dataset_size": 869308742}} | 2023-03-31T03:55:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cnn_random_sample20_10000"
More Information needed | [
"# Dataset Card for \"cnn_random_sample20_10000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cnn_random_sample20_10000\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cnn_random_sample20_10000\"\n\nMore Information needed"
]
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|
11fb8e492a7b12d501e6c416ca6a9d00af05dcbd | # Dataset Card for "cnn_test_random_sample20_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | whu9/cnn_test_random_sample20_1000 | [
"region:us"
]
| 2023-03-31T04:27:21+00:00 | {"dataset_info": {"features": [{"name": "doc", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 86034673, "num_examples": 1000}], "download_size": 0, "dataset_size": 86034673}} | 2023-03-31T04:58:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cnn_test_random_sample20_1000"
More Information needed | [
"# Dataset Card for \"cnn_test_random_sample20_1000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cnn_test_random_sample20_1000\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cnn_test_random_sample20_1000\"\n\nMore Information needed"
]
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|
32bfa94dcd95e7c3bbe09fa281f5b89870921745 | # Dataset Card for "ImageNet-Hard"
[Project Page](https://taesiri.github.io/ZoomIsAllYouNeed/) - [ArXiv](https://arxiv.org/abs/2304.05538) - [Paper](https://huggingface.co/papers/2304.05538) - [Github](https://github.com/taesiri/ZoomIsAllYouNeed) - [Image Browser](https://huggingface.co/spaces/taesiri/ImageNet-Hard-Browser)
## Dataset Summary
**ImageNet-Hard** is a new benchmark that comprises 10,980 images collected from various existing ImageNet-scale benchmarks (ImageNet, ImageNet-V2, ImageNet-Sketch, ImageNet-C, ImageNet-R, ImageNet-ReaL, ImageNet-A, and ObjectNet). This dataset poses a significant challenge to state-of-the-art vision models as merely zooming in often fails to improve their ability to classify images correctly. As a result, even the most advanced models, such as `CLIP-ViT-L/14@336px`, struggle to perform well on this dataset, achieving a mere `2.02%` accuracy.
*ImageNet-Hard-4K*: For the 4K version please refere to [this dataset](https://huggingface.co/datasets/taesiri/imagenet-hard-4K).
### Dataset Distribution
### Classifiers Performance
| Model | Accuracy |
| ------------------- | -------- |
| AlexNet | 7.34 |
| VGG-16 | 12.00 |
| ResNet-18 | 10.86 |
| ResNet-50 | 14.74 |
| ViT-B/32 | 18.52 |
| EfficientNet-B0 | 16.57 |
| EfficientNet-B7 | 23.20 |
| EfficientNet-L2-Ns | 39.00 |
| CLIP-ViT-L/14@224px | 1.86 |
| CLIP-ViT-L/14@336px | 2.02 |
| OpenCLIP-ViT-bigG-14| 15.93 |
| OpenCLIP-ViT-L-14 | 15.60 |
**Evaluation Code**
* CLIP <a target="_blank" href="https://colab.research.google.com/github/taesiri/ZoomIsAllYouNeed/blob/main/src/ImageNet_Hard/Prompt_Engineering_for_ImageNet_Hard.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
* [OpenCLIP](https://github.com/taesiri/ZoomIsAllYouNeed/blob/main/src/ImageNet_Hard/benchmark_openclip.py)
* Other models <a target="_blank" href="https://colab.research.google.com/github/taesiri/ZoomIsAllYouNeed/blob/main/src/ImageNet_Hard/Benchmark_ImageNet_Hard.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
## Supported Tasks
- `image-classification`: The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).
## Languages
The `english_label` field in the dataset are in English.
## Dataset Structure
Data Instances
An example looks like this:
```python
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=575x409 at 0x7F09456B53A0>,
'label': [0],
'origin': 'imagenet_sketch',
'english_label': ['tench']
}
```
### Data Fields
The data instances have the following fields:
- image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
- label: A List[int] collection containing the ground-truth ids.
- origin: A string containing source dataset.
- english_label: A List[str] collection containg the english labels for the ground-truth classes.
<details>
<summary>
Click here to see the full list of ImageNet class labels mapping:
</summary>
|id|Class|
|--|-----|
|0 | tench, Tinca tinca|
|1 | goldfish, Carassius auratus|
|2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias|
|3 | tiger shark, Galeocerdo cuvieri|
|4 | hammerhead, hammerhead shark|
|5 | electric ray, crampfish, numbfish, torpedo|
|6 | stingray|
|7 | cock|
|8 | hen|
|9 | ostrich, Struthio camelus|
|10 | brambling, Fringilla montifringilla|
|11 | goldfinch, Carduelis carduelis|
|12 | house finch, linnet, Carpodacus mexicanus|
|13 | junco, snowbird|
|14 | indigo bunting, indigo finch, indigo bird, Passerina cyanea|
|15 | robin, American robin, Turdus migratorius|
|16 | bulbul|
|17 | jay|
|18 | magpie|
|19 | chickadee|
|20 | water ouzel, dipper|
|21 | kite|
|22 | bald eagle, American eagle, Haliaeetus leucocephalus|
|23 | vulture|
|24 | great grey owl, great gray owl, Strix nebulosa|
|25 | European fire salamander, Salamandra salamandra|
|26 | common newt, Triturus vulgaris|
|27 | eft|
|28 | spotted salamander, Ambystoma maculatum|
|29 | axolotl, mud puppy, Ambystoma mexicanum|
|30 | bullfrog, Rana catesbeiana|
|31 | tree frog, tree-frog|
|32 | tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui|
|33 | loggerhead, loggerhead turtle, Caretta caretta|
|34 | leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea|
|35 | mud turtle|
|36 | terrapin|
|37 | box turtle, box tortoise|
|38 | banded gecko|
|39 | common iguana, iguana, Iguana iguana|
|40 | American chameleon, anole, Anolis carolinensis|
|41 | whiptail, whiptail lizard|
|42 | agama|
|43 | frilled lizard, Chlamydosaurus kingi|
|44 | alligator lizard|
|45 | Gila monster, Heloderma suspectum|
|46 | green lizard, Lacerta viridis|
|47 | African chameleon, Chamaeleo chamaeleon|
|48 | Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis|
|49 | African crocodile, Nile crocodile, Crocodylus niloticus|
|50 | American alligator, Alligator mississipiensis|
|51 | triceratops|
|52 | thunder snake, worm snake, Carphophis amoenus|
|53 | ringneck snake, ring-necked snake, ring snake|
|54 | hognose snake, puff adder, sand viper|
|55 | green snake, grass snake|
|56 | king snake, kingsnake|
|57 | garter snake, grass snake|
|58 | water snake|
|59 | vine snake|
|60 | night snake, Hypsiglena torquata|
|61 | boa constrictor, Constrictor constrictor|
|62 | rock python, rock snake, Python sebae|
|63 | Indian cobra, Naja naja|
|64 | green mamba|
|65 | sea snake|
|66 | horned viper, cerastes, sand viper, horned asp, Cerastes cornutus|
|67 | diamondback, diamondback rattlesnake, Crotalus adamanteus|
|68 | sidewinder, horned rattlesnake, Crotalus cerastes|
|69 | trilobite|
|70 | harvestman, daddy longlegs, Phalangium opilio|
|71 | scorpion|
|72 | black and gold garden spider, Argiope aurantia|
|73 | barn spider, Araneus cavaticus|
|74 | garden spider, Aranea diademata|
|75 | black widow, Latrodectus mactans|
|76 | tarantula|
|77 | wolf spider, hunting spider|
|78 | tick|
|79 | centipede|
|80 | black grouse|
|81 | ptarmigan|
|82 | ruffed grouse, partridge, Bonasa umbellus|
|83 | prairie chicken, prairie grouse, prairie fowl|
|84 | peacock|
|85 | quail|
|86 | partridge|
|87 | African grey, African gray, Psittacus erithacus|
|88 | macaw|
|89 | sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita|
|90 | lorikeet|
|91 | coucal|
|92 | bee eater|
|93 | hornbill|
|94 | hummingbird|
|95 | jacamar|
|96 | toucan|
|97 | drake|
|98 | red-breasted merganser, Mergus serrator|
|99 | goose|
|100 | black swan, Cygnus atratus|
|101 | tusker|
|102 | echidna, spiny anteater, anteater|
|103 | platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus|
|104 | wallaby, brush kangaroo|
|105 | koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus|
|106 | wombat|
|107 | jellyfish|
|108 | sea anemone, anemone|
|109 | brain coral|
|110 | flatworm, platyhelminth|
|111 | nematode, nematode worm, roundworm|
|112 | conch|
|113 | snail|
|114 | slug|
|115 | sea slug, nudibranch|
|116 | chiton, coat-of-mail shell, sea cradle, polyplacophore|
|117 | chambered nautilus, pearly nautilus, nautilus|
|118 | Dungeness crab, Cancer magister|
|119 | rock crab, Cancer irroratus|
|120 | fiddler crab|
|121 | king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica|
|122 | American lobster, Northern lobster, Maine lobster, Homarus americanus|
|123 | spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish|
|124 | crayfish, crawfish, crawdad, crawdaddy|
|125 | hermit crab|
|126 | isopod|
|127 | white stork, Ciconia ciconia|
|128 | black stork, Ciconia nigra|
|129 | spoonbill|
|130 | flamingo|
|131 | little blue heron, Egretta caerulea|
|132 | American egret, great white heron, Egretta albus|
|133 | bittern|
|134 | crane|
|135 | limpkin, Aramus pictus|
|136 | European gallinule, Porphyrio porphyrio|
|137 | American coot, marsh hen, mud hen, water hen, Fulica americana|
|138 | bustard|
|139 | ruddy turnstone, Arenaria interpres|
|140 | red-backed sandpiper, dunlin, Erolia alpina|
|141 | redshank, Tringa totanus|
|142 | dowitcher|
|143 | oystercatcher, oyster catcher|
|144 | pelican|
|145 | king penguin, Aptenodytes patagonica|
|146 | albatross, mollymawk|
|147 | grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus|
|148 | killer whale, killer, orca, grampus, sea wolf, Orcinus orca|
|149 | dugong, Dugong dugon|
|150 | sea lion|
|151 | Chihuahua|
|152 | Japanese spaniel|
|153 | Maltese dog, Maltese terrier, Maltese|
|154 | Pekinese, Pekingese, Peke|
|155 | Shih-Tzu|
|156 | Blenheim spaniel|
|157 | papillon|
|158 | toy terrier|
|159 | Rhodesian ridgeback|
|160 | Afghan hound, Afghan|
|161 | basset, basset hound|
|162 | beagle|
|163 | bloodhound, sleuthhound|
|164 | bluetick|
|165 | black-and-tan coonhound|
|166 | Walker hound, Walker foxhound|
|167 | English foxhound|
|168 | redbone|
|169 | borzoi, Russian wolfhound|
|170 | Irish wolfhound|
|171 | Italian greyhound|
|172 | whippet|
|173 | Ibizan hound, Ibizan Podenco|
|174 | Norwegian elkhound, elkhound|
|175 | otterhound, otter hound|
|176 | Saluki, gazelle hound|
|177 | Scottish deerhound, deerhound|
|178 | Weimaraner|
|179 | Staffordshire bullterrier, Staffordshire bull terrier|
|180 | American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier|
|181 | Bedlington terrier|
|182 | Border terrier|
|183 | Kerry blue terrier|
|184 | Irish terrier|
|185 | Norfolk terrier|
|186 | Norwich terrier|
|187 | Yorkshire terrier|
|188 | wire-haired fox terrier|
|189 | Lakeland terrier|
|190 | Sealyham terrier, Sealyham|
|191 | Airedale, Airedale terrier|
|192 | cairn, cairn terrier|
|193 | Australian terrier|
|194 | Dandie Dinmont, Dandie Dinmont terrier|
|195 | Boston bull, Boston terrier|
|196 | miniature schnauzer|
|197 | giant schnauzer|
|198 | standard schnauzer|
|199 | Scotch terrier, Scottish terrier, Scottie|
|200 | Tibetan terrier, chrysanthemum dog|
|201 | silky terrier, Sydney silky|
|202 | soft-coated wheaten terrier|
|203 | West Highland white terrier|
|204 | Lhasa, Lhasa apso|
|205 | flat-coated retriever|
|206 | curly-coated retriever|
|207 | golden retriever|
|208 | Labrador retriever|
|209 | Chesapeake Bay retriever|
|210 | German short-haired pointer|
|211 | vizsla, Hungarian pointer|
|212 | English setter|
|213 | Irish setter, red setter|
|214 | Gordon setter|
|215 | Brittany spaniel|
|216 | clumber, clumber spaniel|
|217 | English springer, English springer spaniel|
|218 | Welsh springer spaniel|
|219 | cocker spaniel, English cocker spaniel, cocker|
|220 | Sussex spaniel|
|221 | Irish water spaniel|
|222 | kuvasz|
|223 | schipperke|
|224 | groenendael|
|225 | malinois|
|226 | briard|
|227 | kelpie|
|228 | komondor|
|229 | Old English sheepdog, bobtail|
|230 | Shetland sheepdog, Shetland sheep dog, Shetland|
|231 | collie|
|232 | Border collie|
|233 | Bouvier des Flandres, Bouviers des Flandres|
|234 | Rottweiler|
|235 | German shepherd, German shepherd dog, German police dog, alsatian|
|236 | Doberman, Doberman pinscher|
|237 | miniature pinscher|
|238 | Greater Swiss Mountain dog|
|239 | Bernese mountain dog|
|240 | Appenzeller|
|241 | EntleBucher|
|242 | boxer|
|243 | bull mastiff|
|244 | Tibetan mastiff|
|245 | French bulldog|
|246 | Great Dane|
|247 | Saint Bernard, St Bernard|
|248 | Eskimo dog, husky|
|249 | malamute, malemute, Alaskan malamute|
|250 | Siberian husky|
|251 | dalmatian, coach dog, carriage dog|
|252 | affenpinscher, monkey pinscher, monkey dog|
|253 | basenji|
|254 | pug, pug-dog|
|255 | Leonberg|
|256 | Newfoundland, Newfoundland dog|
|257 | Great Pyrenees|
|258 | Samoyed, Samoyede|
|259 | Pomeranian|
|260 | chow, chow chow|
|261 | keeshond|
|262 | Brabancon griffon|
|263 | Pembroke, Pembroke Welsh corgi|
|264 | Cardigan, Cardigan Welsh corgi|
|265 | toy poodle|
|266 | miniature poodle|
|267 | standard poodle|
|268 | Mexican hairless|
|269 | timber wolf, grey wolf, gray wolf, Canis lupus|
|270 | white wolf, Arctic wolf, Canis lupus tundrarum|
|271 | red wolf, maned wolf, Canis rufus, Canis niger|
|272 | coyote, prairie wolf, brush wolf, Canis latrans|
|273 | dingo, warrigal, warragal, Canis dingo|
|274 | dhole, Cuon alpinus|
|275 | African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus|
|276 | hyena, hyaena|
|277 | red fox, Vulpes vulpes|
|278 | kit fox, Vulpes macrotis|
|279 | Arctic fox, white fox, Alopex lagopus|
|280 | grey fox, gray fox, Urocyon cinereoargenteus|
|281 | tabby, tabby cat|
|282 | tiger cat|
|283 | Persian cat|
|284 | Siamese cat, Siamese|
|285 | Egyptian cat|
|286 | cougar, puma, catamount, mountain lion, painter, panther, Felis concolor|
|287 | lynx, catamount|
|288 | leopard, Panthera pardus|
|289 | snow leopard, ounce, Panthera uncia|
|290 | jaguar, panther, Panthera onca, Felis onca|
|291 | lion, king of beasts, Panthera leo|
|292 | tiger, Panthera tigris|
|293 | cheetah, chetah, Acinonyx jubatus|
|294 | brown bear, bruin, Ursus arctos|
|295 | American black bear, black bear, Ursus americanus, Euarctos americanus|
|296 | ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus|
|297 | sloth bear, Melursus ursinus, Ursus ursinus|
|298 | mongoose|
|299 | meerkat, mierkat|
|300 | tiger beetle|
|301 | ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle|
|302 | ground beetle, carabid beetle|
|303 | long-horned beetle, longicorn, longicorn beetle|
|304 | leaf beetle, chrysomelid|
|305 | dung beetle|
|306 | rhinoceros beetle|
|307 | weevil|
|308 | fly|
|309 | bee|
|310 | ant, emmet, pismire|
|311 | grasshopper, hopper|
|312 | cricket|
|313 | walking stick, walkingstick, stick insect|
|314 | cockroach, roach|
|315 | mantis, mantid|
|316 | cicada, cicala|
|317 | leafhopper|
|318 | lacewing, lacewing fly|
|319 | dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk|
|320 | damselfly|
|321 | admiral|
|322 | ringlet, ringlet butterfly|
|323 | monarch, monarch butterfly, milkweed butterfly, Danaus plexippus|
|324 | cabbage butterfly|
|325 | sulphur butterfly, sulfur butterfly|
|326 | lycaenid, lycaenid butterfly|
|327 | starfish, sea star|
|328 | sea urchin|
|329 | sea cucumber, holothurian|
|330 | wood rabbit, cottontail, cottontail rabbit|
|331 | hare|
|332 | Angora, Angora rabbit|
|333 | hamster|
|334 | porcupine, hedgehog|
|335 | fox squirrel, eastern fox squirrel, Sciurus niger|
|336 | marmot|
|337 | beaver|
|338 | guinea pig, Cavia cobaya|
|339 | sorrel|
|340 | zebra|
|341 | hog, pig, grunter, squealer, Sus scrofa|
|342 | wild boar, boar, Sus scrofa|
|343 | warthog|
|344 | hippopotamus, hippo, river horse, Hippopotamus amphibius|
|345 | ox|
|346 | water buffalo, water ox, Asiatic buffalo, Bubalus bubalis|
|347 | bison|
|348 | ram, tup|
|349 | bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis|
|350 | ibex, Capra ibex|
|351 | hartebeest|
|352 | impala, Aepyceros melampus|
|353 | gazelle|
|354 | Arabian camel, dromedary, Camelus dromedarius|
|355 | llama|
|356 | weasel|
|357 | mink|
|358 | polecat, fitch, foulmart, foumart, Mustela putorius|
|359 | black-footed ferret, ferret, Mustela nigripes|
|360 | otter|
|361 | skunk, polecat, wood pussy|
|362 | badger|
|363 | armadillo|
|364 | three-toed sloth, ai, Bradypus tridactylus|
|365 | orangutan, orang, orangutang, Pongo pygmaeus|
|366 | gorilla, Gorilla gorilla|
|367 | chimpanzee, chimp, Pan troglodytes|
|368 | gibbon, Hylobates lar|
|369 | siamang, Hylobates syndactylus, Symphalangus syndactylus|
|370 | guenon, guenon monkey|
|371 | patas, hussar monkey, Erythrocebus patas|
|372 | baboon|
|373 | macaque|
|374 | langur|
|375 | colobus, colobus monkey|
|376 | proboscis monkey, Nasalis larvatus|
|377 | marmoset|
|378 | capuchin, ringtail, Cebus capucinus|
|379 | howler monkey, howler|
|380 | titi, titi monkey|
|381 | spider monkey, Ateles geoffroyi|
|382 | squirrel monkey, Saimiri sciureus|
|383 | Madagascar cat, ring-tailed lemur, Lemur catta|
|384 | indri, indris, Indri indri, Indri brevicaudatus|
|385 | Indian elephant, Elephas maximus|
|386 | African elephant, Loxodonta africana|
|387 | lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens|
|388 | giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca|
|389 | barracouta, snoek|
|390 | eel|
|391 | coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch|
|392 | rock beauty, Holocanthus tricolor|
|393 | anemone fish|
|394 | sturgeon|
|395 | gar, garfish, garpike, billfish, Lepisosteus osseus|
|396 | lionfish|
|397 | puffer, pufferfish, blowfish, globefish|
|398 | abacus|
|399 | abaya|
|400 | academic gown, academic robe, judge's robe|
|401 | accordion, piano accordion, squeeze box|
|402 | acoustic guitar|
|403 | aircraft carrier, carrier, flattop, attack aircraft carrier|
|404 | airliner|
|405 | airship, dirigible|
|406 | altar|
|407 | ambulance|
|408 | amphibian, amphibious vehicle|
|409 | analog clock|
|410 | apiary, bee house|
|411 | apron|
|412 | ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin|
|413 | assault rifle, assault gun|
|414 | backpack, back pack, knapsack, packsack, rucksack, haversack|
|415 | bakery, bakeshop, bakehouse|
|416 | balance beam, beam|
|417 | balloon|
|418 | ballpoint, ballpoint pen, ballpen, Biro|
|419 | Band Aid|
|420 | banjo|
|421 | bannister, banister, balustrade, balusters, handrail|
|422 | barbell|
|423 | barber chair|
|424 | barbershop|
|425 | barn|
|426 | barometer|
|427 | barrel, cask|
|428 | barrow, garden cart, lawn cart, wheelbarrow|
|429 | baseball|
|430 | basketball|
|431 | bassinet|
|432 | bassoon|
|433 | bathing cap, swimming cap|
|434 | bath towel|
|435 | bathtub, bathing tub, bath, tub|
|436 | beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon|
|437 | beacon, lighthouse, beacon light, pharos|
|438 | beaker|
|439 | bearskin, busby, shako|
|440 | beer bottle|
|441 | beer glass|
|442 | bell cote, bell cot|
|443 | bib|
|444 | bicycle-built-for-two, tandem bicycle, tandem|
|445 | bikini, two-piece|
|446 | binder, ring-binder|
|447 | binoculars, field glasses, opera glasses|
|448 | birdhouse|
|449 | boathouse|
|450 | bobsled, bobsleigh, bob|
|451 | bolo tie, bolo, bola tie, bola|
|452 | bonnet, poke bonnet|
|453 | bookcase|
|454 | bookshop, bookstore, bookstall|
|455 | bottlecap|
|456 | bow|
|457 | bow tie, bow-tie, bowtie|
|458 | brass, memorial tablet, plaque|
|459 | brassiere, bra, bandeau|
|460 | breakwater, groin, groyne, mole, bulwark, seawall, jetty|
|461 | breastplate, aegis, egis|
|462 | broom|
|463 | bucket, pail|
|464 | buckle|
|465 | bulletproof vest|
|466 | bullet train, bullet|
|467 | butcher shop, meat market|
|468 | cab, hack, taxi, taxicab|
|469 | caldron, cauldron|
|470 | candle, taper, wax light|
|471 | cannon|
|472 | canoe|
|473 | can opener, tin opener|
|474 | cardigan|
|475 | car mirror|
|476 | carousel, carrousel, merry-go-round, roundabout, whirligig|
|477 | carpenter's kit, tool kit|
|478 | carton|
|479 | car wheel|
|480 | cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM|
|481 | cassette|
|482 | cassette player|
|483 | castle|
|484 | catamaran|
|485 | CD player|
|486 | cello, violoncello|
|487 | cellular telephone, cellular phone, cellphone, cell, mobile phone|
|488 | chain|
|489 | chainlink fence|
|490 | chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour|
|491 | chain saw, chainsaw|
|492 | chest|
|493 | chiffonier, commode|
|494 | chime, bell, gong|
|495 | china cabinet, china closet|
|496 | Christmas stocking|
|497 | church, church building|
|498 | cinema, movie theater, movie theatre, movie house, picture palace|
|499 | cleaver, meat cleaver, chopper|
|500 | cliff dwelling|
|501 | cloak|
|502 | clog, geta, patten, sabot|
|503 | cocktail shaker|
|504 | coffee mug|
|505 | coffeepot|
|506 | coil, spiral, volute, whorl, helix|
|507 | combination lock|
|508 | computer keyboard, keypad|
|509 | confectionery, confectionary, candy store|
|510 | container ship, containership, container vessel|
|511 | convertible|
|512 | corkscrew, bottle screw|
|513 | cornet, horn, trumpet, trump|
|514 | cowboy boot|
|515 | cowboy hat, ten-gallon hat|
|516 | cradle|
|517 | crane_1|
|518 | crash helmet|
|519 | crate|
|520 | crib, cot|
|521 | Crock Pot|
|522 | croquet ball|
|523 | crutch|
|524 | cuirass|
|525 | dam, dike, dyke|
|526 | desk|
|527 | desktop computer|
|528 | dial telephone, dial phone|
|529 | diaper, nappy, napkin|
|530 | digital clock|
|531 | digital watch|
|532 | dining table, board|
|533 | dishrag, dishcloth|
|534 | dishwasher, dish washer, dishwashing machine|
|535 | disk brake, disc brake|
|536 | dock, dockage, docking facility|
|537 | dogsled, dog sled, dog sleigh|
|538 | dome|
|539 | doormat, welcome mat|
|540 | drilling platform, offshore rig|
|541 | drum, membranophone, tympan|
|542 | drumstick|
|543 | dumbbell|
|544 | Dutch oven|
|545 | electric fan, blower|
|546 | electric guitar|
|547 | electric locomotive|
|548 | entertainment center|
|549 | envelope|
|550 | espresso maker|
|551 | face powder|
|552 | feather boa, boa|
|553 | file, file cabinet, filing cabinet|
|554 | fireboat|
|555 | fire engine, fire truck|
|556 | fire screen, fireguard|
|557 | flagpole, flagstaff|
|558 | flute, transverse flute|
|559 | folding chair|
|560 | football helmet|
|561 | forklift|
|562 | fountain|
|563 | fountain pen|
|564 | four-poster|
|565 | freight car|
|566 | French horn, horn|
|567 | frying pan, frypan, skillet|
|568 | fur coat|
|569 | garbage truck, dustcart|
|570 | gasmask, respirator, gas helmet|
|571 | gas pump, gasoline pump, petrol pump, island dispenser|
|572 | goblet|
|573 | go-kart|
|574 | golf ball|
|575 | golfcart, golf cart|
|576 | gondola|
|577 | gong, tam-tam|
|578 | gown|
|579 | grand piano, grand|
|580 | greenhouse, nursery, glasshouse|
|581 | grille, radiator grille|
|582 | grocery store, grocery, food market, market|
|583 | guillotine|
|584 | hair slide|
|585 | hair spray|
|586 | half track|
|587 | hammer|
|588 | hamper|
|589 | hand blower, blow dryer, blow drier, hair dryer, hair drier|
|590 | hand-held computer, hand-held microcomputer|
|591 | handkerchief, hankie, hanky, hankey|
|592 | hard disc, hard disk, fixed disk|
|593 | harmonica, mouth organ, harp, mouth harp|
|594 | harp|
|595 | harvester, reaper|
|596 | hatchet|
|597 | holster|
|598 | home theater, home theatre|
|599 | honeycomb|
|600 | hook, claw|
|601 | hoopskirt, crinoline|
|602 | horizontal bar, high bar|
|603 | horse cart, horse-cart|
|604 | hourglass|
|605 | iPod|
|606 | iron, smoothing iron|
|607 | jack-o'-lantern|
|608 | jean, blue jean, denim|
|609 | jeep, landrover|
|610 | jersey, T-shirt, tee shirt|
|611 | jigsaw puzzle|
|612 | jinrikisha, ricksha, rickshaw|
|613 | joystick|
|614 | kimono|
|615 | knee pad|
|616 | knot|
|617 | lab coat, laboratory coat|
|618 | ladle|
|619 | lampshade, lamp shade|
|620 | laptop, laptop computer|
|621 | lawn mower, mower|
|622 | lens cap, lens cover|
|623 | letter opener, paper knife, paperknife|
|624 | library|
|625 | lifeboat|
|626 | lighter, light, igniter, ignitor|
|627 | limousine, limo|
|628 | liner, ocean liner|
|629 | lipstick, lip rouge|
|630 | Loafer|
|631 | lotion|
|632 | loudspeaker, speaker, speaker unit, loudspeaker system, speaker system|
|633 | loupe, jeweler's loupe|
|634 | lumbermill, sawmill|
|635 | magnetic compass|
|636 | mailbag, postbag|
|637 | mailbox, letter box|
|638 | maillot|
|639 | maillot, tank suit|
|640 | manhole cover|
|641 | maraca|
|642 | marimba, xylophone|
|643 | mask|
|644 | matchstick|
|645 | maypole|
|646 | maze, labyrinth|
|647 | measuring cup|
|648 | medicine chest, medicine cabinet|
|649 | megalith, megalithic structure|
|650 | microphone, mike|
|651 | microwave, microwave oven|
|652 | military uniform|
|653 | milk can|
|654 | minibus|
|655 | miniskirt, mini|
|656 | minivan|
|657 | missile|
|658 | mitten|
|659 | mixing bowl|
|660 | mobile home, manufactured home|
|661 | Model T|
|662 | modem|
|663 | monastery|
|664 | monitor|
|665 | moped|
|666 | mortar|
|667 | mortarboard|
|668 | mosque|
|669 | mosquito net|
|670 | motor scooter, scooter|
|671 | mountain bike, all-terrain bike, off-roader|
|672 | mountain tent|
|673 | mouse, computer mouse|
|674 | mousetrap|
|675 | moving van|
|676 | muzzle|
|677 | nail|
|678 | neck brace|
|679 | necklace|
|680 | nipple|
|681 | notebook, notebook computer|
|682 | obelisk|
|683 | oboe, hautboy, hautbois|
|684 | ocarina, sweet potato|
|685 | odometer, hodometer, mileometer, milometer|
|686 | oil filter|
|687 | organ, pipe organ|
|688 | oscilloscope, scope, cathode-ray oscilloscope, CRO|
|689 | overskirt|
|690 | oxcart|
|691 | oxygen mask|
|692 | packet|
|693 | paddle, boat paddle|
|694 | paddlewheel, paddle wheel|
|695 | padlock|
|696 | paintbrush|
|697 | pajama, pyjama, pj's, jammies|
|698 | palace|
|699 | panpipe, pandean pipe, syrinx|
|700 | paper towel|
|701 | parachute, chute|
|702 | parallel bars, bars|
|703 | park bench|
|704 | parking meter|
|705 | passenger car, coach, carriage|
|706 | patio, terrace|
|707 | pay-phone, pay-station|
|708 | pedestal, plinth, footstall|
|709 | pencil box, pencil case|
|710 | pencil sharpener|
|711 | perfume, essence|
|712 | Petri dish|
|713 | photocopier|
|714 | pick, plectrum, plectron|
|715 | pickelhaube|
|716 | picket fence, paling|
|717 | pickup, pickup truck|
|718 | pier|
|719 | piggy bank, penny bank|
|720 | pill bottle|
|721 | pillow|
|722 | ping-pong ball|
|723 | pinwheel|
|724 | pirate, pirate ship|
|725 | pitcher, ewer|
|726 | plane, carpenter's plane, woodworking plane|
|727 | planetarium|
|728 | plastic bag|
|729 | plate rack|
|730 | plow, plough|
|731 | plunger, plumber's helper|
|732 | Polaroid camera, Polaroid Land camera|
|733 | pole|
|734 | police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria|
|735 | poncho|
|736 | pool table, billiard table, snooker table|
|737 | pop bottle, soda bottle|
|738 | pot, flowerpot|
|739 | potter's wheel|
|740 | power drill|
|741 | prayer rug, prayer mat|
|742 | printer|
|743 | prison, prison house|
|744 | projectile, missile|
|745 | projector|
|746 | puck, hockey puck|
|747 | punching bag, punch bag, punching ball, punchball|
|748 | purse|
|749 | quill, quill pen|
|750 | quilt, comforter, comfort, puff|
|751 | racer, race car, racing car|
|752 | racket, racquet|
|753 | radiator|
|754 | radio, wireless|
|755 | radio telescope, radio reflector|
|756 | rain barrel|
|757 | recreational vehicle, RV, R.V.|
|758 | reel|
|759 | reflex camera|
|760 | refrigerator, icebox|
|761 | remote control, remote|
|762 | restaurant, eating house, eating place, eatery|
|763 | revolver, six-gun, six-shooter|
|764 | rifle|
|765 | rocking chair, rocker|
|766 | rotisserie|
|767 | rubber eraser, rubber, pencil eraser|
|768 | rugby ball|
|769 | rule, ruler|
|770 | running shoe|
|771 | safe|
|772 | safety pin|
|773 | saltshaker, salt shaker|
|774 | sandal|
|775 | sarong|
|776 | sax, saxophone|
|777 | scabbard|
|778 | scale, weighing machine|
|779 | school bus|
|780 | schooner|
|781 | scoreboard|
|782 | screen, CRT screen|
|783 | screw|
|784 | screwdriver|
|785 | seat belt, seatbelt|
|786 | sewing machine|
|787 | shield, buckler|
|788 | shoe shop, shoe-shop, shoe store|
|789 | shoji|
|790 | shopping basket|
|791 | shopping cart|
|792 | shovel|
|793 | shower cap|
|794 | shower curtain|
|795 | ski|
|796 | ski mask|
|797 | sleeping bag|
|798 | slide rule, slipstick|
|799 | sliding door|
|800 | slot, one-armed bandit|
|801 | snorkel|
|802 | snowmobile|
|803 | snowplow, snowplough|
|804 | soap dispenser|
|805 | soccer ball|
|806 | sock|
|807 | solar dish, solar collector, solar furnace|
|808 | sombrero|
|809 | soup bowl|
|810 | space bar|
|811 | space heater|
|812 | space shuttle|
|813 | spatula|
|814 | speedboat|
|815 | spider web, spider's web|
|816 | spindle|
|817 | sports car, sport car|
|818 | spotlight, spot|
|819 | stage|
|820 | steam locomotive|
|821 | steel arch bridge|
|822 | steel drum|
|823 | stethoscope|
|824 | stole|
|825 | stone wall|
|826 | stopwatch, stop watch|
|827 | stove|
|828 | strainer|
|829 | streetcar, tram, tramcar, trolley, trolley car|
|830 | stretcher|
|831 | studio couch, day bed|
|832 | stupa, tope|
|833 | submarine, pigboat, sub, U-boat|
|834 | suit, suit of clothes|
|835 | sundial|
|836 | sunglass|
|837 | sunglasses, dark glasses, shades|
|838 | sunscreen, sunblock, sun blocker|
|839 | suspension bridge|
|840 | swab, swob, mop|
|841 | sweatshirt|
|842 | swimming trunks, bathing trunks|
|843 | swing|
|844 | switch, electric switch, electrical switch|
|845 | syringe|
|846 | table lamp|
|847 | tank, army tank, armored combat vehicle, armoured combat vehicle|
|848 | tape player|
|849 | teapot|
|850 | teddy, teddy bear|
|851 | television, television system|
|852 | tennis ball|
|853 | thatch, thatched roof|
|854 | theater curtain, theatre curtain|
|855 | thimble|
|856 | thresher, thrasher, threshing machine|
|857 | throne|
|858 | tile roof|
|859 | toaster|
|860 | tobacco shop, tobacconist shop, tobacconist|
|861 | toilet seat|
|862 | torch|
|863 | totem pole|
|864 | tow truck, tow car, wrecker|
|865 | toyshop|
|866 | tractor|
|867 | trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi|
|868 | tray|
|869 | trench coat|
|870 | tricycle, trike, velocipede|
|871 | trimaran|
|872 | tripod|
|873 | triumphal arch|
|874 | trolleybus, trolley coach, trackless trolley|
|875 | trombone|
|876 | tub, vat|
|877 | turnstile|
|878 | typewriter keyboard|
|879 | umbrella|
|880 | unicycle, monocycle|
|881 | upright, upright piano|
|882 | vacuum, vacuum cleaner|
|883 | vase|
|884 | vault|
|885 | velvet|
|886 | vending machine|
|887 | vestment|
|888 | viaduct|
|889 | violin, fiddle|
|890 | volleyball|
|891 | waffle iron|
|892 | wall clock|
|893 | wallet, billfold, notecase, pocketbook|
|894 | wardrobe, closet, press|
|895 | warplane, military plane|
|896 | washbasin, handbasin, washbowl, lavabo, wash-hand basin|
|897 | washer, automatic washer, washing machine|
|898 | water bottle|
|899 | water jug|
|900 | water tower|
|901 | whiskey jug|
|902 | whistle|
|903 | wig|
|904 | window screen|
|905 | window shade|
|906 | Windsor tie|
|907 | wine bottle|
|908 | wing|
|909 | wok|
|910 | wooden spoon|
|911 | wool, woolen, woollen|
|912 | worm fence, snake fence, snake-rail fence, Virginia fence|
|913 | wreck|
|914 | yawl|
|915 | yurt|
|916 | web site, website, internet site, site|
|917 | comic book|
|918 | crossword puzzle, crossword|
|919 | street sign|
|920 | traffic light, traffic signal, stoplight|
|921 | book jacket, dust cover, dust jacket, dust wrapper|
|922 | menu|
|923 | plate|
|924 | guacamole|
|925 | consomme|
|926 | hot pot, hotpot|
|927 | trifle|
|928 | ice cream, icecream|
|929 | ice lolly, lolly, lollipop, popsicle|
|930 | French loaf|
|931 | bagel, beigel|
|932 | pretzel|
|933 | cheeseburger|
|934 | hotdog, hot dog, red hot|
|935 | mashed potato|
|936 | head cabbage|
|937 | broccoli|
|938 | cauliflower|
|939 | zucchini, courgette|
|940 | spaghetti squash|
|941 | acorn squash|
|942 | butternut squash|
|943 | cucumber, cuke|
|944 | artichoke, globe artichoke|
|945 | bell pepper|
|946 | cardoon|
|947 | mushroom|
|948 | Granny Smith|
|949 | strawberry|
|950 | orange|
|951 | lemon|
|952 | fig|
|953 | pineapple, ananas|
|954 | banana|
|955 | jackfruit, jak, jack|
|956 | custard apple|
|957 | pomegranate|
|958 | hay|
|959 | carbonara|
|960 | chocolate sauce, chocolate syrup|
|961 | dough|
|962 | meat loaf, meatloaf|
|963 | pizza, pizza pie|
|964 | potpie|
|965 | burrito|
|966 | red wine|
|967 | espresso|
|968 | cup|
|969 | eggnog|
|970 | alp|
|971 | bubble|
|972 | cliff, drop, drop-off|
|973 | coral reef|
|974 | geyser|
|975 | lakeside, lakeshore|
|976 | promontory, headland, head, foreland|
|977 | sandbar, sand bar|
|978 | seashore, coast, seacoast, sea-coast|
|979 | valley, vale|
|980 | volcano|
|981 | ballplayer, baseball player|
|982 | groom, bridegroom|
|983 | scuba diver|
|984 | rapeseed|
|985 | daisy|
|986 | yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum|
|987 | corn|
|988 | acorn|
|989 | hip, rose hip, rosehip|
|990 | buckeye, horse chestnut, conker|
|991 | coral fungus|
|992 | agaric|
|993 | gyromitra|
|994 | stinkhorn, carrion fungus|
|995 | earthstar|
|996 | hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa|
|997 | bolete|
|998 | ear, spike, capitulum|
|999 | toilet tissue, toilet paper, bathroom tissue|
</details>
### Data Splits
This dataset is a validation-only set.
## Dataset Creation
### Source Data
This dataset is sourced from ImageNet, ImageNet-ReaL, ImageNet-V2, ImageNet-A, ImageNet-C, ImageNet-R, ImageNet-Sketch, and ObjectNet.
## Citation Information
```
@article{taesiri2023zoom,
title={ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification},
author={Taesiri, Mohammad Reza and Nguyen, Giang and Habchi, Sarra and Bezemer, Cor-Paul and Nguyen, Anh},
journal={arXiv preprint arXiv:2304.05538},
year={2023}
}
``` | taesiri/imagenet-hard | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"OOD",
"ImageNet",
"Out Of Distribution",
"arxiv:2304.05538",
"region:us"
]
| 2023-03-31T04:48:23+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "pretty_name": "ImageNet-Hard", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "sequence": "int64"}, {"name": "origin", "dtype": "string"}, {"name": "english_label", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 1771418938.94, "num_examples": 10980}], "download_size": 6380094503, "dataset_size": 1771418938.94}, "tags": ["OOD", "ImageNet", "Out Of Distribution"]} | 2023-06-16T17:50:51+00:00 | [
"2304.05538"
]
| [
"en"
]
| TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #OOD #ImageNet #Out Of Distribution #arxiv-2304.05538 #region-us
| Dataset Card for "ImageNet-Hard"
================================
Project Page - ArXiv - Paper - Github - Image Browser
Dataset Summary
---------------
ImageNet-Hard is a new benchmark that comprises 10,980 images collected from various existing ImageNet-scale benchmarks (ImageNet, ImageNet-V2, ImageNet-Sketch, ImageNet-C, ImageNet-R, ImageNet-ReaL, ImageNet-A, and ObjectNet). This dataset poses a significant challenge to state-of-the-art vision models as merely zooming in often fails to improve their ability to classify images correctly. As a result, even the most advanced models, such as 'CLIP-ViT-L/14@336px', struggle to perform well on this dataset, achieving a mere '2.02%' accuracy.
*ImageNet-Hard-4K*: For the 4K version please refere to this dataset.
### Dataset Distribution
!Dataset Distribution
### Classifiers Performance
Evaluation Code
* CLIP <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>
* OpenCLIP
* Other models <a target="\_blank" href="URL <img src="URL alt="Open In Colab"/>
Supported Tasks
---------------
* 'image-classification': The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).
Languages
---------
The 'english\_label' field in the dataset are in English.
Dataset Structure
-----------------
Data Instances
An example looks like this:
### Data Fields
The data instances have the following fields:
* image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
* label: A List[int] collection containing the ground-truth ids.
* origin: A string containing source dataset.
* english\_label: A List[str] collection containg the english labels for the ground-truth classes.
Click here to see the full list of ImageNet class labels mapping:
### Data Splits
This dataset is a validation-only set.
Dataset Creation
----------------
### Source Data
This dataset is sourced from ImageNet, ImageNet-ReaL, ImageNet-V2, ImageNet-A, ImageNet-C, ImageNet-R, ImageNet-Sketch, and ObjectNet.
| [
"### Dataset Distribution\n\n\n!Dataset Distribution",
"### Classifiers Performance\n\n\n\nEvaluation Code\n\n\n* CLIP <a target=\"\\_blank\" href=\"URL <img src=\"URL alt=\"Open In Colab\"/>\n* OpenCLIP\n* Other models <a target=\"\\_blank\" href=\"URL <img src=\"URL alt=\"Open In Colab\"/>\n\n\nSupported Tasks\n---------------\n\n\n* 'image-classification': The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).\n\n\nLanguages\n---------\n\n\nThe 'english\\_label' field in the dataset are in English.\n\n\nDataset Structure\n-----------------\n\n\nData Instances\n\n\nAn example looks like this:",
"### Data Fields\n\n\nThe data instances have the following fields:\n\n\n* image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0][\"image\"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the \"image\" column, i.e. dataset[0][\"image\"] should always be preferred over dataset[\"image\"][0].\n* label: A List[int] collection containing the ground-truth ids.\n* origin: A string containing source dataset.\n* english\\_label: A List[str] collection containg the english labels for the ground-truth classes.\n\n\n\n\n Click here to see the full list of ImageNet class labels mapping:",
"### Data Splits\n\n\nThis dataset is a validation-only set.\n\n\nDataset Creation\n----------------",
"### Source Data\n\n\nThis dataset is sourced from ImageNet, ImageNet-ReaL, ImageNet-V2, ImageNet-A, ImageNet-C, ImageNet-R, ImageNet-Sketch, and ObjectNet."
]
| [
"TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #OOD #ImageNet #Out Of Distribution #arxiv-2304.05538 #region-us \n",
"### Dataset Distribution\n\n\n!Dataset Distribution",
"### Classifiers Performance\n\n\n\nEvaluation Code\n\n\n* CLIP <a target=\"\\_blank\" href=\"URL <img src=\"URL alt=\"Open In Colab\"/>\n* OpenCLIP\n* Other models <a target=\"\\_blank\" href=\"URL <img src=\"URL alt=\"Open In Colab\"/>\n\n\nSupported Tasks\n---------------\n\n\n* 'image-classification': The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).\n\n\nLanguages\n---------\n\n\nThe 'english\\_label' field in the dataset are in English.\n\n\nDataset Structure\n-----------------\n\n\nData Instances\n\n\nAn example looks like this:",
"### Data Fields\n\n\nThe data instances have the following fields:\n\n\n* image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0][\"image\"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the \"image\" column, i.e. dataset[0][\"image\"] should always be preferred over dataset[\"image\"][0].\n* label: A List[int] collection containing the ground-truth ids.\n* origin: A string containing source dataset.\n* english\\_label: A List[str] collection containg the english labels for the ground-truth classes.\n\n\n\n\n Click here to see the full list of ImageNet class labels mapping:",
"### Data Splits\n\n\nThis dataset is a validation-only set.\n\n\nDataset Creation\n----------------",
"### Source Data\n\n\nThis dataset is sourced from ImageNet, ImageNet-ReaL, ImageNet-V2, ImageNet-A, ImageNet-C, ImageNet-R, ImageNet-Sketch, and ObjectNet."
]
| [
56,
11,
165,
200,
23,
53
]
| [
"passage: TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #OOD #ImageNet #Out Of Distribution #arxiv-2304.05538 #region-us \n### Dataset Distribution\n\n\n!Dataset Distribution### Classifiers Performance\n\n\n\nEvaluation Code\n\n\n* CLIP <a target=\"\\_blank\" href=\"URL <img src=\"URL alt=\"Open In Colab\"/>\n* OpenCLIP\n* Other models <a target=\"\\_blank\" href=\"URL <img src=\"URL alt=\"Open In Colab\"/>\n\n\nSupported Tasks\n---------------\n\n\n* 'image-classification': The objective of this task is to classify an image into one or more classes, selected from 1000 ImageNet categories (allowing for multiple ground-truth labels per image).\n\n\nLanguages\n---------\n\n\nThe 'english\\_label' field in the dataset are in English.\n\n\nDataset Structure\n-----------------\n\n\nData Instances\n\n\nAn example looks like this:### Data Fields\n\n\nThe data instances have the following fields:\n\n\n* image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0][\"image\"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the \"image\" column, i.e. dataset[0][\"image\"] should always be preferred over dataset[\"image\"][0].\n* label: A List[int] collection containing the ground-truth ids.\n* origin: A string containing source dataset.\n* english\\_label: A List[str] collection containg the english labels for the ground-truth classes.\n\n\n\n\n Click here to see the full list of ImageNet class labels mapping:### Data Splits\n\n\nThis dataset is a validation-only set.\n\n\nDataset Creation\n----------------"
]
| [
-0.06580813974142075,
0.1686355471611023,
-0.006234031170606613,
0.05025386065244675,
0.07860961556434631,
0.028872050344944,
0.03541364148259163,
0.08991188555955887,
-0.039557792246341705,
0.1638992428779602,
0.05777999758720398,
0.08059478551149368,
0.1174110695719719,
0.15060293674468994,
-0.024601241573691368,
-0.1439288854598999,
0.030130252242088318,
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|
7954f38281bed88e06d48204634df77715d03c27 | # DO NOT USE
> Still working on it.
# Annealing
The [Annealing dataset](https://archive-beta.ics.uci.edu/dataset/3/annealing) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Dataset
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| annealing | Multiclass classification | |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/annealing")["train"]
``` | mstz/annealing | [
"task_categories:tabular-classification",
"size_categories:100<n<1K",
"language:en",
"annealing",
"tabular_classification",
"multiclass_classificaiton",
"region:us"
]
| 2023-03-31T06:52:51+00:00 | {"language": ["en"], "size_categories": ["100<n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Annealing", "tags": ["annealing", "tabular_classification", "multiclass_classificaiton"], "configs": ["annealing"]} | 2023-04-15T10:07:36+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-100<n<1K #language-English #annealing #tabular_classification #multiclass_classificaiton #region-us
| DO NOT USE
==========
>
> Still working on it.
>
>
>
Annealing
=========
The Annealing dataset from the UCI ML repository.
Dataset
Configurations and tasks
========================
Configuration: annealing, Task: Multiclass classification, Description:
Usage
=====
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-100<n<1K #language-English #annealing #tabular_classification #multiclass_classificaiton #region-us \n"
]
| [
51
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-100<n<1K #language-English #annealing #tabular_classification #multiclass_classificaiton #region-us \n"
]
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|
b42b8325f32da6261506bdbfc4cedb84151829c6 | Splits: 80% train, 10% validation, 10% test. | arefm/suggestions_small_py | [
"size_categories:10K<n<100K",
"license:afl-3.0",
"region:us"
]
| 2023-03-31T07:23:33+00:00 | {"license": "afl-3.0", "size_categories": ["10K<n<100K"]} | 2023-04-02T15:49:46+00:00 | []
| []
| TAGS
#size_categories-10K<n<100K #license-afl-3.0 #region-us
| Splits: 80% train, 10% validation, 10% test. | []
| [
"TAGS\n#size_categories-10K<n<100K #license-afl-3.0 #region-us \n"
]
| [
26
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #license-afl-3.0 #region-us \n"
]
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|
16a1564e828868a15d7fea551cf3907833f49d25 | # Dataset Card for "neo4j_java_function"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nguyenminh871/neo4j_java_function | [
"region:us"
]
| 2023-03-31T07:29:35+00:00 | {"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "func", "dtype": "string"}, {"name": "target", "dtype": "bool"}, {"name": "project", "dtype": "string"}], "splits": [{"name": "neo4j_train", "num_bytes": 15276987, "num_examples": 6705}, {"name": "cleaned_neo4j_train", "num_bytes": 13925414, "num_examples": 6705}], "download_size": 7441318, "dataset_size": 29202401}} | 2023-03-31T07:30:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "neo4j_java_function"
More Information needed | [
"# Dataset Card for \"neo4j_java_function\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"neo4j_java_function\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"neo4j_java_function\"\n\nMore Information needed"
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|
09f275a5b4fdef575df61fd3448a7b39edda5a93 | # Dataset Card for "coyo-10-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | YiYiXu/coyo-10-test | [
"region:us"
]
| 2023-03-31T07:47:50+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4146065.0, "num_examples": 10}], "download_size": 4147741, "dataset_size": 4146065.0}} | 2023-03-31T07:47:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "coyo-10-test"
More Information needed | [
"# Dataset Card for \"coyo-10-test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"coyo-10-test\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"coyo-10-test\"\n\nMore Information needed"
]
| [
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|
a8b4ee0a0a828c8568f74b1c0e09b1367af2d8d6 |
## 内容
包含约100万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的中文指令数据。
## 样例
```
{
"instruction": "给定一个文字输入，将其中的所有数字加1。\n“明天的会议在9点开始，记得准时到达。”\n",
"input": "",
"output": "“明天的会议在10点开始，记得准时到达。”"
}
```
### 字段：
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## 使用限制
仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
| BelleGroup/train_1M_CN | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:gpl-3.0",
"region:us"
]
| 2023-03-31T07:53:50+00:00 | {"language": ["zh"], "license": "gpl-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["text2text-generation"]} | 2023-04-03T07:23:17+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us
|
## 内容
包含约100万条由BELLE项目生成的中文指令数据。
## 样例
### 字段：
## 使用限制
仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
| [
"## 内容\n包含约100万条由BELLE项目生成的中文指令数据。",
"## 样例",
"### 字段：",
"## 使用限制\n仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。\n本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。"
]
| [
"TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us \n",
"## 内容\n包含约100万条由BELLE项目生成的中文指令数据。",
"## 样例",
"### 字段：",
"## 使用限制\n仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。\n本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。"
]
| [
44,
19,
4,
6,
80
]
| [
"passage: TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us \n## 内容\n包含约100万条由BELLE项目生成的中文指令数据。## 样例### 字段：## 使用限制\n仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。\n本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。"
]
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|
5457220dac47a97bade45d5238a7ca0af477b61f | # Dataset Card for "Elasticsearch_0_90_11_java_function"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nguyenminh871/Elasticsearch_0_90_11_java_function | [
"region:us"
]
| 2023-03-31T07:55:48+00:00 | {"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "func", "dtype": "string"}, {"name": "target", "dtype": "bool"}, {"name": "project", "dtype": "string"}], "splits": [{"name": "Elasticsearch_0_90_11_train", "num_bytes": 21711944, "num_examples": 6480}], "download_size": 4870218, "dataset_size": 21711944}} | 2023-03-31T07:56:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Elasticsearch_0_90_11_java_function"
More Information needed | [
"# Dataset Card for \"Elasticsearch_0_90_11_java_function\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Elasticsearch_0_90_11_java_function\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Elasticsearch_0_90_11_java_function\"\n\nMore Information needed"
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|
7d209492196ec77adc6955b29734e0ad30b64795 | # Summarization dataset for Norwegian Court Decisions
This data was scraped from www.lovdata.no, April 2023, and contains about 27k samples.
## How to Use
```python
from datasets import load_dataset
data = load_dataset("MasterThesisCBS/Court_Decisions_Lovdata")
```
### Dataset Curators
[John Oskar Holmen Skjeldrum](mailto:[email protected]) and [Peder Tanberg](mailto:[email protected]) | MasterThesisCBS/Court_Decisions_Lovdata | [
"task_categories:text-generation",
"language:no",
"language:nb",
"license:cc-by-4.0",
"summarizaton",
"region:us"
]
| 2023-03-31T08:09:12+00:00 | {"language": ["no", "nb"], "license": "cc-by-4.0", "task_categories": ["text-generation"], "pretty_name": "LovData XSUM", "tags": ["summarizaton"], "dataset_info": {"features": [{"name": "Summary", "dtype": "string"}, {"name": "KeyWords", "dtype": "string"}, {"name": "Full_Text", "dtype": "string"}, {"name": "length", "dtype": "int64"}, {"name": "Summary_w/o_paragraph", "dtype": "string"}, {"name": "KeyWords_w/o_paragraph", "dtype": "string"}, {"name": "Full_Text_w/o_paragraph", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 397486921, "num_examples": 26539}, {"name": "test", "num_bytes": 4564831, "num_examples": 1397}], "download_size": 201357343, "dataset_size": 402051752}} | 2023-04-16T09:31:02+00:00 | []
| [
"no",
"nb"
]
| TAGS
#task_categories-text-generation #language-Norwegian #language-Norwegian Bokmål #license-cc-by-4.0 #summarizaton #region-us
| # Summarization dataset for Norwegian Court Decisions
This data was scraped from URL, April 2023, and contains about 27k samples.
## How to Use
### Dataset Curators
John Oskar Holmen Skjeldrum and Peder Tanberg | [
"# Summarization dataset for Norwegian Court Decisions\n\nThis data was scraped from URL, April 2023, and contains about 27k samples.",
"## How to Use",
"### Dataset Curators\nJohn Oskar Holmen Skjeldrum and Peder Tanberg"
]
| [
"TAGS\n#task_categories-text-generation #language-Norwegian #language-Norwegian Bokmål #license-cc-by-4.0 #summarizaton #region-us \n",
"# Summarization dataset for Norwegian Court Decisions\n\nThis data was scraped from URL, April 2023, and contains about 27k samples.",
"## How to Use",
"### Dataset Curators\nJohn Oskar Holmen Skjeldrum and Peder Tanberg"
]
| [
44,
31,
4,
18
]
| [
"passage: TAGS\n#task_categories-text-generation #language-Norwegian #language-Norwegian Bokmål #license-cc-by-4.0 #summarizaton #region-us \n# Summarization dataset for Norwegian Court Decisions\n\nThis data was scraped from URL, April 2023, and contains about 27k samples.## How to Use### Dataset Curators\nJohn Oskar Holmen Skjeldrum and Peder Tanberg"
]
| [
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|
299ff666cd25c205d6472ac82ec57797fafe4b04 | # Dataset Card for "cleaned_pubmedqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | reginaboateng/cleaned_pubmedqa | [
"language:en",
"region:us"
]
| 2023-03-31T08:23:13+00:00 | {"language": "en", "dataset_info": {"features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "sequence": [{"name": "contexts", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "meshes", "dtype": "string"}]}, {"name": "long_answer", "dtype": "string"}, {"name": "final_decision", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 443501057, "num_examples": 211269}, {"name": "validation", "num_bytes": 2052168, "num_examples": 1000}], "download_size": 234483083, "dataset_size": 445553225}} | 2023-07-21T13:19:05+00:00 | []
| [
"en"
]
| TAGS
#language-English #region-us
| # Dataset Card for "cleaned_pubmedqa"
More Information needed | [
"# Dataset Card for \"cleaned_pubmedqa\"\n\nMore Information needed"
]
| [
"TAGS\n#language-English #region-us \n",
"# Dataset Card for \"cleaned_pubmedqa\"\n\nMore Information needed"
]
| [
10,
17
]
| [
"passage: TAGS\n#language-English #region-us \n# Dataset Card for \"cleaned_pubmedqa\"\n\nMore Information needed"
]
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|
29c35e9b56c7fc91f04a613ed33896f7a19bad54 |
## 内容
包含约50万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的中文指令数据。
## 样例
```
{
"instruction": "给定一个文字输入，将其中的所有数字加1。\n“明天的会议在9点开始，记得准时到达。”\n",
"input": "",
"output": "“明天的会议在10点开始，记得准时到达。”"
}
```
### 字段：
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## 使用限制
仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
| BelleGroup/train_0.5M_CN | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:gpl-3.0",
"region:us"
]
| 2023-03-31T09:17:49+00:00 | {"language": ["zh"], "license": "gpl-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["text2text-generation"]} | 2023-04-03T07:11:22+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us
|
## 内容
包含约50万条由BELLE项目生成的中文指令数据。
## 样例
### 字段：
## 使用限制
仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
| [
"## 内容\n包含约50万条由BELLE项目生成的中文指令数据。",
"## 样例",
"### 字段：",
"## 使用限制\n仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。\n本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。"
]
| [
"TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us \n",
"## 内容\n包含约50万条由BELLE项目生成的中文指令数据。",
"## 样例",
"### 字段：",
"## 使用限制\n仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。\n本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。"
]
| [
44,
19,
4,
6,
80
]
| [
"passage: TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us \n## 内容\n包含约50万条由BELLE项目生成的中文指令数据。## 样例### 字段：## 使用限制\n仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。\n本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。"
]
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|
e5a3351933c278c2cc9396743cb4dcf78859bd9a |
# Dataset Card for MAUPQA Dataset
## Dataset Description
- **Paper:** [MAUPQA: Massive Automatically-created Polish Question Answering Dataset](https://arxiv.org/abs/2305.05486), [SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering](https://arxiv.org/abs/2309.08469)
- **Point of Contact:** [Piotr Rybak](mailto:[email protected])
### Dataset Summary
MAUPQA is a collection of 14 datasets for Polish document retrieval. Most of the datasets are either machine-generated or machine-translated from English. Across all datasets, it consists of over 1M questions, 1M positive, and 7M hard-negative question-passage pairs.
### Supported Tasks and Leaderboards
- `document-retrieval`: The dataset can be used to train a model for document retrieval. Success on this task is typically measured by [top-k retrieval accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html) or [NDCG](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html).
### Languages
The text is in Polish, as spoken by the [Internet users](https://github.com/facebookresearch/cc_net), [Polish Wikipedia](https://pl.wikipedia.org/) editors, or is an output of generative or translation models. The BCP-47 code for Polish is pl-PL.
## Dataset Structure
### Data Instances
The dataset consists of over 8 million question-passage pairs. For each instance, there is a `question`, a passage (`passage_title`, `passage_text`), and a boolean indicator if the passage is `relevant` for the given question (i.e. does it contain the answers).
For a small subset of `question` there is also a list of possible `answers` formulated in a natural language, in a way a Polish
speaker would answer the questions.
```
{
'question_id': 1,
'question': 'Na którym kontynencie leży państwo Gujana, panie Krzysztofie?',
'answers': "['W Ameryce Południowej']",
'passage_title': 'Gujana (ujednoznacznienie)',
'passage_text': 'Gujana (region) – region Ameryki Południowej Gujana – państwo w Ameryce Południowej Gujana Brytyjska – dawna kolonia brytyjska; obecnie państwo Gujana Gujana Francuska – departament zamorski Francji; dawniej kolonia francuska Gujana Holenderska – dawna kolonia holenderska; obecnie państwo Surinam',
'relevant': True,
'passage_source': 'crawling',
'subset': '1z10'
}
```
### Data Fields
Question-passage pairs:
- `question_id`: an integer id of the question
- `question`: a string containing the question
- `passage_title`: a string containing the title of the Wikipedia article
- `passage_text`: a string containing the passage text as extracted by the human annotator
- `relevant`: a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers)
- `annotated_by`: a string containing the name of the annotator who verified the relevance of the pair
- `answers`: a string containing a list of possible short answers to the question
- `passage_source`: a string containing the method of obtaining the passage. One of the following:
- `manual-annotation`: the question-passage pair was manually annotated
- `crawling`: the question-passage pairs were created by taking advantage of the specific structure of crawled website
- `dataset-translation`: the dataset was created by machine-translating the English dataset
- `generative-model`: the question was created by the generative model based on the given passage
- `bm25-negatives`: the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is not relevant
- `bm25-positives`: the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is relevant
- `subset`: a string containing the name of the dataset
### Data Splits
MAUPQA is a collection of 14 datasets and most of them are weakly labeled. Therefore, the intended use of MAUPQA is for training only. As such, all examples belong to a single `train` split. We recommend using the [PolQA](https://huggingface.co/datasets/ipipan/polqa) dataset for evaluation.
Basic statistics of all 14 datasets:
| dataset | # questions | # answers | # positive passages | # negative passages |
|-------------------|------------:|----------:|--------------------:|--------------------:|
| 1z10 | 22,835 | 21,415 | 22,014 | 139,471 |
| czy-wiesz-v2 | 29,078 | - | 29,078 | 143,306 |
| gpt3-cc | 10,146 | 10,146 | 10,177 | 89,203 |
| gpt3.5-cc | 29,591 | 29,583 | 29,720 | 251,959 |
| gpt3.5-wiki | 29,674 | 29,636 | 29,748 | 115,564 |
| mkqa | 4,036 | 4,036 | 3,968 | 19,814 |
| mqa | 172,768 | - | 178,131 | 1,249,659 |
| msmarco | 389,987 | - | 416,763 | 3,006,996 |
| multilingual-NLI | 100,752 | 64,900 | 68,096 | 743,857 |
| nq | 135,781 | - | 139,976 | 797,436 |
| poleval2021-pairs | 1,977 | - | 2,088 | 17,608 |
| poquad | 56,588 | 46,157 | 46,187 | 299,865 |
| templates | 15,993 | 14,504 | 15,993 | 45,228 |
| wiki-def | 18,093 | 18,092 | 18,093 | 84,956 |
| Total | 1,017,299 | 238,469 | 1,010,032 | 7,004,922 |
## Dataset Creation
### Curation Rationale
Open-domain question answering systems rely heavily on annotated datasets to train neural document retrievers. However, manually annotating such datasets is both difficult and time-consuming. To overcome these difficulties, we experimented with several methods for automatically collecting weakly labeled datasets. As a result, MAUPQA enables the development of robust document retrieval systems for Polish.
### Source Data
#### Initial Data Collection and Normalization
Below, we briefly describe each dataset. For a detailed description please refer to the [paper](https://arxiv.org/abs/2305.05486).
* `1z10`: We transcribe 333 recordings of the [Jeden z Dziesięciu](https://pl.wikipedia.org/wiki/Jeden_z_dziesi%C4%99ciu) TV show using the Whisper model and extract the question-answer pairs using GPT-3.5 model. We use the BM25 retriever and the GPT-3.5-based cross-encoder to match questions with Wikipedia passages.
* `czy-wiesz-v2`: We first crawl all questions from the [Did you know?](https://pl.wikipedia.org/wiki/Wikiprojekt:Czy_wiesz/archiwum) section on Polish Wikipedia together with a link to the relevant Wikipedia article. Then, we use the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage.
* `gpt3-cc`: We sample random passages from [CCNet](https://github.com/facebookresearch/cc_net) corpus and prompt GPT-3 to generate a relevant question.
* `gpt3.5-cc`: We sample random passages from [CCNet](https://github.com/facebookresearch/cc_net) corpus and prompt GPT-3.5 to generate a relevant question.
* `gpt3.5-wiki`: We sample random passages from Polish Wikipedia and prompt GPT-3.5 to generate a relevant question.
* `mkqa`: We clean the Polish subset of the [MKQA](https://huggingface.co/datasets/mkqa) dataset by removing questions without answers, requiring long answers (*Why?* and *How?* questions), and ambiguous ones ("Who is the *current* president?*). We use the BM25 retriever and the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage.
* `mqa`: We clean the Polish subset of the [MQA](https://huggingface.co/datasets/clips/mqa) dataset by removing artificially created questions like "What is the best hotel in *{city}*?" for hundreds of different *cities*. To clean the dataset, we cluster lexically similar questions/passages and remove clusters with over 5 questions.
* `msmarco`: We translate the [MS MARCO](https://huggingface.co/datasets/ms_marco) dataset into Polish using the machine translation model.
* `multilingual-NLI`: We extract question-answer pairs from the Polish subset of the [multilingual-NLI](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) dataset. We create questions using the following template: "Czy *{premise}*?" (Eng. "Does *{premise}*?") and use hypotheses as passages. We consider `entailment` and `contradiction` labels as relevant and `neutral` as negative.
* `nq`: We translate the [NQ](https://huggingface.co/datasets/natural_questions) dataset into Polish using the machine translation model.
* `poleval2021-pairs`: We take [allegro/polish-question-passage-pairs](https://huggingface.co/datasets/allegro/polish-question-passage-pairs) without any changes.
* `poquad`: We extract question-passages pairs from the training split of the [PoQuAD](https://huggingface.co/datasets/clarin-pl/poquad) dataset.
* `templates`: We take advantage of the Wikipedia structure to generate questions using predefined templates. For example, list pages group together similar entities (e.g. "Writers born in Poland") which allow generating questions like "Where was *{writer name}* born?". In total, we use 33 templates to generate questions. We use the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage from the linked article.
* `wiki-def`: We use [Wiktionary](https://www.wiktionary.org/) to generate questions based on word definitions. We use definitions that have links to Wikipedia articles to create the question-passage pairs. For example, the definition of "Monday" is "the first day of the week". Based on it, we generate the question "What is the name of *the first day of the week*?".
Additionally, we extend each dataset by sampling the hard negative passages using a BM25 retriever and score using a [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to ensure that passages are not relevant.
#### Who are the source language producers?
The text is in Polish, as spoken by the [Internet users](https://github.com/facebookresearch/cc_net), [Polish Wikipedia](https://pl.wikipedia.org/) editors, or is an output of generative or translation models.
### Annotations
#### Annotation process
The MAUPQA dataset doesn't provide additional annotation except for the annotation present in the source datasets.
#### Who are the annotators?
Please refer to the description of the source datasets.
### Personal and Sensitive Information
The dataset should not contain any personal or sensitive information. However, we use the [CCNet](https://github.com/facebookresearch/cc_net) dataset as a source of passages that we didn't manually inspect for personal and sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was created to promote the research in the open-domain question answering for Polish and allow developing question answering systems.
### Discussion of Biases
The machine-translated datasets might not represent the natural language as used by native Polish speakers. Similarly, the questions generated by the generative models might not be representative or correct.
Most of the question-passage pairs are created automatically using the BM25 retriever and as such it is biased to lexically similar pairs.
### Other Known Limitations
The MAUPQA dataset is mostly automatically generated and can therefore contain a high proportion of noise and incorrectly labeled question-passage pairs.
## Additional Information
### Dataset Curators
The MAUPQA dataset was collected by Piotr Rybak and Maciej Ogrodniczuk from the [Institute of Computer Science, Polish Academy of Sciences](http://zil.ipipan.waw.pl/) but the source datasets were created by many more researchers. Please refer to the original dataset descriptions for the full authorship.
This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@inproceedings{rybak-2023-maupqa,
title = "{MAUPQA}: Massive Automatically-created {P}olish Question Answering Dataset",
author = "Rybak, Piotr",
booktitle = "Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bsnlp-1.2",
pages = "11--16",
abstract = "Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.",
}
```
```
@misc{rybak2023silverretriever,
title={SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering},
author={Piotr Rybak and Maciej Ogrodniczuk},
year={2023},
eprint={2309.08469},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | ipipan/maupqa | [
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:open-domain-qa",
"task_ids:document-retrieval",
"annotations_creators:found",
"annotations_creators:machine-generated",
"size_categories:1M<n<10M",
"language:pl",
"license:cc-by-sa-4.0",
"arxiv:2305.05486",
"arxiv:2309.08469",
"region:us"
]
| 2023-03-31T09:21:18+00:00 | {"annotations_creators": ["found", "machine-generated"], "language": ["pl"], "license": "cc-by-sa-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering", "text-retrieval"], "task_ids": ["open-domain-qa", "document-retrieval"], "pretty_name": "MAUPQA"} | 2023-09-18T06:28:41+00:00 | [
"2305.05486",
"2309.08469"
]
| [
"pl"
]
| TAGS
#task_categories-question-answering #task_categories-text-retrieval #task_ids-open-domain-qa #task_ids-document-retrieval #annotations_creators-found #annotations_creators-machine-generated #size_categories-1M<n<10M #language-Polish #license-cc-by-sa-4.0 #arxiv-2305.05486 #arxiv-2309.08469 #region-us
| Dataset Card for MAUPQA Dataset
===============================
Dataset Description
-------------------
* Paper: MAUPQA: Massive Automatically-created Polish Question Answering Dataset, SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering
* Point of Contact: Piotr Rybak
### Dataset Summary
MAUPQA is a collection of 14 datasets for Polish document retrieval. Most of the datasets are either machine-generated or machine-translated from English. Across all datasets, it consists of over 1M questions, 1M positive, and 7M hard-negative question-passage pairs.
### Supported Tasks and Leaderboards
* 'document-retrieval': The dataset can be used to train a model for document retrieval. Success on this task is typically measured by top-k retrieval accuracy or NDCG.
### Languages
The text is in Polish, as spoken by the Internet users, Polish Wikipedia editors, or is an output of generative or translation models. The BCP-47 code for Polish is pl-PL.
Dataset Structure
-----------------
### Data Instances
The dataset consists of over 8 million question-passage pairs. For each instance, there is a 'question', a passage ('passage\_title', 'passage\_text'), and a boolean indicator if the passage is 'relevant' for the given question (i.e. does it contain the answers).
For a small subset of 'question' there is also a list of possible 'answers' formulated in a natural language, in a way a Polish
speaker would answer the questions.
### Data Fields
Question-passage pairs:
* 'question\_id': an integer id of the question
* 'question': a string containing the question
* 'passage\_title': a string containing the title of the Wikipedia article
* 'passage\_text': a string containing the passage text as extracted by the human annotator
* 'relevant': a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers)
* 'annotated\_by': a string containing the name of the annotator who verified the relevance of the pair
* 'answers': a string containing a list of possible short answers to the question
* 'passage\_source': a string containing the method of obtaining the passage. One of the following:
+ 'manual-annotation': the question-passage pair was manually annotated
+ 'crawling': the question-passage pairs were created by taking advantage of the specific structure of crawled website
+ 'dataset-translation': the dataset was created by machine-translating the English dataset
+ 'generative-model': the question was created by the generative model based on the given passage
+ 'bm25-negatives': the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is not relevant
+ 'bm25-positives': the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is relevant
* 'subset': a string containing the name of the dataset
### Data Splits
MAUPQA is a collection of 14 datasets and most of them are weakly labeled. Therefore, the intended use of MAUPQA is for training only. As such, all examples belong to a single 'train' split. We recommend using the PolQA dataset for evaluation.
Basic statistics of all 14 datasets:
Dataset Creation
----------------
### Curation Rationale
Open-domain question answering systems rely heavily on annotated datasets to train neural document retrievers. However, manually annotating such datasets is both difficult and time-consuming. To overcome these difficulties, we experimented with several methods for automatically collecting weakly labeled datasets. As a result, MAUPQA enables the development of robust document retrieval systems for Polish.
### Source Data
#### Initial Data Collection and Normalization
Below, we briefly describe each dataset. For a detailed description please refer to the paper.
* '1z10': We transcribe 333 recordings of the Jeden z Dziesięciu TV show using the Whisper model and extract the question-answer pairs using GPT-3.5 model. We use the BM25 retriever and the GPT-3.5-based cross-encoder to match questions with Wikipedia passages.
* 'czy-wiesz-v2': We first crawl all questions from the Did you know? section on Polish Wikipedia together with a link to the relevant Wikipedia article. Then, we use the multilingual cross-encoder to choose the most relevant passage.
* 'gpt3-cc': We sample random passages from CCNet corpus and prompt GPT-3 to generate a relevant question.
* 'gpt3.5-cc': We sample random passages from CCNet corpus and prompt GPT-3.5 to generate a relevant question.
* 'gpt3.5-wiki': We sample random passages from Polish Wikipedia and prompt GPT-3.5 to generate a relevant question.
* 'mkqa': We clean the Polish subset of the MKQA dataset by removing questions without answers, requiring long answers (*Why?* and *How?* questions), and ambiguous ones ("Who is the *current* president?\*). We use the BM25 retriever and the multilingual cross-encoder to choose the most relevant passage.
* 'mqa': We clean the Polish subset of the MQA dataset by removing artificially created questions like "What is the best hotel in *{city}*?" for hundreds of different *cities*. To clean the dataset, we cluster lexically similar questions/passages and remove clusters with over 5 questions.
* 'msmarco': We translate the MS MARCO dataset into Polish using the machine translation model.
* 'multilingual-NLI': We extract question-answer pairs from the Polish subset of the multilingual-NLI dataset. We create questions using the following template: "Czy *{premise}*?" (Eng. "Does *{premise}*?") and use hypotheses as passages. We consider 'entailment' and 'contradiction' labels as relevant and 'neutral' as negative.
* 'nq': We translate the NQ dataset into Polish using the machine translation model.
* 'poleval2021-pairs': We take allegro/polish-question-passage-pairs without any changes.
* 'poquad': We extract question-passages pairs from the training split of the PoQuAD dataset.
* 'templates': We take advantage of the Wikipedia structure to generate questions using predefined templates. For example, list pages group together similar entities (e.g. "Writers born in Poland") which allow generating questions like "Where was *{writer name}* born?". In total, we use 33 templates to generate questions. We use the multilingual cross-encoder to choose the most relevant passage from the linked article.
* 'wiki-def': We use Wiktionary to generate questions based on word definitions. We use definitions that have links to Wikipedia articles to create the question-passage pairs. For example, the definition of "Monday" is "the first day of the week". Based on it, we generate the question "What is the name of *the first day of the week*?".
Additionally, we extend each dataset by sampling the hard negative passages using a BM25 retriever and score using a multilingual cross-encoder to ensure that passages are not relevant.
#### Who are the source language producers?
The text is in Polish, as spoken by the Internet users, Polish Wikipedia editors, or is an output of generative or translation models.
### Annotations
#### Annotation process
The MAUPQA dataset doesn't provide additional annotation except for the annotation present in the source datasets.
#### Who are the annotators?
Please refer to the description of the source datasets.
### Personal and Sensitive Information
The dataset should not contain any personal or sensitive information. However, we use the CCNet dataset as a source of passages that we didn't manually inspect for personal and sensitive information.
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
This dataset was created to promote the research in the open-domain question answering for Polish and allow developing question answering systems.
### Discussion of Biases
The machine-translated datasets might not represent the natural language as used by native Polish speakers. Similarly, the questions generated by the generative models might not be representative or correct.
Most of the question-passage pairs are created automatically using the BM25 retriever and as such it is biased to lexically similar pairs.
### Other Known Limitations
The MAUPQA dataset is mostly automatically generated and can therefore contain a high proportion of noise and incorrectly labeled question-passage pairs.
Additional Information
----------------------
### Dataset Curators
The MAUPQA dataset was collected by Piotr Rybak and Maciej Ogrodniczuk from the Institute of Computer Science, Polish Academy of Sciences but the source datasets were created by many more researchers. Please refer to the original dataset descriptions for the full authorship.
This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.
### Licensing Information
CC BY-SA 4.0
| [
"### Dataset Summary\n\n\nMAUPQA is a collection of 14 datasets for Polish document retrieval. Most of the datasets are either machine-generated or machine-translated from English. Across all datasets, it consists of over 1M questions, 1M positive, and 7M hard-negative question-passage pairs.",
"### Supported Tasks and Leaderboards\n\n\n* 'document-retrieval': The dataset can be used to train a model for document retrieval. Success on this task is typically measured by top-k retrieval accuracy or NDCG.",
"### Languages\n\n\nThe text is in Polish, as spoken by the Internet users, Polish Wikipedia editors, or is an output of generative or translation models. The BCP-47 code for Polish is pl-PL.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThe dataset consists of over 8 million question-passage pairs. For each instance, there is a 'question', a passage ('passage\\_title', 'passage\\_text'), and a boolean indicator if the passage is 'relevant' for the given question (i.e. does it contain the answers).\n\n\nFor a small subset of 'question' there is also a list of possible 'answers' formulated in a natural language, in a way a Polish\nspeaker would answer the questions.",
"### Data Fields\n\n\nQuestion-passage pairs:\n\n\n* 'question\\_id': an integer id of the question\n* 'question': a string containing the question\n* 'passage\\_title': a string containing the title of the Wikipedia article\n* 'passage\\_text': a string containing the passage text as extracted by the human annotator\n* 'relevant': a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers)\n* 'annotated\\_by': a string containing the name of the annotator who verified the relevance of the pair\n* 'answers': a string containing a list of possible short answers to the question\n* 'passage\\_source': a string containing the method of obtaining the passage. One of the following:\n\t+ 'manual-annotation': the question-passage pair was manually annotated\n\t+ 'crawling': the question-passage pairs were created by taking advantage of the specific structure of crawled website\n\t+ 'dataset-translation': the dataset was created by machine-translating the English dataset\n\t+ 'generative-model': the question was created by the generative model based on the given passage\n\t+ 'bm25-negatives': the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is not relevant\n\t+ 'bm25-positives': the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is relevant\n* 'subset': a string containing the name of the dataset",
"### Data Splits\n\n\nMAUPQA is a collection of 14 datasets and most of them are weakly labeled. Therefore, the intended use of MAUPQA is for training only. As such, all examples belong to a single 'train' split. We recommend using the PolQA dataset for evaluation.\n\n\nBasic statistics of all 14 datasets:\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nOpen-domain question answering systems rely heavily on annotated datasets to train neural document retrievers. However, manually annotating such datasets is both difficult and time-consuming. To overcome these difficulties, we experimented with several methods for automatically collecting weakly labeled datasets. As a result, MAUPQA enables the development of robust document retrieval systems for Polish.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nBelow, we briefly describe each dataset. For a detailed description please refer to the paper.\n\n\n* '1z10': We transcribe 333 recordings of the Jeden z Dziesięciu TV show using the Whisper model and extract the question-answer pairs using GPT-3.5 model. We use the BM25 retriever and the GPT-3.5-based cross-encoder to match questions with Wikipedia passages.\n* 'czy-wiesz-v2': We first crawl all questions from the Did you know? section on Polish Wikipedia together with a link to the relevant Wikipedia article. Then, we use the multilingual cross-encoder to choose the most relevant passage.\n* 'gpt3-cc': We sample random passages from CCNet corpus and prompt GPT-3 to generate a relevant question.\n* 'gpt3.5-cc': We sample random passages from CCNet corpus and prompt GPT-3.5 to generate a relevant question.\n* 'gpt3.5-wiki': We sample random passages from Polish Wikipedia and prompt GPT-3.5 to generate a relevant question.\n* 'mkqa': We clean the Polish subset of the MKQA dataset by removing questions without answers, requiring long answers (*Why?* and *How?* questions), and ambiguous ones (\"Who is the *current* president?\\*). We use the BM25 retriever and the multilingual cross-encoder to choose the most relevant passage.\n* 'mqa': We clean the Polish subset of the MQA dataset by removing artificially created questions like \"What is the best hotel in *{city}*?\" for hundreds of different *cities*. To clean the dataset, we cluster lexically similar questions/passages and remove clusters with over 5 questions.\n* 'msmarco': We translate the MS MARCO dataset into Polish using the machine translation model.\n* 'multilingual-NLI': We extract question-answer pairs from the Polish subset of the multilingual-NLI dataset. We create questions using the following template: \"Czy *{premise}*?\" (Eng. \"Does *{premise}*?\") and use hypotheses as passages. We consider 'entailment' and 'contradiction' labels as relevant and 'neutral' as negative.\n* 'nq': We translate the NQ dataset into Polish using the machine translation model.\n* 'poleval2021-pairs': We take allegro/polish-question-passage-pairs without any changes.\n* 'poquad': We extract question-passages pairs from the training split of the PoQuAD dataset.\n* 'templates': We take advantage of the Wikipedia structure to generate questions using predefined templates. For example, list pages group together similar entities (e.g. \"Writers born in Poland\") which allow generating questions like \"Where was *{writer name}* born?\". In total, we use 33 templates to generate questions. We use the multilingual cross-encoder to choose the most relevant passage from the linked article.\n* 'wiki-def': We use Wiktionary to generate questions based on word definitions. We use definitions that have links to Wikipedia articles to create the question-passage pairs. For example, the definition of \"Monday\" is \"the first day of the week\". Based on it, we generate the question \"What is the name of *the first day of the week*?\".\n\n\nAdditionally, we extend each dataset by sampling the hard negative passages using a BM25 retriever and score using a multilingual cross-encoder to ensure that passages are not relevant.",
"#### Who are the source language producers?\n\n\nThe text is in Polish, as spoken by the Internet users, Polish Wikipedia editors, or is an output of generative or translation models.",
"### Annotations",
"#### Annotation process\n\n\nThe MAUPQA dataset doesn't provide additional annotation except for the annotation present in the source datasets.",
"#### Who are the annotators?\n\n\nPlease refer to the description of the source datasets.",
"### Personal and Sensitive Information\n\n\nThe dataset should not contain any personal or sensitive information. However, we use the CCNet dataset as a source of passages that we didn't manually inspect for personal and sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThis dataset was created to promote the research in the open-domain question answering for Polish and allow developing question answering systems.",
"### Discussion of Biases\n\n\nThe machine-translated datasets might not represent the natural language as used by native Polish speakers. Similarly, the questions generated by the generative models might not be representative or correct.\n\n\nMost of the question-passage pairs are created automatically using the BM25 retriever and as such it is biased to lexically similar pairs.",
"### Other Known Limitations\n\n\nThe MAUPQA dataset is mostly automatically generated and can therefore contain a high proportion of noise and incorrectly labeled question-passage pairs.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe MAUPQA dataset was collected by Piotr Rybak and Maciej Ogrodniczuk from the Institute of Computer Science, Polish Academy of Sciences but the source datasets were created by many more researchers. Please refer to the original dataset descriptions for the full authorship.\n\n\nThis work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.",
"### Licensing Information\n\n\nCC BY-SA 4.0"
]
| [
"TAGS\n#task_categories-question-answering #task_categories-text-retrieval #task_ids-open-domain-qa #task_ids-document-retrieval #annotations_creators-found #annotations_creators-machine-generated #size_categories-1M<n<10M #language-Polish #license-cc-by-sa-4.0 #arxiv-2305.05486 #arxiv-2309.08469 #region-us \n",
"### Dataset Summary\n\n\nMAUPQA is a collection of 14 datasets for Polish document retrieval. Most of the datasets are either machine-generated or machine-translated from English. Across all datasets, it consists of over 1M questions, 1M positive, and 7M hard-negative question-passage pairs.",
"### Supported Tasks and Leaderboards\n\n\n* 'document-retrieval': The dataset can be used to train a model for document retrieval. Success on this task is typically measured by top-k retrieval accuracy or NDCG.",
"### Languages\n\n\nThe text is in Polish, as spoken by the Internet users, Polish Wikipedia editors, or is an output of generative or translation models. The BCP-47 code for Polish is pl-PL.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThe dataset consists of over 8 million question-passage pairs. For each instance, there is a 'question', a passage ('passage\\_title', 'passage\\_text'), and a boolean indicator if the passage is 'relevant' for the given question (i.e. does it contain the answers).\n\n\nFor a small subset of 'question' there is also a list of possible 'answers' formulated in a natural language, in a way a Polish\nspeaker would answer the questions.",
"### Data Fields\n\n\nQuestion-passage pairs:\n\n\n* 'question\\_id': an integer id of the question\n* 'question': a string containing the question\n* 'passage\\_title': a string containing the title of the Wikipedia article\n* 'passage\\_text': a string containing the passage text as extracted by the human annotator\n* 'relevant': a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers)\n* 'annotated\\_by': a string containing the name of the annotator who verified the relevance of the pair\n* 'answers': a string containing a list of possible short answers to the question\n* 'passage\\_source': a string containing the method of obtaining the passage. One of the following:\n\t+ 'manual-annotation': the question-passage pair was manually annotated\n\t+ 'crawling': the question-passage pairs were created by taking advantage of the specific structure of crawled website\n\t+ 'dataset-translation': the dataset was created by machine-translating the English dataset\n\t+ 'generative-model': the question was created by the generative model based on the given passage\n\t+ 'bm25-negatives': the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is not relevant\n\t+ 'bm25-positives': the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is relevant\n* 'subset': a string containing the name of the dataset",
"### Data Splits\n\n\nMAUPQA is a collection of 14 datasets and most of them are weakly labeled. Therefore, the intended use of MAUPQA is for training only. As such, all examples belong to a single 'train' split. We recommend using the PolQA dataset for evaluation.\n\n\nBasic statistics of all 14 datasets:\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nOpen-domain question answering systems rely heavily on annotated datasets to train neural document retrievers. However, manually annotating such datasets is both difficult and time-consuming. To overcome these difficulties, we experimented with several methods for automatically collecting weakly labeled datasets. As a result, MAUPQA enables the development of robust document retrieval systems for Polish.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nBelow, we briefly describe each dataset. For a detailed description please refer to the paper.\n\n\n* '1z10': We transcribe 333 recordings of the Jeden z Dziesięciu TV show using the Whisper model and extract the question-answer pairs using GPT-3.5 model. We use the BM25 retriever and the GPT-3.5-based cross-encoder to match questions with Wikipedia passages.\n* 'czy-wiesz-v2': We first crawl all questions from the Did you know? section on Polish Wikipedia together with a link to the relevant Wikipedia article. Then, we use the multilingual cross-encoder to choose the most relevant passage.\n* 'gpt3-cc': We sample random passages from CCNet corpus and prompt GPT-3 to generate a relevant question.\n* 'gpt3.5-cc': We sample random passages from CCNet corpus and prompt GPT-3.5 to generate a relevant question.\n* 'gpt3.5-wiki': We sample random passages from Polish Wikipedia and prompt GPT-3.5 to generate a relevant question.\n* 'mkqa': We clean the Polish subset of the MKQA dataset by removing questions without answers, requiring long answers (*Why?* and *How?* questions), and ambiguous ones (\"Who is the *current* president?\\*). We use the BM25 retriever and the multilingual cross-encoder to choose the most relevant passage.\n* 'mqa': We clean the Polish subset of the MQA dataset by removing artificially created questions like \"What is the best hotel in *{city}*?\" for hundreds of different *cities*. To clean the dataset, we cluster lexically similar questions/passages and remove clusters with over 5 questions.\n* 'msmarco': We translate the MS MARCO dataset into Polish using the machine translation model.\n* 'multilingual-NLI': We extract question-answer pairs from the Polish subset of the multilingual-NLI dataset. We create questions using the following template: \"Czy *{premise}*?\" (Eng. \"Does *{premise}*?\") and use hypotheses as passages. We consider 'entailment' and 'contradiction' labels as relevant and 'neutral' as negative.\n* 'nq': We translate the NQ dataset into Polish using the machine translation model.\n* 'poleval2021-pairs': We take allegro/polish-question-passage-pairs without any changes.\n* 'poquad': We extract question-passages pairs from the training split of the PoQuAD dataset.\n* 'templates': We take advantage of the Wikipedia structure to generate questions using predefined templates. For example, list pages group together similar entities (e.g. \"Writers born in Poland\") which allow generating questions like \"Where was *{writer name}* born?\". In total, we use 33 templates to generate questions. We use the multilingual cross-encoder to choose the most relevant passage from the linked article.\n* 'wiki-def': We use Wiktionary to generate questions based on word definitions. We use definitions that have links to Wikipedia articles to create the question-passage pairs. For example, the definition of \"Monday\" is \"the first day of the week\". Based on it, we generate the question \"What is the name of *the first day of the week*?\".\n\n\nAdditionally, we extend each dataset by sampling the hard negative passages using a BM25 retriever and score using a multilingual cross-encoder to ensure that passages are not relevant.",
"#### Who are the source language producers?\n\n\nThe text is in Polish, as spoken by the Internet users, Polish Wikipedia editors, or is an output of generative or translation models.",
"### Annotations",
"#### Annotation process\n\n\nThe MAUPQA dataset doesn't provide additional annotation except for the annotation present in the source datasets.",
"#### Who are the annotators?\n\n\nPlease refer to the description of the source datasets.",
"### Personal and Sensitive Information\n\n\nThe dataset should not contain any personal or sensitive information. However, we use the CCNet dataset as a source of passages that we didn't manually inspect for personal and sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThis dataset was created to promote the research in the open-domain question answering for Polish and allow developing question answering systems.",
"### Discussion of Biases\n\n\nThe machine-translated datasets might not represent the natural language as used by native Polish speakers. Similarly, the questions generated by the generative models might not be representative or correct.\n\n\nMost of the question-passage pairs are created automatically using the BM25 retriever and as such it is biased to lexically similar pairs.",
"### Other Known Limitations\n\n\nThe MAUPQA dataset is mostly automatically generated and can therefore contain a high proportion of noise and incorrectly labeled question-passage pairs.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe MAUPQA dataset was collected by Piotr Rybak and Maciej Ogrodniczuk from the Institute of Computer Science, Polish Academy of Sciences but the source datasets were created by many more researchers. Please refer to the original dataset descriptions for the full authorship.\n\n\nThis work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.",
"### Licensing Information\n\n\nCC BY-SA 4.0"
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"passage: TAGS\n#task_categories-question-answering #task_categories-text-retrieval #task_ids-open-domain-qa #task_ids-document-retrieval #annotations_creators-found #annotations_creators-machine-generated #size_categories-1M<n<10M #language-Polish #license-cc-by-sa-4.0 #arxiv-2305.05486 #arxiv-2309.08469 #region-us \n### Dataset Summary\n\n\nMAUPQA is a collection of 14 datasets for Polish document retrieval. Most of the datasets are either machine-generated or machine-translated from English. Across all datasets, it consists of over 1M questions, 1M positive, and 7M hard-negative question-passage pairs.### Supported Tasks and Leaderboards\n\n\n* 'document-retrieval': The dataset can be used to train a model for document retrieval. Success on this task is typically measured by top-k retrieval accuracy or NDCG.### Languages\n\n\nThe text is in Polish, as spoken by the Internet users, Polish Wikipedia editors, or is an output of generative or translation models. The BCP-47 code for Polish is pl-PL.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nThe dataset consists of over 8 million question-passage pairs. For each instance, there is a 'question', a passage ('passage\\_title', 'passage\\_text'), and a boolean indicator if the passage is 'relevant' for the given question (i.e. does it contain the answers).\n\n\nFor a small subset of 'question' there is also a list of possible 'answers' formulated in a natural language, in a way a Polish\nspeaker would answer the questions.",
"passage: ### Data Fields\n\n\nQuestion-passage pairs:\n\n\n* 'question\\_id': an integer id of the question\n* 'question': a string containing the question\n* 'passage\\_title': a string containing the title of the Wikipedia article\n* 'passage\\_text': a string containing the passage text as extracted by the human annotator\n* 'relevant': a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers)\n* 'annotated\\_by': a string containing the name of the annotator who verified the relevance of the pair\n* 'answers': a string containing a list of possible short answers to the question\n* 'passage\\_source': a string containing the method of obtaining the passage. One of the following:\n\t+ 'manual-annotation': the question-passage pair was manually annotated\n\t+ 'crawling': the question-passage pairs were created by taking advantage of the specific structure of crawled website\n\t+ 'dataset-translation': the dataset was created by machine-translating the English dataset\n\t+ 'generative-model': the question was created by the generative model based on the given passage\n\t+ 'bm25-negatives': the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is not relevant\n\t+ 'bm25-positives': the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is relevant\n* 'subset': a string containing the name of the dataset### Data Splits\n\n\nMAUPQA is a collection of 14 datasets and most of them are weakly labeled. Therefore, the intended use of MAUPQA is for training only. As such, all examples belong to a single 'train' split. We recommend using the PolQA dataset for evaluation.\n\n\nBasic statistics of all 14 datasets:\n\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nOpen-domain question answering systems rely heavily on annotated datasets to train neural document retrievers. However, manually annotating such datasets is both difficult and time-consuming. To overcome these difficulties, we experimented with several methods for automatically collecting weakly labeled datasets. As a result, MAUPQA enables the development of robust document retrieval systems for Polish.### Source Data"
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|
8dcd4ec1d77ae594f1bbc3259a02847052e1e4da |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | lambdasec/cve-single-line-fixes | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:code",
"license:apache-2.0",
"code",
"region:us"
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| 2023-03-31T09:56:03+00:00 | {"language": ["code"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "Single line fixes for CVEs", "tags": ["code"]} | 2023-04-11T01:53:45+00:00 | []
| [
"code"
]
| TAGS
#task_categories-text-generation #size_categories-n<1K #language-code #license-apache-2.0 #code #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: lambdasec@URL
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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89038cb8de3c96581bf12569441600c89fea4dad | # Dataset Card for "alpaca_data_cleaned_validations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | argilla/alpaca_data_cleaned_validations | [
"region:us"
]
| 2023-03-31T10:28:53+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "_instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 8789520, "num_examples": 465}], "download_size": 6973054, "dataset_size": 8789520}} | 2023-03-31T14:10:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "alpaca_data_cleaned_validations"
More Information needed | [
"# Dataset Card for \"alpaca_data_cleaned_validations\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"alpaca_data_cleaned_validations\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"alpaca_data_cleaned_validations\"\n\nMore Information needed"
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|
98c5326ebd300e8abd77b9d5f387b63b6e02087c |
This Dataset is a Hugging Face interface to the [Sefaria database export](https://github.com/Sefaria/Sefaria-Export)
Sefaria is a large collection of early Jewish texts, mostly in ancient Hebrew, but also some are in
Aramaic, and some are translations into English.
| mehdie/sefaria | [
"language:he",
"language:en",
"license:cc-by-4.0",
"History",
"Rabbinic",
"region:us"
]
| 2023-03-31T11:08:29+00:00 | {"language": ["he", "en"], "license": "cc-by-4.0", "pretty_name": "Sefaria HF Dataset", "tags": ["History", "Rabbinic"]} | 2023-05-01T07:39:56+00:00 | []
| [
"he",
"en"
]
| TAGS
#language-Hebrew #language-English #license-cc-by-4.0 #History #Rabbinic #region-us
|
This Dataset is a Hugging Face interface to the Sefaria database export
Sefaria is a large collection of early Jewish texts, mostly in ancient Hebrew, but also some are in
Aramaic, and some are translations into English.
| []
| [
"TAGS\n#language-Hebrew #language-English #license-cc-by-4.0 #History #Rabbinic #region-us \n"
]
| [
31
]
| [
"passage: TAGS\n#language-Hebrew #language-English #license-cc-by-4.0 #History #Rabbinic #region-us \n"
]
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|
f6580feea4f95c1beb80020bbe4e3994747ec5b3 | # Annealing
The [Madelon dataset](https://archive-beta.ics.uci.edu/dataset/171/madelon) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Artificial dataset with continuous input variables.
Highly non-linear classification problem.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| madelon | Binary classification | |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/madelon")["train"]
``` | mstz/madelon | [
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"madelon",
"tabular_classification",
"UCI",
"region:us"
]
| 2023-03-31T11:35:05+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "Madelon", "tags": ["madelon", "tabular_classification", "UCI"], "configs": ["Madelon"]} | 2023-04-16T16:34:04+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #license-cc #madelon #tabular_classification #UCI #region-us
| Annealing
=========
The Madelon dataset from the UCI ML repository.
Artificial dataset with continuous input variables.
Highly non-linear classification problem.
Configurations and tasks
========================
Configuration: madelon, Task: Binary classification, Description:
Usage
=====
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #license-cc #madelon #tabular_classification #UCI #region-us \n"
]
| [
50
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #license-cc #madelon #tabular_classification #UCI #region-us \n"
]
| [
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|
4987a51f6118b2b3b9b766addf28fde26e1f1dbc | # Dataset Card for "squad3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Mel-Iza0/squad3 | [
"region:us"
]
| 2023-03-31T11:52:13+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "input_ids", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 3833665, "num_examples": 5928}, {"name": "validation", "num_bytes": 982887, "num_examples": 1482}], "download_size": 2412739, "dataset_size": 4816552}} | 2023-03-31T15:14:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "squad3"
More Information needed | [
"# Dataset Card for \"squad3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"squad3\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"squad3\"\n\nMore Information needed"
]
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|
14b93c6f0dda0ecd2046c4fdc50ae7fb819085cd | # Dataset Card for "vehicle_segmentation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Onegafer/vehicle_segmentation | [
"region:us"
]
| 2023-03-31T11:52:57+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 160555965.0, "num_examples": 320}], "download_size": 0, "dataset_size": 160555965.0}} | 2023-04-25T12:52:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vehicle_segmentation"
More Information needed | [
"# Dataset Card for \"vehicle_segmentation\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vehicle_segmentation\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vehicle_segmentation\"\n\nMore Information needed"
]
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|
2a0c9c3ea6fd68733f9507fab98efe58fa989ad0 |
<div align="center">
<img width="640" alt="nflechas/recycling_app" src="https://huggingface.co/datasets/nflechas/recycling_app/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['biodegradable', 'cardboard', 'glass', 'metal', 'paper', 'plastic']
```
### Number of Images
```json
{'valid': 2098, 'test': 1042, 'train': 7324}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("nflechas/recycling_app", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2](https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2?ref=roboflow2huggingface)
### Citation
```
@misc{ garbage-classification-3_dataset,
title = { GARBAGE CLASSIFICATION 3 Dataset },
type = { Open Source Dataset },
author = { Material Identification },
howpublished = { \\url{ https://universe.roboflow.com/material-identification/garbage-classification-3 } },
url = { https://universe.roboflow.com/material-identification/garbage-classification-3 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { mar },
note = { visited on 2023-03-31 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on July 27, 2022 at 5:44 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 10464 images.
GARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 416x416 (Stretch)
The following augmentation was applied to create 1 versions of each source image:
* 50% probability of horizontal flip
* 50% probability of vertical flip
* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
| nflechas/recycling_app | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"Manufacturing",
"region:us"
]
| 2023-03-31T12:26:20+00:00 | {"task_categories": ["object-detection"], "tags": ["roboflow", "roboflow2huggingface", "Manufacturing"]} | 2023-03-31T12:26:33+00:00 | []
| []
| TAGS
#task_categories-object-detection #roboflow #roboflow2huggingface #Manufacturing #region-us
|
<div align="center">
<img width="640" alt="nflechas/recycling_app" src="URL
</div>
### Dataset Labels
### Number of Images
### How to Use
- Install datasets:
- Load the dataset:
### Roboflow Dataset Page
URL
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via URL on July 27, 2022 at 5:44 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 10464 images.
GARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 416x416 (Stretch)
The following augmentation was applied to create 1 versions of each source image:
* 50% probability of horizontal flip
* 50% probability of vertical flip
* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
| [
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"### Number of Images",
"### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:",
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"### License\nCC BY 4.0",
"### Dataset Summary\nThis dataset was exported via URL on July 27, 2022 at 5:44 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 10464 images.\nGARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Stretch)\n\nThe following augmentation was applied to create 1 versions of each source image:\n* 50% probability of horizontal flip\n* 50% probability of vertical flip\n* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down"
]
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"TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #Manufacturing #region-us \n",
"### Dataset Labels",
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"### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:",
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"### Dataset Summary\nThis dataset was exported via URL on July 27, 2022 at 5:44 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 10464 images.\nGARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Stretch)\n\nThe following augmentation was applied to create 1 versions of each source image:\n* 50% probability of horizontal flip\n* 50% probability of vertical flip\n* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down"
]
| [
32,
5,
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| [
"passage: TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #Manufacturing #region-us \n### Dataset Labels### Number of Images### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:### Roboflow Dataset Page\nURL### License\nCC BY 4.0### Dataset Summary\nThis dataset was exported via URL on July 27, 2022 at 5:44 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 10464 images.\nGARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 416x416 (Stretch)\n\nThe following augmentation was applied to create 1 versions of each source image:\n* 50% probability of horizontal flip\n* 50% probability of vertical flip\n* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down"
]
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|
72ae206f21f416f3e7981183537e9ab071aff006 | # Covertype
Classification of pixels into 7 forest cover types based on attributes such as elevation, aspect, slope, hillshade, soil-type, and more.
The [Covertype dataset](https://archive-beta.ics.uci.edu/dataset/31/covertype) from the [UCI ML repository](https://archive-beta.ics.uci.edu).
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| covertype | Multiclass classification | Classify the area as one of 7 cover classes. |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/covertype")["train"]
``` | mstz/covertype | [
"task_categories:tabular-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc",
"biology",
"UCI",
"binary_classification",
"multiclass_classification",
"region:us"
]
| 2023-03-31T12:33:53+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["100K<n<1M"], "task_categories": ["tabular-classification"], "pretty_name": "Covertype", "tags": ["biology", "UCI", "binary_classification", "multiclass_classification"]} | 2023-05-29T09:09:11+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-100K<n<1M #language-English #license-cc #biology #UCI #binary_classification #multiclass_classification #region-us
| Covertype
=========
Classification of pixels into 7 forest cover types based on attributes such as elevation, aspect, slope, hillshade, soil-type, and more.
The Covertype dataset from the UCI ML repository.
Configuration: covertype, Task: Multiclass classification, Description: Classify the area as one of 7 cover classes.
Usage
=====
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-100K<n<1M #language-English #license-cc #biology #UCI #binary_classification #multiclass_classification #region-us \n"
]
| [
56
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-100K<n<1M #language-English #license-cc #biology #UCI #binary_classification #multiclass_classification #region-us \n"
]
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|
f4de14db58cd402f76f6224b585563d3b93a8954 |
</b>Testing purpose only. Do not redistribute. </b>
Original contents: [url] https://huggingface.co/datasets/tatsu-lab/alpaca
Ko-alpaca: [url] https://github.com/Beomi/KoAlpaca/blob/main/ko_alpaca_data.json | royboy0416/ko-alpaca | [
"task_categories:text-generation",
"language:ko",
"license:cc-by-4.0",
"region:us"
]
| 2023-03-31T13:16:10+00:00 | {"language": ["ko"], "license": "cc-by-4.0", "task_categories": ["text-generation"]} | 2023-03-31T20:14:40+00:00 | []
| [
"ko"
]
| TAGS
#task_categories-text-generation #language-Korean #license-cc-by-4.0 #region-us
|
</b>Testing purpose only. Do not redistribute. </b>
Original contents: [url] URL
Ko-alpaca: [url] URL | []
| [
"TAGS\n#task_categories-text-generation #language-Korean #license-cc-by-4.0 #region-us \n"
]
| [
31
]
| [
"passage: TAGS\n#task_categories-text-generation #language-Korean #license-cc-by-4.0 #region-us \n"
]
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|
c9e69154e45534b41951ef9beeaf1d54be5eb9b6 | # AutoTrain Dataset for project: severe-js100-sentiment
## Dataset Description
This dataset has been automatically processed by AutoTrain for project severe-js100-sentiment.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "00:58 #\u0e2d\u0e38\u0e1a\u0e31\u0e15\u0e34\u0e40\u0e2b\u0e15\u0e38 #\u0e16\u0e19\u0e19\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e193 \u0e0a\u0e48\u0e27\u0e07\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e32\u0e23\u0e2a\u0e32\u0e2a\u0e19\u0e4c\u0e27\u0e34\u0e40\u0e17\u0e28\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e19 >\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e01\u0e23\u0e1e\u0e34\u0e17\u0e31\u0e01\u0e29\u0e4c\u0e28\u0e36\u0e01\u0e29\u0e32 \u0e1b\u0e32\u0e01\u0e0b\u0e2d\u0e22\u0e1a\u0e32\u0e07\u0e1a\u0e2d\u0e193\u0e0b\u0e2d\u0e225 \u0e23\u0e16\u0e08\u0e31\u0e01\u0e23\u0e22\u0e32\u0e19\u0e22\u0e19\u0e15\u0e4c\u0e40\u0e2a\u0e35\u0e22\u0e2b\u0e25\u0e31\u0e01\u0e25\u0e49\u0e21 \u0e02\u0e27\u0e32\u0e07\u0e0a\u0e48\u0e2d\u0e07\u0e17\u0e32\u0e07\u0e0b\u0e49\u0e32\u0e22",
"target": 2
},
{
"text": "03:22 #\u0e2d\u0e38\u0e1a\u0e31\u0e15\u0e34\u0e40\u0e2b\u0e15\u0e38 #\u0e16\u0e19\u0e19\u0e01\u0e32\u0e0d\u0e08\u0e19\u0e32\u0e20\u0e34\u0e40\u0e29\u0e01 \u0e0a\u0e48\u0e27\u0e07\u0e2a\u0e30\u0e1e\u0e32\u0e19\u0e02\u0e49\u0e32\u0e21\u0e04\u0e25\u0e2d\u0e07\u0e20\u0e32\u0e29\u0e35\u0e40\u0e08\u0e23\u0e34\u0e0d >\u0e41\u0e22\u0e01\u0e1a\u0e32\u0e07\u0e41\u0e27\u0e01 \u0e1a\u0e19\u0e15\u0e48\u0e32\u0e07\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e40\u0e1e\u0e0a\u0e23\u0e40\u0e01\u0e29\u0e21 \u0e23\u0e16\u0e1b\u0e34\u0e04\u0e2d\u0e31\u0e1e\u0e40\u0e2a\u0e35\u0e22\u0e2b\u0e25\u0e31\u0e01\u0e0a\u0e19\u0e02\u0e2d\u0e1a\u0e17\u0e32\u0e07 \u0e02\u0e27\u0e32\u0e07\u0e0a\u0e48\u0e2d\u0e07\u0e17\u0e32\u0e07\u0e0b\u0e49\u0e32\u0e22",
"target": 2
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07', '\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07\u0e21\u0e32\u0e01', '\u0e44\u0e21\u0e48\u0e23\u0e38\u0e19\u0e41\u0e23\u0e07'], 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 | 5348 |
| valid | 1339 |
| Garfieldgx/DataSet_for_thesis | [
"task_categories:text-classification",
"region:us"
]
| 2023-03-31T13:28:27+00:00 | {"task_categories": ["text-classification"]} | 2023-03-31T13:33:01+00:00 | []
| []
| TAGS
#task_categories-text-classification #region-us
| AutoTrain Dataset for project: severe-js100-sentiment
=====================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project severe-js100-sentiment.
### Languages
The BCP-47 code for the dataset's language is unk.
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 unk.\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-text-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-text-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\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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|
9afa55076fe28de3c6ca9fa0c1f5cf02897fed49 | # Sonar
The [Sonar dataset](https://archive-beta.ics.uci.edu/dataset/151/connectionist+bench+sonar+mines+vs+rocks) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Dataset to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| sonar | Binary classification | Is the sonar detecting a rock? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/sonar")["train"]
``` | mstz/sonar | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"adult",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
]
| 2023-03-31T13:43:15+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Sonar", "tags": ["adult", "tabular_classification", "binary_classification", "UCI"], "configs": ["sonar"]} | 2023-04-16T17:02:16+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #adult #tabular_classification #binary_classification #UCI #region-us
| Sonar
=====
The Sonar dataset from the UCI ML repository.
Dataset to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
Configurations and tasks
========================
Configuration: sonar, Task: Binary classification, Description: Is the sonar detecting a rock?
Usage
=====
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #adult #tabular_classification #binary_classification #UCI #region-us \n"
]
| [
53
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #adult #tabular_classification #binary_classification #UCI #region-us \n"
]
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|
46ac8bc5eac1461e5a1f7f212f09044fc06623a8 | # Adult
The [Toxicity dataset](https://archive-beta.ics.uci.edu/dataset/728/toxicity) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
The dataset includes 171 molecules designed for functional domains of a core clock protein, CRY1, responsible for generating circadian rhythm.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| toxicity | Binary classification | Is the molecule toxic? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/toxicity")["train"]
``` | mstz/toxicity | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"toxicity",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
]
| 2023-03-31T13:59:54+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Toxicity", "tags": ["toxicity", "tabular_classification", "binary_classification", "multiclass_classification", "UCI"], "configs": ["encoding", "income", "income-no race", "race"]} | 2023-04-16T17:03:37+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #toxicity #tabular_classification #binary_classification #multiclass_classification #UCI #region-us
| Adult
=====
The Toxicity dataset from the UCI ML repository.
The dataset includes 171 molecules designed for functional domains of a core clock protein, CRY1, responsible for generating circadian rhythm.
Configurations and tasks
========================
Configuration: toxicity, Task: Binary classification, Description: Is the molecule toxic?
Usage
=====
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #toxicity #tabular_classification #binary_classification #multiclass_classification #UCI #region-us \n"
]
| [
60
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #toxicity #tabular_classification #binary_classification #multiclass_classification #UCI #region-us \n"
]
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|
1ffb2b525d281093c631cd4092bbdf7460250e92 | # Dataset Card for "ner_naamapadam"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | livinNector/ner_naamapadam | [
"region:us"
]
| 2023-03-31T14:06:29+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": 186464659, "num_examples": 497882}, {"name": "test", "num_bytes": 319747, "num_examples": 758}, {"name": "validation", "num_bytes": 1046343, "num_examples": 2795}], "download_size": 48623255, "dataset_size": 187830749}} | 2023-04-04T06:09:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ner_naamapadam"
More Information needed | [
"# Dataset Card for \"ner_naamapadam\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ner_naamapadam\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ner_naamapadam\"\n\nMore Information needed"
]
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|
581ef135892badd87c59f2c8f4ad09c4f555968a | # Dataset Card for "ner_wikiann"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | livinNector/ner_wikiann | [
"region:us"
]
| 2023-03-31T14:06:36+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": "validation", "num_bytes": 229847, "num_examples": 1000}, {"name": "test", "num_bytes": 230672, "num_examples": 1000}, {"name": "train", "num_bytes": 3413046, "num_examples": 15000}], "download_size": 1020943, "dataset_size": 3873565}} | 2023-04-04T06:09:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ner_wikiann"
More Information needed | [
"# Dataset Card for \"ner_wikiann\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ner_wikiann\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ner_wikiann\"\n\nMore Information needed"
]
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|
616e5b00cefb18166abcad33b7dc2b2431b4b964 | # Dataset Card for "ner_xtreme"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | livinNector/ner_xtreme | [
"region:us"
]
| 2023-03-31T14:06:40+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": 3413046, "num_examples": 15000}, {"name": "validation", "num_bytes": 229847, "num_examples": 1000}, {"name": "test", "num_bytes": 230672, "num_examples": 1000}], "download_size": 1020943, "dataset_size": 3873565}} | 2023-04-04T06:09:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ner_xtreme"
More Information needed | [
"# Dataset Card for \"ner_xtreme\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ner_xtreme\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ner_xtreme\"\n\nMore Information needed"
]
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|
4b704ccb2df29dcd654e2f37640e03c89fc7f931 | # Dataset Card for "ner_indic_glue"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | livinNector/ner_indic_glue | [
"region:us"
]
| 2023-03-31T14:06:43+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": 4687618, "num_examples": 20466}, {"name": "validation", "num_bytes": 591516, "num_examples": 2586}, {"name": "test", "num_bytes": 585925, "num_examples": 2611}], "download_size": 1381725, "dataset_size": 5865059}} | 2023-04-04T06:09:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ner_indic_glue"
More Information needed | [
"# Dataset Card for \"ner_indic_glue\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ner_indic_glue\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ner_indic_glue\"\n\nMore Information needed"
]
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|
861b51c0ad56beba65c0d47d75105681cf7915b3 | # Dataset Card for "cyber-threat-intelligence_v2"
updated version of mrmoor/cyber-threat-intelligence
RE and NER Dataset for Cyber Threat Intelegence (CTI)
T5 Model trained on NYT and this dataset: Olec/cyber_rebel
This dataset only contains sentences with realtions.
Full dataset is available at mrmoor/cyber-threat-intelligence. | Olec/cyber-threat-intelligence_v2 | [
"region:us"
]
| 2023-03-31T14:08:08+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "end_offset", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "start_offset", "dtype": "int64"}]}, {"name": "relations", "list": [{"name": "from_id", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "to_id", "dtype": "int64"}, {"name": "type", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 29518, "num_examples": 72}, {"name": "train", "num_bytes": 147723, "num_examples": 332}, {"name": "validation", "num_bytes": 36580, "num_examples": 76}], "download_size": 119557, "dataset_size": 213821}} | 2023-04-15T10:00:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cyber-threat-intelligence_v2"
updated version of mrmoor/cyber-threat-intelligence
RE and NER Dataset for Cyber Threat Intelegence (CTI)
T5 Model trained on NYT and this dataset: Olec/cyber_rebel
This dataset only contains sentences with realtions.
Full dataset is available at mrmoor/cyber-threat-intelligence. | [
"# Dataset Card for \"cyber-threat-intelligence_v2\"\n\nupdated version of mrmoor/cyber-threat-intelligence\n\nRE and NER Dataset for Cyber Threat Intelegence (CTI)\n\nT5 Model trained on NYT and this dataset: Olec/cyber_rebel\n\nThis dataset only contains sentences with realtions.\nFull dataset is available at mrmoor/cyber-threat-intelligence."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cyber-threat-intelligence_v2\"\n\nupdated version of mrmoor/cyber-threat-intelligence\n\nRE and NER Dataset for Cyber Threat Intelegence (CTI)\n\nT5 Model trained on NYT and this dataset: Olec/cyber_rebel\n\nThis dataset only contains sentences with realtions.\nFull dataset is available at mrmoor/cyber-threat-intelligence."
]
| [
6,
103
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cyber-threat-intelligence_v2\"\n\nupdated version of mrmoor/cyber-threat-intelligence\n\nRE and NER Dataset for Cyber Threat Intelegence (CTI)\n\nT5 Model trained on NYT and this dataset: Olec/cyber_rebel\n\nThis dataset only contains sentences with realtions.\nFull dataset is available at mrmoor/cyber-threat-intelligence."
]
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|
0263ae881810b1a40f5dbce699b7265dd5679b0b | # Dataset Card for "ner_ta4ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | livinNector/ner_ta4ner | [
"region:us"
]
| 2023-03-31T14:29:07+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": 197978369, "num_examples": 548348}, {"name": "test", "num_bytes": 1367016, "num_examples": 5369}, {"name": "validation", "num_bytes": 2097553, "num_examples": 7381}], "download_size": 52034852, "dataset_size": 201442938}} | 2023-03-31T14:29:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ner_ta4ner"
More Information needed | [
"# Dataset Card for \"ner_ta4ner\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ner_ta4ner\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ner_ta4ner\"\n\nMore Information needed"
]
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]
|
6243af4425a17ae2f3a8377948a7b29a21fee195 |
# Dataset Card for Mtg Pauper BLIP captions
Dataset inspired by [Pokemon Blip Captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/tree/main)
BLIP generated captions for Magic: the gathering pauper cards artworks from _BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research)_
For each row the dataset contains `image` and `caption` keys. `image` is a varying size PIL jpeg, and `caption` is the accompanying text caption. Only a train split is provided.
## Examples
> a drawing of a small blue man holding a yellow toy
For more in-depth informations in how this dataset was created, check the [Google Colab](https://colab.research.google.com/drive/14Hxsm8o25rGOeYozqPmy6dBNMTudkw_H?usp=sharing)
## Citation
If you use this dataset, please cite it as:
```
@misc{mazzola2022pauper,
author = {Matteo Mazzola},
title = {Magic:the Gatering pauper BLIP captions},
year={2023},
howpublished= {\url{https://huggingface.co/datasets/vedalken/mtg-pauper-blip-captions}}
}
```
### Acknowledgments:
This dataset was made possible by [Scryfall](https://scryfall.com/).
Portions of artwork on Scryfall are unofficial Fan Content permitted under the Wizards of the Coast Fan Content Policy. The literal and graphical information presented on this site about Magic: The Gathering, including card images, mana symbols, and Oracle text, is copyright Wizards of the Coast, LLC, a subsidiary of Hasbro, This dataset is not produced by or endorsed by Wizards of the Coast. | vedalken/mtg-pauper-blip-captions | [
"region:us"
]
| 2023-03-31T16:51:14+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 253832724.0, "num_examples": 3000}], "download_size": 0, "dataset_size": 253832724.0}} | 2023-04-06T11:50:14+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Mtg Pauper BLIP captions
Dataset inspired by Pokemon Blip Captions
BLIP generated captions for Magic: the gathering pauper cards artworks from _BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research)_
For each row the dataset contains 'image' and 'caption' keys. 'image' is a varying size PIL jpeg, and 'caption' is the accompanying text caption. Only a train split is provided.
## Examples
!URL
> a drawing of a small blue man holding a yellow toy
For more in-depth informations in how this dataset was created, check the Google Colab
If you use this dataset, please cite it as:
### Acknowledgments:
This dataset was made possible by Scryfall.
Portions of artwork on Scryfall are unofficial Fan Content permitted under the Wizards of the Coast Fan Content Policy. The literal and graphical information presented on this site about Magic: The Gathering, including card images, mana symbols, and Oracle text, is copyright Wizards of the Coast, LLC, a subsidiary of Hasbro, This dataset is not produced by or endorsed by Wizards of the Coast. | [
"# Dataset Card for Mtg Pauper BLIP captions\n\nDataset inspired by Pokemon Blip Captions\n\nBLIP generated captions for Magic: the gathering pauper cards artworks from _BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research)_\nFor each row the dataset contains 'image' and 'caption' keys. 'image' is a varying size PIL jpeg, and 'caption' is the accompanying text caption. Only a train split is provided.",
"## Examples\n\n\n!URL\n> a drawing of a small blue man holding a yellow toy\n\nFor more in-depth informations in how this dataset was created, check the Google Colab\n\nIf you use this dataset, please cite it as:",
"### Acknowledgments: \nThis dataset was made possible by Scryfall. \nPortions of artwork on Scryfall are unofficial Fan Content permitted under the Wizards of the Coast Fan Content Policy. The literal and graphical information presented on this site about Magic: The Gathering, including card images, mana symbols, and Oracle text, is copyright Wizards of the Coast, LLC, a subsidiary of Hasbro, This dataset is not produced by or endorsed by Wizards of the Coast."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for Mtg Pauper BLIP captions\n\nDataset inspired by Pokemon Blip Captions\n\nBLIP generated captions for Magic: the gathering pauper cards artworks from _BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research)_\nFor each row the dataset contains 'image' and 'caption' keys. 'image' is a varying size PIL jpeg, and 'caption' is the accompanying text caption. Only a train split is provided.",
"## Examples\n\n\n!URL\n> a drawing of a small blue man holding a yellow toy\n\nFor more in-depth informations in how this dataset was created, check the Google Colab\n\nIf you use this dataset, please cite it as:",
"### Acknowledgments: \nThis dataset was made possible by Scryfall. \nPortions of artwork on Scryfall are unofficial Fan Content permitted under the Wizards of the Coast Fan Content Policy. The literal and graphical information presented on this site about Magic: The Gathering, including card images, mana symbols, and Oracle text, is copyright Wizards of the Coast, LLC, a subsidiary of Hasbro, This dataset is not produced by or endorsed by Wizards of the Coast."
]
| [
6,
127,
48,
114
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for Mtg Pauper BLIP captions\n\nDataset inspired by Pokemon Blip Captions\n\nBLIP generated captions for Magic: the gathering pauper cards artworks from _BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research)_\nFor each row the dataset contains 'image' and 'caption' keys. 'image' is a varying size PIL jpeg, and 'caption' is the accompanying text caption. Only a train split is provided.## Examples\n\n\n!URL\n> a drawing of a small blue man holding a yellow toy\n\nFor more in-depth informations in how this dataset was created, check the Google Colab\n\nIf you use this dataset, please cite it as:### Acknowledgments: \nThis dataset was made possible by Scryfall. \nPortions of artwork on Scryfall are unofficial Fan Content permitted under the Wizards of the Coast Fan Content Policy. The literal and graphical information presented on this site about Magic: The Gathering, including card images, mana symbols, and Oracle text, is copyright Wizards of the Coast, LLC, a subsidiary of Hasbro, This dataset is not produced by or endorsed by Wizards of the Coast."
]
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|
819c6ecbccbe0da7a5c70593c2dbe02aac56fb96 | # Dataset Card for "alpaca-gigo-detector"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | argilla/alpaca-gigo-detector | [
"task_categories:text-classification",
"language:en",
"region:us"
]
| 2023-03-31T16:52:24+00:00 | {"language": ["en"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "_instruction", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ALL GOOD", "1": "BAD INSTRUCTION"}}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 545007, "num_examples": 697}, {"name": "test", "num_bytes": 58515, "num_examples": 78}], "download_size": 364798, "dataset_size": 603522}} | 2023-04-02T18:40:38+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #language-English #region-us
| # Dataset Card for "alpaca-gigo-detector"
More Information needed | [
"# Dataset Card for \"alpaca-gigo-detector\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-text-classification #language-English #region-us \n",
"# Dataset Card for \"alpaca-gigo-detector\"\n\nMore Information needed"
]
| [
21,
19
]
| [
"passage: TAGS\n#task_categories-text-classification #language-English #region-us \n# Dataset Card for \"alpaca-gigo-detector\"\n\nMore Information needed"
]
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|
976019b896a111193e5fb4ddeb7df0b717737574 | # Dataset Card for "cracks2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | GarayMC/cracks2 | [
"region:us"
]
| 2023-03-31T16:53:02+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3434976.0, "num_examples": 55}, {"name": "validation", "num_bytes": 691668.0, "num_examples": 10}], "download_size": 0, "dataset_size": 4126644.0}} | 2023-03-31T19:00:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cracks2"
More Information needed | [
"# Dataset Card for \"cracks2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cracks2\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cracks2\"\n\nMore Information needed"
]
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|
8201958666658a69d9a952cd7a46842fd9ea1ea3 | # Dataset Card for "digit_mask_augmented_raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mazkooleg/digit_mask_augmented_raw | [
"region:us"
]
| 2023-03-31T16:56:51+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 58513564703.2, "num_examples": 1825800}, {"name": "test", "num_bytes": 195044953.756, "num_examples": 6086}, {"name": "validation", "num_bytes": 169086020.324, "num_examples": 5276}], "download_size": 54506700314, "dataset_size": 58877695677.27999}} | 2023-04-01T00:03:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "digit_mask_augmented_raw"
More Information needed | [
"# Dataset Card for \"digit_mask_augmented_raw\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"digit_mask_augmented_raw\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"digit_mask_augmented_raw\"\n\nMore Information needed"
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|
6bc7a2902d99b1815a50cae2e69544c52562e096 | # Dataset Card for "translated_german_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LEL-A/translated_german_alpaca | [
"region:us"
]
| 2023-03-31T17:08:19+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "_instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "null"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "original_id", "dtype": "int64"}, {"name": "translation_model", "dtype": "string"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 1004916509, "num_examples": 51759}], "download_size": 690637366, "dataset_size": 1004916509}} | 2023-04-10T08:32:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "translated_german_alpaca"
More Information needed | [
"# Dataset Card for \"translated_german_alpaca\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"translated_german_alpaca\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"translated_german_alpaca\"\n\nMore Information needed"
]
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|
44a6b95864bcb51df8f2ffe032049e399b0fce28 | # Dataset Card for "soda_input_output-clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AlekseyKorshuk/soda_input_output-clean | [
"region:us"
]
| 2023-03-31T19:05:40+00:00 | {"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 842581512.2664871, "num_examples": 940754}], "download_size": 495782858, "dataset_size": 842581512.2664871}} | 2023-03-31T19:31:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "soda_input_output-clean"
More Information needed | [
"# Dataset Card for \"soda_input_output-clean\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"soda_input_output-clean\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"soda_input_output-clean\"\n\nMore Information needed"
]
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|
86be64b30075120dfa60f829972e06d936a0909f | # AutoTrain Dataset for project: pegasus-subreddit-comments-summarizer
## Dataset Description
This dataset has been automatically processed by AutoTrain for project pegasus-subreddit-comments-summarizer.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "I go through this every single year. We have an Ironman competition that is 2 miles from my hotel, and I sell out for that weekend almost a year in advance. Without fail I will have some nitwit who will come up on their checkout day and ask to extend, when I tell them I can't they lose their mind at me. It's their room, they paid for it, they're already in there how can I just give it away. People do not understand how reservations work.",
"target": "The commenter experiences this every year - they sell out their hotel almost a year in advance for an Ironman competition nearby. Despite this, some customers still ask to extend their stay at checkout and get angry when told it's not possible because they don't understand how reservations work."
},
{
"text": "Can i just say .. thanks for going back to make sure you hadn't overreacted. Im sure that made things so much easier on all the staff, with it being their first days back, being understaffed, I'm sure, and trying to get back into the swing of things. I think you handled that really well :)",
"target": "The commenter appreciates the poster's effort in going back to verify if they had overreacted. The commenter believes this action might have made things easier for the understaffed team during their first days back. The commenter commends the poster for handling the situation well."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', 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 | 7177 |
| valid | 1796 |
| stevied67/autotrain-data-pegasus-subreddit-comments-summarizer | [
"task_categories:summarization",
"language:en",
"region:us"
]
| 2023-03-31T19:09:49+00:00 | {"language": ["en"], "task_categories": ["summarization"]} | 2023-03-31T19:10:36+00:00 | []
| [
"en"
]
| TAGS
#task_categories-summarization #language-English #region-us
| AutoTrain Dataset for project: pegasus-subreddit-comments-summarizer
====================================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project pegasus-subreddit-comments-summarizer.
### Languages
The BCP-47 code for the dataset's language is en.
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 en.\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-summarization #language-English #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\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:"
]
| [
20,
26,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-summarization #language-English #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\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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|
ac493c22f1e9e1da2ea35511ad8dfb216cd7632f | # 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) | sebinbusra/turkishReviews-ds-mini | [
"region:us"
]
| 2023-03-31T20:04:14+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": 896649, "dataset_size": 1392332.0}} | 2023-03-31T20:04:31+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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|
45bd2671a067b851549201eb07d2421d0c47ae4a | # Dataset Card for Criticality Prediction
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Legal Criticality Prediction (LCP) is a multilingual, diachronic dataset of 139K Swiss Federal Supreme Court (FSCS) cases annotated with two criticality labels. The bge_label i a binary label (critical, non-critical), while the citation label has 5 classes (critical-1, critical-2, critical-3, critical-4, non-critical). Critical classes of the citation_label are distinct subsets of the critical class of the bge_label. This dataset creates a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
### Supported Tasks and Leaderboards
LCP can be used as text classification task
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
German (91k), French (33k), Italian (15k)
## Dataset Structure
```
{
"decision_id": "008d8a52-f0ea-4820-a18c-d06066dbb407",
"language": "fr",
"year": "2018",
"chamber": "CH_BGer_004",
"region": "Federation",
"origin_chamber": "338.0",
"origin_court": "127.0",
"origin_canton": "24.0",
"law_area": "civil_law",
"law_sub_area": ,
"bge_label": "critical",
"citation_label": "critical-1",
"facts": "Faits : A. A.a. Le 17 août 2007, C.X._, née le 14 février 1944 et domiciliée...",
"considerations": "Considérant en droit : 1. Interjeté en temps utile (art. 100 al. 1 LTF) par les défendeurs qui ont succombé dans leurs conclusions (art. 76 LTF) contre une décision...",
"rulings": "Par ces motifs, le Tribunal fédéral prononce : 1. Le recours est rejeté. 2. Les frais judiciaires, arrêtés à 10'000 fr., sont mis solidairement à la charge des recourants...",
}
```
### Data Fields
```
decision_id: (str) a unique identifier of the for the document
language: (str) one of (de, fr, it)
year: (int) the publication year
chamber: (str) the chamber of the case
region: (str) the region of the case
origin_chamber: (str) the chamber of the origin case
origin_court: (str) the court of the origin case
origin_canton: (str) the canton of the origin case
law_area: (str) the law area of the case
law_sub_area:(str) the law sub area of the case
bge_label: (str) critical or non-critical
citation_label: (str) critical-1, critical-2, critical-3, critical-4, non-critical
facts: (str) the facts of the case
considerations: (str) the considerations of the case
rulings: (str) the rulings of the case
```
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
The dataset was split date-stratisfied
- Train: 2002-2015
- Validation: 2016-2017
- Test: 2018-2022
| Language | Subset | Number of Documents (Training/Validation/Test) |
|------------|------------|--------------------------------------------|
| German | **de** | 81'264 (56592 / 19601 / 5071) |
| French | **fr** | 49'354 (29263 / 11117 / 8974) |
| Italian | **it** | 7913 (5220 / 1901 / 792) |
## Dataset Creation
### Curation Rationale
The dataset was created by Stern (2023).
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
bge_label:
1. all bger_references in the bge header were extracted (for bge see rcds/swiss_rulings).
2. bger file_names are compared with the found references
citation_label:
1. count all citations for all bger cases and weight citations
2. divide cited cases in four different classes, depending on amount of citations
#### Who are the annotators?
Stern processed data and introduced bge and citation-label
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Stern5497](https://github.com/stern5497) for adding this dataset. | rcds/swiss_criticality_prediction | [
"task_categories:text-classification",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"region:us"
]
| 2023-03-31T20:21:30+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["de", "fr", "it"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "pretty_name": "Legal Criticality Prediction", "tags": []} | 2023-07-20T06:39:07+00:00 | [
"2306.09237"
]
| [
"de",
"fr",
"it"
]
| TAGS
#task_categories-text-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us
| Dataset Card for Criticality Prediction
=======================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ 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
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage:
* Repository:
* Paper:
* Leaderboard:
* Point of Contact:
### Dataset Summary
Legal Criticality Prediction (LCP) is a multilingual, diachronic dataset of 139K Swiss Federal Supreme Court (FSCS) cases annotated with two criticality labels. The bge\_label i a binary label (critical, non-critical), while the citation label has 5 classes (critical-1, critical-2, critical-3, critical-4, non-critical). Critical classes of the citation\_label are distinct subsets of the critical class of the bge\_label. This dataset creates a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
### Supported Tasks and Leaderboards
LCP can be used as text classification task
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
German (91k), French (33k), Italian (15k)
Dataset Structure
-----------------
### Data Fields
### Data Instances
### Data Fields
### Data Splits
The dataset was split date-stratisfied
* Train: 2002-2015
* Validation: 2016-2017
* Test: 2018-2022
Language: German, Subset: de, Number of Documents (Training/Validation/Test): 81'264 (56592 / 19601 / 5071)
Language: French, Subset: fr, Number of Documents (Training/Validation/Test): 49'354 (29263 / 11117 / 8974)
Language: Italian, Subset: it, Number of Documents (Training/Validation/Test): 7913 (5220 / 1901 / 792)
Dataset Creation
----------------
### Curation Rationale
The dataset was created by Stern (2023).
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
bge\_label:
1. all bger\_references in the bge header were extracted (for bge see rcds/swiss\_rulings).
2. bger file\_names are compared with the found references
citation\_label:
1. count all citations for all bger cases and weight citations
2. divide cited cases in four different classes, depending on amount of citations
#### Who are the annotators?
Stern processed data and introduced bge and citation-label
Metadata is published by the Swiss Federal Supreme Court (URL).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (URL
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: URL
Please cite our ArXiv-Preprint
### Contributions
Thanks to @Stern5497 for adding this dataset.
| [
"### Dataset Summary\n\n\nLegal Criticality Prediction (LCP) is a multilingual, diachronic dataset of 139K Swiss Federal Supreme Court (FSCS) cases annotated with two criticality labels. The bge\\_label i a binary label (critical, non-critical), while the citation label has 5 classes (critical-1, critical-2, critical-3, critical-4, non-critical). Critical classes of the citation\\_label are distinct subsets of the critical class of the bge\\_label. This dataset creates a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.",
"### Supported Tasks and Leaderboards\n\n\nLCP can be used as text classification task",
"### Languages\n\n\nSwitzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.\nGerman (91k), French (33k), Italian (15k)\n\n\nDataset Structure\n-----------------",
"### Data Fields",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\nThe dataset was split date-stratisfied\n\n\n* Train: 2002-2015\n* Validation: 2016-2017\n* Test: 2018-2022\n\n\nLanguage: German, Subset: de, Number of Documents (Training/Validation/Test): 81'264 (56592 / 19601 / 5071)\nLanguage: French, Subset: fr, Number of Documents (Training/Validation/Test): 49'354 (29263 / 11117 / 8974)\nLanguage: Italian, Subset: it, Number of Documents (Training/Validation/Test): 7913 (5220 / 1901 / 792)\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was created by Stern (2023).",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.",
"### Annotations",
"#### Annotation process\n\n\nbge\\_label:\n\n\n1. all bger\\_references in the bge header were extracted (for bge see rcds/swiss\\_rulings).\n2. bger file\\_names are compared with the found references\n\n\ncitation\\_label:\n\n\n1. count all citations for all bger cases and weight citations\n2. divide cited cases in four different classes, depending on amount of citations",
"#### Who are the annotators?\n\n\nStern processed data and introduced bge and citation-label\nMetadata is published by the Swiss Federal Supreme Court (URL).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint",
"### Contributions\n\n\nThanks to @Stern5497 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us \n",
"### Dataset Summary\n\n\nLegal Criticality Prediction (LCP) is a multilingual, diachronic dataset of 139K Swiss Federal Supreme Court (FSCS) cases annotated with two criticality labels. The bge\\_label i a binary label (critical, non-critical), while the citation label has 5 classes (critical-1, critical-2, critical-3, critical-4, non-critical). Critical classes of the citation\\_label are distinct subsets of the critical class of the bge\\_label. This dataset creates a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.",
"### Supported Tasks and Leaderboards\n\n\nLCP can be used as text classification task",
"### Languages\n\n\nSwitzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.\nGerman (91k), French (33k), Italian (15k)\n\n\nDataset Structure\n-----------------",
"### Data Fields",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\nThe dataset was split date-stratisfied\n\n\n* Train: 2002-2015\n* Validation: 2016-2017\n* Test: 2018-2022\n\n\nLanguage: German, Subset: de, Number of Documents (Training/Validation/Test): 81'264 (56592 / 19601 / 5071)\nLanguage: French, Subset: fr, Number of Documents (Training/Validation/Test): 49'354 (29263 / 11117 / 8974)\nLanguage: Italian, Subset: it, Number of Documents (Training/Validation/Test): 7913 (5220 / 1901 / 792)\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was created by Stern (2023).",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.",
"### Annotations",
"#### Annotation process\n\n\nbge\\_label:\n\n\n1. all bger\\_references in the bge header were extracted (for bge see rcds/swiss\\_rulings).\n2. bger file\\_names are compared with the found references\n\n\ncitation\\_label:\n\n\n1. count all citations for all bger cases and weight citations\n2. divide cited cases in four different classes, depending on amount of citations",
"#### Who are the annotators?\n\n\nStern processed data and introduced bge and citation-label\nMetadata is published by the Swiss Federal Supreme Court (URL).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint",
"### Contributions\n\n\nThanks to @Stern5497 for adding this dataset."
]
| [
104,
175,
20,
65,
5,
6,
5,
141,
17,
4,
50,
30,
5,
97,
36,
55,
7,
8,
14,
6,
110,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us \n### Dataset Summary\n\n\nLegal Criticality Prediction (LCP) is a multilingual, diachronic dataset of 139K Swiss Federal Supreme Court (FSCS) cases annotated with two criticality labels. The bge\\_label i a binary label (critical, non-critical), while the citation label has 5 classes (critical-1, critical-2, critical-3, critical-4, non-critical). Critical classes of the citation\\_label are distinct subsets of the critical class of the bge\\_label. This dataset creates a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.### Supported Tasks and Leaderboards\n\n\nLCP can be used as text classification task### Languages\n\n\nSwitzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.\nGerman (91k), French (33k), Italian (15k)\n\n\nDataset Structure\n-----------------### Data Fields### Data Instances### Data Fields"
]
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|
fe0ee68f0e3083ece02c3229a4e1fac9f06daca8 | # Dataset Card for "my_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bbooth/my_dataset | [
"region:us"
]
| 2023-03-31T20:22:15+00:00 | {"dataset_info": {"features": [{"name": "data", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 80000, "num_examples": 10000}], "download_size": 96280, "dataset_size": 80000}} | 2023-04-05T05:24:52+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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|
a5123bb354d480dac3db82d2e9004d876a66a222 | # Dataset Card for "reklamation24_unterhaltung-kultur-freizeit-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_unterhaltung-kultur-freizeit-full | [
"region:us"
]
| 2023-03-31T21:21:11+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 25643117, "num_examples": 4699}], "download_size": 0, "dataset_size": 25643117}} | 2023-04-25T13:16:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_unterhaltung-kultur-freizeit-full"
More Information needed | [
"# Dataset Card for \"reklamation24_unterhaltung-kultur-freizeit-full\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_unterhaltung-kultur-freizeit-full\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_unterhaltung-kultur-freizeit-full\"\n\nMore Information needed"
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f8198fa687d790ad5828e2f3ded5dd56668477d4 | # Dataset Card for "reklamation24_mode-schmuck-zubehoer-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_mode-schmuck-zubehoer-full | [
"region:us"
]
| 2023-03-31T21:23:51+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 68271429, "num_examples": 12923}], "download_size": 0, "dataset_size": 68271429}} | 2023-04-25T13:01:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_mode-schmuck-zubehoer-full"
More Information needed | [
"# Dataset Card for \"reklamation24_mode-schmuck-zubehoer-full\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_mode-schmuck-zubehoer-full\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_mode-schmuck-zubehoer-full\"\n\nMore Information needed"
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|
d1f06516d89246d3d187217bbf42e572baf14c03 | # Dataset Card for "reklamation24_finanzen-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_finanzen-full | [
"region:us"
]
| 2023-03-31T21:25:02+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 24578022, "num_examples": 4334}], "download_size": 0, "dataset_size": 24578022}} | 2023-04-25T13:04:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_finanzen-full"
More Information needed | [
"# Dataset Card for \"reklamation24_finanzen-full\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_finanzen-full\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_finanzen-full\"\n\nMore Information needed"
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|
3e5689d2fd4e6dec06df10b2cfec5fde484783aa | # Dataset Card for "reklamation24_moebel-einrichtungshaeuser-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_moebel-einrichtungshaeuser-full | [
"region:us"
]
| 2023-03-31T21:26:52+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 48126857, "num_examples": 8452}], "download_size": 0, "dataset_size": 48126857}} | 2023-04-25T13:03:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_moebel-einrichtungshaeuser-full"
More Information needed | [
"# Dataset Card for \"reklamation24_moebel-einrichtungshaeuser-full\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_moebel-einrichtungshaeuser-full\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_moebel-einrichtungshaeuser-full\"\n\nMore Information needed"
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|
1a91a050d010189f40d680f179936035f5540814 | # Dataset Card for "reklamation24_supermaerkte-drogerien-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_supermaerkte-drogerien-full | [
"region:us"
]
| 2023-03-31T21:30:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 84943744, "num_examples": 15366}], "download_size": 0, "dataset_size": 84943744}} | 2023-04-25T12:57:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_supermaerkte-drogerien-full"
More Information needed | [
"# Dataset Card for \"reklamation24_supermaerkte-drogerien-full\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_supermaerkte-drogerien-full\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_supermaerkte-drogerien-full\"\n\nMore Information needed"
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|
cbf963e6b721b7e5e3d87fc23ad97a93bd27a6f2 | # Dataset Card for "wikipedia_stage2_coverage_20230331"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MartinKu/wikipedia_stage2_coverage_20230331 | [
"region:us"
]
| 2023-03-31T22:05:15+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "S_V_position", "sequence": "int64"}, {"name": "O_C_position", "sequence": "int64"}, {"name": "start_point_list", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 60121841137, "num_examples": 1089628}], "download_size": 18558792841, "dataset_size": 60121841137}} | 2023-04-02T13:44:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikipedia_stage2_coverage_20230331"
More Information needed | [
"# Dataset Card for \"wikipedia_stage2_coverage_20230331\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia_stage2_coverage_20230331\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia_stage2_coverage_20230331\"\n\nMore Information needed"
]
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|
82c70da478a4f7604256ed2b2805957b1a0f5240 | # Dataset Card for "wikipedia_stage1_SV_20230331"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MartinKu/wikipedia_stage1_SV_20230331 | [
"region:us"
]
| 2023-03-31T22:32:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3712575761, "num_examples": 124985104}], "download_size": 2439087910, "dataset_size": 3712575761}} | 2023-04-02T18:32:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikipedia_stage1_SV_20230331"
More Information needed | [
"# Dataset Card for \"wikipedia_stage1_SV_20230331\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia_stage1_SV_20230331\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia_stage1_SV_20230331\"\n\nMore Information needed"
]
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|
854cc2d12d9b8e5b06fa121e709d2e4eab0797be | # Dataset Card for "wikipedia_stage1_OC_20230331"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MartinKu/wikipedia_stage1_OC_20230331 | [
"region:us"
]
| 2023-03-31T22:32:48+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10763367877, "num_examples": 152512467}], "download_size": 6671458208, "dataset_size": 10763367877}} | 2023-04-02T18:41:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikipedia_stage1_OC_20230331"
More Information needed | [
"# Dataset Card for \"wikipedia_stage1_OC_20230331\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia_stage1_OC_20230331\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia_stage1_OC_20230331\"\n\nMore Information needed"
]
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|
266b2f170c42b55a693ab45943aefb53656a8831 | # Dataset Card for "AICE_dataset"
[AICE project on Kaggle](https://www.kaggle.com/datasets/capsuleyolo/kyucapsule)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zxcej/AICE_dataset | [
"region:us"
]
| 2023-03-31T23:30:18+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "SMT", "1": "angiodysplasia", "2": "bleeding", "3": "diverticulum", "4": "erosion", "5": "erythema", "6": "foreign body", "7": "lymph follicle", "8": "lymphangiectasia", "9": "no_class", "10": "polyp-like", "11": "stenosis"}}}}], "splits": [{"name": "train", "num_bytes": 993869095.1352087, "num_examples": 14784}, {"name": "test", "num_bytes": 247932424.8427913, "num_examples": 3697}], "download_size": 1242057657, "dataset_size": 1241801519.978}} | 2023-04-04T20:17:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AICE_dataset"
AICE project on Kaggle
More Information needed | [
"# Dataset Card for \"AICE_dataset\"\n\nAICE project on Kaggle\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AICE_dataset\"\n\nAICE project on Kaggle\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AICE_dataset\"\n\nAICE project on Kaggle\n\nMore Information needed"
]
| [
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|
660971b7d5e5336aab0b59288734b6ed9a8eb3d8 | # Dataset Card for "little_dataset-combined"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/little_dataset-combined | [
"region:us"
]
| 2023-03-31T23:47:26+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3528300.0, "num_examples": 10}], "download_size": 355277, "dataset_size": 3528300.0}} | 2023-03-31T23:47:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "little_dataset-combined"
More Information needed | [
"# Dataset Card for \"little_dataset-combined\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"little_dataset-combined\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"little_dataset-combined\"\n\nMore Information needed"
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|
a0225c44fb2d96a53fcf340391a04cc8d7e5de0a | [
{ "question": "я", "answer": "I" },
{ "question": "не", "answer": "not" },
{ "question": "в", "answer": "in" },
{ "question": "що", "answer": "what" },
{ "question": "на", "answer": "on" },
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{ "question": "як", "answer": "how" },
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{ "question": "за", "answer": "for" },
{ "question": "його", "answer": "his" },
{ "question": "її", "answer": "her" },
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{ "question": "так", "answer": "yes" },
{ "question": "або", "answer": "or" },
{ "question": "щоб", "answer": "to" },
{ "question": "неї", "answer": "hers" },
{ "question": "той", "answer": "that" },
{ "question": "зі", "answer": "with" },
{ "question": "де", "answer": "where" },
{ "question": "йому", "answer": "him" },
{ "question": "їм", "answer": "them" },
{ "question": "вже", "answer": "already" },
{ "question": "ні", "answer": "no" },
{ "question": "та", "answer": "and" },
{ "question": "є", "answer": "is" },
{ "question": "все", "answer": "all" },
{ "question": "до", "answer": "to" },
{ "question": "ще", "answer": "still" },
{ "question": "тут", "answer": "here" },
{ "question": "мій", "answer": "my" },
{"question": "Привіт", "answer": "Hello"},
{"question": "Як справи?", "answer": "How are you?"},
{"question": "Добре", "answer": "Good"},
{"question": "Погано", "answer": "Bad"},
{"question": "Я не розумію", "answer": "I don't understand"},
{"question": "Будь ласка", "answer": "Please"},
{"question": "Дякую", "answer": "Thank you"},
{"question": "Без проблем", "answer": "No problem"},
{"question": "До побачення", "answer": "Goodbye"},
{"question": "Я згоден", "answer": "I agree"},
{"question": "Я не згоден", "answer": "I disagree"},
{"question": "Це можливо?", "answer": "Is it possible?"},
{"question": "Допоможіть мені, будь ласка", "answer": "Help me, please"},
{"question": "Як вас звати?", "answer": "What's your name?"},
{"question": "Мене звуть...", "answer": "My name is..."},
{"question": "Скільки це коштує?", "answer": "How much does it cost?"},
{"question": "Як довго це займає?", "answer": "How long does it take?"},
{"question": "Я не впевнений", "answer": "I'm not sure"},
{"question": "Де знаходиться туалет?", "answer": "Where is the bathroom?"},
{"question": "Де знаходиться готель?", "answer": "Where is the hotel?"},
{"question": "Де знаходиться ресторан?", "answer": "Where is the restaurant?"},
{"question": "Де знаходиться банк?", "answer": "Where is the bank?"},
{"question": "Де знаходиться аеропорт?", "answer": "Where is the airport?"},
{"question": "Де знаходиться вокзал?", "answer": "Where is the train station?"},
{"question": "Я не знаю", "answer": "I don't know"},
{"question": "Як тебе справи?", "answer": "How are you doing?"},
{"question": "Дуже добре, дякую", "answer": "Very well, thank you"},
{"question": "Чим ти займаєшся?", "answer": "What do you do?"},
{"question": "Я студент", "answer": "I am a student"},
{"question": "Я працюю в IT", "answer": "I work in IT"}
]
| sherkoo/language | [
"region:us"
]
| 2023-04-01T00:00:28+00:00 | {} | 2023-04-01T00:01:40+00:00 | []
| []
| TAGS
#region-us
| [
{ "question": "я", "answer": "I" },
{ "question": "не", "answer": "not" },
{ "question": "в", "answer": "in" },
{ "question": "що", "answer": "what" },
{ "question": "на", "answer": "on" },
{ "question": "з", "answer": "from" },
{ "question": "як", "answer": "how" },
{ "question": "ти", "answer": "you" },
{ "question": "він", "answer": "he" },
{ "question": "вона", "answer": "she" },
{ "question": "це", "answer": "this" },
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{ "question": "за", "answer": "for" },
{ "question": "його", "answer": "his" },
{ "question": "її", "answer": "her" },
{ "question": "їх", "answer": "their" },
{ "question": "так", "answer": "yes" },
{ "question": "або", "answer": "or" },
{ "question": "щоб", "answer": "to" },
{ "question": "неї", "answer": "hers" },
{ "question": "той", "answer": "that" },
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{ "question": "де", "answer": "where" },
{ "question": "йому", "answer": "him" },
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{ "question": "та", "answer": "and" },
{ "question": "є", "answer": "is" },
{ "question": "все", "answer": "all" },
{ "question": "до", "answer": "to" },
{ "question": "ще", "answer": "still" },
{ "question": "тут", "answer": "here" },
{ "question": "мій", "answer": "my" },
{"question": "Привіт", "answer": "Hello"},
{"question": "Як справи?", "answer": "How are you?"},
{"question": "Добре", "answer": "Good"},
{"question": "Погано", "answer": "Bad"},
{"question": "Я не розумію", "answer": "I don't understand"},
{"question": "Будь ласка", "answer": "Please"},
{"question": "Дякую", "answer": "Thank you"},
{"question": "Без проблем", "answer": "No problem"},
{"question": "До побачення", "answer": "Goodbye"},
{"question": "Я згоден", "answer": "I agree"},
{"question": "Я не згоден", "answer": "I disagree"},
{"question": "Це можливо?", "answer": "Is it possible?"},
{"question": "Допоможіть мені, будь ласка", "answer": "Help me, please"},
{"question": "Як вас звати?", "answer": "What's your name?"},
{"question": "Мене звуть...", "answer": "My name is..."},
{"question": "Скільки це коштує?", "answer": "How much does it cost?"},
{"question": "Як довго це займає?", "answer": "How long does it take?"},
{"question": "Я не впевнений", "answer": "I'm not sure"},
{"question": "Де знаходиться туалет?", "answer": "Where is the bathroom?"},
{"question": "Де знаходиться готель?", "answer": "Where is the hotel?"},
{"question": "Де знаходиться ресторан?", "answer": "Where is the restaurant?"},
{"question": "Де знаходиться банк?", "answer": "Where is the bank?"},
{"question": "Де знаходиться аеропорт?", "answer": "Where is the airport?"},
{"question": "Де знаходиться вокзал?", "answer": "Where is the train station?"},
{"question": "Я не знаю", "answer": "I don't know"},
{"question": "Як тебе справи?", "answer": "How are you doing?"},
{"question": "Дуже добре, дякую", "answer": "Very well, thank you"},
{"question": "Чим ти займаєшся?", "answer": "What do you do?"},
{"question": "Я студент", "answer": "I am a student"},
{"question": "Я працюю в IT", "answer": "I work in IT"}
]
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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0251eed5a7b6f5bbc6c7418292e29430a99f0f2d | # Dataset Card for "CVEFixes_Python_with_norm_vul_lines"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | EddieChen372/CVEFixes_Python_with_norm_vul_lines | [
"region:us"
]
| 2023-04-01T00:25:11+00:00 | {"dataset_info": {"features": [{"name": "lines", "sequence": "string"}, {"name": "raw_lines", "sequence": "string"}, {"name": "label", "sequence": "int64"}, {"name": "type", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 5568381, "num_examples": 4584}, {"name": "test", "num_bytes": 1334879, "num_examples": 1146}], "download_size": 2782844, "dataset_size": 6903260}} | 2023-04-01T00:25:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "CVEFixes_Python_with_norm_vul_lines"
More Information needed | [
"# Dataset Card for \"CVEFixes_Python_with_norm_vul_lines\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"CVEFixes_Python_with_norm_vul_lines\"\n\nMore Information needed"
]
| [
6,
26
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"CVEFixes_Python_with_norm_vul_lines\"\n\nMore Information needed"
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|
c89a3c5c755d45a083685d3a0c1ca99ff85af5db | # Dataset Card for "AICE_binary_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zxcej/AICE_binary_dataset | [
"region:us"
]
| 2023-04-01T01:53:36+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Disease", "1": "Healthy"}}}}], "splits": [{"name": "train", "num_bytes": 996171511.8039621, "num_examples": 14784}, {"name": "test", "num_bytes": 247564754.71103784, "num_examples": 3697}], "download_size": 1242379757, "dataset_size": 1243736266.5149999}} | 2023-04-01T01:58:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AICE_binary_dataset"
More Information needed | [
"# Dataset Card for \"AICE_binary_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AICE_binary_dataset\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AICE_binary_dataset\"\n\nMore Information needed"
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|
7a1fc31c5242497edd26fea455e7894deb946027 | # Dataset Card for "korra-lite_captioned-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/korra-lite_captioned-augmented | [
"region:us"
]
| 2023-04-01T02:27:13+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 254281033.375, "num_examples": 1173}], "download_size": 254182437, "dataset_size": 254281033.375}} | 2023-04-01T02:27:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "korra-lite_captioned-augmented"
More Information needed | [
"# Dataset Card for \"korra-lite_captioned-augmented\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"korra-lite_captioned-augmented\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"korra-lite_captioned-augmented\"\n\nMore Information needed"
]
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|
44f7a7919e5eebac84c9d47e3fa4a478d7891c71 |
Scraped conversations of the LIHKG forum. Content scraped by Ayaka: https://github.com/ayaka14732/lihkg-scraper
| AlienKevin/LIHKG | [
"size_categories:1M<n<10M",
"language:yue",
"license:mit",
"region:us"
]
| 2023-04-01T02:36:12+00:00 | {"language": ["yue"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "\u9023\u767b"} | 2023-05-08T13:38:36+00:00 | []
| [
"yue"
]
| TAGS
#size_categories-1M<n<10M #language-Yue Chinese #license-mit #region-us
|
Scraped conversations of the LIHKG forum. Content scraped by Ayaka: URL
| []
| [
"TAGS\n#size_categories-1M<n<10M #language-Yue Chinese #license-mit #region-us \n"
]
| [
29
]
| [
"passage: TAGS\n#size_categories-1M<n<10M #language-Yue Chinese #license-mit #region-us \n"
]
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|
08eb3409e2ad117d0eec8efb0ac9ff2ca4d6a790 | # Dataset Card for "ask2democracy-cqa-salud"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jorge-henao/ask2democracy-cqa-salud | [
"region:us"
]
| 2023-04-01T02:41:20+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "topics", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 849585, "num_examples": 445}], "download_size": 132480, "dataset_size": 849585}} | 2023-04-01T02:41:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ask2democracy-cqa-salud"
More Information needed | [
"# Dataset Card for \"ask2democracy-cqa-salud\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ask2democracy-cqa-salud\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ask2democracy-cqa-salud\"\n\nMore Information needed"
]
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|
866b2a89ab700c377a1598853c2643542be477ec | # Dataset Card for "movie-love-actually_captioned-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/movie-love-actually_captioned-augmented | [
"region:us"
]
| 2023-04-01T02:55:41+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 228506362.0, "num_examples": 732}], "download_size": 228474429, "dataset_size": 228506362.0}} | 2023-04-01T02:55:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "movie-love-actually_captioned-augmented"
More Information needed | [
"# Dataset Card for \"movie-love-actually_captioned-augmented\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"movie-love-actually_captioned-augmented\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"movie-love-actually_captioned-augmented\"\n\nMore Information needed"
]
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|
9aebbaf2949f6d71ea97f6b147dab0e1235493fd |
# Arabic Translated Conceptual Captions Dataset
## Overview
This dataset consists of conceptual captions translated into Arabic using the Google Translate API. It serves as a resource for researchers and developers interested in exploring the vision-language tasks and biases introduced during the translation process.
## Dataset Information
- **Source Dataset**: Conceptual Captions
- **Translation Tool**: Google Translate API
- **Translation Language**: English to Arabic
## Important Notes
1. **Translation Quality**: The translations are machine-generated and may contain errors, inaccuracies, or cultural nuances that were not appropriately captured. Researchers are encouraged to verify translations for accuracy.
2. **Biases**: The dataset is prone to various types of biases, including but not limited to gender bias. Google Translate API, like any other machine translation tool, may inadvertently introduce biases present in its training data.
3. **Usage Guidelines**: Please refer to the original Conceptual Captions dataset usage guidelines, as they apply to this translated version. Respect copyright and licensing agreements associated with the source dataset.
## Description
the dataset contains two file train and validation and each has:
- **`arabic_caption`**: This column includes the machine-translated captions in Arabic, generated using the Google Translate API.
- **`caption`**: This column contains the original captions in English sourced from the Conceptual Captions dataset.
- **`link`**: This column contains links to images corresponding to the captions in the dataset.
| LinaAlhuri/ArabicConceptualCaptions3M | [
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"language:ar",
"region:us"
]
| 2023-04-01T04:20:06+00:00 | {"language": ["ar"], "size_categories": ["1M<n<10M"], "task_categories": ["image-to-text"], "pretty_name": "ArabicConceptualCaptions3M"} | 2023-11-15T09:24:55+00:00 | []
| [
"ar"
]
| TAGS
#task_categories-image-to-text #size_categories-1M<n<10M #language-Arabic #region-us
|
# Arabic Translated Conceptual Captions Dataset
## Overview
This dataset consists of conceptual captions translated into Arabic using the Google Translate API. It serves as a resource for researchers and developers interested in exploring the vision-language tasks and biases introduced during the translation process.
## Dataset Information
- Source Dataset: Conceptual Captions
- Translation Tool: Google Translate API
- Translation Language: English to Arabic
## Important Notes
1. Translation Quality: The translations are machine-generated and may contain errors, inaccuracies, or cultural nuances that were not appropriately captured. Researchers are encouraged to verify translations for accuracy.
2. Biases: The dataset is prone to various types of biases, including but not limited to gender bias. Google Translate API, like any other machine translation tool, may inadvertently introduce biases present in its training data.
3. Usage Guidelines: Please refer to the original Conceptual Captions dataset usage guidelines, as they apply to this translated version. Respect copyright and licensing agreements associated with the source dataset.
## Description
the dataset contains two file train and validation and each has:
- 'arabic_caption': This column includes the machine-translated captions in Arabic, generated using the Google Translate API.
- 'caption': This column contains the original captions in English sourced from the Conceptual Captions dataset.
- 'link': This column contains links to images corresponding to the captions in the dataset.
| [
"# Arabic Translated Conceptual Captions Dataset",
"## Overview\n\nThis dataset consists of conceptual captions translated into Arabic using the Google Translate API. It serves as a resource for researchers and developers interested in exploring the vision-language tasks and biases introduced during the translation process.",
"## Dataset Information\n\n- Source Dataset: Conceptual Captions\n- Translation Tool: Google Translate API\n- Translation Language: English to Arabic",
"## Important Notes\n\n1. Translation Quality: The translations are machine-generated and may contain errors, inaccuracies, or cultural nuances that were not appropriately captured. Researchers are encouraged to verify translations for accuracy.\n\n2. Biases: The dataset is prone to various types of biases, including but not limited to gender bias. Google Translate API, like any other machine translation tool, may inadvertently introduce biases present in its training data.\n\n3. Usage Guidelines: Please refer to the original Conceptual Captions dataset usage guidelines, as they apply to this translated version. Respect copyright and licensing agreements associated with the source dataset.",
"## Description\nthe dataset contains two file train and validation and each has:\n- 'arabic_caption': This column includes the machine-translated captions in Arabic, generated using the Google Translate API.\n- 'caption': This column contains the original captions in English sourced from the Conceptual Captions dataset.\n- 'link': This column contains links to images corresponding to the captions in the dataset."
]
| [
"TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Arabic #region-us \n",
"# Arabic Translated Conceptual Captions Dataset",
"## Overview\n\nThis dataset consists of conceptual captions translated into Arabic using the Google Translate API. It serves as a resource for researchers and developers interested in exploring the vision-language tasks and biases introduced during the translation process.",
"## Dataset Information\n\n- Source Dataset: Conceptual Captions\n- Translation Tool: Google Translate API\n- Translation Language: English to Arabic",
"## Important Notes\n\n1. Translation Quality: The translations are machine-generated and may contain errors, inaccuracies, or cultural nuances that were not appropriately captured. Researchers are encouraged to verify translations for accuracy.\n\n2. Biases: The dataset is prone to various types of biases, including but not limited to gender bias. Google Translate API, like any other machine translation tool, may inadvertently introduce biases present in its training data.\n\n3. Usage Guidelines: Please refer to the original Conceptual Captions dataset usage guidelines, as they apply to this translated version. Respect copyright and licensing agreements associated with the source dataset.",
"## Description\nthe dataset contains two file train and validation and each has:\n- 'arabic_caption': This column includes the machine-translated captions in Arabic, generated using the Google Translate API.\n- 'caption': This column contains the original captions in English sourced from the Conceptual Captions dataset.\n- 'link': This column contains links to images corresponding to the captions in the dataset."
]
| [
35,
11,
57,
28,
155,
105
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-1M<n<10M #language-Arabic #region-us \n# Arabic Translated Conceptual Captions Dataset## Overview\n\nThis dataset consists of conceptual captions translated into Arabic using the Google Translate API. It serves as a resource for researchers and developers interested in exploring the vision-language tasks and biases introduced during the translation process.## Dataset Information\n\n- Source Dataset: Conceptual Captions\n- Translation Tool: Google Translate API\n- Translation Language: English to Arabic## Important Notes\n\n1. Translation Quality: The translations are machine-generated and may contain errors, inaccuracies, or cultural nuances that were not appropriately captured. Researchers are encouraged to verify translations for accuracy.\n\n2. Biases: The dataset is prone to various types of biases, including but not limited to gender bias. Google Translate API, like any other machine translation tool, may inadvertently introduce biases present in its training data.\n\n3. Usage Guidelines: Please refer to the original Conceptual Captions dataset usage guidelines, as they apply to this translated version. Respect copyright and licensing agreements associated with the source dataset.## Description\nthe dataset contains two file train and validation and each has:\n- 'arabic_caption': This column includes the machine-translated captions in Arabic, generated using the Google Translate API.\n- 'caption': This column contains the original captions in English sourced from the Conceptual Captions dataset.\n- 'link': This column contains links to images corresponding to the captions in the dataset."
]
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]
|
f552854050c1626fb34efb07a9d8157f5558d32f |
# Arabic Text-Image Dataset from Wikipedia
## Overview
This repository contains a dataset created by scraping images and their captions from Wikipedia, focusing on content that represents the Arab world. The goal is to provide a diverse and representative collection of images with corresponding Arabic captions to support research and development in natural language processing (NLP), computer vision, and cross-modal applications.
## Dataset Structure
The dataset contains four columns:
1. **source:** Indicates the origin of the image-text pair.
2. **link:** Provides the link to the Wikipedia image.
3. **caption:** Arabic captions corresponding to each image.
4. **extension:** Specifies the file extension of each image.
## Recommended Data Preprocessing
It is recommended to incorporate various filtering techniques inspired by established practices:
1. **Text-Based Filtering:**
- Discarded images with captions containing fewer than three tokens.
- Maintained digits in captions to increase dataset complexity.
- Preserved Latin words to minimize information loss for foreign entities or scientific terms.
2. **Diacritics Removal:**
- Employed the Araby library in Python for diacritics removal in captions.
3. **Speech Tagger Utilization:**
- Utilized the CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa tagger to eliminate captions that are mostly proper nouns to generate higher-quality captions.
- Aimed to prioritize learning more generic concepts over fine details.
5. **Image-Based Filtering:**
- Removed images with fewer than one hundred pixels to supply our model with detail-rich images.
- Included only JPEG images in the final dataset due to potential information loss in other formats.
6. **Handling Non-JPEG Images:**
- Excluded images of other extensions as they usually contain maps, logos or non-rich information content.
7. **Color System Standardization:**
- Converted images into RGB using Python OpenCV to ensure a standardized color space.
**Note:** The dataset lacks protection for individual personal images due to the absence of Arabic graph tools that substitute specific concepts for more generic ones. This presents an opportunity for future research and improvement in image privacy and protection.
## Usage
Researchers and developers are encouraged to use this preprocessed dataset for tasks such as image captioning, cross-modal learning, and other NLP and computer vision applications. Please adhere to ethical standards and ensure that the usage of this dataset aligns with Wikipedia's terms of service and licensing.
| LinaAlhuri/WikipediaArabicPairs | [
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ar",
"region:us"
]
| 2023-04-01T04:21:52+00:00 | {"language": ["ar"], "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"], "pretty_name": "WAP"} | 2023-11-15T10:03:44+00:00 | []
| [
"ar"
]
| TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Arabic #region-us
|
# Arabic Text-Image Dataset from Wikipedia
## Overview
This repository contains a dataset created by scraping images and their captions from Wikipedia, focusing on content that represents the Arab world. The goal is to provide a diverse and representative collection of images with corresponding Arabic captions to support research and development in natural language processing (NLP), computer vision, and cross-modal applications.
## Dataset Structure
The dataset contains four columns:
1. source: Indicates the origin of the image-text pair.
2. link: Provides the link to the Wikipedia image.
3. caption: Arabic captions corresponding to each image.
4. extension: Specifies the file extension of each image.
## Recommended Data Preprocessing
It is recommended to incorporate various filtering techniques inspired by established practices:
1. Text-Based Filtering:
- Discarded images with captions containing fewer than three tokens.
- Maintained digits in captions to increase dataset complexity.
- Preserved Latin words to minimize information loss for foreign entities or scientific terms.
2. Diacritics Removal:
- Employed the Araby library in Python for diacritics removal in captions.
3. Speech Tagger Utilization:
- Utilized the CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa tagger to eliminate captions that are mostly proper nouns to generate higher-quality captions.
- Aimed to prioritize learning more generic concepts over fine details.
5. Image-Based Filtering:
- Removed images with fewer than one hundred pixels to supply our model with detail-rich images.
- Included only JPEG images in the final dataset due to potential information loss in other formats.
6. Handling Non-JPEG Images:
- Excluded images of other extensions as they usually contain maps, logos or non-rich information content.
7. Color System Standardization:
- Converted images into RGB using Python OpenCV to ensure a standardized color space.
Note: The dataset lacks protection for individual personal images due to the absence of Arabic graph tools that substitute specific concepts for more generic ones. This presents an opportunity for future research and improvement in image privacy and protection.
## Usage
Researchers and developers are encouraged to use this preprocessed dataset for tasks such as image captioning, cross-modal learning, and other NLP and computer vision applications. Please adhere to ethical standards and ensure that the usage of this dataset aligns with Wikipedia's terms of service and licensing.
| [
"# Arabic Text-Image Dataset from Wikipedia",
"## Overview\n\nThis repository contains a dataset created by scraping images and their captions from Wikipedia, focusing on content that represents the Arab world. The goal is to provide a diverse and representative collection of images with corresponding Arabic captions to support research and development in natural language processing (NLP), computer vision, and cross-modal applications.",
"## Dataset Structure\n\nThe dataset contains four columns:\n\n1. source: Indicates the origin of the image-text pair.\n2. link: Provides the link to the Wikipedia image.\n3. caption: Arabic captions corresponding to each image.\n4. extension: Specifies the file extension of each image.",
"## Recommended Data Preprocessing\n\nIt is recommended to incorporate various filtering techniques inspired by established practices:\n\n1. Text-Based Filtering:\n - Discarded images with captions containing fewer than three tokens.\n - Maintained digits in captions to increase dataset complexity.\n - Preserved Latin words to minimize information loss for foreign entities or scientific terms.\n\n2. Diacritics Removal:\n - Employed the Araby library in Python for diacritics removal in captions.\n\n3. Speech Tagger Utilization:\n - Utilized the CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa tagger to eliminate captions that are mostly proper nouns to generate higher-quality captions.\n - Aimed to prioritize learning more generic concepts over fine details.\n\n5. Image-Based Filtering:\n - Removed images with fewer than one hundred pixels to supply our model with detail-rich images.\n - Included only JPEG images in the final dataset due to potential information loss in other formats.\n\n6. Handling Non-JPEG Images:\n - Excluded images of other extensions as they usually contain maps, logos or non-rich information content.\n\n7. Color System Standardization:\n - Converted images into RGB using Python OpenCV to ensure a standardized color space.\n\nNote: The dataset lacks protection for individual personal images due to the absence of Arabic graph tools that substitute specific concepts for more generic ones. This presents an opportunity for future research and improvement in image privacy and protection.",
"## Usage\n\nResearchers and developers are encouraged to use this preprocessed dataset for tasks such as image captioning, cross-modal learning, and other NLP and computer vision applications. Please adhere to ethical standards and ensure that the usage of this dataset aligns with Wikipedia's terms of service and licensing."
]
| [
"TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Arabic #region-us \n",
"# Arabic Text-Image Dataset from Wikipedia",
"## Overview\n\nThis repository contains a dataset created by scraping images and their captions from Wikipedia, focusing on content that represents the Arab world. The goal is to provide a diverse and representative collection of images with corresponding Arabic captions to support research and development in natural language processing (NLP), computer vision, and cross-modal applications.",
"## Dataset Structure\n\nThe dataset contains four columns:\n\n1. source: Indicates the origin of the image-text pair.\n2. link: Provides the link to the Wikipedia image.\n3. caption: Arabic captions corresponding to each image.\n4. extension: Specifies the file extension of each image.",
"## Recommended Data Preprocessing\n\nIt is recommended to incorporate various filtering techniques inspired by established practices:\n\n1. Text-Based Filtering:\n - Discarded images with captions containing fewer than three tokens.\n - Maintained digits in captions to increase dataset complexity.\n - Preserved Latin words to minimize information loss for foreign entities or scientific terms.\n\n2. Diacritics Removal:\n - Employed the Araby library in Python for diacritics removal in captions.\n\n3. Speech Tagger Utilization:\n - Utilized the CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa tagger to eliminate captions that are mostly proper nouns to generate higher-quality captions.\n - Aimed to prioritize learning more generic concepts over fine details.\n\n5. Image-Based Filtering:\n - Removed images with fewer than one hundred pixels to supply our model with detail-rich images.\n - Included only JPEG images in the final dataset due to potential information loss in other formats.\n\n6. Handling Non-JPEG Images:\n - Excluded images of other extensions as they usually contain maps, logos or non-rich information content.\n\n7. Color System Standardization:\n - Converted images into RGB using Python OpenCV to ensure a standardized color space.\n\nNote: The dataset lacks protection for individual personal images due to the absence of Arabic graph tools that substitute specific concepts for more generic ones. This presents an opportunity for future research and improvement in image privacy and protection.",
"## Usage\n\nResearchers and developers are encouraged to use this preprocessed dataset for tasks such as image captioning, cross-modal learning, and other NLP and computer vision applications. Please adhere to ethical standards and ensure that the usage of this dataset aligns with Wikipedia's terms of service and licensing."
]
| [
35,
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| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Arabic #region-us \n# Arabic Text-Image Dataset from Wikipedia## Overview\n\nThis repository contains a dataset created by scraping images and their captions from Wikipedia, focusing on content that represents the Arab world. The goal is to provide a diverse and representative collection of images with corresponding Arabic captions to support research and development in natural language processing (NLP), computer vision, and cross-modal applications.## Dataset Structure\n\nThe dataset contains four columns:\n\n1. source: Indicates the origin of the image-text pair.\n2. link: Provides the link to the Wikipedia image.\n3. caption: Arabic captions corresponding to each image.\n4. extension: Specifies the file extension of each image."
]
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|
b3c7428796e86c3eaaaa1c760db6d88bdd30ad2f | # Dataset Card for "coyo1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | YiYiXu/coyo1k | [
"region:us"
]
| 2023-04-01T05:22:49+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 766412778.0, "num_examples": 1000}], "download_size": 766320726, "dataset_size": 766412778.0}} | 2023-04-01T05:23:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "coyo1k"
More Information needed | [
"# Dataset Card for \"coyo1k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"coyo1k\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"coyo1k\"\n\nMore Information needed"
]
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|
da3b0d669a54d4a47cf13ef761aa9ae62c0c7341 |
# Arabic Translated ImageNet Dataset
---
## Overview
Welcome to the Arabic Translated ImageNet Dataset! This dataset is a version of the ImageNet dataset, translated into Arabic. ImageNet is a widely used benchmark for image classification tasks, and this translation aims to facilitate research and development in the Arabic language.
## Contents
1. **Arabic_query_short:** Arabic translations of the original ImageNet class labels.
2. **Arabic_query_long:** Arabic translations of the original ImageNet class labels within a simple template.
## Usage
- **Image Classification:** Use this dataset for training and evaluating models in the domain of image classification with a focus on the Arabic language.
## Dataset Translation and Considerations
This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically, and some categories may not have accurate or complete Arabic translations. Researchers and users are advised to be mindful of these limitations and consider the implications of incomplete or potentially inaccurate translations in their analyses.
| LinaAlhuri/ArabicImageNet | [
"task_categories:zero-shot-classification",
"task_categories:image-classification",
"size_categories:n<1K",
"language:ar",
"region:us"
]
| 2023-04-01T05:43:01+00:00 | {"language": ["ar"], "size_categories": ["n<1K"], "task_categories": ["zero-shot-classification", "image-classification"], "pretty_name": "ArabicImageNet"} | 2023-11-15T08:46:51+00:00 | []
| [
"ar"
]
| TAGS
#task_categories-zero-shot-classification #task_categories-image-classification #size_categories-n<1K #language-Arabic #region-us
|
# Arabic Translated ImageNet Dataset
---
## Overview
Welcome to the Arabic Translated ImageNet Dataset! This dataset is a version of the ImageNet dataset, translated into Arabic. ImageNet is a widely used benchmark for image classification tasks, and this translation aims to facilitate research and development in the Arabic language.
## Contents
1. Arabic_query_short: Arabic translations of the original ImageNet class labels.
2. Arabic_query_long: Arabic translations of the original ImageNet class labels within a simple template.
## Usage
- Image Classification: Use this dataset for training and evaluating models in the domain of image classification with a focus on the Arabic language.
## Dataset Translation and Considerations
This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically, and some categories may not have accurate or complete Arabic translations. Researchers and users are advised to be mindful of these limitations and consider the implications of incomplete or potentially inaccurate translations in their analyses.
| [
"# Arabic Translated ImageNet Dataset\n---",
"## Overview\n\nWelcome to the Arabic Translated ImageNet Dataset! This dataset is a version of the ImageNet dataset, translated into Arabic. ImageNet is a widely used benchmark for image classification tasks, and this translation aims to facilitate research and development in the Arabic language.",
"## Contents\n\n1. Arabic_query_short: Arabic translations of the original ImageNet class labels.\n\n2. Arabic_query_long: Arabic translations of the original ImageNet class labels within a simple template.",
"## Usage\n\n- Image Classification: Use this dataset for training and evaluating models in the domain of image classification with a focus on the Arabic language.",
"## Dataset Translation and Considerations\n\nThis dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically, and some categories may not have accurate or complete Arabic translations. Researchers and users are advised to be mindful of these limitations and consider the implications of incomplete or potentially inaccurate translations in their analyses."
]
| [
"TAGS\n#task_categories-zero-shot-classification #task_categories-image-classification #size_categories-n<1K #language-Arabic #region-us \n",
"# Arabic Translated ImageNet Dataset\n---",
"## Overview\n\nWelcome to the Arabic Translated ImageNet Dataset! This dataset is a version of the ImageNet dataset, translated into Arabic. ImageNet is a widely used benchmark for image classification tasks, and this translation aims to facilitate research and development in the Arabic language.",
"## Contents\n\n1. Arabic_query_short: Arabic translations of the original ImageNet class labels.\n\n2. Arabic_query_long: Arabic translations of the original ImageNet class labels within a simple template.",
"## Usage\n\n- Image Classification: Use this dataset for training and evaluating models in the domain of image classification with a focus on the Arabic language.",
"## Dataset Translation and Considerations\n\nThis dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically, and some categories may not have accurate or complete Arabic translations. Researchers and users are advised to be mindful of these limitations and consider the implications of incomplete or potentially inaccurate translations in their analyses."
]
| [
45,
10,
64,
47,
33,
108
]
| [
"passage: TAGS\n#task_categories-zero-shot-classification #task_categories-image-classification #size_categories-n<1K #language-Arabic #region-us \n# Arabic Translated ImageNet Dataset\n---## Overview\n\nWelcome to the Arabic Translated ImageNet Dataset! This dataset is a version of the ImageNet dataset, translated into Arabic. ImageNet is a widely used benchmark for image classification tasks, and this translation aims to facilitate research and development in the Arabic language.## Contents\n\n1. Arabic_query_short: Arabic translations of the original ImageNet class labels.\n\n2. Arabic_query_long: Arabic translations of the original ImageNet class labels within a simple template.## Usage\n\n- Image Classification: Use this dataset for training and evaluating models in the domain of image classification with a focus on the Arabic language.## Dataset Translation and Considerations\n\nThis dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically, and some categories may not have accurate or complete Arabic translations. Researchers and users are advised to be mindful of these limitations and consider the implications of incomplete or potentially inaccurate translations in their analyses."
]
| [
0.015634052455425262,
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|
a5d5fa7ae6db9b1ad6ca576da36aa79c7e467029 |
# Arabic Translated COCO Validation Dataset
---
## Overview
Welcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language.
## Contents
1. **coco_url:** This column includes images URL which makes a subset of the COCO validation images.
2. **arabic_caption:** Arabic translations of the original COCO annotations, providing detailed information about image captions.
## Usage
- **Research and Development:** Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.
- **Benchmarking:** Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research.
## Dataset Translation and Bias
This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses.
| LinaAlhuri/Arabic-COCO2014-Validation | [
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ar",
"region:us"
]
| 2023-04-01T06:09:45+00:00 | {"language": ["ar"], "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"], "pretty_name": "Arabic COCO 2014 Validation"} | 2023-11-15T08:38:07+00:00 | []
| [
"ar"
]
| TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Arabic #region-us
|
# Arabic Translated COCO Validation Dataset
---
## Overview
Welcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language.
## Contents
1. coco_url: This column includes images URL which makes a subset of the COCO validation images.
2. arabic_caption: Arabic translations of the original COCO annotations, providing detailed information about image captions.
## Usage
- Research and Development: Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.
- Benchmarking: Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research.
## Dataset Translation and Bias
This dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses.
| [
"# Arabic Translated COCO Validation Dataset\n\n---",
"## Overview\n\nWelcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language.",
"## Contents\n\n1. coco_url: This column includes images URL which makes a subset of the COCO validation images.\n\n2. arabic_caption: Arabic translations of the original COCO annotations, providing detailed information about image captions.",
"## Usage\n\n- Research and Development: Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.\n\n- Benchmarking: Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research.",
"## Dataset Translation and Bias\n\nThis dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses."
]
| [
"TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Arabic #region-us \n",
"# Arabic Translated COCO Validation Dataset\n\n---",
"## Overview\n\nWelcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language.",
"## Contents\n\n1. coco_url: This column includes images URL which makes a subset of the COCO validation images.\n\n2. arabic_caption: Arabic translations of the original COCO annotations, providing detailed information about image captions.",
"## Usage\n\n- Research and Development: Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.\n\n- Benchmarking: Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research.",
"## Dataset Translation and Bias\n\nThis dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses."
]
| [
35,
13,
83,
55,
74,
113
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Arabic #region-us \n# Arabic Translated COCO Validation Dataset\n\n---## Overview\n\nWelcome to the Arabic Translated COCO Validation Dataset! This dataset is a version of the Common Objects in Context (COCO) dataset, specifically translated into Arabic. The COCO dataset is a widely used benchmark for image captioning and object detection tasks, and this translation aims to facilitate research and development in the Arabic language.## Contents\n\n1. coco_url: This column includes images URL which makes a subset of the COCO validation images.\n\n2. arabic_caption: Arabic translations of the original COCO annotations, providing detailed information about image captions.## Usage\n\n- Research and Development: Use this dataset for training and evaluating models in the domain of image captioning and object detection with a focus on the Arabic language.\n\n- Benchmarking: Evaluate the performance of your algorithms on this translated COCO dataset to contribute to the advancement of Arabic-language computer vision research.## Dataset Translation and Bias\n\nThis dataset has been translated using the Google Translation API. It's important to note that automated translation methods, including machine translation, may introduce biases and inaccuracies. The translations are generated algorithmically and might not capture the full context or cultural nuances or might contain gender bias, leading to potential biases in the dataset. Researchers and users are advised to be mindful of these limitations and consider the implications of bias in their analyses."
]
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3ee4f82be33fc80700c0b5e169f346b5a42c7589 | # Dataset Card for "dtd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jxie/dtd | [
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| 2023-04-01T06:20:53+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "banded", "1": "blotchy", "2": "braided", "3": "bubbly", "4": "bumpy", "5": "chequered", "6": "cobwebbed", "7": "cracked", "8": "crosshatched", "9": "crystalline", "10": "dotted", "11": "fibrous", "12": "flecked", "13": "freckled", "14": "frilly", "15": "gauzy", "16": "grid", "17": "grooved", "18": "honeycombed", "19": "interlaced", "20": "knitted", "21": "lacelike", "22": "lined", "23": "marbled", "24": "matted", "25": "meshed", "26": "paisley", "27": "perforated", "28": "pitted", "29": "pleated", "30": "polka-dotted", "31": "porous", "32": "potholed", "33": "scaly", "34": "smeared", "35": "spiralled", "36": "sprinkled", "37": "stained", "38": "stratified", "39": "striped", "40": "studded", "41": "swirly", "42": "veined", "43": "waffled", "44": "woven", "45": "wrinkled", "46": "zigzagged"}}}}], "splits": [{"name": "train", "num_bytes": 226313270.04, "num_examples": 1880}, {"name": "test", "num_bytes": 172035822.0, "num_examples": 1880}, {"name": "validation", "num_bytes": 222278767.48, "num_examples": 1880}], "download_size": 629310459, "dataset_size": 620627859.52}} | 2023-04-01T06:25:40+00:00 | []
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More Information needed | [
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|
adc248ab67cd256066762340c776076fb71dabfc | # Dataset Card for "alpaca-german-validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dvilasuero/alpaca-german-validation | [
"region:us"
]
| 2023-04-01T06:43:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "_instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "original_id", "dtype": "int64"}, {"name": "translation_model", "dtype": "string"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 38104, "num_examples": 2}], "download_size": 0, "dataset_size": 38104}} | 2023-04-01T07:23:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "alpaca-german-validation"
More Information needed | [
"# Dataset Card for \"alpaca-german-validation\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"alpaca-german-validation\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"alpaca-german-validation\"\n\nMore Information needed"
]
| [
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|
ba53c8dc642c4ed50f8d978e41b4bd43409d178b | # Dataset Card for "translated_tasks_de_google_52k"
Copy of : https://github.com/thisserand/alpaca-lora-finetune-language/tree/main/data/translated | philschmid/translated_tasks_de_google_52k | [
"region:us"
]
| 2023-04-01T08:05:24+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22108071, "num_examples": 51664}], "download_size": 13686739, "dataset_size": 22108071}} | 2023-04-01T08:06:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "translated_tasks_de_google_52k"
Copy of : URL | [
"# Dataset Card for \"translated_tasks_de_google_52k\"\n\n\nCopy of : URL"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"translated_tasks_de_google_52k\"\n\n\nCopy of : URL"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"translated_tasks_de_google_52k\"\n\n\nCopy of : URL"
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|
567359d2de171a4010f47c3dde018756ee7ab268 | # Dataset Card for "qa_pairs_1st"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tulip4attoo/qa_pairs_1st | [
"region:us"
]
| 2023-04-01T08:23:44+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 969432, "num_examples": 2668}], "download_size": 612591, "dataset_size": 969432}} | 2023-04-01T08:23:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "qa_pairs_1st"
More Information needed | [
"# Dataset Card for \"qa_pairs_1st\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"qa_pairs_1st\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"qa_pairs_1st\"\n\nMore Information needed"
]
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|
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