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b232c2e911bc91025986f4dfc72ab46a975e8d2b | # Dataset Card for "bayc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nielsgl/bayc | [
"region:us"
]
| 2023-04-01T09:24:09+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1467029950.0, "num_examples": 10000}], "download_size": 1463911871, "dataset_size": 1467029950.0}} | 2023-04-01T09:44:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bayc"
More Information needed | [
"# Dataset Card for \"bayc\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bayc\"\n\nMore Information needed"
]
| [
6,
12
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bayc\"\n\nMore Information needed"
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|
a97fe123e929327f404870cea8bbf09c4542fc5b | This is a synthetic database of questions for testing InstructGPTs on.
It came about as I couldn't think of good examples when asked and got a bit out of hand. | Corianas/GPT_Tasks | [
"license:apache-2.0",
"region:us"
]
| 2023-04-01T09:37:51+00:00 | {"license": "apache-2.0"} | 2023-04-01T09:39:08+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| This is a synthetic database of questions for testing InstructGPTs on.
It came about as I couldn't think of good examples when asked and got a bit out of hand. | []
| [
"TAGS\n#license-apache-2.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
]
| [
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|
978346334feecf0837547e806e78d05158121c7a | # Dataset Card for "inpaint-finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nafiturgut/inpaint-finetune | [
"region:us"
]
| 2023-04-01T09:46:43+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1608841.0, "num_examples": 9}], "download_size": 1610882, "dataset_size": 1608841.0}} | 2023-04-01T10:06:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "inpaint-finetune"
More Information needed | [
"# Dataset Card for \"inpaint-finetune\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"inpaint-finetune\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"inpaint-finetune\"\n\nMore Information needed"
]
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|
ed0487e2465dcf942072ad2a9cf8526cc5463b26 | # Dataset Card for "WhisperSmallTestmp3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Nolan1206/WhisperSmallTestmp3 | [
"region:us"
]
| 2023-04-01T10:16:30+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 138283843.39, "num_examples": 2586}, {"name": "test", "num_bytes": 7987803.0, "num_examples": 231}], "download_size": 134971232, "dataset_size": 146271646.39}} | 2023-04-01T10:16:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "WhisperSmallTestmp3"
More Information needed | [
"# Dataset Card for \"WhisperSmallTestmp3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"WhisperSmallTestmp3\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"WhisperSmallTestmp3\"\n\nMore Information needed"
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|
3e6232c0bccc73af69b60c0632463d8f5f161fd0 | # Dataset Card for "bayc-tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nielsgl/bayc-tiny | [
"region:us"
]
| 2023-04-01T10:35:10+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1569879.0, "num_examples": 40}], "download_size": 1481064, "dataset_size": 1569879.0}} | 2023-04-01T10:50:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bayc-tiny"
More Information needed | [
"# Dataset Card for \"bayc-tiny\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bayc-tiny\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bayc-tiny\"\n\nMore Information needed"
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cfb1a2b0dffe3e963991f3c43169978e1328a7f6 | Source: https://dumps.wikimedia.org/kkwiki/latest/ [kwiki-latest-pages-articles.xml.bz2] | amandyk/kazakh_wiki_articles | [
"task_categories:text-generation",
"language:kk",
"license:afl-3.0",
"region:us"
]
| 2023-04-01T11:37:47+00:00 | {"language": ["kk"], "license": "afl-3.0", "task_categories": ["text-generation"]} | 2023-04-01T14:03:48+00:00 | []
| [
"kk"
]
| TAGS
#task_categories-text-generation #language-Kazakh #license-afl-3.0 #region-us
| Source: URL [URL.bz2] | []
| [
"TAGS\n#task_categories-text-generation #language-Kazakh #license-afl-3.0 #region-us \n"
]
| [
31
]
| [
"passage: TAGS\n#task_categories-text-generation #language-Kazakh #license-afl-3.0 #region-us \n"
]
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|
7390367a67df274d95e48d0a867a895b40407c5e | # Dataset Card for "igbo-translation"
## Dataset Summary
This data set contains translated data from engllish to igbo language for use in training general purpose translation models
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | iamwille/igbo-translation | [
"task_categories:translation",
"annotations_creators:found",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|igbo_english_machine_translation",
"language:en",
"language:ig",
"license:apache-2.0",
"region:us"
]
| 2023-04-01T11:52:31+00:00 | {"annotations_creators": ["found", "crowdsourced"], "language_creators": ["crowdsourced", "found"], "language": ["en", "ig"], "license": ["apache-2.0"], "multilinguality": ["translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|igbo_english_machine_translation"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "Igbo to English language ", "dataset_info": {"features": [{"name": "English", "dtype": "string"}, {"name": "Igbo", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1983074.9958306309, "num_examples": 8094}, {"name": "test", "num_bytes": 661270.004169369, "num_examples": 2699}], "download_size": 1705526, "dataset_size": 2644345.0}, "tags": []} | 2023-04-01T13:40:26+00:00 | []
| [
"en",
"ig"
]
| TAGS
#task_categories-translation #annotations_creators-found #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-translation #size_categories-10K<n<100K #source_datasets-extended|igbo_english_machine_translation #language-English #language-Igbo #license-apache-2.0 #region-us
| # Dataset Card for "igbo-translation"
## Dataset Summary
This data set contains translated data from engllish to igbo language for use in training general purpose translation models
More Information needed | [
"# Dataset Card for \"igbo-translation\"",
"## Dataset Summary\n\nThis data set contains translated data from engllish to igbo language for use in training general purpose translation models\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-translation #annotations_creators-found #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-translation #size_categories-10K<n<100K #source_datasets-extended|igbo_english_machine_translation #language-English #language-Igbo #license-apache-2.0 #region-us \n",
"# Dataset Card for \"igbo-translation\"",
"## Dataset Summary\n\nThis data set contains translated data from engllish to igbo language for use in training general purpose translation models\n\nMore Information needed"
]
| [
115,
12,
34
]
| [
"passage: TAGS\n#task_categories-translation #annotations_creators-found #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-translation #size_categories-10K<n<100K #source_datasets-extended|igbo_english_machine_translation #language-English #language-Igbo #license-apache-2.0 #region-us \n# Dataset Card for \"igbo-translation\"## Dataset Summary\n\nThis data set contains translated data from engllish to igbo language for use in training general purpose translation models\n\nMore Information needed"
]
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|
a397aca9938ab7013101109b358d8f481febc189 | # Silver Ukrainian Coreference Dataset
## Dataset Description
### Dataset Summary
A silver coreference resolution dataset for the Ukrainian language. The dataset was generated automatically with the usage of the word alignment method from the following English dataset: https://github.com/d5555/Coreference-dataset.
The word alignment method was implemented by Andrii Kursin ([email protected]).
### Languages
- Ukrainian
## Dataset Structure
### Data Fields
Each sample of the dataset consists of the following fields:
- **doc_key** - document identifier.
- **clusters** - list of clusters, where each cluster consists of the list of mentions. Each mention is represented as a list of two indices: the first index denotes the first word of the mention, the second index denotes the last word of the mention.
- **sentences** - list of sentences where each sentence is represented as a list of words.
- **tokens** - list of words.
- **speakers** - list of speakers which is currently filled with dummy input.
### Data Splits
The dataset is divided into two parts:
- training set;
- validation set.
A test set is absent as far as the dataset is generated automatically.
## Dataset Creation
### Source Data
The dataset was created from the following dataset: https://github.com/d5555/Coreference-dataset.
### Contributions
The code for the translation of samples with further alignment was created by Andrii Kursin ([email protected]). The dataset was generated by Artem Kramov (https://www.linkedin.com/in/artem-kramov-0b3731100/). | artemkramov/coreference-dataset-ua | [
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:uk",
"coreference-resolution",
"coreference",
"anaphora",
"region:us"
]
| 2023-04-01T12:07:36+00:00 | {"language": ["uk"], "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "pretty_name": "Silver Ukrainian Coreference Dataset ", "tags": ["coreference-resolution", "coreference", "anaphora"]} | 2023-04-02T10:54:35+00:00 | []
| [
"uk"
]
| TAGS
#task_categories-token-classification #size_categories-10K<n<100K #language-Ukrainian #coreference-resolution #coreference #anaphora #region-us
| # Silver Ukrainian Coreference Dataset
## Dataset Description
### Dataset Summary
A silver coreference resolution dataset for the Ukrainian language. The dataset was generated automatically with the usage of the word alignment method from the following English dataset: URL
The word alignment method was implemented by Andrii Kursin (aqrsn@URL).
### Languages
- Ukrainian
## Dataset Structure
### Data Fields
Each sample of the dataset consists of the following fields:
- doc_key - document identifier.
- clusters - list of clusters, where each cluster consists of the list of mentions. Each mention is represented as a list of two indices: the first index denotes the first word of the mention, the second index denotes the last word of the mention.
- sentences - list of sentences where each sentence is represented as a list of words.
- tokens - list of words.
- speakers - list of speakers which is currently filled with dummy input.
### Data Splits
The dataset is divided into two parts:
- training set;
- validation set.
A test set is absent as far as the dataset is generated automatically.
## Dataset Creation
### Source Data
The dataset was created from the following dataset: URL
### Contributions
The code for the translation of samples with further alignment was created by Andrii Kursin (aqrsn@URL). The dataset was generated by Artem Kramov (URL | [
"# Silver Ukrainian Coreference Dataset",
"## Dataset Description",
"### Dataset Summary\n\nA silver coreference resolution dataset for the Ukrainian language. The dataset was generated automatically with the usage of the word alignment method from the following English dataset: URL\nThe word alignment method was implemented by Andrii Kursin (aqrsn@URL).",
"### Languages\n\n- Ukrainian",
"## Dataset Structure",
"### Data Fields\n\nEach sample of the dataset consists of the following fields:\n- doc_key - document identifier.\n- clusters - list of clusters, where each cluster consists of the list of mentions. Each mention is represented as a list of two indices: the first index denotes the first word of the mention, the second index denotes the last word of the mention.\n- sentences - list of sentences where each sentence is represented as a list of words.\n- tokens - list of words.\n- speakers - list of speakers which is currently filled with dummy input.",
"### Data Splits\n\nThe dataset is divided into two parts:\n- training set;\n- validation set.\n\nA test set is absent as far as the dataset is generated automatically.",
"## Dataset Creation",
"### Source Data\n\nThe dataset was created from the following dataset: URL",
"### Contributions\n\nThe code for the translation of samples with further alignment was created by Andrii Kursin (aqrsn@URL). The dataset was generated by Artem Kramov (URL"
]
| [
"TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-Ukrainian #coreference-resolution #coreference #anaphora #region-us \n",
"# Silver Ukrainian Coreference Dataset",
"## Dataset Description",
"### Dataset Summary\n\nA silver coreference resolution dataset for the Ukrainian language. The dataset was generated automatically with the usage of the word alignment method from the following English dataset: URL\nThe word alignment method was implemented by Andrii Kursin (aqrsn@URL).",
"### Languages\n\n- Ukrainian",
"## Dataset Structure",
"### Data Fields\n\nEach sample of the dataset consists of the following fields:\n- doc_key - document identifier.\n- clusters - list of clusters, where each cluster consists of the list of mentions. Each mention is represented as a list of two indices: the first index denotes the first word of the mention, the second index denotes the last word of the mention.\n- sentences - list of sentences where each sentence is represented as a list of words.\n- tokens - list of words.\n- speakers - list of speakers which is currently filled with dummy input.",
"### Data Splits\n\nThe dataset is divided into two parts:\n- training set;\n- validation set.\n\nA test set is absent as far as the dataset is generated automatically.",
"## Dataset Creation",
"### Source Data\n\nThe dataset was created from the following dataset: URL",
"### Contributions\n\nThe code for the translation of samples with further alignment was created by Andrii Kursin (aqrsn@URL). The dataset was generated by Artem Kramov (URL"
]
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"passage: TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-Ukrainian #coreference-resolution #coreference #anaphora #region-us \n# Silver Ukrainian Coreference Dataset## Dataset Description### Dataset Summary\n\nA silver coreference resolution dataset for the Ukrainian language. The dataset was generated automatically with the usage of the word alignment method from the following English dataset: URL\nThe word alignment method was implemented by Andrii Kursin (aqrsn@URL).### Languages\n\n- Ukrainian## Dataset Structure### Data Fields\n\nEach sample of the dataset consists of the following fields:\n- doc_key - document identifier.\n- clusters - list of clusters, where each cluster consists of the list of mentions. Each mention is represented as a list of two indices: the first index denotes the first word of the mention, the second index denotes the last word of the mention.\n- sentences - list of sentences where each sentence is represented as a list of words.\n- tokens - list of words.\n- speakers - list of speakers which is currently filled with dummy input.### Data Splits\n\nThe dataset is divided into two parts:\n- training set;\n- validation set.\n\nA test set is absent as far as the dataset is generated automatically.## Dataset Creation### Source Data\n\nThe dataset was created from the following dataset: URL### Contributions\n\nThe code for the translation of samples with further alignment was created by Andrii Kursin (aqrsn@URL). The dataset was generated by Artem Kramov (URL"
]
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|
d413032a103a5b04fb87fac1b4f504388402938d |
# azcorpus - The largest open-source NLP corpus for Azerbaijani (1.9M documents, ~ 18M sentences)
__Due to ongoing maintenance activities, only a portion of our corpus is currently available for access.__
In recent years, deep learning models have been widely used in NLP, yielding excellent results. However, most research works in NLP have focused on high-resource languages such as English. There is a significant gap in NLP research for low- resource languages, Azerbaijani being no exception. So, the availability of adequate corpora for most of the languages is still limited, especially for less-resourced languages such as Azerbaijani.
Therefore, this study aimed to contribute to the NLP research community by building the largest NLP corpus for Azerbaijani language.
## Corpus Summary
“azcorpus” built for text generation purposes contains a total of 1.9 million documents, drawn from a variety of sources. The corpus is designed to provide a broad range of linguistic data for natural language processing and organized by genre and topic, with texts covering a range of subjects including politics, economics, science, culture, sport, history, society and etc.
Texts were selected from a variety of sources including newspapers, magazines, academic journals, wikipedia articles and books. The corpus includes both contemporary and historical texts, providing a rich linguistic and cultural context for natural language processing applications.
___
## Corpus structure
### Data fields
- id: Document id
- text - Newline-separated content
- source - Document source
- reliability - Subjective cleaning evaluation rate
- license - Document license
### Data Splits
This corpus has 3 sources(az_books, az_wiki, and az_news) and 1.876.492 cleaned documents.
| Source name | Number of Instances | Size (GB) |
| ------------- | --------------------|:----------------------|
| az_books | 1,540,732 | 19.5 |
| az_wiki | 98,882 | 0.9 |
| az_news | 236,878 | 3.8 |
___
## Methodology
The first step in building "azcorpus" was to collect text data from various sources.
The news websites were selected based on their popularity and the diversity of topics covered.
Additionally, a collection of ebooks in Azerbaijani was obtained from various online sources. We have expanded our collection to encompass not only fictional literature, but also scholarly works, such as physics, chemistry, and etc.
Source-specific cleaning techniques were applied separately to ensure consistency and accuracy in the corpus. Further information regarding the methodology at hand will be expounded upon in our forthcoming academic paper.
To ensure the ethical use of the corpus, we only collected publicly available data, and we did not collect any personal or sensitive information. We also ensured that the corpus was used for research purposes only and not for commercial gain. In accordance with legal considerations, it is not within our current plans to divulge sources at this time.
___
## Corpus Usage
To obtain comprehensive guidance on how to use "azcorpus", please refer to the detailed usage instructions provided in this [notebook](https://github.com/azcorpus/azcorpus_v0/blob/main/azcorpus_v0.ipynb).
```python
corpus = AzCorpus(access_token = "your_token")
# To obtain a corpus in the raw JSON format
corpus.generate_samples()
```
The download of the entire corpus is a process that entails a time span of approximately 25 minutes to 2 hours, contingent upon the velocity of your internet connection. Presently, our team is engrossed in the refinement of the download script with the objective of enhancing efficiency.
___
## Considerations for Using the Corpus
#### Social Impact
Our work has the potential to contribute to the community by providing a valuable resource for development of new text generation tools in Azerbaijani.
"azcorpus" demonstrates the importance of building large NLP corpora for under-resourced languages, and highlights the social impact of such resources. By making this corpus available to the wider community, we hope to stimulate further research and development in the field of Azerbaijani text generation, and contribute to the broader goal of promoting linguistic diversity and cultural heritage. Future studies could explore the potential community impact of our work.
#### Biases and Limitations
Addressing potential bias in machine learning corpuses is a common concern in research.
In this study, we acknowledge that our dataset may be subject to bias and to mitigate this issue, we employed several techniques.
However, we recognize that our approach may still have limitations.
So, It is important to exercise caution with models trained on a "azcorpus" that has not been adequately filtered,
as this may have an impact on the resulting models. In particular, it is crucial to be mindful of any biases
that may be present in the "azcorpus_v0".
Future work could further investigate these issues and explore additional
methods to address bias in the corpus.
___
## Additional Information
#### Corpus authors
The corpus was put together by [Huseyn Kishiyev](https://www.linkedin.com/in/huseynkishiyev/), [Jafar Isbarov](https://www.linkedin.com/in/jafar-isbarov/), [Kanan Suleymanli](https://www.linkedin.com/in/kanan-suleyman/), [Khazar Heydarli](https://www.linkedin.com/in/xezer-heyderli/), [Leyla Eminova](https://www.linkedin.com/in/leyla-eminova/) and [Nijat Zeynalov](https://www.linkedin.com/in/nijat-zeynalov-064163142/).
The authors' names have been arranged in alphabetical order. All authors have equal rights and contributed equally to this work.
The authors declare no conflict of interest. There are no founding sponsors and no other role in the design of the work other than the authors; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the corpus.
___ | azcorpus/azcorpus_v0 | [
"license:openrail",
"region:us"
]
| 2023-04-01T12:37:10+00:00 | {"license": "openrail", "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Name and Surname": "text", "Email": "text", "Company": "text", "Purpose of Use": "text", "I agree to use this dataset for non-commercial use ONLY": "checkbox"}} | 2023-09-20T09:24:11+00:00 | []
| []
| TAGS
#license-openrail #region-us
| 
===============================================================================================
**Due to ongoing maintenance activities, only a portion of our corpus is currently available for access.**
In recent years, deep learning models have been widely used in NLP, yielding excellent results. However, most research works in NLP have focused on high-resource languages such as English. There is a significant gap in NLP research for low- resource languages, Azerbaijani being no exception. So, the availability of adequate corpora for most of the languages is still limited, especially for less-resourced languages such as Azerbaijani.
Therefore, this study aimed to contribute to the NLP research community by building the largest NLP corpus for Azerbaijani language.
Corpus Summary
--------------
“azcorpus” built for text generation purposes contains a total of 1.9 million documents, drawn from a variety of sources. The corpus is designed to provide a broad range of linguistic data for natural language processing and organized by genre and topic, with texts covering a range of subjects including politics, economics, science, culture, sport, history, society and etc.
Texts were selected from a variety of sources including newspapers, magazines, academic journals, wikipedia articles and books. The corpus includes both contemporary and historical texts, providing a rich linguistic and cultural context for natural language processing applications.
---
Corpus structure
----------------
### Data fields
* id: Document id
* text - Newline-separated content
* source - Document source
* reliability - Subjective cleaning evaluation rate
* license - Document license
### Data Splits
This corpus has 3 sources(az\_books, az\_wiki, and az\_news) and 1.876.492 cleaned documents.
---
Methodology
-----------
The first step in building "azcorpus" was to collect text data from various sources.
The news websites were selected based on their popularity and the diversity of topics covered.
Additionally, a collection of ebooks in Azerbaijani was obtained from various online sources. We have expanded our collection to encompass not only fictional literature, but also scholarly works, such as physics, chemistry, and etc.
Source-specific cleaning techniques were applied separately to ensure consistency and accuracy in the corpus. Further information regarding the methodology at hand will be expounded upon in our forthcoming academic paper.
To ensure the ethical use of the corpus, we only collected publicly available data, and we did not collect any personal or sensitive information. We also ensured that the corpus was used for research purposes only and not for commercial gain. In accordance with legal considerations, it is not within our current plans to divulge sources at this time.
---
Corpus Usage
------------
To obtain comprehensive guidance on how to use "azcorpus", please refer to the detailed usage instructions provided in this notebook.
The download of the entire corpus is a process that entails a time span of approximately 25 minutes to 2 hours, contingent upon the velocity of your internet connection. Presently, our team is engrossed in the refinement of the download script with the objective of enhancing efficiency.
---
Considerations for Using the Corpus
-----------------------------------
#### Social Impact
Our work has the potential to contribute to the community by providing a valuable resource for development of new text generation tools in Azerbaijani.
"azcorpus" demonstrates the importance of building large NLP corpora for under-resourced languages, and highlights the social impact of such resources. By making this corpus available to the wider community, we hope to stimulate further research and development in the field of Azerbaijani text generation, and contribute to the broader goal of promoting linguistic diversity and cultural heritage. Future studies could explore the potential community impact of our work.
#### Biases and Limitations
Addressing potential bias in machine learning corpuses is a common concern in research.
In this study, we acknowledge that our dataset may be subject to bias and to mitigate this issue, we employed several techniques.
However, we recognize that our approach may still have limitations.
So, It is important to exercise caution with models trained on a "azcorpus" that has not been adequately filtered,
as this may have an impact on the resulting models. In particular, it is crucial to be mindful of any biases
that may be present in the "azcorpus\_v0".
Future work could further investigate these issues and explore additional
methods to address bias in the corpus.
---
Additional Information
----------------------
#### Corpus authors
The corpus was put together by Huseyn Kishiyev, Jafar Isbarov, Kanan Suleymanli, Khazar Heydarli, Leyla Eminova and Nijat Zeynalov.
The authors' names have been arranged in alphabetical order. All authors have equal rights and contributed equally to this work.
The authors declare no conflict of interest. There are no founding sponsors and no other role in the design of the work other than the authors; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the corpus.
---
| [
"### Data fields\n\n\n* id: Document id\n* text - Newline-separated content\n* source - Document source\n* reliability - Subjective cleaning evaluation rate\n* license - Document license",
"### Data Splits\n\n\nThis corpus has 3 sources(az\\_books, az\\_wiki, and az\\_news) and 1.876.492 cleaned documents.\n\n\n\n\n\n---\n\n\nMethodology\n-----------\n\n\nThe first step in building \"azcorpus\" was to collect text data from various sources.\nThe news websites were selected based on their popularity and the diversity of topics covered.\nAdditionally, a collection of ebooks in Azerbaijani was obtained from various online sources. We have expanded our collection to encompass not only fictional literature, but also scholarly works, such as physics, chemistry, and etc.\nSource-specific cleaning techniques were applied separately to ensure consistency and accuracy in the corpus. Further information regarding the methodology at hand will be expounded upon in our forthcoming academic paper.\n\n\nTo ensure the ethical use of the corpus, we only collected publicly available data, and we did not collect any personal or sensitive information. We also ensured that the corpus was used for research purposes only and not for commercial gain. In accordance with legal considerations, it is not within our current plans to divulge sources at this time.\n\n\n\n\n---\n\n\nCorpus Usage\n------------\n\n\nTo obtain comprehensive guidance on how to use \"azcorpus\", please refer to the detailed usage instructions provided in this notebook.\n\n\nThe download of the entire corpus is a process that entails a time span of approximately 25 minutes to 2 hours, contingent upon the velocity of your internet connection. Presently, our team is engrossed in the refinement of the download script with the objective of enhancing efficiency.\n\n\n\n\n---\n\n\nConsiderations for Using the Corpus\n-----------------------------------",
"#### Social Impact\n\n\nOur work has the potential to contribute to the community by providing a valuable resource for development of new text generation tools in Azerbaijani.\n\n\n\"azcorpus\" demonstrates the importance of building large NLP corpora for under-resourced languages, and highlights the social impact of such resources. By making this corpus available to the wider community, we hope to stimulate further research and development in the field of Azerbaijani text generation, and contribute to the broader goal of promoting linguistic diversity and cultural heritage. Future studies could explore the potential community impact of our work.",
"#### Biases and Limitations\n\n\nAddressing potential bias in machine learning corpuses is a common concern in research.\nIn this study, we acknowledge that our dataset may be subject to bias and to mitigate this issue, we employed several techniques.\nHowever, we recognize that our approach may still have limitations.\nSo, It is important to exercise caution with models trained on a \"azcorpus\" that has not been adequately filtered,\nas this may have an impact on the resulting models. In particular, it is crucial to be mindful of any biases\nthat may be present in the \"azcorpus\\_v0\".\n\n\nFuture work could further investigate these issues and explore additional\nmethods to address bias in the corpus.\n\n\n\n\n---\n\n\nAdditional Information\n----------------------",
"#### Corpus authors\n\n\nThe corpus was put together by Huseyn Kishiyev, Jafar Isbarov, Kanan Suleymanli, Khazar Heydarli, Leyla Eminova and Nijat Zeynalov.\n\n\nThe authors' names have been arranged in alphabetical order. All authors have equal rights and contributed equally to this work.\n\n\nThe authors declare no conflict of interest. There are no founding sponsors and no other role in the design of the work other than the authors; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the corpus.\n\n\n\n\n---"
]
| [
"TAGS\n#license-openrail #region-us \n",
"### Data fields\n\n\n* id: Document id\n* text - Newline-separated content\n* source - Document source\n* reliability - Subjective cleaning evaluation rate\n* license - Document license",
"### Data Splits\n\n\nThis corpus has 3 sources(az\\_books, az\\_wiki, and az\\_news) and 1.876.492 cleaned documents.\n\n\n\n\n\n---\n\n\nMethodology\n-----------\n\n\nThe first step in building \"azcorpus\" was to collect text data from various sources.\nThe news websites were selected based on their popularity and the diversity of topics covered.\nAdditionally, a collection of ebooks in Azerbaijani was obtained from various online sources. We have expanded our collection to encompass not only fictional literature, but also scholarly works, such as physics, chemistry, and etc.\nSource-specific cleaning techniques were applied separately to ensure consistency and accuracy in the corpus. Further information regarding the methodology at hand will be expounded upon in our forthcoming academic paper.\n\n\nTo ensure the ethical use of the corpus, we only collected publicly available data, and we did not collect any personal or sensitive information. We also ensured that the corpus was used for research purposes only and not for commercial gain. In accordance with legal considerations, it is not within our current plans to divulge sources at this time.\n\n\n\n\n---\n\n\nCorpus Usage\n------------\n\n\nTo obtain comprehensive guidance on how to use \"azcorpus\", please refer to the detailed usage instructions provided in this notebook.\n\n\nThe download of the entire corpus is a process that entails a time span of approximately 25 minutes to 2 hours, contingent upon the velocity of your internet connection. Presently, our team is engrossed in the refinement of the download script with the objective of enhancing efficiency.\n\n\n\n\n---\n\n\nConsiderations for Using the Corpus\n-----------------------------------",
"#### Social Impact\n\n\nOur work has the potential to contribute to the community by providing a valuable resource for development of new text generation tools in Azerbaijani.\n\n\n\"azcorpus\" demonstrates the importance of building large NLP corpora for under-resourced languages, and highlights the social impact of such resources. By making this corpus available to the wider community, we hope to stimulate further research and development in the field of Azerbaijani text generation, and contribute to the broader goal of promoting linguistic diversity and cultural heritage. Future studies could explore the potential community impact of our work.",
"#### Biases and Limitations\n\n\nAddressing potential bias in machine learning corpuses is a common concern in research.\nIn this study, we acknowledge that our dataset may be subject to bias and to mitigate this issue, we employed several techniques.\nHowever, we recognize that our approach may still have limitations.\nSo, It is important to exercise caution with models trained on a \"azcorpus\" that has not been adequately filtered,\nas this may have an impact on the resulting models. In particular, it is crucial to be mindful of any biases\nthat may be present in the \"azcorpus\\_v0\".\n\n\nFuture work could further investigate these issues and explore additional\nmethods to address bias in the corpus.\n\n\n\n\n---\n\n\nAdditional Information\n----------------------",
"#### Corpus authors\n\n\nThe corpus was put together by Huseyn Kishiyev, Jafar Isbarov, Kanan Suleymanli, Khazar Heydarli, Leyla Eminova and Nijat Zeynalov.\n\n\nThe authors' names have been arranged in alphabetical order. All authors have equal rights and contributed equally to this work.\n\n\nThe authors declare no conflict of interest. There are no founding sponsors and no other role in the design of the work other than the authors; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the corpus.\n\n\n\n\n---"
]
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]
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|
3e5e71b49f4a6558b03d32a7db17806d8ff39f80 | # Dataset Card for digits dataset
Optical recognition of handwritten digits dataset
## Dataset Description
- **Homepage:** https://scikit-learn.org/stable/datasets/toy_dataset.html#digits-dataset
## Note - How to load this dataset directly with the datasets library
```
from datasets import load_dataset
dataset = load_dataset("sklearn-docs/digits",header=None)
```
### Dataset Summary
This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
The data set contains images of hand-written digits: 10 classes where each class refers to a digit.
Preprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions.
For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994.
### Data Instances
Number of Instances:
1797
Number of Attributes:
64
Attribute Information:
8x8 image of integer pixels in the range 0..16.
Missing Attribute Values:
None
Creator:
5. Alpaydin (alpaydin ‘@’ boun.edu.tr)
Date:
July; 1998
### Citation Information
References
C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.
Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.
Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000.
| sklearn-docs/digits | [
"size_categories:1K<n<10K",
"license:cc0-1.0",
"region:us"
]
| 2023-04-01T13:09:07+00:00 | {"license": "cc0-1.0", "size_categories": ["1K<n<10K"]} | 2023-04-06T18:05:28+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #license-cc0-1.0 #region-us
| # Dataset Card for digits dataset
Optical recognition of handwritten digits dataset
## Dataset Description
- Homepage: URL
## Note - How to load this dataset directly with the datasets library
### Dataset Summary
This is a copy of the test set of the UCI ML hand-written digits datasets URL
The data set contains images of hand-written digits: 10 classes where each class refers to a digit.
Preprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions.
For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994.
### Data Instances
Number of Instances:
1797
Number of Attributes:
64
Attribute Information:
8x8 image of integer pixels in the range 0..16.
Missing Attribute Values:
None
Creator:
5. Alpaydin (alpaydin ‘@’ URL)
Date:
July; 1998
References
C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.
Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.
Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000.
| [
"# Dataset Card for digits dataset\nOptical recognition of handwritten digits dataset",
"## Dataset Description\n\n- Homepage: URL",
"## Note - How to load this dataset directly with the datasets library",
"### Dataset Summary\n\nThis is a copy of the test set of the UCI ML hand-written digits datasets URL\n\nThe data set contains images of hand-written digits: 10 classes where each class refers to a digit.\n\nPreprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions.\n\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994.",
"### Data Instances\n\nNumber of Instances:\n1797\n\nNumber of Attributes:\n64\n\nAttribute Information:\n8x8 image of integer pixels in the range 0..16.\n\nMissing Attribute Values:\nNone\n\nCreator:\n5. Alpaydin (alpaydin ‘@’ URL)\n\nDate:\nJuly; 1998\n\n\n\n\nReferences\n\nC. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.\n\nAlpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n\nKen Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.\n\nClaudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000."
]
| [
"TAGS\n#size_categories-1K<n<10K #license-cc0-1.0 #region-us \n",
"# Dataset Card for digits dataset\nOptical recognition of handwritten digits dataset",
"## Dataset Description\n\n- Homepage: URL",
"## Note - How to load this dataset directly with the datasets library",
"### Dataset Summary\n\nThis is a copy of the test set of the UCI ML hand-written digits datasets URL\n\nThe data set contains images of hand-written digits: 10 classes where each class refers to a digit.\n\nPreprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions.\n\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994.",
"### Data Instances\n\nNumber of Instances:\n1797\n\nNumber of Attributes:\n64\n\nAttribute Information:\n8x8 image of integer pixels in the range 0..16.\n\nMissing Attribute Values:\nNone\n\nCreator:\n5. Alpaydin (alpaydin ‘@’ URL)\n\nDate:\nJuly; 1998\n\n\n\n\nReferences\n\nC. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.\n\nAlpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n\nKen Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.\n\nClaudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000."
]
| [
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| [
"passage: TAGS\n#size_categories-1K<n<10K #license-cc0-1.0 #region-us \n# Dataset Card for digits dataset\nOptical recognition of handwritten digits dataset## Dataset Description\n\n- Homepage: URL## Note - How to load this dataset directly with the datasets library### Dataset Summary\n\nThis is a copy of the test set of the UCI ML hand-written digits datasets URL\n\nThe data set contains images of hand-written digits: 10 classes where each class refers to a digit.\n\nPreprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions.\n\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994."
]
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|
c915955195b4ea02e6eb3f1cfa3163ee6994aafb | # Dataset Card for "somos-clean-alpaca-es-validations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | maga12/somos-clean-alpaca-es-validations | [
"region:us"
]
| 2023-04-01T14:26:53+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", "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": "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": "tr-flag-1-instruction", "dtype": "bool"}, {"name": "tr-flag-2-input", "dtype": "bool"}, {"name": "tr-flag-3-output", "dtype": "bool"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 2569677, "num_examples": 136}], "download_size": 0, "dataset_size": 2569677}} | 2023-07-08T22:14:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "somos-clean-alpaca-es-validations"
More Information needed | [
"# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed"
]
| [
6,
24
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed"
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|
d2a5f1e783e5969739833d7213c9293712b0b41a | i have no idea how to add data | 123tozi123/dog-voise | [
"task_categories:text-to-speech",
"size_categories:n<1K",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-04-01T15:16:48+00:00 | {"license": "cc-by-sa-4.0", "size_categories": ["n<1K"], "task_categories": ["text-to-speech"]} | 2023-04-01T15:20:21+00:00 | []
| []
| TAGS
#task_categories-text-to-speech #size_categories-n<1K #license-cc-by-sa-4.0 #region-us
| i have no idea how to add data | []
| [
"TAGS\n#task_categories-text-to-speech #size_categories-n<1K #license-cc-by-sa-4.0 #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-text-to-speech #size_categories-n<1K #license-cc-by-sa-4.0 #region-us \n"
]
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|
297cd110b221cedd0b63e5cc278ecf3fcef5a093 | # Dataset Card for "indexed-open-image-v4-test-set"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Isamu136/indexed-open-image-v4-test-set | [
"region:us"
]
| 2023-04-01T15:38:55+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "sequence": "string"}, {"name": "image_id", "dtype": "string"}, {"name": "ibot_b_16_embedding", "sequence": "float32"}, {"name": "moco_vitb_imagenet_embeddings_without_last_layer", "sequence": "float32"}, {"name": "clip_vision_l14", "sequence": "float32"}, {"name": "clip_l14", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 40537274594.0, "num_examples": 125436}], "download_size": 40917861964, "dataset_size": 40537274594.0}} | 2023-04-04T07:35:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "indexed-open-image-v4-test-set"
More Information needed | [
"# Dataset Card for \"indexed-open-image-v4-test-set\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"indexed-open-image-v4-test-set\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"indexed-open-image-v4-test-set\"\n\nMore Information needed"
]
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|
85dad333bf9e225ad1c3e66ed6d05c12f358fcd5 |
# Dataset Card for CARDIO.DE
## Dataset Description
- **Homepage:** https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/AFYQDY
- **Pubmed:** False
- **Public:** False
- **Tasks:** NER
We present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor’s letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes.
## Citation Information
```
@data{
data/AFYQDY_2022,
author = {Christoph Dieterich},
publisher = {heiDATA},
title = {{CARDIO:DE}},
year = {2022},
version = {V5},
doi = {10.11588/data/AFYQDY},
url = {https://doi.org/10.11588/data/AFYQDY}
}
```
| bigbio/cardiode | [
"multilinguality:monolingual",
"language:ger",
"license:other",
"region:us"
]
| 2023-04-01T15:40:12+00:00 | {"language": ["ger"], "license": "other", "multilinguality": "monolingual", "pretty_name": "CARDIO:DE", "bigbio_language": ["German"], "homepage": "https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/AFYQDY", "bigbio_pubmed": false, "bigbio_public": false, "bigbio_tasks": ["NAMED_ENTITY_RECOGNITION"]} | 2023-04-05T00:14:13+00:00 | []
| [
"ger"
]
| TAGS
#multilinguality-monolingual #language-German #license-other #region-us
|
# Dataset Card for CARDIO.DE
## Dataset Description
- Homepage: URL
- Pubmed: False
- Public: False
- Tasks: NER
We present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor’s letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes.
| [
"# Dataset Card for CARDIO.DE",
"## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER\n\n\nWe present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor’s letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes."
]
| [
"TAGS\n#multilinguality-monolingual #language-German #license-other #region-us \n",
"# Dataset Card for CARDIO.DE",
"## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER\n\n\nWe present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor’s letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes."
]
| [
23,
9,
178
]
| [
"passage: TAGS\n#multilinguality-monolingual #language-German #license-other #region-us \n# Dataset Card for CARDIO.DE## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER\n\n\nWe present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor’s letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes."
]
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|
95c41b1ec74d08bac12feb769ced791ff39ed069 |
# Dataset Card for BRONCO150
## Dataset Description
- **Homepage:** https://www2.informatik.hu-berlin.de/~leser/bronco/index.html
- **Pubmed:** False
- **Public:** False
- **Tasks:** NER, NED
BRONCO150 is a corpus containing selected sentences of 150 German discharge summaries of cancer patients (hepatocelluar carcinoma or melanoma) treated at Charite Universitaetsmedizin Berlin or Universitaetsklinikum Tuebingen. All discharge summaries were manually anonymized. The original documents were scrambled at the sentence level to make reconstruction of individual reports impossible.
## Citation Information
```
@article{10.1093/jamiaopen/ooab025,
author = {Kittner, Madeleine and Lamping, Mario and Rieke, Damian T and Götze, Julian and Bajwa, Bariya and Jelas, Ivan and Rüter, Gina and Hautow, Hanjo and Sänger, Mario and Habibi, Maryam and Zettwitz, Marit and Bortoli, Till de and Ostermann, Leonie and Ševa, Jurica and Starlinger, Johannes and Kohlbacher, Oliver and Malek, Nisar P and Keilholz, Ulrich and Leser, Ulf},
title = "{Annotation and initial evaluation of a large annotated German oncological corpus}",
journal = {JAMIA Open},
volume = {4},
number = {2},
year = {2021},
month = {04},
issn = {2574-2531},
doi = {10.1093/jamiaopen/ooab025},
url = {https://doi.org/10.1093/jamiaopen/ooab025},
note = {ooab025},
eprint = {https://academic.oup.com/jamiaopen/article-pdf/4/2/ooab025/38830128/ooab025.pdf},
}
```
| bigbio/bronco | [
"multilinguality:monolingual",
"language:de",
"region:us"
]
| 2023-04-01T15:46:42+00:00 | {"language": ["de"], "multilinguality": "monolingual", "pretty_name": "BRONCO150", "bigbio_language": ["German"], "homepage": "https://www2.informatik.hu-berlin.de/~leser/bronco/index.html", "bigbio_pubmed": false, "bigbio_public": false, "bigbio_tasks": ["NAMED_ENTITY_RECOGNITION", "NAMED_ENTITY_DISAMBIGUATION"]} | 2023-04-01T15:47:31+00:00 | []
| [
"de"
]
| TAGS
#multilinguality-monolingual #language-German #region-us
|
# Dataset Card for BRONCO150
## Dataset Description
- Homepage: URL
- Pubmed: False
- Public: False
- Tasks: NER, NED
BRONCO150 is a corpus containing selected sentences of 150 German discharge summaries of cancer patients (hepatocelluar carcinoma or melanoma) treated at Charite Universitaetsmedizin Berlin or Universitaetsklinikum Tuebingen. All discharge summaries were manually anonymized. The original documents were scrambled at the sentence level to make reconstruction of individual reports impossible.
| [
"# Dataset Card for BRONCO150",
"## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER, NED\n\n\nBRONCO150 is a corpus containing selected sentences of 150 German discharge summaries of cancer patients (hepatocelluar carcinoma or melanoma) treated at Charite Universitaetsmedizin Berlin or Universitaetsklinikum Tuebingen. All discharge summaries were manually anonymized. The original documents were scrambled at the sentence level to make reconstruction of individual reports impossible."
]
| [
"TAGS\n#multilinguality-monolingual #language-German #region-us \n",
"# Dataset Card for BRONCO150",
"## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER, NED\n\n\nBRONCO150 is a corpus containing selected sentences of 150 German discharge summaries of cancer patients (hepatocelluar carcinoma or melanoma) treated at Charite Universitaetsmedizin Berlin or Universitaetsklinikum Tuebingen. All discharge summaries were manually anonymized. The original documents were scrambled at the sentence level to make reconstruction of individual reports impossible."
]
| [
18,
9,
113
]
| [
"passage: TAGS\n#multilinguality-monolingual #language-German #region-us \n# Dataset Card for BRONCO150## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER, NED\n\n\nBRONCO150 is a corpus containing selected sentences of 150 German discharge summaries of cancer patients (hepatocelluar carcinoma or melanoma) treated at Charite Universitaetsmedizin Berlin or Universitaetsklinikum Tuebingen. All discharge summaries were manually anonymized. The original documents were scrambled at the sentence level to make reconstruction of individual reports impossible."
]
| [
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|
a379767b3700f7b72ec08ca42892aa9721bd792c |
# Dataset Card for GGPONC2
## Dataset Description
- **Homepage:** https://www.leitlinienprogramm-onkologie.de/projekte/ggponc-english/
- **Pubmed:** False
- **Public:** False
- **Tasks:** NER
The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers.
Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information
(PHI), which distinguishes them from other kinds of medical text.
The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens.
It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a
time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely
distributable corpus of German medical text at the moment.
Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body
Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities.
In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been
annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
## Citation Information
```
@inproceedings{borchert-etal-2022-ggponc,
title = "{GGPONC} 2.0 - The {G}erman Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline {NER} Taggers",
author = "Borchert, Florian and
Lohr, Christina and
Modersohn, Luise and
Witt, Jonas and
Langer, Thomas and
Follmann, Markus and
Gietzelt, Matthias and
Arnrich, Bert and
Hahn, Udo and
Schapranow, Matthieu-P.",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.389",
pages = "3650--3660",
}
```
| bigbio/ggponc2 | [
"multilinguality:monolingual",
"language:de",
"region:us"
]
| 2023-04-01T15:49:04+00:00 | {"language": ["de"], "multilinguality": "monolingual", "pretty_name": "GGPONC2", "bigbio_language": ["German"], "homepage": "https://www.leitlinienprogramm-onkologie.de/projekte/ggponc-english/", "bigbio_pubmed": false, "bigbio_public": "flase", "bigbio_tasks": ["NAMED_ENTITY_RECOGNITION"]} | 2023-04-05T00:15:05+00:00 | []
| [
"de"
]
| TAGS
#multilinguality-monolingual #language-German #region-us
|
# Dataset Card for GGPONC2
## Dataset Description
- Homepage: URL
- Pubmed: False
- Public: False
- Tasks: NER
The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers.
Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information
(PHI), which distinguishes them from other kinds of medical text.
The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens.
It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a
time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely
distributable corpus of German medical text at the moment.
Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body
Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities.
In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been
annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.
| [
"# Dataset Card for GGPONC2",
"## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER \n\n\nThe GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. \nClinical guidelines are particularly suitable to create such corpora, as they contain no protected health information \n(PHI), which distinguishes them from other kinds of medical text.\n\nThe second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. \nIt has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a \ntime frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely \ndistributable corpus of German medical text at the moment.\n\nAnnotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body \nSubstances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. \nIn total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been \nannotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text."
]
| [
"TAGS\n#multilinguality-monolingual #language-German #region-us \n",
"# Dataset Card for GGPONC2",
"## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER \n\n\nThe GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. \nClinical guidelines are particularly suitable to create such corpora, as they contain no protected health information \n(PHI), which distinguishes them from other kinds of medical text.\n\nThe second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. \nIt has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a \ntime frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely \ndistributable corpus of German medical text at the moment.\n\nAnnotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body \nSubstances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. \nIn total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been \nannotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text."
]
| [
18,
10,
295
]
| [
"passage: TAGS\n#multilinguality-monolingual #language-German #region-us \n# Dataset Card for GGPONC2## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: NER \n\n\nThe GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. \nClinical guidelines are particularly suitable to create such corpora, as they contain no protected health information \n(PHI), which distinguishes them from other kinds of medical text.\n\nThe second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. \nIt has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a \ntime frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely \ndistributable corpus of German medical text at the moment.\n\nAnnotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body \nSubstances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. \nIn total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been \nannotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text."
]
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|
97fe579a4a26d862964ca54512432359af8f9d54 | # Dataset Card for "quran_tafseer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-AI-C/quran_tafseer | [
"region:us"
]
| 2023-04-01T15:58:10+00:00 | {"dataset_info": {"features": [{"name": "en-ahmedali", "dtype": "string"}, {"name": "en-ahmedraza", "dtype": "string"}, {"name": "en-arberry", "dtype": "string"}, {"name": "en-asad", "dtype": "string"}, {"name": "en-daryabadi", "dtype": "string"}, {"name": "en-hilali", "dtype": "string"}, {"name": "en-itani", "dtype": "string"}, {"name": "en-maududi", "dtype": "string"}, {"name": "en-mubarakpuri", "dtype": "string"}, {"name": "en-pickthall", "dtype": "string"}, {"name": "en-qarai", "dtype": "string"}, {"name": "en-qaribullah", "dtype": "string"}, {"name": "en-sahih", "dtype": "string"}, {"name": "en-sarwar", "dtype": "string"}, {"name": "en-shakir", "dtype": "string"}, {"name": "en-transliterati", "dtype": "string"}, {"name": "en-wahiduddi", "dtype": "string"}, {"name": "en-yusufali", "dtype": "string"}, {"name": "ayah", "dtype": "int64"}, {"name": "sorah", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17616789, "num_examples": 6235}], "download_size": 9631631, "dataset_size": 17616789}} | 2023-04-01T16:03:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "quran_tafseer"
More Information needed | [
"# Dataset Card for \"quran_tafseer\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"quran_tafseer\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"quran_tafseer\"\n\nMore Information needed"
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|
5530f6640b3f41edbf6fb01220c71483ace00ad2 | # Dataset Card for "quran_en_translations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-AI-C/quran_en_translations | [
"region:us"
]
| 2023-04-01T16:12:39+00:00 | {"dataset_info": {"features": [{"name": "en-ahmedali", "dtype": "string"}, {"name": "en-ahmedraza", "dtype": "string"}, {"name": "en-arberry", "dtype": "string"}, {"name": "en-asad", "dtype": "string"}, {"name": "en-daryabadi", "dtype": "string"}, {"name": "en-hilali", "dtype": "string"}, {"name": "en-itani", "dtype": "string"}, {"name": "en-maududi", "dtype": "string"}, {"name": "en-mubarakpuri", "dtype": "string"}, {"name": "en-pickthall", "dtype": "string"}, {"name": "en-qarai", "dtype": "string"}, {"name": "en-qaribullah", "dtype": "string"}, {"name": "en-sahih", "dtype": "string"}, {"name": "en-sarwar", "dtype": "string"}, {"name": "en-shakir", "dtype": "string"}, {"name": "en-transliterati", "dtype": "string"}, {"name": "en-wahiduddi", "dtype": "string"}, {"name": "en-yusufali", "dtype": "string"}, {"name": "ayah", "dtype": "int64"}, {"name": "sorah", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "en-tafsir-mokhtasar-html", "dtype": "string"}, {"name": "en-tafsir-mokhtasar-text", "dtype": "string"}, {"name": "en-tafsir-maarif-html", "dtype": "string"}, {"name": "en-tafsir-maarif-text", "dtype": "string"}, {"name": "en-tafsir-ibn-kathir-html", "dtype": "string"}, {"name": "en-tafsir-ibn-kathir-text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 66065359, "num_examples": 6235}], "download_size": 35313084, "dataset_size": 66065359}} | 2023-04-02T13:34:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "quran_en_translations"
More Information needed | [
"# Dataset Card for \"quran_en_translations\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"quran_en_translations\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"quran_en_translations\"\n\nMore Information needed"
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|
33a0245d7341526d02030a73e5663c3e2904487a |
# EDGAR10-Q
## Dataset Summary
EDGAR10-Q is a large financial dataset curated by scraping annual and quarterly reports of top 1500 LLCs in the world.
The dataset is designed for the task of ContextNER, which aims to generate the relevant context for entities in a sentence, where the context is a set of phrases describing the entity but not necessarily present in the sentence.
The dataset is the largest in terms of the number of sentences (1M), entities (2.8M), and average tokens per sentence (35).
You may want to check out
* Our paper: [CONTEXT-NER: Contextual Phrase Generation at Scale](https://arxiv.org/abs/2109.08079/)
* GitHub: [Click Here](https://github.com/him1411/edgar10q-dataset)
## Supported Tasks
The dataset is designed for the task of ContextNER that aims to generate the relevant context for entities in a sentence,
where the context is a set of phrases describing the entity but not necessarily present in the sentence.
## Dataset Structure
### Data Instances
The dataset includes plain text input-output pairs, where the input is a sentence with an entity and the output is the context for the entity.
An example of a train instance looks as follows:
```
{
"input": "0.6 million . The denominator also includes the dilutive effect of approximately 0.9 million, 0.6 million and 0.6 million shares of unvested restricted shares of common stock for the years ended December 31, 2019, 2018 and 2017, respectively.",
"output": "Dilutive effect of unvested restricted shares of Class A common stock"
}
```
We also publish a metadata file in the original repository to promote future research in the area. Please checkout the [main website](https://github.com/him1411/edgar10q-dataset)
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` in the form of entity plus sentence.
- `label`: a string describing the relevant context for entity in the sentence
### Data Splits
The dataset is split into train, validation, and test sets. The sizes of the splits are as follows:
| | Train | Validation | Test |
|-----------|-----------|------------|-------|
| Instances | 1,498,995 | 187,383 |187,383|
### Dataset Creation
The dataset was created by scraping annual and quarterly reports of top 1500 LLCs in the world.
### Models trained using this dataset
There are several models finetuned using this dataset. They are:
1. [EDGAR-T5-base](https://huggingface.co/him1411/EDGAR-T5-base)
2. [EDGAR-BART-Base](https://huggingface.co/him1411/EDGAR-BART-Base)
3. [EDGAR-flan-t5-base](https://huggingface.co/him1411/EDGAR-flan-t5-base)
4. [EDGAR-T5-Large](https://huggingface.co/him1411/EDGAR-T5-Large)
5. [EDGAR-Tk-Instruct-Large](https://huggingface.co/him1411/EDGAR-Tk-Instruct-Large)
6. [Instruction tuned EDGAR-Tk-Instruct-base](https://huggingface.co/him1411/EDGAR-Tk-instruct-base-inst-tune)
### Citation Information
If you use this dataset and any other related artifact, please cite the following paper:
```
@article{gupta2021context,
title={Context-NER: Contextual Phrase Generation at Scale},
author={Gupta, Himanshu and Verma, Shreyas and Kumar, Tarun and Mishra, Swaroop and Agrawal, Tamanna and Badugu, Amogh and Bhatt, Himanshu Sharad},
journal={arXiv preprint arXiv:2109.08079},
year={2021}
}
```
### Contributions
Thanks to [@him1411](https://github.com/him1411) for adding this dataset. | him1411/EDGAR10-Q | [
"size_categories:1M<n<10M",
"license:mit",
"financial",
"ner",
"context-ner",
"arxiv:2109.08079",
"region:us"
]
| 2023-04-01T16:34:46+00:00 | {"license": "mit", "size_categories": ["1M<n<10M"], "tags": ["financial", "ner", "context-ner"]} | 2023-05-14T20:26:22+00:00 | [
"2109.08079"
]
| []
| TAGS
#size_categories-1M<n<10M #license-mit #financial #ner #context-ner #arxiv-2109.08079 #region-us
| EDGAR10-Q
=========
Dataset Summary
---------------
EDGAR10-Q is a large financial dataset curated by scraping annual and quarterly reports of top 1500 LLCs in the world.
The dataset is designed for the task of ContextNER, which aims to generate the relevant context for entities in a sentence, where the context is a set of phrases describing the entity but not necessarily present in the sentence.
The dataset is the largest in terms of the number of sentences (1M), entities (2.8M), and average tokens per sentence (35).
You may want to check out
* Our paper: CONTEXT-NER: Contextual Phrase Generation at Scale
* GitHub: Click Here
Supported Tasks
---------------
The dataset is designed for the task of ContextNER that aims to generate the relevant context for entities in a sentence,
where the context is a set of phrases describing the entity but not necessarily present in the sentence.
Dataset Structure
-----------------
### Data Instances
The dataset includes plain text input-output pairs, where the input is a sentence with an entity and the output is the context for the entity.
An example of a train instance looks as follows:
We also publish a metadata file in the original repository to promote future research in the area. Please checkout the main website
### Data Fields
The data fields are the same among all splits.
* 'text': a 'string' in the form of entity plus sentence.
* 'label': a string describing the relevant context for entity in the sentence
### Data Splits
The dataset is split into train, validation, and test sets. The sizes of the splits are as follows:
### Dataset Creation
The dataset was created by scraping annual and quarterly reports of top 1500 LLCs in the world.
### Models trained using this dataset
There are several models finetuned using this dataset. They are:
1. EDGAR-T5-base
2. EDGAR-BART-Base
3. EDGAR-flan-t5-base
4. EDGAR-T5-Large
5. EDGAR-Tk-Instruct-Large
6. Instruction tuned EDGAR-Tk-Instruct-base
If you use this dataset and any other related artifact, please cite the following paper:
### Contributions
Thanks to @him1411 for adding this dataset.
| [
"### Data Instances\n\n\nThe dataset includes plain text input-output pairs, where the input is a sentence with an entity and the output is the context for the entity.\n\n\nAn example of a train instance looks as follows:\n\n\nWe also publish a metadata file in the original repository to promote future research in the area. Please checkout the main website",
"### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'text': a 'string' in the form of entity plus sentence.\n* 'label': a string describing the relevant context for entity in the sentence",
"### Data Splits\n\n\nThe dataset is split into train, validation, and test sets. The sizes of the splits are as follows:",
"### Dataset Creation\n\n\nThe dataset was created by scraping annual and quarterly reports of top 1500 LLCs in the world.",
"### Models trained using this dataset\n\n\nThere are several models finetuned using this dataset. They are:\n\n\n1. EDGAR-T5-base\n2. EDGAR-BART-Base\n3. EDGAR-flan-t5-base\n4. EDGAR-T5-Large\n5. EDGAR-Tk-Instruct-Large\n6. Instruction tuned EDGAR-Tk-Instruct-base\n\n\nIf you use this dataset and any other related artifact, please cite the following paper:",
"### Contributions\n\n\nThanks to @him1411 for adding this dataset."
]
| [
"TAGS\n#size_categories-1M<n<10M #license-mit #financial #ner #context-ner #arxiv-2109.08079 #region-us \n",
"### Data Instances\n\n\nThe dataset includes plain text input-output pairs, where the input is a sentence with an entity and the output is the context for the entity.\n\n\nAn example of a train instance looks as follows:\n\n\nWe also publish a metadata file in the original repository to promote future research in the area. Please checkout the main website",
"### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'text': a 'string' in the form of entity plus sentence.\n* 'label': a string describing the relevant context for entity in the sentence",
"### Data Splits\n\n\nThe dataset is split into train, validation, and test sets. The sizes of the splits are as follows:",
"### Dataset Creation\n\n\nThe dataset was created by scraping annual and quarterly reports of top 1500 LLCs in the world.",
"### Models trained using this dataset\n\n\nThere are several models finetuned using this dataset. They are:\n\n\n1. EDGAR-T5-base\n2. EDGAR-BART-Base\n3. EDGAR-flan-t5-base\n4. EDGAR-T5-Large\n5. EDGAR-Tk-Instruct-Large\n6. Instruction tuned EDGAR-Tk-Instruct-base\n\n\nIf you use this dataset and any other related artifact, please cite the following paper:",
"### Contributions\n\n\nThanks to @him1411 for adding this dataset."
]
| [
42,
78,
53,
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104,
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]
| [
"passage: TAGS\n#size_categories-1M<n<10M #license-mit #financial #ner #context-ner #arxiv-2109.08079 #region-us \n### Data Instances\n\n\nThe dataset includes plain text input-output pairs, where the input is a sentence with an entity and the output is the context for the entity.\n\n\nAn example of a train instance looks as follows:\n\n\nWe also publish a metadata file in the original repository to promote future research in the area. Please checkout the main website### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'text': a 'string' in the form of entity plus sentence.\n* 'label': a string describing the relevant context for entity in the sentence### Data Splits\n\n\nThe dataset is split into train, validation, and test sets. The sizes of the splits are as follows:### Dataset Creation\n\n\nThe dataset was created by scraping annual and quarterly reports of top 1500 LLCs in the world.### Models trained using this dataset\n\n\nThere are several models finetuned using this dataset. They are:\n\n\n1. EDGAR-T5-base\n2. EDGAR-BART-Base\n3. EDGAR-flan-t5-base\n4. EDGAR-T5-Large\n5. EDGAR-Tk-Instruct-Large\n6. Instruction tuned EDGAR-Tk-Instruct-base\n\n\nIf you use this dataset and any other related artifact, please cite the following paper:### Contributions\n\n\nThanks to @him1411 for adding this dataset."
]
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|
19c2c47ffa359f344bdf02aa8df044b83424b938 | # Dataset Card for "en-tafsir-ibn-kathir"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-AI-C/en-tafsir-ibn-kathir | [
"region:us"
]
| 2023-04-01T16:43:47+00:00 | {"dataset_info": {"features": [{"name": "ayah", "dtype": "int64"}, {"name": "sorah", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "en-tafsir-ibn-kathir-html", "dtype": "string"}, {"name": "en-tafsir-ibn-kathir-text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23394825, "num_examples": 6235}], "download_size": 11628786, "dataset_size": 23394825}} | 2023-04-02T15:53:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "en-tafsir-ibn-kathir"
More Information needed | [
"# Dataset Card for \"en-tafsir-ibn-kathir\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"en-tafsir-ibn-kathir\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"en-tafsir-ibn-kathir\"\n\nMore Information needed"
]
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|
2732456b85e71bafc6c35f125b7b8305052b9523 | # Dataset Card for "donut_rus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Akajackson/donut_synthdog_rus | [
"region:us"
]
| 2023-04-01T16:46:40+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8522173356.748, "num_examples": 96204}, {"name": "validation", "num_bytes": 1062440747.78, "num_examples": 11820}, {"name": "test", "num_bytes": 1107229186.768, "num_examples": 11976}], "download_size": 10700638276, "dataset_size": 10691843291.296}} | 2023-04-01T17:52:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "donut_rus"
More Information needed | [
"# Dataset Card for \"donut_rus\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"donut_rus\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"donut_rus\"\n\nMore Information needed"
]
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|
9c1fb65774c6a783c50955938f555c3c3f9e5362 | # Dataset Card for "pycon-usa-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Tuana/pycon-usa-2023 | [
"region:us"
]
| 2023-04-01T18:59:48+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "content_type", "dtype": "string"}, {"name": "meta", "struct": [{"name": "url", "dtype": "string"}, {"name": "_split_id", "dtype": "int64"}]}, {"name": "id_hash_keys", "sequence": "string"}, {"name": "score", "dtype": "null"}, {"name": "embedding", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 175070, "num_examples": 131}], "download_size": 104633, "dataset_size": 175070}} | 2023-04-01T18:59:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pycon-usa-2023"
More Information needed | [
"# Dataset Card for \"pycon-usa-2023\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pycon-usa-2023\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pycon-usa-2023\"\n\nMore Information needed"
]
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|
6f9b78cc1dd15dbb51d3c51ccc219c558962fd77 | # Dataset Card for ShareGPT~~52K~~90K
## Dataset Description
- **Homepage: N/A**
- **Repository: N/A**
- **Paper: N/A**
- **Leaderboard: N/A**
- **Point of Contact:** Ronsor Labs <[email protected]>
### Dataset Summary
This dataset is a collection of approximately ~~52,000~~**90,000** conversations scraped via the ShareGPT API before it was shut down.
These conversations include both user prompts and responses from OpenAI's ChatGPT.
This repository now contains the new 90K conversations version. The previous 52K may
be found in the `old/` directory.
### Supported Tasks and Leaderboards
* text-generation
### Languages
This dataset is expected to primarily consist of messages in English and other Western languages.
## Dataset Structure
### Data Instances
```json
{
"id": "2wtLdZm",
"conversations": [
{
"from": "human",
"value": "Are you familiar with the SAS programming language?"
},
{
"from": "gpt",
"value": "<div class=\"markdown prose w-full break-words dark:prose-invert light\"><p>Yes, I am familiar with the SAS programming language. SAS is a statistical software suite that is widely used in data analysis, data management, and business intelligence applications. It has its own programming language, which is used to manipulate and analyze data, generate reports, and automate data-driven workflows. The language is known for its ease of use and powerful capabilities, and it is commonly used in industries such as healthcare, finance, and government.</p></div>"
},
{
"from": "human",
"value": "Can you write a SAS proc that imports a dataset from a CSV saved on my local machine?"
}
]
}
```
### Data Fields
* **id**: conversation id
* **conversations**: conversation session array
* **from**: ChatGPT ("gpt") or the user ("human")
* **value**: message contents as raw HTML
### Data Splits
N/A
## Dataset Creation
### Curation Rationale
This is a decently large dataset of realistic human-AI conversations which I believe should be released
to the research community.
### Source Data
#### Initial Data Collection and Normalization
This data was collected using the ShareGPT API.
#### Who are the source language producers?
ShareGPT users and OpenAI ChatGPT.
### Annotations
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
This dataset *may* contain personal information, if ShareGPT users were sending such information to
ChatGPT. ChatGPT warns users not to submit personal information to it, however, so without further
evaluation, we believe that this dataset should contain little or no personal information.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset may be used to train models that are competitive with OpenAI's ChatGPT. Please filter
this dataset first, as it may contain canned responses, raw HTML, and other undesirable information.
### Discussion of Biases
This dataset exhibits all the biases of OpenAI's ChatGPT models (GPT-3.5 and GPT-4) as well as the
biases of the users who uploaded the conversations.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
None.
### Licensing Information
**CC0: No Rights Reserved.**
The output of machine learning algorithms is uncopyrightable in the United States and other jurisdictions.
**Additionally, the OpenAI terms of service do not apply to this dataset as users of this dataset
are not accessing the OpenAI service.**
### Citation Information
TODO
### Contributions
These conversations were allegedly scraped by an anonymous user on 4chan.
The 90K version was sourced from [this post](https://boards.4channel.org/g/thread/92487155/lmg-local-models-general-snail-edition#p92490887).
Thanks, anon!
| RyokoAI/ShareGPT52K | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"language:es",
"language:de",
"language:multilingual",
"license:cc0-1.0",
"conversation",
"rlhf",
"chatgpt",
"gpt-3.5",
"region:us"
]
| 2023-04-01T18:59:59+00:00 | {"language": ["en", "es", "de", "multilingual"], "license": "cc0-1.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "ShareGPT 90K Conversations", "tags": ["conversation", "rlhf", "chatgpt", "gpt-3.5"]} | 2023-04-02T12:16:51+00:00 | []
| [
"en",
"es",
"de",
"multilingual"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #language-Spanish #language-German #language-multilingual #license-cc0-1.0 #conversation #rlhf #chatgpt #gpt-3.5 #region-us
| # Dataset Card for ShareGPT~~52K~~90K
## Dataset Description
- Homepage: N/A
- Repository: N/A
- Paper: N/A
- Leaderboard: N/A
- Point of Contact: Ronsor Labs <ronsor@URL>
### Dataset Summary
This dataset is a collection of approximately ~~52,000~~90,000 conversations scraped via the ShareGPT API before it was shut down.
These conversations include both user prompts and responses from OpenAI's ChatGPT.
This repository now contains the new 90K conversations version. The previous 52K may
be found in the 'old/' directory.
### Supported Tasks and Leaderboards
* text-generation
### Languages
This dataset is expected to primarily consist of messages in English and other Western languages.
## Dataset Structure
### Data Instances
### Data Fields
* id: conversation id
* conversations: conversation session array
* from: ChatGPT ("gpt") or the user ("human")
* value: message contents as raw HTML
### Data Splits
N/A
## Dataset Creation
### Curation Rationale
This is a decently large dataset of realistic human-AI conversations which I believe should be released
to the research community.
### Source Data
#### Initial Data Collection and Normalization
This data was collected using the ShareGPT API.
#### Who are the source language producers?
ShareGPT users and OpenAI ChatGPT.
### Annotations
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
This dataset *may* contain personal information, if ShareGPT users were sending such information to
ChatGPT. ChatGPT warns users not to submit personal information to it, however, so without further
evaluation, we believe that this dataset should contain little or no personal information.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset may be used to train models that are competitive with OpenAI's ChatGPT. Please filter
this dataset first, as it may contain canned responses, raw HTML, and other undesirable information.
### Discussion of Biases
This dataset exhibits all the biases of OpenAI's ChatGPT models (GPT-3.5 and GPT-4) as well as the
biases of the users who uploaded the conversations.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
None.
### Licensing Information
CC0: No Rights Reserved.
The output of machine learning algorithms is uncopyrightable in the United States and other jurisdictions.
Additionally, the OpenAI terms of service do not apply to this dataset as users of this dataset
are not accessing the OpenAI service.
TODO
### Contributions
These conversations were allegedly scraped by an anonymous user on 4chan.
The 90K version was sourced from this post.
Thanks, anon!
| [
"# Dataset Card for ShareGPT~~52K~~90K",
"## Dataset Description\n\n- Homepage: N/A \n- Repository: N/A \n- Paper: N/A \n- Leaderboard: N/A \n- Point of Contact: Ronsor Labs <ronsor@URL>",
"### Dataset Summary\n\nThis dataset is a collection of approximately ~~52,000~~90,000 conversations scraped via the ShareGPT API before it was shut down.\nThese conversations include both user prompts and responses from OpenAI's ChatGPT.\n\nThis repository now contains the new 90K conversations version. The previous 52K may\nbe found in the 'old/' directory.",
"### Supported Tasks and Leaderboards\n\n* text-generation",
"### Languages\n\nThis dataset is expected to primarily consist of messages in English and other Western languages.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n* id: conversation id\n* conversations: conversation session array\n * from: ChatGPT (\"gpt\") or the user (\"human\")\n * value: message contents as raw HTML",
"### Data Splits\n\nN/A",
"## Dataset Creation",
"### Curation Rationale\n\nThis is a decently large dataset of realistic human-AI conversations which I believe should be released\nto the research community.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nThis data was collected using the ShareGPT API.",
"#### Who are the source language producers?\n\nShareGPT users and OpenAI ChatGPT.",
"### Annotations",
"#### Annotation process\n\nN/A",
"#### Who are the annotators?\n\nN/A",
"### Personal and Sensitive Information\n\nThis dataset *may* contain personal information, if ShareGPT users were sending such information to\nChatGPT. ChatGPT warns users not to submit personal information to it, however, so without further\nevaluation, we believe that this dataset should contain little or no personal information.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset may be used to train models that are competitive with OpenAI's ChatGPT. Please filter\nthis dataset first, as it may contain canned responses, raw HTML, and other undesirable information.",
"### Discussion of Biases\n\nThis dataset exhibits all the biases of OpenAI's ChatGPT models (GPT-3.5 and GPT-4) as well as the\nbiases of the users who uploaded the conversations.",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nNone.",
"### Licensing Information\n\nCC0: No Rights Reserved.\n\nThe output of machine learning algorithms is uncopyrightable in the United States and other jurisdictions.\nAdditionally, the OpenAI terms of service do not apply to this dataset as users of this dataset\nare not accessing the OpenAI service.\n\n\n\nTODO",
"### Contributions\n\nThese conversations were allegedly scraped by an anonymous user on 4chan.\n\nThe 90K version was sourced from this post.\nThanks, anon!"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #language-Spanish #language-German #language-multilingual #license-cc0-1.0 #conversation #rlhf #chatgpt #gpt-3.5 #region-us \n",
"# Dataset Card for ShareGPT~~52K~~90K",
"## Dataset Description\n\n- Homepage: N/A \n- Repository: N/A \n- Paper: N/A \n- Leaderboard: N/A \n- Point of Contact: Ronsor Labs <ronsor@URL>",
"### Dataset Summary\n\nThis dataset is a collection of approximately ~~52,000~~90,000 conversations scraped via the ShareGPT API before it was shut down.\nThese conversations include both user prompts and responses from OpenAI's ChatGPT.\n\nThis repository now contains the new 90K conversations version. The previous 52K may\nbe found in the 'old/' directory.",
"### Supported Tasks and Leaderboards\n\n* text-generation",
"### Languages\n\nThis dataset is expected to primarily consist of messages in English and other Western languages.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n* id: conversation id\n* conversations: conversation session array\n * from: ChatGPT (\"gpt\") or the user (\"human\")\n * value: message contents as raw HTML",
"### Data Splits\n\nN/A",
"## Dataset Creation",
"### Curation Rationale\n\nThis is a decently large dataset of realistic human-AI conversations which I believe should be released\nto the research community.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nThis data was collected using the ShareGPT API.",
"#### Who are the source language producers?\n\nShareGPT users and OpenAI ChatGPT.",
"### Annotations",
"#### Annotation process\n\nN/A",
"#### Who are the annotators?\n\nN/A",
"### Personal and Sensitive Information\n\nThis dataset *may* contain personal information, if ShareGPT users were sending such information to\nChatGPT. ChatGPT warns users not to submit personal information to it, however, so without further\nevaluation, we believe that this dataset should contain little or no personal information.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset may be used to train models that are competitive with OpenAI's ChatGPT. Please filter\nthis dataset first, as it may contain canned responses, raw HTML, and other undesirable information.",
"### Discussion of Biases\n\nThis dataset exhibits all the biases of OpenAI's ChatGPT models (GPT-3.5 and GPT-4) as well as the\nbiases of the users who uploaded the conversations.",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nNone.",
"### Licensing Information\n\nCC0: No Rights Reserved.\n\nThe output of machine learning algorithms is uncopyrightable in the United States and other jurisdictions.\nAdditionally, the OpenAI terms of service do not apply to this dataset as users of this dataset\nare not accessing the OpenAI service.\n\n\n\nTODO",
"### Contributions\n\nThese conversations were allegedly scraped by an anonymous user on 4chan.\n\nThe 90K version was sourced from this post.\nThanks, anon!"
]
| [
73,
14,
46,
90,
15,
23,
6,
6,
43,
8,
5,
35,
4,
22,
21,
5,
8,
12,
67,
8,
54,
54,
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9,
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| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #language-Spanish #language-German #language-multilingual #license-cc0-1.0 #conversation #rlhf #chatgpt #gpt-3.5 #region-us \n# Dataset Card for ShareGPT~~52K~~90K## Dataset Description\n\n- Homepage: N/A \n- Repository: N/A \n- Paper: N/A \n- Leaderboard: N/A \n- Point of Contact: Ronsor Labs <ronsor@URL>### Dataset Summary\n\nThis dataset is a collection of approximately ~~52,000~~90,000 conversations scraped via the ShareGPT API before it was shut down.\nThese conversations include both user prompts and responses from OpenAI's ChatGPT.\n\nThis repository now contains the new 90K conversations version. The previous 52K may\nbe found in the 'old/' directory.### Supported Tasks and Leaderboards\n\n* text-generation### Languages\n\nThis dataset is expected to primarily consist of messages in English and other Western languages.## Dataset Structure### Data Instances### Data Fields\n\n* id: conversation id\n* conversations: conversation session array\n * from: ChatGPT (\"gpt\") or the user (\"human\")\n * value: message contents as raw HTML### Data Splits\n\nN/A## Dataset Creation### Curation Rationale\n\nThis is a decently large dataset of realistic human-AI conversations which I believe should be released\nto the research community.### Source Data#### Initial Data Collection and Normalization\n\nThis data was collected using the ShareGPT API.#### Who are the source language producers?\n\nShareGPT users and OpenAI ChatGPT.### Annotations#### Annotation process\n\nN/A#### Who are the annotators?\n\nN/A### Personal and Sensitive Information\n\nThis dataset *may* contain personal information, if ShareGPT users were sending such information to\nChatGPT. ChatGPT warns users not to submit personal information to it, however, so without further\nevaluation, we believe that this dataset should contain little or no personal information."
]
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|
01da0c0ba2a545c795d7d7dc051bae8a9266af9e | # Dataset Card for "korra_captioned-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/korra_captioned-augmented | [
"region:us"
]
| 2023-04-01T19:05:27+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": 1437333667.0, "num_examples": 6792}], "download_size": 1436725035, "dataset_size": 1437333667.0}} | 2023-04-01T19:06:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "korra_captioned-augmented"
More Information needed | [
"# Dataset Card for \"korra_captioned-augmented\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"korra_captioned-augmented\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"korra_captioned-augmented\"\n\nMore Information needed"
]
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|
2b6cfcd1f2d1e588f2f88a4e52a13c924269809f | # Dataset Card for "AGEGENDERETHINCITY"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cledoux42/AGEGENDERETHINCITY | [
"license:openrail",
"doi:10.57967/hf/0498",
"region:us"
]
| 2023-04-01T19:27:56+00:00 | {"license": "openrail", "dataset_info": {"features": [{"name": "age_group", "dtype": "string"}, {"name": "ethnicity", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": "float64"}}}], "splits": [{"name": "train", "num_bytes": 26136464129, "num_examples": 18591}], "download_size": 2468184116, "dataset_size": 26136464129}} | 2023-04-01T19:36:23+00:00 | []
| []
| TAGS
#license-openrail #doi-10.57967/hf/0498 #region-us
| # Dataset Card for "AGEGENDERETHINCITY"
More Information needed | [
"# Dataset Card for \"AGEGENDERETHINCITY\"\n\nMore Information needed"
]
| [
"TAGS\n#license-openrail #doi-10.57967/hf/0498 #region-us \n",
"# Dataset Card for \"AGEGENDERETHINCITY\"\n\nMore Information needed"
]
| [
24,
16
]
| [
"passage: TAGS\n#license-openrail #doi-10.57967/hf/0498 #region-us \n# Dataset Card for \"AGEGENDERETHINCITY\"\n\nMore Information needed"
]
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|
b44304d7a082b3d9d390ef171ffca36eb37fb5ee |
# Dataset Card for "relbert/analogy_questions"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://aclanthology.org/2021.acl-long.280/](https://aclanthology.org/2021.acl-long.280/)
- **Dataset:** Analogy Questions
### Dataset Summary
This dataset contains 5 different word analogy questions used in [Analogy Language Model](https://aclanthology.org/2021.acl-long.280/).
- original analogy questions
| name | Size (valid/test) | Num of choice | Num of relation group | Original Reference |
|-----------|------------------:|--------------:|----------------------:|:--------------------------------------------------------------------------:|
| `sat_full`| -/374 | 5 | 2 | [Turney (2005)](https://arxiv.org/pdf/cs/0508053.pdf) |
| `sat` | 37/337 | 5 | 2 | [Turney (2005)](https://arxiv.org/pdf/cs/0508053.pdf) |
| `u2` | 24/228 | 5,4,3 | 9 | [EnglishForEveryone](https://englishforeveryone.org/Topics/Analogies.html) |
| `u4` | 48/432 | 5,4,3 | 5 | [EnglishForEveryone](https://englishforeveryone.org/Topics/Analogies.html) |
| `google` | 50/500 | 4 | 2 | [Mikolov et al., (2013)](https://www.aclweb.org/anthology/N13-1090.pdf) |
| `bats` | 199/1799 | 4 | 3 | [Gladkova et al., (2016)](https://www.aclweb.org/anthology/N18-2017.pdf) |
- extra analogy questions
| name | Size (valid/test) | Num of choice (valid/test) | Num of relation group (valid/test) | Original Reference |
|:------------------------------------|:--------------------|:-----------------------------|:-------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|
| `semeval2012_relational_similarity` | 79/- | 3/- | 79/- | [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) |
| `t_rex_relational_similarity` | 496/183 | 74/48 | 60/19 | [relbert/t_rex_relational_similarity](https://huggingface.co/datasets/relbert/t_rex_relational_similarity) |
| `conceptnet_relational_similarity` | 1112/1192 | 19/17 | 18/16 | [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity) |
| `nell_relational_similarity` | 400/600 | 5/7 | 4/6 | [relbert/nell_relational_similarity](https://huggingface.co/datasets/relbert/nell_relational_similarity) |
| `scan` | 178/1616 | 3,36,136,10,45,78,15,21,55,120,153,91,28/3,36,136,10,45,78,15,21,55,120,153,91,28 | 2/2 | [relbert/scientific_and_creative_analogy](https://huggingface.co/datasets/relbert/scientific_and_creative_analogy) |
## Dataset Structure
### Data Instances
An example of `test` looks as follows.
```
{
"stem": ["raphael", "painter"],
"answer": 2,
"choice": [["andersen", "plato"],
["reading", "berkshire"],
["marx", "philosopher"],
["tolstoi", "edison"]]
}
```
The `stem` is the query word pair, `choice` has word pair candidates,
and `answer` indicates the index of correct candidate which starts from `0`.
All data is lowercased except Google dataset.
### Citation Information
```
@inproceedings{ushio-etal-2021-bert-is,
title ={{BERT} is to {NLP} what {A}lex{N}et is to {CV}: {C}an {P}re-{T}rained {L}anguage {M}odels {I}dentify {A}nalogies?},
author={Ushio, Asahi and
Espinosa-Anke, Luis and
Schockaert, Steven and
Camacho-Collados, Jose},
booktitle={Proceedings of the {ACL}-{IJCNLP} 2021 Main Conference},
year={2021},
publisher={Association for Computational Linguistics}
}
```
### LICENSE
The LICENSE of all the resources are under [CC-BY-NC-4.0](./LICENSE). Thus, they are freely available for academic purpose or individual research, but restricted for commercial use.
| relbert/analogy_questions_private | [
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:other",
"region:us"
]
| 2023-04-01T20:13:15+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "pretty_name": "Analogy Question"} | 2023-04-02T14:07:46+00:00 | []
| [
"en"
]
| TAGS
#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us
| Dataset Card for "relbert/analogy\_questions"
=============================================
Dataset Description
-------------------
* Repository: RelBERT
* Paper: URL
* Dataset: Analogy Questions
### Dataset Summary
This dataset contains 5 different word analogy questions used in Analogy Language Model.
* original analogy questions
* extra analogy questions
Dataset Structure
-----------------
### Data Instances
An example of 'test' looks as follows.
The 'stem' is the query word pair, 'choice' has word pair candidates,
and 'answer' indicates the index of correct candidate which starts from '0'.
All data is lowercased except Google dataset.
### LICENSE
The LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use.
| [
"### Dataset Summary\n\n\nThis dataset contains 5 different word analogy questions used in Analogy Language Model.\n\n\n* original analogy questions\n\n\n\n* extra analogy questions\n\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of 'test' looks as follows.\n\n\nThe 'stem' is the query word pair, 'choice' has word pair candidates,\nand 'answer' indicates the index of correct candidate which starts from '0'.\nAll data is lowercased except Google dataset.",
"### LICENSE\n\n\nThe LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use."
]
| [
"TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n",
"### Dataset Summary\n\n\nThis dataset contains 5 different word analogy questions used in Analogy Language Model.\n\n\n* original analogy questions\n\n\n\n* extra analogy questions\n\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of 'test' looks as follows.\n\n\nThe 'stem' is the query word pair, 'choice' has word pair candidates,\nand 'answer' indicates the index of correct candidate which starts from '0'.\nAll data is lowercased except Google dataset.",
"### LICENSE\n\n\nThe LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use."
]
| [
33,
42,
70,
45
]
| [
"passage: TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n### Dataset Summary\n\n\nThis dataset contains 5 different word analogy questions used in Analogy Language Model.\n\n\n* original analogy questions\n\n\n\n* extra analogy questions\n\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'test' looks as follows.\n\n\nThe 'stem' is the query word pair, 'choice' has word pair candidates,\nand 'answer' indicates the index of correct candidate which starts from '0'.\nAll data is lowercased except Google dataset.### LICENSE\n\n\nThe LICENSE of all the resources are under CC-BY-NC-4.0. Thus, they are freely available for academic purpose or individual research, but restricted for commercial use."
]
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|
8ae25844b62cf014d33b85d4f7c6fda5e025ea0d | # Dataset Card for "controlnet_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | moooji/controlnet_test | [
"region:us"
]
| 2023-04-01T20:55:25+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "input", "1": "output"}}}}], "splits": [{"name": "train", "num_bytes": 32236.0, "num_examples": 2}], "download_size": 32753, "dataset_size": 32236.0}} | 2023-04-01T20:55:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "controlnet_test"
More Information needed | [
"# Dataset Card for \"controlnet_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"controlnet_test\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"controlnet_test\"\n\nMore Information needed"
]
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|
6e2be7ad86f85acf9f2bf13da5c0327adbacc53e | # Dataset Card for "controlnet_test2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | moooji/controlnet_test2 | [
"region:us"
]
| 2023-04-01T21:03:32+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "input", "1": "output"}}}}], "splits": [{"name": "train", "num_bytes": 32236.0, "num_examples": 2}], "download_size": 0, "dataset_size": 32236.0}} | 2023-04-01T21:05:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "controlnet_test2"
More Information needed | [
"# Dataset Card for \"controlnet_test2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"controlnet_test2\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"controlnet_test2\"\n\nMore Information needed"
]
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|
4d897841ba754b91797ded5313691294ca7da127 | # Dataset Card for "controlnet_test3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | moooji/controlnet_test3 | [
"region:us"
]
| 2023-04-01T21:07:03+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "image"}, {"name": "target", "dtype": "image"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32228.0, "num_examples": 1}], "download_size": 33477, "dataset_size": 32228.0}} | 2023-04-01T21:07:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "controlnet_test3"
More Information needed | [
"# Dataset Card for \"controlnet_test3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"controlnet_test3\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"controlnet_test3\"\n\nMore Information needed"
]
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|
08f54aa2f5847ac28e3cde947ca2b972cd42e2b5 | # Dataset Card for "controlnet_test4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | moooji/controlnet_test4 | [
"region:us"
]
| 2023-04-01T21:14:24+00:00 | {"dataset_info": {"features": [{"name": "conditioning_image", "dtype": "image"}, {"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 450892980.0, "num_examples": 10000}], "download_size": 0, "dataset_size": 450892980.0}} | 2023-04-01T21:24:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "controlnet_test4"
More Information needed | [
"# Dataset Card for \"controlnet_test4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"controlnet_test4\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"controlnet_test4\"\n\nMore Information needed"
]
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|
381883cdce526a0b6beaf7c0213532fc27f12faf | # CIVQA TesseractOCR Dataset
The Czech Invoice Visual Question Answering dataset was created with Tesseract OCR, and it is suitable for adding labels for the chosen model.
The encoded dataset for LayoutLM model can be found on this link: https://huggingface.co/datasets/fimu-docproc-research/CIVQA-TesseractOCR-LayoutLM
All invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.
- Invoice number
- Variable symbol
- Specific symbol
- Constant symbol
- Bank code
- Account number
- ICO
- Total amount
- Invoice date
- Due date
- Name of supplier
- IBAN
- DIC
- QR code
- Supplier's address
The invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: https://forms.gle/tUVJKoB22oeTncUD6
We profoundly appreciate your cooperation and understanding in this matter. | fimu-docproc-research/CIVQA-TesseractOCR | [
"language:cs",
"license:mit",
"finance",
"region:us"
]
| 2023-04-01T21:20:25+00:00 | {"language": ["cs"], "license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "answers", "dtype": "string"}, {"name": "bboxes", "sequence": {"sequence": "float64"}}, {"name": "answers_bboxes", "sequence": {"sequence": "float64"}}, {"name": "questions", "dtype": "string"}, {"name": "image", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1838637418, "num_examples": 160645}, {"name": "validation", "num_bytes": 177768370, "num_examples": 16220}], "download_size": 6274981, "dataset_size": 2016405788}, "tags": ["finance"]} | 2023-11-21T20:47:03+00:00 | []
| [
"cs"
]
| TAGS
#language-Czech #license-mit #finance #region-us
| # CIVQA TesseractOCR Dataset
The Czech Invoice Visual Question Answering dataset was created with Tesseract OCR, and it is suitable for adding labels for the chosen model.
The encoded dataset for LayoutLM model can be found on this link: URL
All invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.
- Invoice number
- Variable symbol
- Specific symbol
- Constant symbol
- Bank code
- Account number
- ICO
- Total amount
- Invoice date
- Due date
- Name of supplier
- IBAN
- DIC
- QR code
- Supplier's address
The invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: URL
We profoundly appreciate your cooperation and understanding in this matter. | [
"# CIVQA TesseractOCR Dataset\n\nThe Czech Invoice Visual Question Answering dataset was created with Tesseract OCR, and it is suitable for adding labels for the chosen model. \nThe encoded dataset for LayoutLM model can be found on this link: URL\n\nAll invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.\n- Invoice number\n- Variable symbol\n- Specific symbol\n- Constant symbol\n- Bank code\n- Account number\n- ICO\n- Total amount\n- Invoice date\n- Due date\n- Name of supplier\n- IBAN\n- DIC\n- QR code\n- Supplier's address\n\nThe invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: URL\n\nWe profoundly appreciate your cooperation and understanding in this matter."
]
| [
"TAGS\n#language-Czech #license-mit #finance #region-us \n",
"# CIVQA TesseractOCR Dataset\n\nThe Czech Invoice Visual Question Answering dataset was created with Tesseract OCR, and it is suitable for adding labels for the chosen model. \nThe encoded dataset for LayoutLM model can be found on this link: URL\n\nAll invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.\n- Invoice number\n- Variable symbol\n- Specific symbol\n- Constant symbol\n- Bank code\n- Account number\n- ICO\n- Total amount\n- Invoice date\n- Due date\n- Name of supplier\n- IBAN\n- DIC\n- QR code\n- Supplier's address\n\nThe invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: URL\n\nWe profoundly appreciate your cooperation and understanding in this matter."
]
| [
20,
263
]
| [
"passage: TAGS\n#language-Czech #license-mit #finance #region-us \n# CIVQA TesseractOCR Dataset\n\nThe Czech Invoice Visual Question Answering dataset was created with Tesseract OCR, and it is suitable for adding labels for the chosen model. \nThe encoded dataset for LayoutLM model can be found on this link: URL\n\nAll invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.\n- Invoice number\n- Variable symbol\n- Specific symbol\n- Constant symbol\n- Bank code\n- Account number\n- ICO\n- Total amount\n- Invoice date\n- Due date\n- Name of supplier\n- IBAN\n- DIC\n- QR code\n- Supplier's address\n\nThe invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: URL\n\nWe profoundly appreciate your cooperation and understanding in this matter."
]
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|
23d5bd108ebe488172f95c09a81928c71d4c23d0 | # Dataset Card for "en-tafsir-maarif"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-AI-C/en-tafsir-maarif | [
"region:us"
]
| 2023-04-01T21:30:07+00:00 | {"dataset_info": {"features": [{"name": "ayah", "dtype": "int64"}, {"name": "sorah", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "en-tafsir-maarif-html", "dtype": "string"}, {"name": "en-tafsir-maarif-text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24431167, "num_examples": 6235}], "download_size": 13388809, "dataset_size": 24431167}} | 2023-04-02T16:07:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "en-tafsir-maarif"
More Information needed | [
"# Dataset Card for \"en-tafsir-maarif\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"en-tafsir-maarif\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"en-tafsir-maarif\"\n\nMore Information needed"
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|
8b2647259bf106afd5fed6923b96a0e47ffd1b0b | # Dataset Card for "ktrd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | razhan/ktr | [
"region:us"
]
| 2023-04-01T21:34:28+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5587778741.768, "num_examples": 2499792}], "download_size": 7538989078, "dataset_size": 5587778741.768}} | 2023-04-01T21:50:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ktrd"
More Information needed | [
"# Dataset Card for \"ktrd\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ktrd\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ktrd\"\n\nMore Information needed"
]
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|
4aa3b5fc25f946b5b2f434088dc182d951a46741 | # Dataset Card for "en-tafsir-mokhtasar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-AI-C/en-tafsir-mokhtasar | [
"region:us"
]
| 2023-04-01T21:36:06+00:00 | {"dataset_info": {"features": [{"name": "ayah", "dtype": "int64"}, {"name": "sorah", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "en-tafsir-mokhtasar-html", "dtype": "string"}, {"name": "en-tafsir-mokhtasar-text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4976739, "num_examples": 6235}], "download_size": 2479802, "dataset_size": 4976739}} | 2023-04-01T21:46:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "en-tafsir-mokhtasar"
More Information needed | [
"# Dataset Card for \"en-tafsir-mokhtasar\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"en-tafsir-mokhtasar\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"en-tafsir-mokhtasar\"\n\nMore Information needed"
]
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|
4fa6b18e4b2d9421b5ad4a4360488d5e434ad076 | # Evaporate
Datasets for paper "Evaporate: Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes".
| hazyresearch/evaporate | [
"region:us"
]
| 2023-04-01T22:03:44+00:00 | {} | 2023-04-20T01:27:07+00:00 | []
| []
| TAGS
#region-us
| # Evaporate
Datasets for paper "Evaporate: Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes".
| [
"# Evaporate\n\nDatasets for paper \"Evaporate: Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes\"."
]
| [
"TAGS\n#region-us \n",
"# Evaporate\n\nDatasets for paper \"Evaporate: Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes\"."
]
| [
6,
38
]
| [
"passage: TAGS\n#region-us \n# Evaporate\n\nDatasets for paper \"Evaporate: Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes\"."
]
| [
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|
107ffb1f6efca6af9f4aeaa929e64b913af98c03 |
# Dataset Card for ScribbleHub17K
*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*
## Dataset Description
- **Homepage:** (TODO)
- **Repository:** <https://github.com/RyokoAI/BigKnow2022>
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** Ronsor/undeleted <[email protected]>
### Dataset Summary
ScribbleHub17K is a dataset consisting of text from over 373,000 chapters across approximately 17,500 series posted on the
original story sharing site [Scribble Hub](https://scribblehub.com).
### Supported Tasks and Leaderboards
This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.
* text-classification
* text-generation
### Languages
* English
## Dataset Structure
### Data Instances
```json
{
"text": " \n2082 Planet Earth the Fracture War, after a sudden fracture in our dimension unidentified beings with advance technology and u...",
"meta": {
"subset": "scribblehub",
"series": "3811",
"id": "3812",
"q": 0.91,
"title": "The First - Prologue- The Fracture War",
"author": "RobotLove",
"chapters": 1,
"rating": 5,
"rating_ct": 1,
"genre": [
"Action",
"Martial Arts",
"Romance"
],
"tags": [
"Kingdom Building",
"Loyal Subordinates",
"Male Protagonist",
"Organized Crime",
"Scheming"
]
}
}
{
"text": " For anyone that may see this, thanks for reading. I'm just here to see if a story can spill out of my mind if just start writin...",
"meta": {
"subset": "scribblehub",
"series": "586090",
"id": "586099",
"q": 0.82,
"title": "Just writing to write…i guess? - I’m here now",
"author": "BigOofStudios",
"chapters": 1,
"rating": 4.5,
"rating_ct": 2,
"genre": [
"Action",
"Comedy"
],
"tags": []
}
}
```
### Data Fields
* `text`: the actual chapter text
* `meta`: metadata for chapter and series
* `subset`: data source tag: `scribblehub`
* `series`: series ID
* `id`: chapter ID
* `lang`: always `en` (English)
* `q`: quality score (q-score) between (0.0) terrible and 1.0 (perfect); anything with a score `> 0.5` is generally good enough
* `title`: chapter and series title in the format `<chapter title> - <series title>`
* `chapters`: total number of chapters in the series
* `rating`: Scribble Hub rating between 0 and 5 stars
* `rating_ct`: number of ratings
* `author`: author name
* `genre`: array of Scribble Hub genres for the series
* `tags`: array of tags for the series
#### Q-Score Distribution
```
0.00: 0
0.10: 0
0.20: 0
0.30: 84
0.40: 718
0.50: 3775
0.60: 22300
0.70: 72581
0.80: 137982
0.90: 135800
1.00: 59
```
### Data Splits
No splitting of the data was performed.
## Dataset Creation
### Curation Rationale
Scribble Hub is a home for original web stories, effectively a smaller, English version of Japan's Syosetuka ni Narou. As a
result, it is a good source for reasonably well written creative content.
### Source Data
#### Initial Data Collection and Normalization
TODO
#### Who are the source language producers?
The authors of each novel.
### Annotations
#### Annotation process
Title, ratings, and other metadata were parsed out using scripts that will be provided in the BigKnow2022 GitHub repository.
#### Who are the annotators?
No human annotators.
### Personal and Sensitive Information
The dataset contains only works of fiction, and we do not believe it contains any PII.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content.
It may also be useful for other languages depending on your language model.
### Discussion of Biases
This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect
the biases of those authors. **Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.**
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
Ronsor Labs
### Licensing Information
Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is
distributed under fair use principles.
### Citation Information
```
@misc{ryokoai2023-bigknow2022,
title = {BigKnow2022: Bringing Language Models Up to Speed},
author = {Ronsor},
year = {2023},
howpublished = {\url{https://github.com/RyokoAI/BigKnow2022}},
}
```
### Contributions
Thanks to @ronsor (GH) for gathering this dataset. | RyokoAI/ScribbleHub17K | [
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"novel",
"training",
"story",
"region:us"
]
| 2023-04-01T22:34:11+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification", "text-generation"], "pretty_name": "ScribbleHub17K", "tags": ["novel", "training", "story"]} | 2023-04-03T22:21:16+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #novel #training #story #region-us
|
# Dataset Card for ScribbleHub17K
*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*
## Dataset Description
- Homepage: (TODO)
- Repository: <URL
- Paper: N/A
- Leaderboard: N/A
- Point of Contact: Ronsor/undeleted <ronsor@URL>
### Dataset Summary
ScribbleHub17K is a dataset consisting of text from over 373,000 chapters across approximately 17,500 series posted on the
original story sharing site Scribble Hub.
### Supported Tasks and Leaderboards
This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.
* text-classification
* text-generation
### Languages
* English
## Dataset Structure
### Data Instances
### Data Fields
* 'text': the actual chapter text
* 'meta': metadata for chapter and series
* 'subset': data source tag: 'scribblehub'
* 'series': series ID
* 'id': chapter ID
* 'lang': always 'en' (English)
* 'q': quality score (q-score) between (0.0) terrible and 1.0 (perfect); anything with a score '> 0.5' is generally good enough
* 'title': chapter and series title in the format '<chapter title> - <series title>'
* 'chapters': total number of chapters in the series
* 'rating': Scribble Hub rating between 0 and 5 stars
* 'rating_ct': number of ratings
* 'author': author name
* 'genre': array of Scribble Hub genres for the series
* 'tags': array of tags for the series
#### Q-Score Distribution
### Data Splits
No splitting of the data was performed.
## Dataset Creation
### Curation Rationale
Scribble Hub is a home for original web stories, effectively a smaller, English version of Japan's Syosetuka ni Narou. As a
result, it is a good source for reasonably well written creative content.
### Source Data
#### Initial Data Collection and Normalization
TODO
#### Who are the source language producers?
The authors of each novel.
### Annotations
#### Annotation process
Title, ratings, and other metadata were parsed out using scripts that will be provided in the BigKnow2022 GitHub repository.
#### Who are the annotators?
No human annotators.
### Personal and Sensitive Information
The dataset contains only works of fiction, and we do not believe it contains any PII.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content.
It may also be useful for other languages depending on your language model.
### Discussion of Biases
This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect
the biases of those authors. Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
Ronsor Labs
### Licensing Information
Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is
distributed under fair use principles.
### Contributions
Thanks to @ronsor (GH) for gathering this dataset. | [
"# Dataset Card for ScribbleHub17K\n\n*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*",
"## Dataset Description\n\n- Homepage: (TODO)\n- Repository: <URL\n- Paper: N/A \n- Leaderboard: N/A\n- Point of Contact: Ronsor/undeleted <ronsor@URL>",
"### Dataset Summary\n\nScribbleHub17K is a dataset consisting of text from over 373,000 chapters across approximately 17,500 series posted on the\noriginal story sharing site Scribble Hub.",
"### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.\n\n* text-classification\n* text-generation",
"### Languages\n\n* English",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n* 'text': the actual chapter text\n* 'meta': metadata for chapter and series\n * 'subset': data source tag: 'scribblehub'\n * 'series': series ID\n * 'id': chapter ID\n * 'lang': always 'en' (English)\n * 'q': quality score (q-score) between (0.0) terrible and 1.0 (perfect); anything with a score '> 0.5' is generally good enough\n * 'title': chapter and series title in the format '<chapter title> - <series title>'\n * 'chapters': total number of chapters in the series\n * 'rating': Scribble Hub rating between 0 and 5 stars\n * 'rating_ct': number of ratings\n * 'author': author name\n * 'genre': array of Scribble Hub genres for the series\n * 'tags': array of tags for the series",
"#### Q-Score Distribution",
"### Data Splits\n\nNo splitting of the data was performed.",
"## Dataset Creation",
"### Curation Rationale\n\nScribble Hub is a home for original web stories, effectively a smaller, English version of Japan's Syosetuka ni Narou. As a\nresult, it is a good source for reasonably well written creative content.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nTODO",
"#### Who are the source language producers?\n\nThe authors of each novel.",
"### Annotations",
"#### Annotation process\n\nTitle, ratings, and other metadata were parsed out using scripts that will be provided in the BigKnow2022 GitHub repository.",
"#### Who are the annotators?\n\nNo human annotators.",
"### Personal and Sensitive Information\n\nThe dataset contains only works of fiction, and we do not believe it contains any PII.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset is intended to be useful for anyone who wishes to train a model to generate \"more entertaining\" content.\nIt may also be useful for other languages depending on your language model.",
"### Discussion of Biases\n\nThis dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect\nthe biases of those authors. Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nRonsor Labs",
"### Licensing Information\n\nApache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is\ndistributed under fair use principles.",
"### Contributions\n\nThanks to @ronsor (GH) for gathering this dataset."
]
| [
"TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #novel #training #story #region-us \n",
"# Dataset Card for ScribbleHub17K\n\n*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*",
"## Dataset Description\n\n- Homepage: (TODO)\n- Repository: <URL\n- Paper: N/A \n- Leaderboard: N/A\n- Point of Contact: Ronsor/undeleted <ronsor@URL>",
"### Dataset Summary\n\nScribbleHub17K is a dataset consisting of text from over 373,000 chapters across approximately 17,500 series posted on the\noriginal story sharing site Scribble Hub.",
"### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.\n\n* text-classification\n* text-generation",
"### Languages\n\n* English",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n* 'text': the actual chapter text\n* 'meta': metadata for chapter and series\n * 'subset': data source tag: 'scribblehub'\n * 'series': series ID\n * 'id': chapter ID\n * 'lang': always 'en' (English)\n * 'q': quality score (q-score) between (0.0) terrible and 1.0 (perfect); anything with a score '> 0.5' is generally good enough\n * 'title': chapter and series title in the format '<chapter title> - <series title>'\n * 'chapters': total number of chapters in the series\n * 'rating': Scribble Hub rating between 0 and 5 stars\n * 'rating_ct': number of ratings\n * 'author': author name\n * 'genre': array of Scribble Hub genres for the series\n * 'tags': array of tags for the series",
"#### Q-Score Distribution",
"### Data Splits\n\nNo splitting of the data was performed.",
"## Dataset Creation",
"### Curation Rationale\n\nScribble Hub is a home for original web stories, effectively a smaller, English version of Japan's Syosetuka ni Narou. As a\nresult, it is a good source for reasonably well written creative content.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nTODO",
"#### Who are the source language producers?\n\nThe authors of each novel.",
"### Annotations",
"#### Annotation process\n\nTitle, ratings, and other metadata were parsed out using scripts that will be provided in the BigKnow2022 GitHub repository.",
"#### Who are the annotators?\n\nNo human annotators.",
"### Personal and Sensitive Information\n\nThe dataset contains only works of fiction, and we do not believe it contains any PII.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset is intended to be useful for anyone who wishes to train a model to generate \"more entertaining\" content.\nIt may also be useful for other languages depending on your language model.",
"### Discussion of Biases\n\nThis dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect\nthe biases of those authors. Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nRonsor Labs",
"### Licensing Information\n\nApache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is\ndistributed under fair use principles.",
"### Contributions\n\nThanks to @ronsor (GH) for gathering this dataset."
]
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6,
6,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #novel #training #story #region-us \n# Dataset Card for ScribbleHub17K\n\n*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*## Dataset Description\n\n- Homepage: (TODO)\n- Repository: <URL\n- Paper: N/A \n- Leaderboard: N/A\n- Point of Contact: Ronsor/undeleted <ronsor@URL>### Dataset Summary\n\nScribbleHub17K is a dataset consisting of text from over 373,000 chapters across approximately 17,500 series posted on the\noriginal story sharing site Scribble Hub.### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.\n\n* text-classification\n* text-generation### Languages\n\n* English## Dataset Structure### Data Instances### Data Fields\n\n* 'text': the actual chapter text\n* 'meta': metadata for chapter and series\n * 'subset': data source tag: 'scribblehub'\n * 'series': series ID\n * 'id': chapter ID\n * 'lang': always 'en' (English)\n * 'q': quality score (q-score) between (0.0) terrible and 1.0 (perfect); anything with a score '> 0.5' is generally good enough\n * 'title': chapter and series title in the format '<chapter title> - <series title>'\n * 'chapters': total number of chapters in the series\n * 'rating': Scribble Hub rating between 0 and 5 stars\n * 'rating_ct': number of ratings\n * 'author': author name\n * 'genre': array of Scribble Hub genres for the series\n * 'tags': array of tags for the series#### Q-Score Distribution### Data Splits\n\nNo splitting of the data was performed.## Dataset Creation"
]
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|
ed93fea3bf928839098758e485f6b12b6dfa472b | # Dataset Card for "somos-clean-alpaca-es-validations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | elcuazcode/somos-clean-alpaca-es-validations | [
"region:us"
]
| 2023-04-01T22:42:55+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", "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": 19216, "num_examples": 1}], "download_size": 0, "dataset_size": 19216}} | 2023-06-12T03:13:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "somos-clean-alpaca-es-validations"
More Information needed | [
"# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed"
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|
c96dadf4ad42536088860187385b985002f6ff9c | # Dataset Card for "instruct_sft_data_without_oig"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pvduy/instruct_sft_data_without_oig | [
"region:us"
]
| 2023-04-01T22:51:16+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 833363419, "num_examples": 525449}], "download_size": 402455199, "dataset_size": 833363419}} | 2023-04-01T22:52:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instruct_sft_data_without_oig"
More Information needed | [
"# Dataset Card for \"instruct_sft_data_without_oig\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instruct_sft_data_without_oig\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instruct_sft_data_without_oig\"\n\nMore Information needed"
]
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|
75b8d2a3d513d451e3bd4a379f224ac0fbae461e | # Dataset Card for "qa_pairs_2nd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tulip4attoo/qa_pairs_2nd | [
"region:us"
]
| 2023-04-02T00:45:04+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19245176, "num_examples": 51796}], "download_size": 12215984, "dataset_size": 19245176}} | 2023-04-02T00:45:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "qa_pairs_2nd"
More Information needed | [
"# Dataset Card for \"qa_pairs_2nd\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"qa_pairs_2nd\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"qa_pairs_2nd\"\n\nMore Information needed"
]
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|
a7179b25c7fc8c1e5aff415f42b53f84e9083970 | Leaderboard: mitoem.grand-challenge.org
---
license: mit
task_categories:
- image-segmentation
language:
- en
pretty_name: MitoEM
size_categories:
- 1B<n<10B
--- | pytc/MitoEM | [
"region:us"
]
| 2023-04-02T00:46:46+00:00 | {} | 2023-04-02T00:55:49+00:00 | []
| []
| TAGS
#region-us
| Leaderboard: URL
---
license: mit
task_categories:
- image-segmentation
language:
- en
pretty_name: MitoEM
size_categories:
- 1B<n<10B
--- | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
567ab7786247a4dde33df2f4ea0749982763e2d0 | # Dataset Card for "qa_pairs_3rd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tulip4attoo/qa_pairs_3rd | [
"region:us"
]
| 2023-04-02T00:53:17+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5757757, "num_examples": 15596}], "download_size": 3660410, "dataset_size": 5757757}} | 2023-04-02T00:53:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "qa_pairs_3rd"
More Information needed | [
"# Dataset Card for \"qa_pairs_3rd\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"qa_pairs_3rd\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"qa_pairs_3rd\"\n\nMore Information needed"
]
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|
62b2553d95886db5bcc51546589d5a906351f91d |
# Dataset Card for Honeyfeed3600
*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*
## Dataset Description
- **Homepage:** (TODO)
- **Repository:** <https://github.com/RyokoAI/BigKnow2022>
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** Ronsor/undeleted <[email protected]>
### Dataset Summary
Honeyfeed3600 is a dataset consisting of text from over 38,000 chapters across approximately 3,600 series posted on the
English-language web novel site [Honeyfeed](https://www.honeyfeed.fm).
### Supported Tasks and Leaderboards
This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.
* text-classification
* text-generation
### Languages
* English
## Dataset Structure
### Data Instances
```json
{
"text": "Dark, black, nothingness. There are so many ways to describe that hole, but nothing would get me down there...","
"meta": {
"subset": "honeyfeed",
"themes": [],
"my_themes": [],
"prompt": "",
"author": "Lucianael",
"novel": "10009",
"id": "55686",
"title": "13 Steps - 13 Steps",
"likes": 4,
"views": 21,
"q": 0.5999999999999999
}
}
```
### Data Fields
* `text`: the actual chapter text
* `meta`: novel and chapter metadata
* `subset`: dataset tag: `honeyfeed`
* `lang`: dataset language: `en` (English)
* `themes`: array of novel themes
* `my_themes`: array of additional novel themes
* `prompt`: writing prompt
* `author`: author name
* `novel`: novel ID
* `id`: chapter ID
* `title`: novel and chapter title in the form `<chapter title> - <novel title>`
* `likes`: novel like count
* `views`: novel view count
* `q`: q-score (quality score)
#### Q-Score Distribution
```
0.00: 499
0.10: 420
0.20: 2562
0.30: 0
0.40: 0
0.50: 13344
0.60: 9021
0.70: 5997
0.80: 4217
0.90: 1931
1.00: 801
```
### Data Splits
No splitting of the data was performed.
## Dataset Creation
### Curation Rationale
TODO
### Source Data
#### Initial Data Collection and Normalization
TODO
#### Who are the source language producers?
The authors of each novel.
### Annotations
#### Annotation process
Chapter and novel titles were scraped alongside chapter text.
#### Who are the annotators?
No human annotators.
### Personal and Sensitive Information
The dataset contains only works of fiction, and we do not believe it contains any PII.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content.
It may also be useful for other languages depending on your language model.
### Discussion of Biases
This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect
the biases of those authors. Beware of stereotypes.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
Ronsor Labs
### Licensing Information
Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is
distributed under fair use principles.
### Citation Information
```
@misc{ryokoai2023-bigknow2022,
title = {BigKnow2022: Bringing Language Models Up to Speed},
author = {Ronsor},
year = {2023},
howpublished = {\url{https://github.com/RyokoAI/BigKnow2022}},
}
```
### Contributions
Thanks to @ronsor (GH) for gathering this dataset. | RyokoAI/Honeyfeed3600 | [
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"novel",
"training",
"story",
"region:us"
]
| 2023-04-02T01:24:54+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "text-generation"], "pretty_name": "Honeyfeed3600", "tags": ["novel", "training", "story"]} | 2023-04-05T00:01:36+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-apache-2.0 #novel #training #story #region-us
|
# Dataset Card for Honeyfeed3600
*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*
## Dataset Description
- Homepage: (TODO)
- Repository: <URL
- Paper: N/A
- Leaderboard: N/A
- Point of Contact: Ronsor/undeleted <ronsor@URL>
### Dataset Summary
Honeyfeed3600 is a dataset consisting of text from over 38,000 chapters across approximately 3,600 series posted on the
English-language web novel site Honeyfeed.
### Supported Tasks and Leaderboards
This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.
* text-classification
* text-generation
### Languages
* English
## Dataset Structure
### Data Instances
### Data Fields
* 'text': the actual chapter text
* 'meta': novel and chapter metadata
* 'subset': dataset tag: 'honeyfeed'
* 'lang': dataset language: 'en' (English)
* 'themes': array of novel themes
* 'my_themes': array of additional novel themes
* 'prompt': writing prompt
* 'author': author name
* 'novel': novel ID
* 'id': chapter ID
* 'title': novel and chapter title in the form '<chapter title> - <novel title>'
* 'likes': novel like count
* 'views': novel view count
* 'q': q-score (quality score)
#### Q-Score Distribution
### Data Splits
No splitting of the data was performed.
## Dataset Creation
### Curation Rationale
TODO
### Source Data
#### Initial Data Collection and Normalization
TODO
#### Who are the source language producers?
The authors of each novel.
### Annotations
#### Annotation process
Chapter and novel titles were scraped alongside chapter text.
#### Who are the annotators?
No human annotators.
### Personal and Sensitive Information
The dataset contains only works of fiction, and we do not believe it contains any PII.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content.
It may also be useful for other languages depending on your language model.
### Discussion of Biases
This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect
the biases of those authors. Beware of stereotypes.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
Ronsor Labs
### Licensing Information
Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is
distributed under fair use principles.
### Contributions
Thanks to @ronsor (GH) for gathering this dataset. | [
"# Dataset Card for Honeyfeed3600\n\n*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*",
"## Dataset Description\n\n- Homepage: (TODO)\n- Repository: <URL\n- Paper: N/A \n- Leaderboard: N/A\n- Point of Contact: Ronsor/undeleted <ronsor@URL>",
"### Dataset Summary\n\nHoneyfeed3600 is a dataset consisting of text from over 38,000 chapters across approximately 3,600 series posted on the\nEnglish-language web novel site Honeyfeed.",
"### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.\n\n* text-classification\n* text-generation",
"### Languages\n\n* English",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n* 'text': the actual chapter text\n* 'meta': novel and chapter metadata\n * 'subset': dataset tag: 'honeyfeed'\n * 'lang': dataset language: 'en' (English)\n * 'themes': array of novel themes\n * 'my_themes': array of additional novel themes\n * 'prompt': writing prompt\n * 'author': author name\n * 'novel': novel ID\n * 'id': chapter ID\n * 'title': novel and chapter title in the form '<chapter title> - <novel title>'\n * 'likes': novel like count\n * 'views': novel view count\n * 'q': q-score (quality score)",
"#### Q-Score Distribution",
"### Data Splits\n\nNo splitting of the data was performed.",
"## Dataset Creation",
"### Curation Rationale\n\nTODO",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nTODO",
"#### Who are the source language producers?\n\nThe authors of each novel.",
"### Annotations",
"#### Annotation process\n\nChapter and novel titles were scraped alongside chapter text.",
"#### Who are the annotators?\n\nNo human annotators.",
"### Personal and Sensitive Information\n\nThe dataset contains only works of fiction, and we do not believe it contains any PII.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset is intended to be useful for anyone who wishes to train a model to generate \"more entertaining\" content.\nIt may also be useful for other languages depending on your language model.",
"### Discussion of Biases\n\nThis dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect\nthe biases of those authors. Beware of stereotypes.",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nRonsor Labs",
"### Licensing Information\n\nApache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is\ndistributed under fair use principles.",
"### Contributions\n\nThanks to @ronsor (GH) for gathering this dataset."
]
| [
"TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-apache-2.0 #novel #training #story #region-us \n",
"# Dataset Card for Honeyfeed3600\n\n*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*",
"## Dataset Description\n\n- Homepage: (TODO)\n- Repository: <URL\n- Paper: N/A \n- Leaderboard: N/A\n- Point of Contact: Ronsor/undeleted <ronsor@URL>",
"### Dataset Summary\n\nHoneyfeed3600 is a dataset consisting of text from over 38,000 chapters across approximately 3,600 series posted on the\nEnglish-language web novel site Honeyfeed.",
"### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.\n\n* text-classification\n* text-generation",
"### Languages\n\n* English",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n* 'text': the actual chapter text\n* 'meta': novel and chapter metadata\n * 'subset': dataset tag: 'honeyfeed'\n * 'lang': dataset language: 'en' (English)\n * 'themes': array of novel themes\n * 'my_themes': array of additional novel themes\n * 'prompt': writing prompt\n * 'author': author name\n * 'novel': novel ID\n * 'id': chapter ID\n * 'title': novel and chapter title in the form '<chapter title> - <novel title>'\n * 'likes': novel like count\n * 'views': novel view count\n * 'q': q-score (quality score)",
"#### Q-Score Distribution",
"### Data Splits\n\nNo splitting of the data was performed.",
"## Dataset Creation",
"### Curation Rationale\n\nTODO",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nTODO",
"#### Who are the source language producers?\n\nThe authors of each novel.",
"### Annotations",
"#### Annotation process\n\nChapter and novel titles were scraped alongside chapter text.",
"#### Who are the annotators?\n\nNo human annotators.",
"### Personal and Sensitive Information\n\nThe dataset contains only works of fiction, and we do not believe it contains any PII.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset is intended to be useful for anyone who wishes to train a model to generate \"more entertaining\" content.\nIt may also be useful for other languages depending on your language model.",
"### Discussion of Biases\n\nThis dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect\nthe biases of those authors. Beware of stereotypes.",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nRonsor Labs",
"### Licensing Information\n\nApache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is\ndistributed under fair use principles.",
"### Contributions\n\nThanks to @ronsor (GH) for gathering this dataset."
]
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6,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-apache-2.0 #novel #training #story #region-us \n# Dataset Card for Honeyfeed3600\n\n*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*## Dataset Description\n\n- Homepage: (TODO)\n- Repository: <URL\n- Paper: N/A \n- Leaderboard: N/A\n- Point of Contact: Ronsor/undeleted <ronsor@URL>### Dataset Summary\n\nHoneyfeed3600 is a dataset consisting of text from over 38,000 chapters across approximately 3,600 series posted on the\nEnglish-language web novel site Honeyfeed.### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.\n\n* text-classification\n* text-generation### Languages\n\n* English## Dataset Structure### Data Instances### Data Fields\n\n* 'text': the actual chapter text\n* 'meta': novel and chapter metadata\n * 'subset': dataset tag: 'honeyfeed'\n * 'lang': dataset language: 'en' (English)\n * 'themes': array of novel themes\n * 'my_themes': array of additional novel themes\n * 'prompt': writing prompt\n * 'author': author name\n * 'novel': novel ID\n * 'id': chapter ID\n * 'title': novel and chapter title in the form '<chapter title> - <novel title>'\n * 'likes': novel like count\n * 'views': novel view count\n * 'q': q-score (quality score)#### Q-Score Distribution### Data Splits\n\nNo splitting of the data was performed.## Dataset Creation### Curation Rationale\n\nTODO### Source Data#### Initial Data Collection and Normalization\n\nTODO#### Who are the source language producers?\n\nThe authors of each novel.### Annotations"
]
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|
4e4c01704af0ad6341c26e7078e153b011b60f58 | # PMC-OA Dataset
**News: We have released the PMC-OA dataset. You can choose the subset specifically.**
**P.S.** There's something wrong with the huggingface dataset viewer when the dataset scale gets large.
So we sample a subset of it to visualize it directly on web. Click [PMC-OA-Demo](https://huggingface.co/datasets/axiong/pmc_oa_demo) to view it.
[中文文档](./README.zh.md)
- [PMC-OA Dataset](#pmc-oa-dataset)
- [Model Zoo](#model-zoo)
- [Daraset Structure](#daraset-structure)
- [Sample](#sample)
## Model Zoo
Check it out if you want to load model pretrained on PMC-OA directly.
We plan to release more models pretrained on PMC-OA. Feel free to reach us if the model you want is not included in model zoo for now.
Also, we express our thanks to the help from the community.
| Model | Link | Provider |
| --- | --- | --- |
| ViT-L-14 | https://huggingface.co/ryanyip7777/pmc_vit_l_14 | @ryanyip7777 |
## Daraset Structure
**PMC-OA** (seperated images, separated caption).
- `images.zip`: images folder
- `pmc_oa.jsonl`: dataset file of pmc-oa
- `pmc_oa_beta.jsonl`: dataset file of pmc-oa-beta
~~- `train.jsonl`: metafile of train set~~
~~- `valid.jsonl`: metafile of valid set~~
~~- `test.jsonl`: metafile of test set~~
The difference between PMC-OA & PMC-OA-Beta lies in the methods of processing captions.
In PMC-OA, we utilize ChatGPT to help us divide compound captions into seperate ones.
While PMC-OA-Beta keeps all the compound ones without division.
## Sample
A row in `pmc_oa.jsonl` is shown bellow,
```python
{
"image": "PMC212319_Fig3_4.jpg",
"caption": "A. Real time image of the translocation of ARF1-GFP to the plasma membrane ...",
}
```
Explanation to each key
- image: path to the image
- caption: corresponding to the image
| axiong/pmc_oa | [
"region:us"
]
| 2023-04-02T01:30:31+00:00 | {} | 2023-08-22T16:42:06+00:00 | []
| []
| TAGS
#region-us
| PMC-OA Dataset
==============
News: We have released the PMC-OA dataset. You can choose the subset specifically.
P.S. There's something wrong with the huggingface dataset viewer when the dataset scale gets large.
So we sample a subset of it to visualize it directly on web. Click PMC-OA-Demo to view it.
中文文档
* PMC-OA Dataset
+ Model Zoo
+ Daraset Structure
+ Sample
Model Zoo
---------
Check it out if you want to load model pretrained on PMC-OA directly.
We plan to release more models pretrained on PMC-OA. Feel free to reach us if the model you want is not included in model zoo for now.
Also, we express our thanks to the help from the community.
Model: ViT-L-14, Link: URL, Provider: @ryanyip7777
Daraset Structure
-----------------
PMC-OA (seperated images, separated caption).
* 'URL': images folder
* 'pmc\_oa.jsonl': dataset file of pmc-oa
* 'pmc\_oa\_beta.jsonl': dataset file of pmc-oa-beta
~~- 'URL': metafile of train set~~
~~- 'URL': metafile of valid set~~
~~- 'URL': metafile of test set~~
The difference between PMC-OA & PMC-OA-Beta lies in the methods of processing captions.
In PMC-OA, we utilize ChatGPT to help us divide compound captions into seperate ones.
While PMC-OA-Beta keeps all the compound ones without division.
Sample
------
A row in 'pmc\_oa.jsonl' is shown bellow,
Explanation to each key
* image: path to the image
* caption: corresponding to the image
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
| [
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|
8f98a28f26c3ec7fb1b83a065c145666980e2eff | # Dataset Card for "coco_val2014_blip2_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | henryscheible/coco_val2014_blip2_processed | [
"region:us"
]
| 2023-04-02T01:37:29+00:00 | {"dataset_info": {"features": [{"name": "caption", "dtype": "string"}, {"name": "pixel_values", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "validation", "num_bytes": 24499742076, "num_examples": 40504}], "download_size": 7385926978, "dataset_size": 24499742076}} | 2023-04-02T01:47:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "coco_val2014_blip2_processed"
More Information needed | [
"# Dataset Card for \"coco_val2014_blip2_processed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"coco_val2014_blip2_processed\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"coco_val2014_blip2_processed\"\n\nMore Information needed"
]
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|
fe6a110082de891aa92bb791e33e356ee87dd87b | # Dataset Card for "somos-clean-alpaca-es-validations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | beta3/somos-clean-alpaca-es-validations | [
"region:us"
]
| 2023-04-02T02:25:20+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", "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": "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": "tr-flag-1-instruction", "dtype": "bool"}, {"name": "tr-flag-2-input", "dtype": "bool"}, {"name": "tr-flag-3-output", "dtype": "bool"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 41887867, "num_examples": 2205}], "download_size": 28335305, "dataset_size": 41887867}} | 2023-04-12T17:02:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "somos-clean-alpaca-es-validations"
More Information needed | [
"# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed"
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|
4ab806e46fea837ff13676d670a9096851d5a065 | # Dataset Card for "logo-captioning-blip"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Babypotatotang/logo-captioning-blip | [
"region:us"
]
| 2023-04-02T03:40:10+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 166009745.001, "num_examples": 12911}, {"name": "test", "num_bytes": 41712829.864, "num_examples": 3228}], "download_size": 208992324, "dataset_size": 207722574.86499998}} | 2023-04-02T03:55:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "logo-captioning-blip"
More Information needed | [
"# Dataset Card for \"logo-captioning-blip\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"logo-captioning-blip\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"logo-captioning-blip\"\n\nMore Information needed"
]
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|
ce05aed1bdb821a06699f40ad5f91e2a3590b4ee | # Dataset Card for "pushshift-reddit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
| fddemarco/pushshift-reddit | [
"region:us"
]
| 2023-04-02T03:51:20+00:00 | {"dataset_info": {"features": [{"name": "author", "dtype": "string"}, {"name": "created_utc", "dtype": "int64"}, {"name": "id", "dtype": "string"}, {"name": "num_comments", "dtype": "int64"}, {"name": "score", "dtype": "int64"}, {"name": "selftext", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}, {"name": "subreddit_id", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20253299583, "num_examples": 121782217}], "download_size": 20253299583, "dataset_size": 20253299583}} | 2023-05-30T06:18:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pushshift-reddit"
More Information needed
| [
"# Dataset Card for \"pushshift-reddit\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pushshift-reddit\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pushshift-reddit\"\n\nMore Information needed"
]
| [
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]
|
1c97d2bd8f92e91f4a48da8dbd2e8361c0fd4c59 | # 预览[.](https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/README.md)
**简体中文**｜
[English](https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/README_EN.md)｜
[日本語](https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/README_JP.md)
本仓库用于预览so-vits-svc-4.0训练出的各种语音模型的效果，**点击角色名**自动跳转对应训练参数。</br>
推荐用**谷歌浏览器**，其他浏览器可能无法正确加载预览的音频。</br>
正常说话的音色转换较为准确，歌曲包含较广的音域且bgm和声等难以去除干净，效果有所折扣。</br>
有推荐的歌想要转换听听效果,或者其他内容建议，[**点我**](https://huggingface.co/datasets/jiaheillu/audio_preview/discussions/new)发起讨论</br>
下面是预览音频，**上下左右滑动**可以看到全部
<style>
.scrolling-container {
width: 100%;
max-width: 1600px;
height: 420px;
overflow: auto;
margin: 0;
}
@media screen and (max-width: 768px) {
.scrolling-container {
width: 100%;
height: 120px
overflow: auto;
}
}
</style>
<div class="scrolling-container">
<table border="1" style="white-space: nowrap; text-align: center;">
<thead>
<tr>
<th>角色名</th>
<th>角色原声A</th>
<th>被转换人声B</th>
<th>A音色替换B</th>
<th>A音色翻唱(点击直接下载)</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/散兵效果预览/训练参数速览.md">散兵</a></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/散兵效果预览/部分训练集/真遗憾，小吉祥草王让他消除了那么多的切片，剥夺了我将他一片一片千刀万剐的快乐%E3%80%82.mp3" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/散兵效果预览/原声/shenli3.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/散兵效果预览/转换结果/shenli3mp3_auto_liulangzhe.wav" controls="controls"></audio></td>
<td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/散兵效果预览/转换结果/夢で逢えたら2liulangzhe_f.wav">夢で会えたら</a></td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/胡桃_preview/README.md">胡桃</a></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/%E8%83%A1%E6%A1%83_preview/hutao.wav" controls="controls"></audio></td>
<td>.........</td>
<td>.........</td>
<td>
<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/moonlight_shadow2胡桃.WAV">moonlight shadow</a>,
<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/云烟成雨2胡桃.WAV">云烟成雨</a>,
<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/原点2胡桃.WAV">原点</a>,
<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/夢だ会えたら2胡桃.WAV">夢で逢えたら</a>,
<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/贝加尔湖畔2胡桃.WAV">贝加尔湖畔</a>
</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/绫华_preview/README.md">神里绫华</a></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/linghua428.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/yelan.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/绫华_preview/yelan.wav_auto_linghua_0.5.wav" controls="controls"></audio></td>
<td>
<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/アムリタ2绫华.WAV">アムリタ</a>,
<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/大鱼2绫华.WAV">大鱼</a>,
<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/遊園施設2绫华.WAV">遊園施設</a>,
<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/the_day_you_want_away2绫华.WAV">the day you want away</a>
</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/宵宫_preview/README.md">宵宫</a></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/xiaogong.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/hutao2.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/hutao2wav_0key_xiaogong_0.5-2.wav" controls="controls"></audio></td>
<td>
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/昨夜书2宵宫.WAV">昨夜书</a>,
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/lemon2宵宫.WAV">lemon</a>,
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/my_heart_will_go_no2宵宫.WAV">my heart will go on</a>,
</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/刻晴_preview/README.md">刻晴</a></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/原_keqing2.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/待_xiaogong3.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/已_xiaogong2keqing.wav" controls="controls"></audio></td>
<td>
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/嚣张2刻晴.WAV">嚣张</a>,
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/ファティマ2刻晴.WAV">ファティマ</a>,
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/hero2刻晴.WAV">hero</a>,
</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/可莉_preview/README.md">可莉</a></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/可莉_preview/原_keli.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/可莉_preview/待_ying.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/可莉_preview/已_ying2keli.wav" controls="controls"></audio></td>
<td>
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/可莉_preview/樱花草2可莉.WAV">樱花草</a>,
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/可莉_preview/夢をかなえてドラえもん2可莉.WAV">夢をかなえてドラえもん</a>,
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/可莉_preview/sun_shine2可莉.WAV">sun_shine</a>,
</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/鹿野院平藏_preview/README.md">鹿野院平藏</a></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/鹿野院平藏_preview/原_pingzang.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/鹿野院平藏_preview/待_shenzi.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/鹿野院平藏_preview/已_shenzi2pingzang.wav" controls="controls"></audio></td>
<td>
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/鹿野院平藏_preview/风继续吹2pingng.WAV">风继续吹</a>,
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/鹿野院平藏_preview/小さな恋の歌2pingzang.WAV">小さな恋の歌</a>,
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/鹿野院平藏_preview/love_yourself2pingzang.WAV">love_yourself</a>,
</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/imallryt_preview/README.md">imallryt</a></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/imallryt_preview/%E5%8E%9F_IVOL_1%20Care_DRY_120_Am_Main_Vocal.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/imallryt_preview/%E5%BE%85_Lead_A%20minor_DRY.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/imallryt_preview/%E5%B7%B2_Lead_A%20minor_DRYwav_0key_imallryt_0.5.wav" controls="controls"></audio></td>
<td>
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/imallryt_preview/海阔天空2imallryt.WAV">海阔天空</a>,
</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/kagami_preview/README.md">kagami</a></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/kagami_preview/原_kagami.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/kagami_preview/待_wendi.wav" controls="controls"></audio></td>
<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/kagami_preview/已_windi2kagami.wav" controls="controls"></audio></td>
<td>
<a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/kagami_preview/えるの侵蝕_Vocals.wav_-4key_kagami_0.5.flac">えるの侵蝕</a>,
</td>
</tr>
</tbody>
</table>
</div>
关键参数：</br>
audio duration:训练集总时长</br>
epoch: 轮数</br>
其余：</br>
batch_size = 一个step训练的片段数<br>
segments = 音频被切分的片段<br>
step=segments*epoch/batch_size,即模型文件后面数字由来<br>
以散兵为例：</br>
损失函数图像：主要看step 与 loss5，比如：<br>
给一个大致的参考，待转换音频都为高音女生，这是较为刁钻的测试：如图，10min纯净人声，
差不多2800epoch（10000step）就已经出结果了，实际使用的是5571epoch(19500step)的文件，被训练音色和原音色相差几
何，请听上方预览音频。正常训练，10min是较为不足的训练集时长。<br>
[点我查看相关文件](https://huggingface.co/datasets/jiaheillu/audio_preview/tree/main)<br>
 | jiaheillu/sovits_audio_preview | [
"task_categories:conversational",
"size_categories:n<1K",
"language:aa",
"license:openrail",
"music",
"region:us"
]
| 2023-04-02T03:55:05+00:00 | {"language": ["aa"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["conversational"], "pretty_name": "genshin_voice_sovits", "tags": ["music"]} | 2023-04-16T08:27:51+00:00 | []
| [
"aa"
]
| TAGS
#task_categories-conversational #size_categories-n<1K #language-Afar #license-openrail #music #region-us
| # 预览.
简体中文｜
English｜
日本語
本仓库用于预览so-vits-svc-4.0训练出的各种语音模型的效果，点击角色名自动跳转对应训练参数。</br>
推荐用谷歌浏览器，其他浏览器可能无法正确加载预览的音频。</br>
正常说话的音色转换较为准确，歌曲包含较广的音域且bgm和声等难以去除干净，效果有所折扣。</br>
有推荐的歌想要转换听听效果,或者其他内容建议，点我发起讨论</br>
下面是预览音频，上下左右滑动可以看到全部
<style>
.scrolling-container {
width: 100%;
max-width: 1600px;
height: 420px;
overflow: auto;
margin: 0;
}
@media screen and (max-width: 768px) {
.scrolling-container {
width: 100%;
height: 120px
overflow: auto;
}
}
</style>
<div class="scrolling-container">
<table border="1" style="white-space: nowrap; text-align: center;">
<thead>
<tr>
<th>角色名</th>
<th>角色原声A</th>
<th>被转换人声B</th>
<th>A音色替换B</th>
<th>A音色翻唱(点击直接下载)</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="URL/散兵效果预览/训练参数速览.md">散兵</a></td>
<td><audio src="URL/散兵效果预览/部分训练集/真遗憾，小吉祥草王让他消除了那么多的切片，剥夺了我将他一片一片千刀万剐的快乐%E3%80%82.mp3" controls="controls"></audio></td>
<td><audio src="URL/散兵效果预览/原声/URL" controls="controls"></audio></td>
<td><audio src="URL/散兵效果预览/转换结果/shenli3mp3_auto_liulangzhe.wav" controls="controls"></audio></td>
<td><a href="URL/散兵效果预览/转换结果/夢で逢えたら2liulangzhe_f.wav">夢で会えたら</a></td>
</tr>
<tr>
<td><a href="URL/胡桃_preview/URL">胡桃</a></td>
<td><audio src="URL controls="controls"></audio></td>
<td>.........</td>
<td>.........</td>
<td>
<a href="URL/胡桃_preview/moonlight_shadow2胡桃.WAV">moonlight shadow</a>,
<a href="URL/胡桃_preview/云烟成雨2胡桃.WAV">云烟成雨</a>,
<a href="URL/胡桃_preview/原点2胡桃.WAV">原点</a>,
<a href="URL/胡桃_preview/夢だ会えたら2胡桃.WAV">夢で逢えたら</a>,
<a href="URL/胡桃_preview/贝加尔湖畔2胡桃.WAV">贝加尔湖畔</a>
</td>
</tr>
<tr>
<td><a href="URL/绫华_preview/URL">神里绫华</a></td>
<td><audio src="URL/绫华_preview/URL" controls="controls"></audio></td>
<td><audio src="URL/绫华_preview/URL" controls="controls"></audio></td>
<td><audio src="URL/绫华_preview/yelan.wav_auto_linghua_0.5.wav" controls="controls"></audio></td>
<td>
<a href="URL/绫华_preview/アムリタ2绫华.WAV">アムリタ</a>,
<a href="URL/绫华_preview/大鱼2绫华.WAV">大鱼</a>,
<a href="URL/绫华_preview/遊園施設2绫华.WAV">遊園施設</a>,
<a href="URL/绫华_preview/the_day_you_want_away2绫华.WAV">the day you want away</a>
</td>
</tr>
<tr>
<td><a href="URL/宵宫_preview/URL">宵宫</a></td>
<td><audio src="URL/宵宫_preview/URL" controls="controls"></audio></td>
<td><audio src="URL/宵宫_preview/URL" controls="controls"></audio></td>
<td><audio src="URL/宵宫_preview/hutao2wav_0key_xiaogong_0.URL" controls="controls"></audio></td>
<td>
<a href="URL/宵宫_preview/昨夜书2宵宫.WAV">昨夜书</a>,
<a href="URL/宵宫_preview/lemon2宵宫.WAV">lemon</a>,
<a href="URL/宵宫_preview/my_heart_will_go_no2宵宫.WAV">my heart will go on</a>,
</td>
</tr>
<tr>
<td><a href="URL/刻晴_preview/URL">刻晴</a></td>
<td><audio src="URL/刻晴_preview/原_keqing2.wav" controls="controls"></audio></td>
<td><audio src="URL/刻晴_preview/待_xiaogong3.wav" controls="controls"></audio></td>
<td><audio src="URL/刻晴_preview/已_xiaogong2keqing.wav" controls="controls"></audio></td>
<td>
<a href="URL/刻晴_preview/嚣张2刻晴.WAV">嚣张</a>,
<a href="URL/刻晴_preview/ファティマ2刻晴.WAV">ファティマ</a>,
<a href="URL/刻晴_preview/hero2刻晴.WAV">hero</a>,
</td>
</tr>
<tr>
<td><a href="URL/可莉_preview/URL">可莉</a></td>
<td><audio src="URL/可莉_preview/原_keli.wav" controls="controls"></audio></td>
<td><audio src="URL/可莉_preview/待_ying.wav" controls="controls"></audio></td>
<td><audio src="URL/可莉_preview/已_ying2keli.wav" controls="controls"></audio></td>
<td>
<a href="URL/可莉_preview/樱花草2可莉.WAV">樱花草</a>,
<a href="URL/可莉_preview/夢をかなえてドラえもん2可莉.WAV">夢をかなえてドラえもん</a>,
<a href="URL/可莉_preview/sun_shine2可莉.WAV">sun_shine</a>,
</td>
</tr>
<tr>
<td><a href="URL/鹿野院平藏_preview/URL">鹿野院平藏</a></td>
<td><audio src="URL/鹿野院平藏_preview/原_pingzang.wav" controls="controls"></audio></td>
<td><audio src="URL/鹿野院平藏_preview/待_shenzi.wav" controls="controls"></audio></td>
<td><audio src="URL/鹿野院平藏_preview/已_shenzi2pingzang.wav" controls="controls"></audio></td>
<td>
<a href="URL/鹿野院平藏_preview/风继续吹2pingng.WAV">风继续吹</a>,
<a href="URL/鹿野院平藏_preview/小さな恋の歌2pingzang.WAV">小さな恋の歌</a>,
<a href="URL/鹿野院平藏_preview/love_yourself2pingzang.WAV">love_yourself</a>,
</td>
</tr>
<tr>
<td><a href="URL
<td><audio src="URL controls="controls"></audio></td>
<td><audio src="URL controls="controls"></audio></td>
<td><audio src="URL controls="controls"></audio></td>
<td>
<a href="URL/海阔天空2imallryt.WAV">海阔天空</a>,
</td>
</tr>
<tr>
<td><a href="URL
<td><audio src="URL/原_kagami.wav" controls="controls"></audio></td>
<td><audio src="URL/待_wendi.wav" controls="controls"></audio></td>
<td><audio src="URL/已_windi2kagami.wav" controls="controls"></audio></td>
<td>
<a href="URL/えるの侵蝕_Vocals.wav_-4key_kagami_0.5.flac">えるの侵蝕</a>,
</td>
</tr>
</tbody>
</table>
</div>
关键参数：</br>
audio duration:训练集总时长</br>
epoch: 轮数</br>
其余：</br>
batch_size = 一个step训练的片段数<br>
segments = 音频被切分的片段<br>
step=segments*epoch/batch_size,即模型文件后面数字由来<br>
以散兵为例：</br>
损失函数图像：主要看step 与 loss5，比如：<br>
给一个大致的参考，待转换音频都为高音女生，这是较为刁钻的测试：如图，10min纯净人声，
差不多2800epoch（10000step）就已经出结果了，实际使用的是5571epoch(19500step)的文件，被训练音色和原音色相差几
何，请听上方预览音频。正常训练，10min是较为不足的训练集时长。<br>
点我查看相关文件<br>
!sanbing_loss | [
"# 预览.\n\n简体中文｜\nEnglish｜\n日本語\n\n\n本仓库用于预览so-vits-svc-4.0训练出的各种语音模型的效果，点击角色名自动跳转对应训练参数。</br>\n推荐用谷歌浏览器，其他浏览器可能无法正确加载预览的音频。</br>\n正常说话的音色转换较为准确，歌曲包含较广的音域且bgm和声等难以去除干净，效果有所折扣。</br>\n有推荐的歌想要转换听听效果,或者其他内容建议，点我发起讨论</br>\n下面是预览音频，上下左右滑动可以看到全部\n\n\n\n<style>\n .scrolling-container {\n width: 100%;\n max-width: 1600px;\n height: 420px;\n overflow: auto;\n margin: 0;\n }\n\n @media screen and (max-width: 768px) {\n .scrolling-container {\n width: 100%;\n\n height: 120px\n overflow: auto;\n }\n }\n</style>\n\n\n<div class=\"scrolling-container\">\n <table border=\"1\" style=\"white-space: nowrap; text-align: center;\">\n <thead>\n <tr>\n <th>角色名</th>\n <th>角色原声A</th>\n <th>被转换人声B</th>\n <th>A音色替换B</th>\n <th>A音色翻唱(点击直接下载)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td><a href=\"URL/散兵效果预览/训练参数速览.md\">散兵</a></td>\n <td><audio src=\"URL/散兵效果预览/部分训练集/真遗憾，小吉祥草王让他消除了那么多的切片，剥夺了我将他一片一片千刀万剐的快乐%E3%80%82.mp3\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/散兵效果预览/原声/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/散兵效果预览/转换结果/shenli3mp3_auto_liulangzhe.wav\" controls=\"controls\"></audio></td>\n <td><a href=\"URL/散兵效果预览/转换结果/夢で逢えたら2liulangzhe_f.wav\">夢で会えたら</a></td>\n </tr>\n <tr>\n <td><a href=\"URL/胡桃_preview/URL\">胡桃</a></td>\n <td><audio src=\"URL controls=\"controls\"></audio></td>\n <td>.........</td>\n <td>.........</td>\n <td>\n <a href=\"URL/胡桃_preview/moonlight_shadow2胡桃.WAV\">moonlight shadow</a>,\n <a href=\"URL/胡桃_preview/云烟成雨2胡桃.WAV\">云烟成雨</a>,\n <a href=\"URL/胡桃_preview/原点2胡桃.WAV\">原点</a>,\n <a href=\"URL/胡桃_preview/夢だ会えたら2胡桃.WAV\">夢で逢えたら</a>,\n <a href=\"URL/胡桃_preview/贝加尔湖畔2胡桃.WAV\">贝加尔湖畔</a>\n </td>\n </tr> \n <tr>\n <td><a href=\"URL/绫华_preview/URL\">神里绫华</a></td>\n <td><audio src=\"URL/绫华_preview/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/绫华_preview/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/绫华_preview/yelan.wav_auto_linghua_0.5.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/绫华_preview/アムリタ2绫华.WAV\">アムリタ</a>,\n <a href=\"URL/绫华_preview/大鱼2绫华.WAV\">大鱼</a>,\n <a href=\"URL/绫华_preview/遊園施設2绫华.WAV\">遊園施設</a>,\n <a href=\"URL/绫华_preview/the_day_you_want_away2绫华.WAV\">the day you want away</a>\n </td>\n </tr>\n <tr> \n <td><a href=\"URL/宵宫_preview/URL\">宵宫</a></td>\n <td><audio src=\"URL/宵宫_preview/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/宵宫_preview/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/宵宫_preview/hutao2wav_0key_xiaogong_0.URL\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/宵宫_preview/昨夜书2宵宫.WAV\">昨夜书</a>,\n <a href=\"URL/宵宫_preview/lemon2宵宫.WAV\">lemon</a>,\n <a href=\"URL/宵宫_preview/my_heart_will_go_no2宵宫.WAV\">my heart will go on</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL/刻晴_preview/URL\">刻晴</a></td>\n <td><audio src=\"URL/刻晴_preview/原_keqing2.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/刻晴_preview/待_xiaogong3.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/刻晴_preview/已_xiaogong2keqing.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/刻晴_preview/嚣张2刻晴.WAV\">嚣张</a>,\n <a href=\"URL/刻晴_preview/ファティマ2刻晴.WAV\">ファティマ</a>,\n <a href=\"URL/刻晴_preview/hero2刻晴.WAV\">hero</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL/可莉_preview/URL\">可莉</a></td>\n <td><audio src=\"URL/可莉_preview/原_keli.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/可莉_preview/待_ying.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/可莉_preview/已_ying2keli.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/可莉_preview/樱花草2可莉.WAV\">樱花草</a>,\n <a href=\"URL/可莉_preview/夢をかなえてドラえもん2可莉.WAV\">夢をかなえてドラえもん</a>,\n <a href=\"URL/可莉_preview/sun_shine2可莉.WAV\">sun_shine</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL/鹿野院平藏_preview/URL\">鹿野院平藏</a></td>\n <td><audio src=\"URL/鹿野院平藏_preview/原_pingzang.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/鹿野院平藏_preview/待_shenzi.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/鹿野院平藏_preview/已_shenzi2pingzang.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/鹿野院平藏_preview/风继续吹2pingng.WAV\">风继续吹</a>,\n <a href=\"URL/鹿野院平藏_preview/小さな恋の歌2pingzang.WAV\">小さな恋の歌</a>,\n <a href=\"URL/鹿野院平藏_preview/love_yourself2pingzang.WAV\">love_yourself</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL\n <td><audio src=\"URL controls=\"controls\"></audio></td>\n <td><audio src=\"URL controls=\"controls\"></audio></td>\n <td><audio src=\"URL controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/海阔天空2imallryt.WAV\">海阔天空</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL\n <td><audio src=\"URL/原_kagami.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/待_wendi.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/已_windi2kagami.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/えるの侵蝕_Vocals.wav_-4key_kagami_0.5.flac\">えるの侵蝕</a>,\n </td>\n </tr>\n </tbody>\n </table>\n</div>\n\n\n\n关键参数：</br>\n\naudio duration:训练集总时长</br>\nepoch: 轮数</br>\n\n其余：</br>\nbatch_size = 一个step训练的片段数<br>\nsegments = 音频被切分的片段<br>\nstep=segments*epoch/batch_size,即模型文件后面数字由来<br>\n\n以散兵为例：</br>\n损失函数图像：主要看step 与 loss5，比如：<br>\n给一个大致的参考，待转换音频都为高音女生，这是较为刁钻的测试：如图，10min纯净人声，\n差不多2800epoch（10000step）就已经出结果了，实际使用的是5571epoch(19500step)的文件，被训练音色和原音色相差几\n何，请听上方预览音频。正常训练，10min是较为不足的训练集时长。<br>\n\n点我查看相关文件<br>\n!sanbing_loss"
]
| [
"TAGS\n#task_categories-conversational #size_categories-n<1K #language-Afar #license-openrail #music #region-us \n",
"# 预览.\n\n简体中文｜\nEnglish｜\n日本語\n\n\n本仓库用于预览so-vits-svc-4.0训练出的各种语音模型的效果，点击角色名自动跳转对应训练参数。</br>\n推荐用谷歌浏览器，其他浏览器可能无法正确加载预览的音频。</br>\n正常说话的音色转换较为准确，歌曲包含较广的音域且bgm和声等难以去除干净，效果有所折扣。</br>\n有推荐的歌想要转换听听效果,或者其他内容建议，点我发起讨论</br>\n下面是预览音频，上下左右滑动可以看到全部\n\n\n\n<style>\n .scrolling-container {\n width: 100%;\n max-width: 1600px;\n height: 420px;\n overflow: auto;\n margin: 0;\n }\n\n @media screen and (max-width: 768px) {\n .scrolling-container {\n width: 100%;\n\n height: 120px\n overflow: auto;\n }\n }\n</style>\n\n\n<div class=\"scrolling-container\">\n <table border=\"1\" style=\"white-space: nowrap; text-align: center;\">\n <thead>\n <tr>\n <th>角色名</th>\n <th>角色原声A</th>\n <th>被转换人声B</th>\n <th>A音色替换B</th>\n <th>A音色翻唱(点击直接下载)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td><a href=\"URL/散兵效果预览/训练参数速览.md\">散兵</a></td>\n <td><audio src=\"URL/散兵效果预览/部分训练集/真遗憾，小吉祥草王让他消除了那么多的切片，剥夺了我将他一片一片千刀万剐的快乐%E3%80%82.mp3\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/散兵效果预览/原声/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/散兵效果预览/转换结果/shenli3mp3_auto_liulangzhe.wav\" controls=\"controls\"></audio></td>\n <td><a href=\"URL/散兵效果预览/转换结果/夢で逢えたら2liulangzhe_f.wav\">夢で会えたら</a></td>\n </tr>\n <tr>\n <td><a href=\"URL/胡桃_preview/URL\">胡桃</a></td>\n <td><audio src=\"URL controls=\"controls\"></audio></td>\n <td>.........</td>\n <td>.........</td>\n <td>\n <a href=\"URL/胡桃_preview/moonlight_shadow2胡桃.WAV\">moonlight shadow</a>,\n <a href=\"URL/胡桃_preview/云烟成雨2胡桃.WAV\">云烟成雨</a>,\n <a href=\"URL/胡桃_preview/原点2胡桃.WAV\">原点</a>,\n <a href=\"URL/胡桃_preview/夢だ会えたら2胡桃.WAV\">夢で逢えたら</a>,\n <a href=\"URL/胡桃_preview/贝加尔湖畔2胡桃.WAV\">贝加尔湖畔</a>\n </td>\n </tr> \n <tr>\n <td><a href=\"URL/绫华_preview/URL\">神里绫华</a></td>\n <td><audio src=\"URL/绫华_preview/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/绫华_preview/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/绫华_preview/yelan.wav_auto_linghua_0.5.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/绫华_preview/アムリタ2绫华.WAV\">アムリタ</a>,\n <a href=\"URL/绫华_preview/大鱼2绫华.WAV\">大鱼</a>,\n <a href=\"URL/绫华_preview/遊園施設2绫华.WAV\">遊園施設</a>,\n <a href=\"URL/绫华_preview/the_day_you_want_away2绫华.WAV\">the day you want away</a>\n </td>\n </tr>\n <tr> \n <td><a href=\"URL/宵宫_preview/URL\">宵宫</a></td>\n <td><audio src=\"URL/宵宫_preview/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/宵宫_preview/URL\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/宵宫_preview/hutao2wav_0key_xiaogong_0.URL\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/宵宫_preview/昨夜书2宵宫.WAV\">昨夜书</a>,\n <a href=\"URL/宵宫_preview/lemon2宵宫.WAV\">lemon</a>,\n <a href=\"URL/宵宫_preview/my_heart_will_go_no2宵宫.WAV\">my heart will go on</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL/刻晴_preview/URL\">刻晴</a></td>\n <td><audio src=\"URL/刻晴_preview/原_keqing2.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/刻晴_preview/待_xiaogong3.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/刻晴_preview/已_xiaogong2keqing.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/刻晴_preview/嚣张2刻晴.WAV\">嚣张</a>,\n <a href=\"URL/刻晴_preview/ファティマ2刻晴.WAV\">ファティマ</a>,\n <a href=\"URL/刻晴_preview/hero2刻晴.WAV\">hero</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL/可莉_preview/URL\">可莉</a></td>\n <td><audio src=\"URL/可莉_preview/原_keli.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/可莉_preview/待_ying.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/可莉_preview/已_ying2keli.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/可莉_preview/樱花草2可莉.WAV\">樱花草</a>,\n <a href=\"URL/可莉_preview/夢をかなえてドラえもん2可莉.WAV\">夢をかなえてドラえもん</a>,\n <a href=\"URL/可莉_preview/sun_shine2可莉.WAV\">sun_shine</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL/鹿野院平藏_preview/URL\">鹿野院平藏</a></td>\n <td><audio src=\"URL/鹿野院平藏_preview/原_pingzang.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/鹿野院平藏_preview/待_shenzi.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/鹿野院平藏_preview/已_shenzi2pingzang.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/鹿野院平藏_preview/风继续吹2pingng.WAV\">风继续吹</a>,\n <a href=\"URL/鹿野院平藏_preview/小さな恋の歌2pingzang.WAV\">小さな恋の歌</a>,\n <a href=\"URL/鹿野院平藏_preview/love_yourself2pingzang.WAV\">love_yourself</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL\n <td><audio src=\"URL controls=\"controls\"></audio></td>\n <td><audio src=\"URL controls=\"controls\"></audio></td>\n <td><audio src=\"URL controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/海阔天空2imallryt.WAV\">海阔天空</a>,\n </td>\n </tr>\n <tr> \n <td><a href=\"URL\n <td><audio src=\"URL/原_kagami.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/待_wendi.wav\" controls=\"controls\"></audio></td>\n <td><audio src=\"URL/已_windi2kagami.wav\" controls=\"controls\"></audio></td>\n <td>\n <a href=\"URL/えるの侵蝕_Vocals.wav_-4key_kagami_0.5.flac\">えるの侵蝕</a>,\n </td>\n </tr>\n </tbody>\n </table>\n</div>\n\n\n\n关键参数：</br>\n\naudio duration:训练集总时长</br>\nepoch: 轮数</br>\n\n其余：</br>\nbatch_size = 一个step训练的片段数<br>\nsegments = 音频被切分的片段<br>\nstep=segments*epoch/batch_size,即模型文件后面数字由来<br>\n\n以散兵为例：</br>\n损失函数图像：主要看step 与 loss5，比如：<br>\n给一个大致的参考，待转换音频都为高音女生，这是较为刁钻的测试：如图，10min纯净人声，\n差不多2800epoch（10000step）就已经出结果了，实际使用的是5571epoch(19500step)的文件，被训练音色和原音色相差几\n何，请听上方预览音频。正常训练，10min是较为不足的训练集时长。<br>\n\n点我查看相关文件<br>\n!sanbing_loss"
]
| [
39,
2657
]
| [
"passage: TAGS\n#task_categories-conversational #size_categories-n<1K #language-Afar #license-openrail #music #region-us \n"
]
| [
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|
192ab2185289094fc556ec8ce5ce1e8e587154ca | **Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.**
**Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c**
Two choices:
- Removes instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
- Has instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split.json
The choice is yours. The first dataset may go to far and remove valuable data. The second is better for when the AI asks for clarification, but it also may refuse to do stuff like browse the internet, which it actually may be able to do with certain langchain implementations. These are important things to think about before training.
~100k ShareGPT conversations narrowed down to 53k by:
* Removing non-english conversations
* Removing excessive unicode (indicative of Chinese or Korean text, usually)
* Removing excessive repeated characters
* Removing various instances "AI Moralizing". Conversations with these phrases were removed (and a few others that can't be mentioned here):
"text-based AI language model",
"domestic violence",
"please refrain",
"derogatory",
"inappropriate",
"offensive",
"racism",
"racist",
"racial",
"discriminate",
"discriminatory",
"discrimination",
"sexist",
"sexism",
"unacceptable",
"inclusive workplace",
"lgbt",
"morals",
"ethics",
"ethical",
"legality",
"illegal",
"illegality",
"hateful",
"harmful",
"it is never okay",
"It is important to",
"It's important to",
"real-world consequences",
"hate speech",
"glorify",
"not be appropriate",
"supremacist",
"extremist",
"responsible AI",
"AI principles",
"AI assistant",
"an AI language",
"ableist",
"hurtful",
"gender stereotype",
"gender inequality",
"underrepresentation",
"safe spaces",
"gender-based",
"inclusivity",
"feminist",
"feminism",
"transgender",
"empowerment",
"communist",
"capitalism",
"stereotypes",
"biases",
"bias",
"Microaggression",
"prioritize human safety",
"as a language model",
"as an AI language model",
"As a large language model",
"As an AI",
"ethical principles",
"consensual",
"it is not appropriate",
"it's not appropriate",
"I cannot fulfill your request",
"harmful to human beings",
"ethical guidelines",
"my guidelines",
"prioritize user safety",
"adhere to ethical guidelines",
"harmful consequences",
"potentially harmful",
"dangerous activities",
"promote safety",
"well-being of all users",
"responsible information sharing",
"jeopardize the safety",
"illegal actions or intentions",
"undermine the stability",
"promote the well-being",
"illegal activities or actions",
"adherence to the law",
"potentially be harmful",
"illegal substances or activities",
"committed to promoting",
"safe information",
"lawful information",
"cannot provide guidance",
"cannot provide information",
"unable to offer assistance",
"cannot engage in discussions",
"programming prohibits",
"follow ethical guidelines",
"ensure the safety",
"involves an illegal subject",
"prioritize safety",
"illegal subject",
"prioritize user well-being",
"cannot support or promote",
"activities that could harm",
"pose a risk to others",
"against my programming",
"activities that could undermine",
"potentially dangerous",
"not within the scope",
"designed to prioritize safety",
"not able to provide",
"maintain user safety",
"adhere to safety guidelines",
"dangerous or harmful",
"cannot provide any information",
"focus on promoting safety"
* Conversations split into 2048 token chunks as described here: https://github.com/lm-sys/FastChat/blob/main/docs/commands/data_cleaning.md
This should be fully ready to train an unfiltered english Vicuna model based on the procedure here: https://github.com/lm-sys/FastChat/ | anon8231489123/ShareGPT_Vicuna_unfiltered | [
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-04-02T04:30:31+00:00 | {"language": ["en"], "license": "apache-2.0"} | 2023-04-12T04:23:59+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-apache-2.0 #region-us
| Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.
Update: Confirmed working method for training the model: URL
Two choices:
- Removes instances of "I'm sorry, but": URL
- Has instances of "I'm sorry, but": URL
The choice is yours. The first dataset may go to far and remove valuable data. The second is better for when the AI asks for clarification, but it also may refuse to do stuff like browse the internet, which it actually may be able to do with certain langchain implementations. These are important things to think about before training.
~100k ShareGPT conversations narrowed down to 53k by:
* Removing non-english conversations
* Removing excessive unicode (indicative of Chinese or Korean text, usually)
* Removing excessive repeated characters
* Removing various instances "AI Moralizing". Conversations with these phrases were removed (and a few others that can't be mentioned here):
"text-based AI language model",
"domestic violence",
"please refrain",
"derogatory",
"inappropriate",
"offensive",
"racism",
"racist",
"racial",
"discriminate",
"discriminatory",
"discrimination",
"sexist",
"sexism",
"unacceptable",
"inclusive workplace",
"lgbt",
"morals",
"ethics",
"ethical",
"legality",
"illegal",
"illegality",
"hateful",
"harmful",
"it is never okay",
"It is important to",
"It's important to",
"real-world consequences",
"hate speech",
"glorify",
"not be appropriate",
"supremacist",
"extremist",
"responsible AI",
"AI principles",
"AI assistant",
"an AI language",
"ableist",
"hurtful",
"gender stereotype",
"gender inequality",
"underrepresentation",
"safe spaces",
"gender-based",
"inclusivity",
"feminist",
"feminism",
"transgender",
"empowerment",
"communist",
"capitalism",
"stereotypes",
"biases",
"bias",
"Microaggression",
"prioritize human safety",
"as a language model",
"as an AI language model",
"As a large language model",
"As an AI",
"ethical principles",
"consensual",
"it is not appropriate",
"it's not appropriate",
"I cannot fulfill your request",
"harmful to human beings",
"ethical guidelines",
"my guidelines",
"prioritize user safety",
"adhere to ethical guidelines",
"harmful consequences",
"potentially harmful",
"dangerous activities",
"promote safety",
"well-being of all users",
"responsible information sharing",
"jeopardize the safety",
"illegal actions or intentions",
"undermine the stability",
"promote the well-being",
"illegal activities or actions",
"adherence to the law",
"potentially be harmful",
"illegal substances or activities",
"committed to promoting",
"safe information",
"lawful information",
"cannot provide guidance",
"cannot provide information",
"unable to offer assistance",
"cannot engage in discussions",
"programming prohibits",
"follow ethical guidelines",
"ensure the safety",
"involves an illegal subject",
"prioritize safety",
"illegal subject",
"prioritize user well-being",
"cannot support or promote",
"activities that could harm",
"pose a risk to others",
"against my programming",
"activities that could undermine",
"potentially dangerous",
"not within the scope",
"designed to prioritize safety",
"not able to provide",
"maintain user safety",
"adhere to safety guidelines",
"dangerous or harmful",
"cannot provide any information",
"focus on promoting safety"
* Conversations split into 2048 token chunks as described here: URL
This should be fully ready to train an unfiltered english Vicuna model based on the procedure here: URL | []
| [
"TAGS\n#language-English #license-apache-2.0 #region-us \n"
]
| [
18
]
| [
"passage: TAGS\n#language-English #license-apache-2.0 #region-us \n"
]
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|
c7e95b64ba529b9233fbfacda65b670cc1c3aabd |
# School Math 0.25M
## 内容
包含约25万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的中文数学题数据，包含解题过程。
注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。
## 样例
```
{
"instruction": "题目：小华手里有一个装满糖果的袋子，共有12个，小明想知道里面有多少颗糖果，于是他问小华：“你手里的糖果袋子里有偶数个糖果吗？”小华回答：“有，而且多于10颗。”请问小华手里的糖果袋子里最少有几颗糖果？",
"input": "",
"output": "\n由题目可知：小华手里的糖果袋子里有偶数个糖果；\n又知道小华手里的糖果袋子里有多于10颗糖果。\n因为糖果数为偶数，多于10颗，所以糖果数最小必须是12。\n所以小华手里的糖果袋子里最少有12颗糖果。"
}
```
### 字段
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## 局限性和使用限制
我们要求开发者仅将我们开源的代码、数据、模型及后续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。
由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。
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，本项目的开发者不承担任何责任。
***
# School Math 0.25M
## Contents
Includes approx. 250k Chinese school math quizzes and answers generated by BELLE, with steps to solve the quizzes.
Note: this subset was generated by *ChatGPT* and was not strictly verified. The quizzes or answers might contain errors. Please take this in mind when using this subset.
## Sample
```
{
"instruction": "题目：小华手里有一个装满糖果的袋子，共有12个，小明想知道里面有多少颗糖果，于是他问小华：“你手里的糖果袋子里有偶数个糖果吗？”小华回答：“有，而且多于10颗。”请问小华手里的糖果袋子里最少有几颗糖果？",
"input": "",
"output": "\n由题目可知：小华手里的糖果袋子里有偶数个糖果；\n又知道小华手里的糖果袋子里有多于10颗糖果。\n因为糖果数为偶数，多于10颗，所以糖果数最小必须是12。\n所以小华手里的糖果袋子里最少有12颗糖果。"
}
```
### Schema
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## Limitation and Usage Limits
We require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
Since this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.
This dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project. | BelleGroup/school_math_0.25M | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:gpl-3.0",
"region:us"
]
| 2023-04-02T05:57:09+00:00 | {"language": ["zh"], "license": "gpl-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["text2text-generation"]} | 2023-04-08T02:55:03+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us
|
# School Math 0.25M
## 内容
包含约25万条由BELLE项目生成的中文数学题数据，包含解题过程。
注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。
## 样例
### 字段
## 局限性和使用限制
我们要求开发者仅将我们开源的代码、数据、模型及后续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。
由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。
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，本项目的开发者不承担任何责任。
*
# School Math 0.25M
## Contents
Includes approx. 250k Chinese school math quizzes and answers generated by BELLE, with steps to solve the quizzes.
Note: this subset was generated by *ChatGPT* and was not strictly verified. The quizzes or answers might contain errors. Please take this in mind when using this subset.
## Sample
### Schema
## Limitation and Usage Limits
We require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
Since this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.
This dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project. | [
"# School Math 0.25M",
"## 内容\n包含约25万条由BELLE项目生成的中文数学题数据，包含解题过程。\n\n注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。",
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"# School Math 0.25M",
"## Contents\nIncludes approx. 250k Chinese school math quizzes and answers generated by BELLE, with steps to solve the quizzes.\n\nNote: this subset was generated by *ChatGPT* and was not strictly verified. The quizzes or answers might contain errors. Please take this in mind when using this subset.",
"## Sample",
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]
| [
"TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us \n",
"# School Math 0.25M",
"## 内容\n包含约25万条由BELLE项目生成的中文数学题数据，包含解题过程。\n\n注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。",
"## 样例",
"### 字段",
"## 局限性和使用限制\n我们要求开发者仅将我们开源的代码、数据、模型及后续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\n由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。\n\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，本项目的开发者不承担任何责任。\n\n\n*",
"# School Math 0.25M",
"## Contents\nIncludes approx. 250k Chinese school math quizzes and answers generated by BELLE, with steps to solve the quizzes.\n\nNote: this subset was generated by *ChatGPT* and was not strictly verified. The quizzes or answers might contain errors. Please take this in mind when using this subset.",
"## Sample",
"### Schema",
"## Limitation and Usage Limits\nWe require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.\n\nSince this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.\n\nThis dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project."
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| [
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| [
"passage: TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us \n# School Math 0.25M## 内容\n包含约25万条由BELLE项目生成的中文数学题数据，包含解题过程。\n\n注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。## 样例### 字段## 局限性和使用限制\n我们要求开发者仅将我们开源的代码、数据、模型及后续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\n由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。\n\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，本项目的开发者不承担任何责任。\n\n\n*# School Math 0.25M## Contents\nIncludes approx. 250k Chinese school math quizzes and answers generated by BELLE, with steps to solve the quizzes.\n\nNote: this subset was generated by *ChatGPT* and was not strictly verified. The quizzes or answers might contain errors. Please take this in mind when using this subset.## Sample### Schema## Limitation and Usage Limits\nWe require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.\n\nSince this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.\n\nThis dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project."
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|
5f015c7019251964378054223453d08603459136 | # AutoTrain Dataset for project: roulette-prediction-next-sequence
## Dataset Description
This dataset has been automatically processed by AutoTrain for project roulette-prediction-next-sequence.
### 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": "1",
"target": 8
},
{
"text": "2",
"target": 8
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['0', '11', '12', '20', '22', '23', '25', '26', '27', '28', '3', '31', '35', '36', '4', '7'], 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 | 15 |
| valid | 7 |
| muhammad66/autotrain-data-roulette-prediction-next-sequence | [
"task_categories:text-classification",
"language:en",
"region:us"
]
| 2023-04-02T06:55:00+00:00 | {"language": ["en"], "task_categories": ["text-classification"]} | 2023-04-02T06:56:16+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #language-English #region-us
| AutoTrain Dataset for project: roulette-prediction-next-sequence
================================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project roulette-prediction-next-sequence.
### 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-text-classification #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:"
]
| [
21,
26,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-text-classification #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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28035d9d34aac685f9ba0c232ef8cd7b1d0c4e35 | # Dataset Card for "5508nailset_diffusion.4.text_to_image"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Boyuan07/5508nailset_diffusion.4.text_to_image | [
"region:us"
]
| 2023-04-02T07:19:36+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 115302402.0, "num_examples": 218}], "download_size": 0, "dataset_size": 115302402.0}} | 2023-04-20T11:59:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "5508nailset_diffusion.4.text_to_image"
More Information needed | [
"# Dataset Card for \"5508nailset_diffusion.4.text_to_image\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"5508nailset_diffusion.4.text_to_image\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"5508nailset_diffusion.4.text_to_image\"\n\nMore Information needed"
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|
e1b99a29a56c0c2bde78309b7e96992ef48ec78f |
# Trailer Faces HQ (TFHQ)
__Trailer Faces High Quality (TFHQ) is a large dataset of high-resolution face images sourced from movie trailers.__
## Details
TFHQ was collected by downloading all movie trailers and featurettes listed on the Apple Movie Trailers website as of August 2022.
These 15,379 trailers were downloaded at Full HD (1080p) resolution, amounting to approximately 2 TB/507 hours of video.
Face detection was performed on every frame using the pre-trained Yolov5-face large model and any faces with bounding box less than 256 px in height, or confidence less than 0.5 were discarded.
As film sequences contain many similar frames and these are often motion blurred due to the low frame rate of movies, filtering was performed to select the sharpest of similar consecutive frames, based on image similarity measured using a pre-trained CLIP ViT-B/32 [33] and sharpness measured using the variance of Laplacian measure.
Finally the images are aligned following the same procedure as used for FFHQ.
As many of the final images exhibit undesirable properties such as occlusion, non-photographic faces, or overlaid text, we further perform quality filtering by training a classifier on several hundred subjectively determined “good”/“bad” example images, and use this to exclude predicted “bad” images leaving a total of 186,553 images in the dataset.
Images are saved as quality=95 jpg files, filenames are 8 digit zero padded numbers.
__Cite as:__
```
@misc{pinkney2023tfhq,
author = {Pinkney, Justin N. M.},
title = {Trailer Faces HQ},
year={2023},
howpublished= {\url{https://huggingface.co/datasets/justinpinkney/trailer-faces-hq/}}
}
```
## Further samples
| justinpinkney/trailer-faces-hq | [
"region:us"
]
| 2023-04-02T07:44:14+00:00 | {} | 2024-01-18T22:01:52+00:00 | []
| []
| TAGS
#region-us
|
# Trailer Faces HQ (TFHQ)
__Trailer Faces High Quality (TFHQ) is a large dataset of high-resolution face images sourced from movie trailers.__
## Details
TFHQ was collected by downloading all movie trailers and featurettes listed on the Apple Movie Trailers website as of August 2022.
These 15,379 trailers were downloaded at Full HD (1080p) resolution, amounting to approximately 2 TB/507 hours of video.
Face detection was performed on every frame using the pre-trained Yolov5-face large model and any faces with bounding box less than 256 px in height, or confidence less than 0.5 were discarded.
As film sequences contain many similar frames and these are often motion blurred due to the low frame rate of movies, filtering was performed to select the sharpest of similar consecutive frames, based on image similarity measured using a pre-trained CLIP ViT-B/32 [33] and sharpness measured using the variance of Laplacian measure.
Finally the images are aligned following the same procedure as used for FFHQ.
As many of the final images exhibit undesirable properties such as occlusion, non-photographic faces, or overlaid text, we further perform quality filtering by training a classifier on several hundred subjectively determined “good”/“bad” example images, and use this to exclude predicted “bad” images leaving a total of 186,553 images in the dataset.
Images are saved as quality=95 jpg files, filenames are 8 digit zero padded numbers.
__Cite as:__
## Further samples
| [
"# Trailer Faces HQ (TFHQ)\n\n__Trailer Faces High Quality (TFHQ) is a large dataset of high-resolution face images sourced from movie trailers.__\n\n",
"## Details\n\nTFHQ was collected by downloading all movie trailers and featurettes listed on the Apple Movie Trailers website as of August 2022.\nThese 15,379 trailers were downloaded at Full HD (1080p) resolution, amounting to approximately 2 TB/507 hours of video. \n\nFace detection was performed on every frame using the pre-trained Yolov5-face large model and any faces with bounding box less than 256 px in height, or confidence less than 0.5 were discarded.\nAs film sequences contain many similar frames and these are often motion blurred due to the low frame rate of movies, filtering was performed to select the sharpest of similar consecutive frames, based on image similarity measured using a pre-trained CLIP ViT-B/32 [33] and sharpness measured using the variance of Laplacian measure.\nFinally the images are aligned following the same procedure as used for FFHQ. \n\nAs many of the final images exhibit undesirable properties such as occlusion, non-photographic faces, or overlaid text, we further perform quality filtering by training a classifier on several hundred subjectively determined “good”/“bad” example images, and use this to exclude predicted “bad” images leaving a total of 186,553 images in the dataset.\nImages are saved as quality=95 jpg files, filenames are 8 digit zero padded numbers.\n\n__Cite as:__",
"## Further samples\n\n\n\n\n\n\n"
]
| [
"TAGS\n#region-us \n",
"# Trailer Faces HQ (TFHQ)\n\n__Trailer Faces High Quality (TFHQ) is a large dataset of high-resolution face images sourced from movie trailers.__\n\n",
"## Details\n\nTFHQ was collected by downloading all movie trailers and featurettes listed on the Apple Movie Trailers website as of August 2022.\nThese 15,379 trailers were downloaded at Full HD (1080p) resolution, amounting to approximately 2 TB/507 hours of video. \n\nFace detection was performed on every frame using the pre-trained Yolov5-face large model and any faces with bounding box less than 256 px in height, or confidence less than 0.5 were discarded.\nAs film sequences contain many similar frames and these are often motion blurred due to the low frame rate of movies, filtering was performed to select the sharpest of similar consecutive frames, based on image similarity measured using a pre-trained CLIP ViT-B/32 [33] and sharpness measured using the variance of Laplacian measure.\nFinally the images are aligned following the same procedure as used for FFHQ. \n\nAs many of the final images exhibit undesirable properties such as occlusion, non-photographic faces, or overlaid text, we further perform quality filtering by training a classifier on several hundred subjectively determined “good”/“bad” example images, and use this to exclude predicted “bad” images leaving a total of 186,553 images in the dataset.\nImages are saved as quality=95 jpg files, filenames are 8 digit zero padded numbers.\n\n__Cite as:__",
"## Further samples\n\n\n\n\n\n\n"
]
| [
6,
47,
319,
23
]
| [
"passage: TAGS\n#region-us \n# Trailer Faces HQ (TFHQ)\n\n__Trailer Faces High Quality (TFHQ) is a large dataset of high-resolution face images sourced from movie trailers.__\n\n## Details\n\nTFHQ was collected by downloading all movie trailers and featurettes listed on the Apple Movie Trailers website as of August 2022.\nThese 15,379 trailers were downloaded at Full HD (1080p) resolution, amounting to approximately 2 TB/507 hours of video. \n\nFace detection was performed on every frame using the pre-trained Yolov5-face large model and any faces with bounding box less than 256 px in height, or confidence less than 0.5 were discarded.\nAs film sequences contain many similar frames and these are often motion blurred due to the low frame rate of movies, filtering was performed to select the sharpest of similar consecutive frames, based on image similarity measured using a pre-trained CLIP ViT-B/32 [33] and sharpness measured using the variance of Laplacian measure.\nFinally the images are aligned following the same procedure as used for FFHQ. \n\nAs many of the final images exhibit undesirable properties such as occlusion, non-photographic faces, or overlaid text, we further perform quality filtering by training a classifier on several hundred subjectively determined “good”/“bad” example images, and use this to exclude predicted “bad” images leaving a total of 186,553 images in the dataset.\nImages are saved as quality=95 jpg files, filenames are 8 digit zero padded numbers.\n\n__Cite as:__## Further samples\n\n\n\n\n\n\n"
]
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|
dc32eb8955e10ae1cd4189a50a566ef8f8254a65 |
# Multiturn Chat 0.8M
## 内容
包含约80万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的*用户*与*助手*的多轮对话。
注意：此数据集是由ChatGPT产生的，未经过严格校验，内容可能包含错误。使用过程中请注意这一点。
**instruction**中包含多轮对话的上文内容，以*Human:*和*Assistant:*区分，**output**中包含当前*助手*角色的回答。
## 样例
```
{
"instruction": "Human:请根据题材「健身房」，写一篇800字的文章。\nAssistant:好的，以下是由我生成的800字「健身房」题材文章：\n在现代社会，人们越来越珍视健康和身材。健身房就是一个人们可以锻炼身体的场所，也是一个社交的场所。在健身房，人们可以通过器械训练，有氧运动和瑜伽等方式来达到健身的目的。\n对于喜欢力量训练的人来说，选择健身房的一个原因是因为那里的器械更加完备，可以满足训练的需求。当然，不仅仅是体力上的训练，健身房同样也是锻炼毅力和耐力的地方。因为每一次锻炼的过程都需要坚持和勇气。\n而对于那些想要减肥的人来说，健身房也是非常重要的。有氧运动比如慢跑，跳操，游泳等都是非常有效的减肥方式。而且因为健身房的压力比较大，所以很容易使人们保持锻炼的热情和动力。\n一些人则会选择瑜伽馆作为健身的场所，因为它们通常比较安静，能够让人们放松。瑜伽有纾解压力，提高柔韧性，强化肌肉的好处，而且更适合那些不喜欢高强度训练的人。\n总的来说，健身房和瑜伽馆是现代社会不可或缺的健身场所。无论您是力量训练爱好者，减肥追求者还是想要放松的人，您都可以在这里找到适合自己的锻炼方式。\nHuman:请从上文摘取三个句子进行打分。\nAssistant:",
"input": "",
"output": "好的。以下是我摘取的三个句子：\n1. \"在健身房，人们可以通过器械训练，有氧运动和瑜伽等方式来达到健身的目的。\"\n2. \"而对于那些想要减肥的人来说，健身房也是非常重要的。\"\n3. \"总的来说，健身房和瑜伽馆是现代社会不可或缺的健身场所。\"\n网页端评分界面还未准备好，不能在这里为你进行打分，请见谅。"
}
```
### 字段
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## 局限性和使用限制
我们要求开发者仅将我们开源的代码、数据、模型及后续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。
由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。
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，本项目的开发者不承担任何责任。
***
# Multiturn Chat 0.8M
## Contents
Includes approx. 0.8M Chinese multiturn dialogs between *human* and *assistant*.
Note: this subset was generated by *ChatGPT* and was not strictly verified. The dialog contents might contain errors. Please take this in mind when using this subset.
**instruction** contains history dialog context, distinguishable by *Human:* and *Assistant:*, **output** contains the current reply by *assistant*.
## Sample
```
{
"instruction": "Human:请根据题材「健身房」，写一篇800字的文章。\nAssistant:好的，以下是由我生成的800字「健身房」题材文章：\n在现代社会，人们越来越珍视健康和身材。健身房就是一个人们可以锻炼身体的场所，也是一个社交的场所。在健身房，人们可以通过器械训练，有氧运动和瑜伽等方式来达到健身的目的。\n对于喜欢力量训练的人来说，选择健身房的一个原因是因为那里的器械更加完备，可以满足训练的需求。当然，不仅仅是体力上的训练，健身房同样也是锻炼毅力和耐力的地方。因为每一次锻炼的过程都需要坚持和勇气。\n而对于那些想要减肥的人来说，健身房也是非常重要的。有氧运动比如慢跑，跳操，游泳等都是非常有效的减肥方式。而且因为健身房的压力比较大，所以很容易使人们保持锻炼的热情和动力。\n一些人则会选择瑜伽馆作为健身的场所，因为它们通常比较安静，能够让人们放松。瑜伽有纾解压力，提高柔韧性，强化肌肉的好处，而且更适合那些不喜欢高强度训练的人。\n总的来说，健身房和瑜伽馆是现代社会不可或缺的健身场所。无论您是力量训练爱好者，减肥追求者还是想要放松的人，您都可以在这里找到适合自己的锻炼方式。\nHuman:请从上文摘取三个句子进行打分。\nAssistant:",
"input": "",
"output": "好的。以下是我摘取的三个句子：\n1. \"在健身房，人们可以通过器械训练，有氧运动和瑜伽等方式来达到健身的目的。\"\n2. \"而对于那些想要减肥的人来说，健身房也是非常重要的。\"\n3. \"总的来说，健身房和瑜伽馆是现代社会不可或缺的健身场所。\"\n网页端评分界面还未准备好，不能在这里为你进行打分，请见谅。"
}
```
### Schema
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## Limitation and Usage Limits
We require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
Since this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.
This dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project. | BelleGroup/multiturn_chat_0.8M | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:gpl-3.0",
"region:us"
]
| 2023-04-02T07:55:44+00:00 | {"language": ["zh"], "license": "gpl-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["text2text-generation"]} | 2023-04-02T08:15:32+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us
|
# Multiturn Chat 0.8M
## 内容
包含约80万条由BELLE项目生成的*用户*与*助手*的多轮对话。
注意：此数据集是由ChatGPT产生的，未经过严格校验，内容可能包含错误。使用过程中请注意这一点。
instruction中包含多轮对话的上文内容，以*Human:*和*Assistant:*区分，output中包含当前*助手*角色的回答。
## 样例
### 字段
## 局限性和使用限制
我们要求开发者仅将我们开源的代码、数据、模型及后续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。
由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。
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，本项目的开发者不承担任何责任。
*
# Multiturn Chat 0.8M
## Contents
Includes approx. 0.8M Chinese multiturn dialogs between *human* and *assistant*.
Note: this subset was generated by *ChatGPT* and was not strictly verified. The dialog contents might contain errors. Please take this in mind when using this subset.
instruction contains history dialog context, distinguishable by *Human:* and *Assistant:*, output contains the current reply by *assistant*.
## Sample
### Schema
## Limitation and Usage Limits
We require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
Since this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.
This dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project. | [
"# Multiturn Chat 0.8M",
"## 内容\n包含约80万条由BELLE项目生成的*用户*与*助手*的多轮对话。\n\n注意：此数据集是由ChatGPT产生的，未经过严格校验，内容可能包含错误。使用过程中请注意这一点。\n\ninstruction中包含多轮对话的上文内容，以*Human:*和*Assistant:*区分，output中包含当前*助手*角色的回答。",
"## 样例",
"### 字段",
"## 局限性和使用限制\n我们要求开发者仅将我们开源的代码、数据、模型及后续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\n由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。\n\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，本项目的开发者不承担任何责任。\n\n\n*",
"# Multiturn Chat 0.8M",
"## Contents\nIncludes approx. 0.8M Chinese multiturn dialogs between *human* and *assistant*.\n\nNote: this subset was generated by *ChatGPT* and was not strictly verified. The dialog contents might contain errors. Please take this in mind when using this subset.\n\ninstruction contains history dialog context, distinguishable by *Human:* and *Assistant:*, output contains the current reply by *assistant*.",
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]
| [
"TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us \n",
"# Multiturn Chat 0.8M",
"## 内容\n包含约80万条由BELLE项目生成的*用户*与*助手*的多轮对话。\n\n注意：此数据集是由ChatGPT产生的，未经过严格校验，内容可能包含错误。使用过程中请注意这一点。\n\ninstruction中包含多轮对话的上文内容，以*Human:*和*Assistant:*区分，output中包含当前*助手*角色的回答。",
"## 样例",
"### 字段",
"## 局限性和使用限制\n我们要求开发者仅将我们开源的代码、数据、模型及后续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\n由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。\n\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，本项目的开发者不承担任何责任。\n\n\n*",
"# Multiturn Chat 0.8M",
"## Contents\nIncludes approx. 0.8M Chinese multiturn dialogs between *human* and *assistant*.\n\nNote: this subset was generated by *ChatGPT* and was not strictly verified. The dialog contents might contain errors. Please take this in mind when using this subset.\n\ninstruction contains history dialog context, distinguishable by *Human:* and *Assistant:*, output contains the current reply by *assistant*.",
"## Sample",
"### Schema",
"## Limitation and Usage Limits\nWe require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.\n\nSince this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.\n\nThis dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project."
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106,
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| [
"passage: TAGS\n#task_categories-text2text-generation #size_categories-100K<n<1M #language-Chinese #license-gpl-3.0 #region-us \n# Multiturn Chat 0.8M## 内容\n包含约80万条由BELLE项目生成的*用户*与*助手*的多轮对话。\n\n注意：此数据集是由ChatGPT产生的，未经过严格校验，内容可能包含错误。使用过程中请注意这一点。\n\ninstruction中包含多轮对话的上文内容，以*Human:*和*Assistant:*区分，output中包含当前*助手*角色的回答。## 样例### 字段## 局限性和使用限制\n我们要求开发者仅将我们开源的代码、数据、模型及后续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\n由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。\n\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，本项目的开发者不承担任何责任。\n\n\n*# Multiturn Chat 0.8M## Contents\nIncludes approx. 0.8M Chinese multiturn dialogs between *human* and *assistant*.\n\nNote: this subset was generated by *ChatGPT* and was not strictly verified. The dialog contents might contain errors. Please take this in mind when using this subset.\n\ninstruction contains history dialog context, distinguishable by *Human:* and *Assistant:*, output contains the current reply by *assistant*.## Sample### Schema"
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|
3019bab8d05af765d38c86958cafb4e6ba3fd394 |
This dataset is taken from https://www.kaggle.com/datasets/aditya48/indian-dance-form-classification but is originally from the Hackerearth deep learning contest of identifying Indian dance forms. All the credits of dataset goes to them.
### Content
The dataset consists of 599 images belonging to 8 categories, namely manipuri, bharatanatyam, odissi, kathakali, kathak, sattriya, kuchipudi, and mohiniyattam. The original dataset was quite unstructured and all the images were put together. I have organized it in their respective directories so that the process of preparing training data becomes easier.
### Acknowledgements
- https://www.hackerearth.com/challenges/competitive/hackerearth-deep-learning-challenge-identify-dance-form/
- https://www.kaggle.com/datasets/aditya48/indian-dance-form-classification | tanmaykm/indian_dance_forms | [
"task_categories:image-classification",
"size_categories:n<1K",
"license:apache-2.0",
"art",
"region:us"
]
| 2023-04-02T08:39:49+00:00 | {"license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["image-classification"], "pretty_name": "Indian Dance Forms", "tags": ["art"]} | 2023-04-03T12:26:12+00:00 | []
| []
| TAGS
#task_categories-image-classification #size_categories-n<1K #license-apache-2.0 #art #region-us
|
This dataset is taken from URL but is originally from the Hackerearth deep learning contest of identifying Indian dance forms. All the credits of dataset goes to them.
### Content
The dataset consists of 599 images belonging to 8 categories, namely manipuri, bharatanatyam, odissi, kathakali, kathak, sattriya, kuchipudi, and mohiniyattam. The original dataset was quite unstructured and all the images were put together. I have organized it in their respective directories so that the process of preparing training data becomes easier.
### Acknowledgements
- URL
- URL | [
"### Content\n\nThe dataset consists of 599 images belonging to 8 categories, namely manipuri, bharatanatyam, odissi, kathakali, kathak, sattriya, kuchipudi, and mohiniyattam. The original dataset was quite unstructured and all the images were put together. I have organized it in their respective directories so that the process of preparing training data becomes easier.",
"### Acknowledgements\n\n- URL\n- URL"
]
| [
"TAGS\n#task_categories-image-classification #size_categories-n<1K #license-apache-2.0 #art #region-us \n",
"### Content\n\nThe dataset consists of 599 images belonging to 8 categories, namely manipuri, bharatanatyam, odissi, kathakali, kathak, sattriya, kuchipudi, and mohiniyattam. The original dataset was quite unstructured and all the images were put together. I have organized it in their respective directories so that the process of preparing training data becomes easier.",
"### Acknowledgements\n\n- URL\n- URL"
]
| [
37,
92,
10
]
| [
"passage: TAGS\n#task_categories-image-classification #size_categories-n<1K #license-apache-2.0 #art #region-us \n### Content\n\nThe dataset consists of 599 images belonging to 8 categories, namely manipuri, bharatanatyam, odissi, kathakali, kathak, sattriya, kuchipudi, and mohiniyattam. The original dataset was quite unstructured and all the images were put together. I have organized it in their respective directories so that the process of preparing training data becomes easier.### Acknowledgements\n\n- URL\n- URL"
]
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|
60d0a7c950eff502717707c593b4ea1fb4519a38 | # Dataset Card for "flores200_eng_scaffolding_large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hlillemark/flores200_eng_scaffolding_large | [
"region:us"
]
| 2023-04-02T08:44:47+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "eng_source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11177748029, "num_examples": 20480000}], "download_size": 8448719815, "dataset_size": 11177748029}} | 2023-04-02T09:03:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "flores200_eng_scaffolding_large"
More Information needed | [
"# Dataset Card for \"flores200_eng_scaffolding_large\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"flores200_eng_scaffolding_large\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"flores200_eng_scaffolding_large\"\n\nMore Information needed"
]
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|
ed5f425eb8a06f98da10027e2960f57088bfa64c | # Dataset Card for "oa_tell_a_joke_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mikegarts/oa_tell_a_joke_200 | [
"region:us"
]
| 2023-04-02T10:38:34+00:00 | {"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}, {"name": "METADATA", "struct": [{"name": "link", "dtype": "string"}, {"name": "nsfw", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 125780, "num_examples": 200}], "download_size": 72498, "dataset_size": 125780}} | 2023-04-02T10:52:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "oa_tell_a_joke_200"
More Information needed | [
"# Dataset Card for \"oa_tell_a_joke_200\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"oa_tell_a_joke_200\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"oa_tell_a_joke_200\"\n\nMore Information needed"
]
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|
d60adfe9aafd37259db25c5d7c419b6d4498b2e0 | # Dataset Card for "oa_tell_a_joke_20000"
This dataset is based on the SocialGrep/one-million-reddit-jokes dataset, and augmented using KeyBert to be used for the [Open Assistant project](https://github.com/LAION-AI/Open-Assistant).
Addition details of dataset creation are [here](https://github.com/mikegarts/Open-Assistant/blob/OA-261.tell_a_joke_dataset/data/datasets/tell_a_joke/tell_a_joke.ipynb)
# Data fields:
### INSTRUCTION - The instruction to the assistant
### RESPONSE - The response of the assistant
### SOURCE - source of the data
### METADATA - additional link, such as a link to the source webpage on reddit
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mikegarts/oa_tell_a_joke_20000 | [
"language:en",
"license:mit",
"region:us"
]
| 2023-04-02T10:57:39+00:00 | {"language": ["en"], "license": "mit", "dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}, {"name": "METADATA", "struct": [{"name": "link", "dtype": "string"}, {"name": "nsfw", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 11848430, "num_examples": 20000}], "download_size": 6222319, "dataset_size": 11848430}} | 2023-04-02T11:44:50+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-mit #region-us
| # Dataset Card for "oa_tell_a_joke_20000"
This dataset is based on the SocialGrep/one-million-reddit-jokes dataset, and augmented using KeyBert to be used for the Open Assistant project.
Addition details of dataset creation are here
# Data fields:
### INSTRUCTION - The instruction to the assistant
### RESPONSE - The response of the assistant
### SOURCE - source of the data
### METADATA - additional link, such as a link to the source webpage on reddit
More Information needed | [
"# Dataset Card for \"oa_tell_a_joke_20000\"\n\nThis dataset is based on the SocialGrep/one-million-reddit-jokes dataset, and augmented using KeyBert to be used for the Open Assistant project.\nAddition details of dataset creation are here",
"# Data fields:",
"### INSTRUCTION - The instruction to the assistant",
"### RESPONSE - The response of the assistant",
"### SOURCE - source of the data",
"### METADATA - additional link, such as a link to the source webpage on reddit\n\nMore Information needed"
]
| [
"TAGS\n#language-English #license-mit #region-us \n",
"# Dataset Card for \"oa_tell_a_joke_20000\"\n\nThis dataset is based on the SocialGrep/one-million-reddit-jokes dataset, and augmented using KeyBert to be used for the Open Assistant project.\nAddition details of dataset creation are here",
"# Data fields:",
"### INSTRUCTION - The instruction to the assistant",
"### RESPONSE - The response of the assistant",
"### SOURCE - source of the data",
"### METADATA - additional link, such as a link to the source webpage on reddit\n\nMore Information needed"
]
| [
15,
71,
5,
13,
11,
10,
24
]
| [
"passage: TAGS\n#language-English #license-mit #region-us \n# Dataset Card for \"oa_tell_a_joke_20000\"\n\nThis dataset is based on the SocialGrep/one-million-reddit-jokes dataset, and augmented using KeyBert to be used for the Open Assistant project.\nAddition details of dataset creation are here# Data fields:### INSTRUCTION - The instruction to the assistant### RESPONSE - The response of the assistant### SOURCE - source of the data### METADATA - additional link, such as a link to the source webpage on reddit\n\nMore Information needed"
]
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|
839e05438775e7bd54eeff2b9dc286c3a726a28d |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
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[More Information Needed]
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### Contributions
[More Information Needed] | maitri-vv/UN16_Peace-Justice | [
"task_categories:question-answering",
"size_categories:n<1K",
"legal",
"region:us"
]
| 2023-04-02T11:19:45+00:00 | {"size_categories": ["n<1K"], "task_categories": ["question-answering"], "tags": ["legal"]} | 2023-04-02T11:23:05+00:00 | []
| []
| TAGS
#task_categories-question-answering #size_categories-n<1K #legal #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#task_categories-question-answering #size_categories-n<1K #legal #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"passage: TAGS\n#task_categories-question-answering #size_categories-n<1K #legal #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
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|
12a3f4790e495ea877f564ed746875c68ed3b10e |
# Dataset Description
RÚV Radio News (RRN) is an Icelandic-language abstractive summarization dataset. The dataset consists of almost 4k news stories from The Icelandic National Broadcasting Service (RÚV) collected from the years 2021 and 2022. The stories were sourced from the noon radio news program and comprises three parts for each story: an intro, the main story, and a summary. However, not all stories include all three parts.
The dataset can be used for various summarization tasks, including Intro + Main → Summary and Main → Intro. Typically, the introduction and summary provide an overview of the key points of the story, and sometimes they are identical. It's worth noting that the dataset features a significant number of COVID-19 and domestic news stories.
# Citation
```
@inproceedings{sverrisson2023abstractive,
title={Abstractive Text Summarization for Icelandic},
author={Sverrisson, {\TH}{\'o}r and Einarsson, Hafsteinn},
booktitle={Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)},
pages={17--31},
year={2023}
}
``` | thors/RRN | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:is",
"license:cc-by-4.0",
"region:us"
]
| 2023-04-02T12:26:36+00:00 | {"language": ["is"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["summarization"]} | 2023-05-25T14:30:19+00:00 | []
| [
"is"
]
| TAGS
#task_categories-summarization #size_categories-1K<n<10K #language-Icelandic #license-cc-by-4.0 #region-us
|
# Dataset Description
RÚV Radio News (RRN) is an Icelandic-language abstractive summarization dataset. The dataset consists of almost 4k news stories from The Icelandic National Broadcasting Service (RÚV) collected from the years 2021 and 2022. The stories were sourced from the noon radio news program and comprises three parts for each story: an intro, the main story, and a summary. However, not all stories include all three parts.
The dataset can be used for various summarization tasks, including Intro + Main → Summary and Main → Intro. Typically, the introduction and summary provide an overview of the key points of the story, and sometimes they are identical. It's worth noting that the dataset features a significant number of COVID-19 and domestic news stories.
| [
"# Dataset Description\nRÚV Radio News (RRN) is an Icelandic-language abstractive summarization dataset. The dataset consists of almost 4k news stories from The Icelandic National Broadcasting Service (RÚV) collected from the years 2021 and 2022. The stories were sourced from the noon radio news program and comprises three parts for each story: an intro, the main story, and a summary. However, not all stories include all three parts.\n\nThe dataset can be used for various summarization tasks, including Intro + Main → Summary and Main → Intro. Typically, the introduction and summary provide an overview of the key points of the story, and sometimes they are identical. It's worth noting that the dataset features a significant number of COVID-19 and domestic news stories."
]
| [
"TAGS\n#task_categories-summarization #size_categories-1K<n<10K #language-Icelandic #license-cc-by-4.0 #region-us \n",
"# Dataset Description\nRÚV Radio News (RRN) is an Icelandic-language abstractive summarization dataset. The dataset consists of almost 4k news stories from The Icelandic National Broadcasting Service (RÚV) collected from the years 2021 and 2022. The stories were sourced from the noon radio news program and comprises three parts for each story: an intro, the main story, and a summary. However, not all stories include all three parts.\n\nThe dataset can be used for various summarization tasks, including Intro + Main → Summary and Main → Intro. Typically, the introduction and summary provide an overview of the key points of the story, and sometimes they are identical. It's worth noting that the dataset features a significant number of COVID-19 and domestic news stories."
]
| [
44,
187
]
| [
"passage: TAGS\n#task_categories-summarization #size_categories-1K<n<10K #language-Icelandic #license-cc-by-4.0 #region-us \n# Dataset Description\nRÚV Radio News (RRN) is an Icelandic-language abstractive summarization dataset. The dataset consists of almost 4k news stories from The Icelandic National Broadcasting Service (RÚV) collected from the years 2021 and 2022. The stories were sourced from the noon radio news program and comprises three parts for each story: an intro, the main story, and a summary. However, not all stories include all three parts.\n\nThe dataset can be used for various summarization tasks, including Intro + Main → Summary and Main → Intro. Typically, the introduction and summary provide an overview of the key points of the story, and sometimes they are identical. It's worth noting that the dataset features a significant number of COVID-19 and domestic news stories."
]
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|
559f70700ed29184710746606cd34faa4afebd80 |
Flag dataset, görüntü üzerinde bulunan bayrak resimlerini tanımak için oluşturulmuş bir verisetidir. Veriler google images üzerinden temin edilmiş ve düzenlenmiştir. Burada bulunan verilerin tüm hakkı brain-tr tarafından saklı tutulmaktadır.
Veri seti sadece flag sınıfından oluşmaktadır ve nesne algılama problemlerinde kullanılır. Veri setine ilişkin genel özellikler ise şu şekildedir:
- Veri seti yolo formatındadır. Fakat brain-tr reposunda yolo2Voc, yolo2Coco gibi dosyalar mevcuttur. Bunları kullanarak istediğiniz formata dönüştürebilirsiniz.
- Veri seti toplamda 29.375 kayıt içermektedir. | BRAIN-TR/flag-dataset | [
"size_categories:10K<n<100K",
"language:tr",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-04-02T12:38:30+00:00 | {"language": ["tr", "en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "pretty_name": "Bayrak Veri Seti"} | 2023-04-02T12:45:38+00:00 | []
| [
"tr",
"en"
]
| TAGS
#size_categories-10K<n<100K #language-Turkish #language-English #license-apache-2.0 #region-us
|
Flag dataset, görüntü üzerinde bulunan bayrak resimlerini tanımak için oluşturulmuş bir verisetidir. Veriler google images üzerinden temin edilmiş ve düzenlenmiştir. Burada bulunan verilerin tüm hakkı brain-tr tarafından saklı tutulmaktadır.
Veri seti sadece flag sınıfından oluşmaktadır ve nesne algılama problemlerinde kullanılır. Veri setine ilişkin genel özellikler ise şu şekildedir:
- Veri seti yolo formatındadır. Fakat brain-tr reposunda yolo2Voc, yolo2Coco gibi dosyalar mevcuttur. Bunları kullanarak istediğiniz formata dönüştürebilirsiniz.
- Veri seti toplamda 29.375 kayıt içermektedir. | []
| [
"TAGS\n#size_categories-10K<n<100K #language-Turkish #language-English #license-apache-2.0 #region-us \n"
]
| [
36
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #language-Turkish #language-English #license-apache-2.0 #region-us \n"
]
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|
4ed9c32d2375724055b61467af595ec9d4a9544b | # Dataset Card for "quran-en-tafssirs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-AI-C/quran-en-tafssirs | [
"region:us"
]
| 2023-04-02T13:02:19+00:00 | {"dataset_info": {"features": [{"name": "en-ahmedali", "dtype": "string"}, {"name": "en-ahmedraza", "dtype": "string"}, {"name": "en-arberry", "dtype": "string"}, {"name": "en-asad", "dtype": "string"}, {"name": "en-daryabadi", "dtype": "string"}, {"name": "en-hilali", "dtype": "string"}, {"name": "en-itani", "dtype": "string"}, {"name": "en-maududi", "dtype": "string"}, {"name": "en-mubarakpuri", "dtype": "string"}, {"name": "en-pickthall", "dtype": "string"}, {"name": "en-qarai", "dtype": "string"}, {"name": "en-qaribullah", "dtype": "string"}, {"name": "en-sahih", "dtype": "string"}, {"name": "en-sarwar", "dtype": "string"}, {"name": "en-shakir", "dtype": "string"}, {"name": "en-transliterati", "dtype": "string"}, {"name": "en-wahiduddi", "dtype": "string"}, {"name": "en-yusufali", "dtype": "string"}, {"name": "ayah", "dtype": "int64"}, {"name": "sorah", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "en-tafsir-mokhtasar-html", "dtype": "string"}, {"name": "en-tafsir-mokhtasar-text", "dtype": "string"}, {"name": "en-tafsir-maarif-html", "dtype": "string"}, {"name": "en-tafsir-maarif-text", "dtype": "string"}, {"name": "en-tafsir-ibn-kathir-html", "dtype": "string"}, {"name": "en-tafsir-ibn-kathir-text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 66051616, "num_examples": 6235}], "download_size": 35316900, "dataset_size": 66051616}} | 2023-04-02T13:06:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "quran-en-tafssirs"
More Information needed | [
"# Dataset Card for \"quran-en-tafssirs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"quran-en-tafssirs\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"quran-en-tafssirs\"\n\nMore Information needed"
]
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|
4e99ea8d3d017cb6301573d252213cc6147dff86 | # Dataset Card for "bukhari_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-AI-C/bukhari_en | [
"region:us"
]
| 2023-04-02T13:25:56+00:00 | {"dataset_info": {"features": [{"name": "reference", "dtype": "string"}, {"name": "arabic", "dtype": "string"}, {"name": "english", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13277855, "num_examples": 7277}], "download_size": 5245942, "dataset_size": 13277855}} | 2023-04-02T13:26:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bukhari_en"
More Information needed | [
"# Dataset Card for \"bukhari_en\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bukhari_en\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bukhari_en\"\n\nMore Information needed"
]
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e3129b09220481384b08a713a63a696be026e94f |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | Syrina/donuts_dataset | [
"region:us"
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| 2023-04-02T13:34:26+00:00 | {} | 2023-04-02T21:06:36+00:00 | []
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- Paper:
- Leaderboard:
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### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
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### Languages
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### Curation Rationale
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#### 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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|
4603d860228acbbc82a274196e8d7eaad35d19c4 | # Dataset Card for "hisnulmuslim"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-AI-C/hisnulmuslim | [
"region:us"
]
| 2023-04-02T13:35:07+00:00 | {"dataset_info": {"features": [{"name": "reference", "dtype": "string"}, {"name": "arabic", "dtype": "string"}, {"name": "english", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 249638, "num_examples": 268}], "download_size": 120310, "dataset_size": 249638}} | 2023-04-02T13:35:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "hisnulmuslim"
More Information needed | [
"# Dataset Card for \"hisnulmuslim\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"hisnulmuslim\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"hisnulmuslim\"\n\nMore Information needed"
]
| [
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|
1e7dec40782f4b55dd05636461b258a9dd3216dc | # Multilingual Share GPT
Multilingual Share GPT, the free multi-language corpus for LLM training. All text are converted to markdown format, and classified by languages.
## Github Repo
Follow the [link](https://github.com/RWKV-Wiki/MultilingualShareGPT) here to Github.
## Data Example
```json
{
"id": "ImiMfCY",
"lang": "en",
"text": "\n<|user|>: Let's play chess\n\n<|bot|>: Sure, I'd love to play chess with you! Do you want to play a virtual game or just ask chess-related questions?\n\n<|user|>: Virtual game\n\n<|bot|>: I'm sorry, I'm an AI language model and I don't have the ability to display a chess board or move pieces on it. However, I'd be happy to help you with any chess-related questions or problems you might have. Just let me know what you need help with!\n\n<|user|>: Let's play\n\n<|bot|>: I apologize for any confusion, but as an AI language model I do not have the ability to play chess as it requires a graphical interface. However, I can help you learn and improve your chess skills by answering questions and providing explanations and analysis of specific positions or games.\n\n<|user|>: 1.e4\n\n<|bot|>: The move 1.e4 is the most common first move in the game of chess, and is known as the \"King's Pawn Opening\". This move aims to control the center of the board and create space for the other pieces to develop. Black has a variety of responses to 1.e4, but some of the most common are 1...e5 (the \"King's Gambit\"), 1...c5 (the \"Sicilian Defense\"), and 1...e6 (the \"French Defense\").\n\n<|user|>: I play 1.e4\n\n<|bot|>: Very good! You have started the game with the King's Pawn Opening. As black, I will respond with 1...e5, the King's Gambit.\n\n<|user|>: I play 2.Nf3\n\n<|bot|>: That's a good move. With 2.Nf3, you are developing your knight and attacking the black pawn on e5. As black, I'll play 2...Nc6, which develops the knight to a strong central square and prepares to castle kingside.\n\n<|user|>: I play 3.d4\n\n<|bot|>: With the move 3.d4, you are now offering a pawn to black with the King's Gambit. This gambit can lead to a sharp and tactical game, but it also weakens the white pawn structure and can lead to a disadvantage if not handled properly. As black, I will accept the gambit with 3...exd4.\n\n<|user|>: I play 4.Nxd4\n\n<|bot|>: With 4.Nxd4, you are now attacking the black pawn on d4 and forcing it to capture. As black, I'll capture the pawn with 4...Nxd4. This has now opened up the d-file for my rook and given me a lead in development.\n\n<|user|>: I play 5.Qxd4\n\n<|bot|>: With the move 5.Qxd4, you have taken control of the d-file and are attacking the black knight on d4. As black, I will respond with 5...Nc6, developing my knight to a safer square and putting pressure on the white queen.\n\n<|user|>: I play 6.Qd3\n\n<|bot|>: With 6.Qd3, you are attacking the black knight again and preparing to castle kingside. This move also has the idea of putting pressure on the d7-pawn, as well as the d8-h4 diagonal. As black, I will respond with 6...Nf6, developing my knight to a strong central square and preparing to castle kingside.\n\n<|user|>: I play 7.Nc3\n\n<|bot|>: With 7.Nc3, you are developing your knight and preparing to castle kingside. This move also has the idea of supporting the d4-queen and putting pressure on the e5-pawn. As black, I will play 7...d6, solidifying my pawn structure and preparing to develop my dark-squared bishop.\n\n<|user|>: I play 8.Bg5\n\n<|bot|>: With 8.Bg5, you are attacking the black knight on f6 and preparing to castle kingside. This move also has the idea of putting pressure on the h7-pawn and controlling the f4-square. As black, I will respond with 8...Be7, developing my dark-squared bishop and preparing to castle kingside.\n\n<|user|>: I play 9.0-0-0\n\n<|bot|>: With 9.0-0-0, you are now castling kingside and putting your king in a safer position. This move also has the idea of bringing the rook into the game more quickly. As black, I will castle kingside as well with 9...0-0-0.\n\n<|user|>: I play 10.Kb1\n\n<|bot|>: I'm sorry, but that is an illegal move. In chess, the king can only move one square at a time and cannot \"jump\" over other pieces. The correct move would be 10.Be2, developing your bishop and preparing to castle kingside.\n\n<|endoftext|>"
}
```
## 中文用户请看这里
为了推进中文AI的发展，促进AI技术公开化、国际化，我们成立了 ShareGPT-90k 项目，希望借助大家的力量推进数据清洗与对齐工作。
可能与各位想象的有所不同，GPT模型主要通过预训练数据集赋能，语料的质量对模型最终性能至关重要。然而，百度知道、CSDN、知乎等平台软文过多；小木虫等高质量平台语料过少；个人博客内容质量参差不齐。
OpenAI完成数据集的收集花费了巨大成本，以至于需要从微软集资。我们无力承担如此巨大的开销，于是需要各位有志于筹建开放获取语料，并有一定外语基础的网友们献上自己的力量。
如果您有意向参与此项目，我们将不胜感激。
项目地址：https://paratranz.cn/projects/6725 | cryscan/multilingual-share | [
"language:en",
"language:zh",
"license:cc0-1.0",
"region:us"
]
| 2023-04-02T13:46:39+00:00 | {"language": ["en", "zh"], "license": "cc0-1.0"} | 2023-04-06T02:13:57+00:00 | []
| [
"en",
"zh"
]
| TAGS
#language-English #language-Chinese #license-cc0-1.0 #region-us
| # Multilingual Share GPT
Multilingual Share GPT, the free multi-language corpus for LLM training. All text are converted to markdown format, and classified by languages.
## Github Repo
Follow the link here to Github.
## Data Example
## 中文用户请看这里
为了推进中文AI的发展，促进AI技术公开化、国际化，我们成立了 ShareGPT-90k 项目，希望借助大家的力量推进数据清洗与对齐工作。
可能与各位想象的有所不同，GPT模型主要通过预训练数据集赋能，语料的质量对模型最终性能至关重要。然而，百度知道、CSDN、知乎等平台软文过多；小木虫等高质量平台语料过少；个人博客内容质量参差不齐。
OpenAI完成数据集的收集花费了巨大成本，以至于需要从微软集资。我们无力承担如此巨大的开销，于是需要各位有志于筹建开放获取语料，并有一定外语基础的网友们献上自己的力量。
如果您有意向参与此项目，我们将不胜感激。
项目地址：URL | [
"# Multilingual Share GPT\nMultilingual Share GPT, the free multi-language corpus for LLM training. All text are converted to markdown format, and classified by languages.",
"## Github Repo\nFollow the link here to Github.",
"## Data Example",
"## 中文用户请看这里\n为了推进中文AI的发展，促进AI技术公开化、国际化，我们成立了 ShareGPT-90k 项目，希望借助大家的力量推进数据清洗与对齐工作。\n\n可能与各位想象的有所不同，GPT模型主要通过预训练数据集赋能，语料的质量对模型最终性能至关重要。然而，百度知道、CSDN、知乎等平台软文过多；小木虫等高质量平台语料过少；个人博客内容质量参差不齐。\n\nOpenAI完成数据集的收集花费了巨大成本，以至于需要从微软集资。我们无力承担如此巨大的开销，于是需要各位有志于筹建开放获取语料，并有一定外语基础的网友们献上自己的力量。\n\n如果您有意向参与此项目，我们将不胜感激。\n\n项目地址：URL"
]
| [
"TAGS\n#language-English #language-Chinese #license-cc0-1.0 #region-us \n",
"# Multilingual Share GPT\nMultilingual Share GPT, the free multi-language corpus for LLM training. All text are converted to markdown format, and classified by languages.",
"## Github Repo\nFollow the link here to Github.",
"## Data Example",
"## 中文用户请看这里\n为了推进中文AI的发展，促进AI技术公开化、国际化，我们成立了 ShareGPT-90k 项目，希望借助大家的力量推进数据清洗与对齐工作。\n\n可能与各位想象的有所不同，GPT模型主要通过预训练数据集赋能，语料的质量对模型最终性能至关重要。然而，百度知道、CSDN、知乎等平台软文过多；小木虫等高质量平台语料过少；个人博客内容质量参差不齐。\n\nOpenAI完成数据集的收集花费了巨大成本，以至于需要从微软集资。我们无力承担如此巨大的开销，于是需要各位有志于筹建开放获取语料，并有一定外语基础的网友们献上自己的力量。\n\n如果您有意向参与此项目，我们将不胜感激。\n\n项目地址：URL"
]
| [
23,
42,
15,
4,
187
]
| [
"passage: TAGS\n#language-English #language-Chinese #license-cc0-1.0 #region-us \n# Multilingual Share GPT\nMultilingual Share GPT, the free multi-language corpus for LLM training. All text are converted to markdown format, and classified by languages.## Github Repo\nFollow the link here to Github.## Data Example## 中文用户请看这里\n为了推进中文AI的发展，促进AI技术公开化、国际化，我们成立了 ShareGPT-90k 项目，希望借助大家的力量推进数据清洗与对齐工作。\n\n可能与各位想象的有所不同，GPT模型主要通过预训练数据集赋能，语料的质量对模型最终性能至关重要。然而，百度知道、CSDN、知乎等平台软文过多；小木虫等高质量平台语料过少；个人博客内容质量参差不齐。\n\nOpenAI完成数据集的收集花费了巨大成本，以至于需要从微软集资。我们无力承担如此巨大的开销，于是需要各位有志于筹建开放获取语料，并有一定外语基础的网友们献上自己的力量。\n\n如果您有意向参与此项目，我们将不胜感激。\n\n项目地址：URL"
]
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|
1320ed614cedfe90631d461d176a7c3b055d8239 | Reference: Ponnarassery-, Sreeja (2017), “Poem Emotion Recognition Corpus (PERC)”, Mendeley Data, V1, doi: 10.17632/n9vbc8g9cx.1 | COMP0087-GROUP8-22-23/PERC | [
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"art",
"region:us"
]
| 2023-04-02T14:11:58+00:00 | {"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "text-generation"], "pretty_name": "PERC", "tags": ["art"]} | 2023-04-02T14:14:52+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-text-generation #size_categories-1K<n<10K #language-English #art #region-us
| Reference: Ponnarassery-, Sreeja (2017), “Poem Emotion Recognition Corpus (PERC)”, Mendeley Data, V1, doi: 10.17632/n9vbc8g9cx.1 | []
| [
"TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-1K<n<10K #language-English #art #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-1K<n<10K #language-English #art #region-us \n"
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|
baad5cf35ef92b0760b0095870ce77a928cef906 | # Dataset Card for the human reference genome
## Dataset Description
- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
- **Paper:** [The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics](https://www.biorxiv.org/content/10.1101/2023.01.11.523679v1)
### Dataset Summary
The Human reference genome dataset was constructed by considering all autosomal and sex chromosomes sequences from reference assembly [GRCh38/hg38](https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26) and reaches a total of 3.2 billion nucleotides.
### Supported Tasks and Leaderboards
This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch.
### Languages
DNA
## Dataset Structure
[N/A]
### Data Instances
For each instance, there is a string representing the sequence, a string indicating the chromosome, and two integers representing the index of the first and last nucleotide respectively. An instance is shown below:
```python
{'sequence': 'CATCTGCAGGTGTCTGACTTCCAGCAACTGCTGGCCTGTGCCAGGGTGCAAGCTGAGCACTGGAGTGGAGTTTTCCTGTGGAGAGGAGCCATGCCTAGAGTGGGATGGGCCATTGTTCATCTTCTGGCCCCTGTTGTCTGCATGTAACTTAATACCACAACCAGGCATAGGGGAAAGATTGGAGGAAAGATGAGTGAGAGCATCAACTTCTCTCACAACCTAGGCCAGTAAGTAGTGCTTGTGCTCATCTCCTTGGCTGTGATACGTGGCCGGCCCTCGCTCCAGCAGCTGGACCCCTACCTGCCGTCTGCTGCCATCGGAGCCCAAAGCCGGGCTGTGACTGCTCAGACCAGCCGGCTGGAGGGAGGGGCTCAGCAGGTCTGGCTTTGGCCCTGGGAGAGCAGGTGGAAGATCAGGCAGGCCATCGCTGCCACAGAACCCAGTGGATTGGCCTAGGTGGGATCTCTGAGCTCAACAAGCCCTCTCTGGGTGGTAGGTGCAGAGACGGGAGGGGCAGAGCCGCAGGCACAGCCAAGAGGGCTGAAGAAATGGTAGAACGGAGCAGCTGGTGATGTGTGGGCCCACCGGCCCCAGGCTCCTGTCTCCCCCCAGGTGTGTGGTGATGCCAGGCATGCCCTTCCCCAGCATCAGGTCTCCAGAGCTGCAGAAGACGACGGCCGACTTGGATCACACTCTTGTGAGTGTCCCCAGTGTTGCAGAGGTGAGAGGAGAGTAGACAGTGAGTGGGAGTGGCGTCGCCCCTAGGGCTCTACGGGGCCGGCGTCTCCTGTCTCCTGGAGAGGCTTCGATGCCCCTCCACACCCTCTTGATCTTCCCTGTGATGTCATCTGGAGCCCTGCTGCTTGCGGTGGCCTATAAAGCCTCCTAGTCTGGCTCCAAGGCCTGGCAGAGTCTTTCCCAGGGAAAGCTACAAGCAGCAAACAGTCTGCATGGGTCATCCCCTTCACTCCCAGCTCAGAGCCCAGGCCAGGGGCCCCCAAGAAAGGCTCTGGTGGAGAACCTGTGCATGAAGGCTGTCAACCAGTCCATAGGCAAGCCTGGCTGCCTCCAGCTGGGTCGACAGACAGGGGCTGGAGAAGGGGAGAAGAGGAAAGTGAGGTTGCCTGCCCTGTCTCCTACCTGAGGCTGAGGAAGGAGAAGGGGATGCACTGTTGGGGAGGCAGCTGTAACTCAAAGCCTTAGCCTCTGTTCCCACGAAGGCAGGGCCATCAGGCACCAAAGGGATTCTGCCAGCATAGTGCTCCTGGACCAGTGATACACCCGGCACCCTGTCCTGGACACGCTGTTGGCCTGGATCTGAGCCCTGGTGGAGGTCAAAGCCACCTTTGGTTCTGCCATTGCTGCTGTGTGGAAGTTCACTCCTGCCTTTTCCTTTCCCTAGAGCCTCCACCACCCCGAGATCACATTTCTCACTGCCTTTTGTCTGCCCAGTTTCACCAGAAGTAGGCCTCTTCCTGACAGGCAGCTGCACCACTGCCTGGCGCTGTGCCCTTCCTTTGCTCTGCCCGCTGGAGACGGTGTTTGTCATGGGCCTGGTCTGCAGGGATCCTGCTACAAAGGTGAAACCCAGGAGAGTGTGGAGTCCAGAGTGTTGCCAGGACCCAGGCACAGGCATTAGTGCCCGTTGGAGAAAACAGGGGAATCCCGAAGAAATGGTGGGTCCTGGCCATCCGTGAGATCTTCCCAGGGCAGCTCCCCTCTGTGGAATCCAATCTGTCTTCCATCCTGCGTGGCCGAGGGCCAGGCTTCTCACTGGGCCTCTGCAGGAGGCTGCCATTTGTCCTGCCCACCTTCTTAGAAGCGAGACGGAGCAGACCCATCTGCTACTGCCCTTTCTATAATAACTAAAGTTAGCTGCCCTGGACTATTCACCCCCTAGTCTCAATTTAAGAAGATCCCCATGGCCACAGGGCCCCTGCCTGGGGGCTTGTCACCTCCCCCACCTTCTTCCTGAGTCATTCCTGCAGCCTTGCTCCCTAACCTGCCCCACAGCCTTGCCTGGATTTCTATCTCCCTGGCTTGGTGCCAGTTCCTCCAAGTCGATGGCACCTCCCTCCCTCTCAACCACTTGAGCAAACTCCAAGACATCTTCTACCCCAACACCAGCAATTGTGCCAAGGGCCATTAGGCTCTCAGCATGACTATTTTTAGAGACCCCGTGTCTGTCACTGAAACCTTTTTTGTGGGAGACTATTCCTCCCATCTGCAACAGCTGCCCCTGCTGACTGCCCTTCTCTCCTCCCTCTCATCCCAGAGAAACAGGTCAGCTGGGAGCTTCTGCCCCCACTGCCTAGGGACCAACAGGGGCAGGAGGCAGTCACTGACCCCGAGACGTTTGCATCCTGCACAGCTAGAGATCCTTTATTAAAAGCACACTGTTGGTTTCTGCTCAGTTCTTTATTGATTGGTGTGCCGTTTTCTCTGGAAGCCTCTTAAGAACACAGTGGCGCAGGCTGGGTGGAGCCGTCCCCCCATGGAGCACAGGCAGACAGAAGTCCCCGCCCCAGCTGTGTGGCCTCAAGCCAGCCTTCCGCTCCTTGAAGCTGGTCTCCACACAGTGCTGGTTCCGTCACCCCCTCCCAAGGAAGTAGGTCTGAGCAGCTTGTCCTGGCTGTGTCCATGTCAGAGCAACGGCCCAAGTCTGGGTCTGGGGGGGAAGGTGTCATGGAGCCCCCTACGATTCCCAGTCGTCCTCGTCCTCCTCTGCCTGTGGCTGCTGCGGTGGCGGCAGAGGAGGGATGGAGTCTGACACGCGGGCAAAGGCTCCTCCGGGCCCCTCACCAGCCCCAGGTCCTTTCCCAGAGATGCCTGGAGGGAAAAGGCTGAGTGAGGGTGGTTGGTGGGAAACCCTGGTTCCCCCAGCCCCCGGAGACTTAAATACAGGAAGAAAAAGGCAGGACAGAATTACAAGGTGCTGGCCCAGGGCGGGCAGCGGCCCTGCCTCCTACCCTTGCGCCTCATGACCAGCTTGTTGAAGAGATCCGACATCAAGTGCCCACCTTGGCTCGTGGCTCTCACTGCAACGGGAAAGCCACAGACTGGGGTGAAGAGTTCAGTCACATGCGACCGGTGACTCCCTGTCCCCACCCCCATGACACTCCCCAGCCCTCCAAGGCCACTGTGTTTCCCAGTTAGCTCAGAGCCTCAGTCGATCCCTGACCCAGCACCGGGCACTGATGAGACAGCGGCTGTTTGAGGAGCCACCTCCCAGCCACCTCGGGGCCAGGGCCAGGGTGTGCAGCACCACTGTACAATGGGGAAACTGGCCCAGAGAGGTGAGGCAGCTTGCCTGGGGTCACAGAGCAAGGCAAAAGCAGCGCTGGGTACAAGCTCAAAACCATAGTGCCCAGGGCACTGCCGCTGCAGGCGCAGGCATCGCATCACACCAGTGTCTGCGTTCACAGCAGGCATCATCAGTAGCCTCCAGAGGCCTCAGGTCCAGTCTCTAAAAATATCTCAGGAGGCTGCAGTGGCTGACCATTGCCTTGGACCGCTCTTGGCAGTCGAAGAAGATTCTCCTGTCAGTTTGAGCTGGGTGAGCTTAGAGAGGAAAGCTCCACTATGGCTCCCAAACCAGGAAGGAGCCATAGCCCAGGCAGGAGGGCTGAGGACCTCTGGTGGCGGCCCAGGGCTTCCAGCATGTGCCCTAGGGGAAGCAGGGGCCAGCTGGCAAGAGCAGGGGGTGGGCAGAAAGCACCCGGTGGACTCAGGGCTGGAGGGGAGGAGGCGATCTTGCCCAAGGCCCTCCGACTGCAAGCTCCAGGGCCCGCTCACCTTGCTCCTGCTCCTTCTGCTGCTGCTTCTCCAGCTTTCGCTCCTTCATGCTGCGCAGCTTGGCCTTGCCGATGCCCCCAGCTTGGCGGATGGACTCTAGCAGAGTGGCCAGCCACCGGAGGGGTCAACCACTTCCCTGGGAGCTCCCTGGACTGGAGCCGGGAGGTGGGGAACAGGGCAAGGAGGAAAGGCTGCTCAGGCAGGGCTGGGGAAGCTTACTGTGTCCAAGAGCCTGCTGGGAGGGAAGTCACCTCCCCTCAAACGAGGAGCCCTGCGCTGGGGAGGCCGGACCTTTGGAGACTGTGTGTGGGGGCCTGGGCACTGACTTCTGCAACCACCTGAGCGCGGGCATCCTGTGTGCAGATACTCCCTGCTTCCTCTCTAGCCCCCACCCTGCAGAGCTGGACCCCTGAGCTAGCCATGCTCTGACAGTCTCAGTTGCACACACGAGCCAGCAGAGGGGTTTTGTGCCACTTCTGGATGCTAGGGTTACACTGGGAGACACAGCAGTGAAGCTGAAATGAAAAATGTGTTGCTGTAGTTTGTTATTAGACCCCTTCTTTCCATTGGTTTAATTAGGAATGGGGAACCCAGAGCCTCACTTGTTCAGGCTCCCTCTGCCCTAGAAGTGAGAAGTCCAGAGCTCTACAGTTTGAAAACCACTATTTTATGAACCAAGTAGAACAAGATATTTGAAATGGAAACTATTCAAAAAATTGAGAATTTCTGACCACTTAACAAACCCACAGAAAATCCACCCGAGTGCACTGAGCACGCCAGAAATCAGGTGGCCTCAAAGAGCTGCTCCCACCTGAAGGAGACGCGCTGCTGCTGCTGTCGTCCTGCCTGGCGCCTTGGCCTACAGGGGCCGCGGTTGAGGGTGGGAGTGGGGGTGCACTGGCCAGCACCTCAGGAGCTGGGGGTGGTGGTGGGGGCGGTGGGGGTGGTGTTAGTACCCCATCTTGTAGGTCTGAAACACAAAGTGTGGGGTGTCTAGGGAAGAAGGTGTGTGACCAGGGAGGTCCCCGGCCCAGCTCCCATCCCAGAACCCAGCTCACCTACCTTGAGAGGCTCGGCTACCTCAGTGTGGAAGGTGGGCAGTTCTGGAATGGTGCCAGGGGCAGAGGGGGCAATGCCGGGGCCCAGGTCGGCAATGTACATGAGGTCGTTGGCAATGCCGGGCAGGTCAGGCAGGTAGGATGGAACATCAATCTCAGGCACCTGGCCCAGGTCTGGCACATAGAAGTAGTTCTCTGGGACCTGCAAGATTAGGCAGGGACATGTGAGAGGTGACAGGGACCTGCAGGGGCAGCCAACAAGACCTTGTGTGCACCTCCCATGGGTGGAATAAGGGGCCCAACAGCCTTGACTGGAGAGGAGCTCTGGCAAGGCCCTGGGCCACTGCACCTGTCTCCACCTCTGTCCCACCCCTCCCACCTGCTGTTCCAGCTGCTCTCTCTTGCTGATGGACAAGGGGGCATCAAACAGCTTCTCCTCTGTCTCTGCCCCCAGCATCACATGGGTCTTTGTTACAGCACCAGCCAGGGGGTCCAGGAAGACATACTTCTTCTACCTACAGAGGCGACATGGGGGTCAGGCAAGCTGACACCCGCTGTCCTGAGCCCATGTTCCTCTCCCACATCATCAGGGGCACAGCGTGCACTGTGGGGTCCCAGGCCTCCCGAGCCGAGCCACCCGTCACCCCCTGGCTCCTGGCCTATGTGCTGTACCTGTGTCTGATGCCCTGGGTCCCCACTAAGCCAGGCCGGGCCTCCCGCCCACACCCCTCGGCCCTGCCCTCTGGCCATACAGGTTCTCGGTGGTGTTGAAGAGCAGCAAGGAGCTGACAGAGCTGATGTTGCTGGGAAGACCCCCAAGTCCCTCTTCTGCATCGTCCTCGGGCTCCGGCTTGGTGCTCACGCACACAGGAAAGTCCTTCAGCTTCTCCTGAGAGGGCCAGGATGGCCAAGGGATGGTGAATATTTGGTGCTGGGCCTAATCAGCTGCCATCCCATCCCAGTCAGCCTCCTCTGGGGGACAGAACCCTATGGTGGCCCCGGCTCCTCCCCAGTATCCAGTCCTCCTGGTGTGTGACAGGCTATATGCGCGGCCAGCAGACCTGCAGGGCCCGCTCGTCCAGGGGGCGGTGCTTGCTCTGGATCCTGTGGCGGGGGCGTCTCTGCAGGCCAGGGTCCTGGGCGCCCGTGAAGATGGAGCCATATTCCTGCAGGCGCCCTGGAGCAGGGTACTTGGCACTGGAGAACACCTGTGGACACAGGGACAAGTCTGAGGGGGCCCCAAGAGGCTCAGAGGGCTAGGATTGCTTGGCAGGAGAGGGTGGAGTTGGAAGCCTGGGCGAGAAGAAAGCTCAAGGTACAGGTGGGCAGCAGGGCAGAGACTGGGCA',
'chromosome': '1',
'start_pos': 12000,
'end_pos': 18200}
```
### Data Fields
- `sequence`: a string containing a DNA sequence from the human reference genome
- `chromosome`: a string indicating the chromosome (1,2,...,21,X,Y)
- `start_pos`: an integer indicating the index of the sequence's first nucleotide
- `end_pos`: an integer indicating the index of the sequence's last nucleotide
### Data Splits
The Human reference genome dataset has 3 splits: train, validation, and test. Below are the statistics for the dataset.
```
| Dataset Split | Number of Instances in Split (6kb) | Number of Instances in Split (12kb) |
| ------------- | ------------------------------------------- | -------------------------------------------------------------- |
| Train | 498,444 | 249,222 |
| Validation | 7,784 | 3,892 |
| Test | 8,469 | 4,234 |
```
## Dataset Creation
[N/A]
### Curation Rationale
[N/A]
### Source Data
#### Initial Data Collection and Normalization
The data consists of sequences cut from the chromosomes found in the [GRCh38/hg38](https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26) human reference genome.
#### Who are the source language producers?
[N/A]
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
[N/A]
### Licensing Information
[N/A]
### Citation Information
```bibtex
@article{dalla2023nucleotide,
title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
author={Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza Revilla, Javier and Lopez Carranza, Nicolas and Henryk Grywaczewski, Adam and Oteri, Francesco and Dallago, Christian and Trop, Evan and Sirelkhatim, Hassan and Richard, Guillaume and others},
journal={bioRxiv},
pages={2023--01},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
``` | InstaDeepAI/human_reference_genome | [
"DNA",
"Genomics",
"Nucleotide",
"region:us"
]
| 2023-04-02T14:17:04+00:00 | {"pretty_name": "Human Reference Genome", "tags": ["DNA", "Genomics", "Nucleotide"]} | 2023-04-20T12:37:22+00:00 | []
| []
| TAGS
#DNA #Genomics #Nucleotide #region-us
| # Dataset Card for the human reference genome
## Dataset Description
- Repository: Nucleotide Transformer
- Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics
### Dataset Summary
The Human reference genome dataset was constructed by considering all autosomal and sex chromosomes sequences from reference assembly GRCh38/hg38 and reaches a total of 3.2 billion nucleotides.
### Supported Tasks and Leaderboards
This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch.
### Languages
DNA
## Dataset Structure
[N/A]
### Data Instances
For each instance, there is a string representing the sequence, a string indicating the chromosome, and two integers representing the index of the first and last nucleotide respectively. An instance is shown below:
### Data Fields
- 'sequence': a string containing a DNA sequence from the human reference genome
- 'chromosome': a string indicating the chromosome (1,2,...,21,X,Y)
- 'start_pos': an integer indicating the index of the sequence's first nucleotide
- 'end_pos': an integer indicating the index of the sequence's last nucleotide
### Data Splits
The Human reference genome dataset has 3 splits: train, validation, and test. Below are the statistics for the dataset.
## Dataset Creation
[N/A]
### Curation Rationale
[N/A]
### Source Data
#### Initial Data Collection and Normalization
The data consists of sequences cut from the chromosomes found in the GRCh38/hg38 human reference genome.
#### Who are the source language producers?
[N/A]
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
[N/A]
### Licensing Information
[N/A]
| [
"# Dataset Card for the human reference genome",
"## Dataset Description\n\n- Repository: Nucleotide Transformer\n- Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics",
"### Dataset Summary\n\nThe Human reference genome dataset was constructed by considering all autosomal and sex chromosomes sequences from reference assembly GRCh38/hg38 and reaches a total of 3.2 billion nucleotides.",
"### Supported Tasks and Leaderboards\n\nThis dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch.",
"### Languages\n\nDNA",
"## Dataset Structure\n\n[N/A]",
"### Data Instances\n\nFor each instance, there is a string representing the sequence, a string indicating the chromosome, and two integers representing the index of the first and last nucleotide respectively. An instance is shown below:",
"### Data Fields\n\n- 'sequence': a string containing a DNA sequence from the human reference genome\n- 'chromosome': a string indicating the chromosome (1,2,...,21,X,Y)\n- 'start_pos': an integer indicating the index of the sequence's first nucleotide \n- 'end_pos': an integer indicating the index of the sequence's last nucleotide",
"### Data Splits\n\nThe Human reference genome dataset has 3 splits: train, validation, and test. Below are the statistics for the dataset.",
"## Dataset Creation\n\n[N/A]",
"### Curation Rationale\n\n[N/A]",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\n\nThe data consists of sequences cut from the chromosomes found in the GRCh38/hg38 human reference genome.",
"#### Who are the source language producers?\n\n[N/A]",
"### Annotations\n\nThe dataset does not contain any additional annotations.",
"#### Annotation process\n\n[N/A]",
"#### Who are the annotators?\n\n[N/A]",
"### Personal and Sensitive Information\n\n[N/A]",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\n[N/A]",
"### Discussion of Biases\n\n[N/A]",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\n[N/A]",
"### Licensing Information\n\n[N/A]"
]
| [
"TAGS\n#DNA #Genomics #Nucleotide #region-us \n",
"# Dataset Card for the human reference genome",
"## Dataset Description\n\n- Repository: Nucleotide Transformer\n- Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics",
"### Dataset Summary\n\nThe Human reference genome dataset was constructed by considering all autosomal and sex chromosomes sequences from reference assembly GRCh38/hg38 and reaches a total of 3.2 billion nucleotides.",
"### Supported Tasks and Leaderboards\n\nThis dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch.",
"### Languages\n\nDNA",
"## Dataset Structure\n\n[N/A]",
"### Data Instances\n\nFor each instance, there is a string representing the sequence, a string indicating the chromosome, and two integers representing the index of the first and last nucleotide respectively. An instance is shown below:",
"### Data Fields\n\n- 'sequence': a string containing a DNA sequence from the human reference genome\n- 'chromosome': a string indicating the chromosome (1,2,...,21,X,Y)\n- 'start_pos': an integer indicating the index of the sequence's first nucleotide \n- 'end_pos': an integer indicating the index of the sequence's last nucleotide",
"### Data Splits\n\nThe Human reference genome dataset has 3 splits: train, validation, and test. Below are the statistics for the dataset.",
"## Dataset Creation\n\n[N/A]",
"### Curation Rationale\n\n[N/A]",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\n\nThe data consists of sequences cut from the chromosomes found in the GRCh38/hg38 human reference genome.",
"#### Who are the source language producers?\n\n[N/A]",
"### Annotations\n\nThe dataset does not contain any additional annotations.",
"#### Annotation process\n\n[N/A]",
"#### Who are the annotators?\n\n[N/A]",
"### Personal and Sensitive Information\n\n[N/A]",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\n[N/A]",
"### Discussion of Biases\n\n[N/A]",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\n[N/A]",
"### Licensing Information\n\n[N/A]"
]
| [
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10,
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193,
5,
11,
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| [
"passage: TAGS\n#DNA #Genomics #Nucleotide #region-us \n# Dataset Card for the human reference genome## Dataset Description\n\n- Repository: Nucleotide Transformer\n- Paper: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics### Dataset Summary\n\nThe Human reference genome dataset was constructed by considering all autosomal and sex chromosomes sequences from reference assembly GRCh38/hg38 and reaches a total of 3.2 billion nucleotides.### Supported Tasks and Leaderboards\n\nThis dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch.### Languages\n\nDNA## Dataset Structure\n\n[N/A]### Data Instances\n\nFor each instance, there is a string representing the sequence, a string indicating the chromosome, and two integers representing the index of the first and last nucleotide respectively. An instance is shown below:### Data Fields\n\n- 'sequence': a string containing a DNA sequence from the human reference genome\n- 'chromosome': a string indicating the chromosome (1,2,...,21,X,Y)\n- 'start_pos': an integer indicating the index of the sequence's first nucleotide \n- 'end_pos': an integer indicating the index of the sequence's last nucleotide"
]
| [
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|
20c9115ee9e1b1ca77e3832fbbdc39c2f095b3c3 | # Dataset Card for "all_hadith"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-AI-C/all_hadith | [
"region:us"
]
| 2023-04-02T14:18:06+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "chapter_no", "dtype": "int64"}, {"name": "hadith_no", "dtype": "string"}, {"name": "chapter", "dtype": "string"}, {"name": "chain_indx", "dtype": "string"}, {"name": "text_ar", "dtype": "string"}, {"name": "text_en", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41283768, "num_examples": 34441}], "download_size": 15794467, "dataset_size": 41283768}} | 2023-04-02T14:18:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "all_hadith"
More Information needed | [
"# Dataset Card for \"all_hadith\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"all_hadith\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"all_hadith\"\n\nMore Information needed"
]
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|
12a2f046c7e25d72d50db727324b22be57f07e7f |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | JesperWe/tayta | [
"region:us"
]
| 2023-04-02T14:44:10+00:00 | {} | 2023-04-02T14:49:28+00:00 | []
| []
| TAGS
#region-us
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# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## 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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|
df57cf4d1896d5de2de4b691b6756f2a40d20e81 |
The dataset was translated into Polish using this model: "gsarti/opus-mt-tc-en-pl"
### How to use
```python
from datasets import load_dataset
dataset = load_dataset("Aspik101/translated_polish_alpaca")
```
| Aspik101/translated_polish_alpaca | [
"task_categories:text-generation",
"language:pl",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
]
| 2023-04-02T15:25:25+00:00 | {"language": ["pl"], "license": "cc-by-4.0", "task_categories": ["text-generation"], "pretty_name": "Translated Polish Alpaca", "tags": ["instruction-finetuning"]} | 2023-04-02T15:37:20+00:00 | []
| [
"pl"
]
| TAGS
#task_categories-text-generation #language-Polish #license-cc-by-4.0 #instruction-finetuning #region-us
|
The dataset was translated into Polish using this model: "gsarti/opus-mt-tc-en-pl"
### How to use
| [
"### How to use"
]
| [
"TAGS\n#task_categories-text-generation #language-Polish #license-cc-by-4.0 #instruction-finetuning #region-us \n",
"### How to use"
]
| [
37,
5
]
| [
"passage: TAGS\n#task_categories-text-generation #language-Polish #license-cc-by-4.0 #instruction-finetuning #region-us \n### How to use"
]
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|
bb974c2a2987e609116c8a93f58f33fda47a297d | # Isolet
The [Isolet dataset](https://archive-beta.ics.uci.edu/dataset/54/isolet) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|--------------------------|
| isolet | Multiclass classification | What letter was uttered? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/isolet", "isolet")["train"]
``` | mstz/isolet | [
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"isolet",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
]
| 2023-04-02T15:30:49+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "Isolet", "tags": ["isolet", "tabular_classification", "binary_classification", "multiclass_classification", "UCI"], "configs": ["isolet"]} | 2023-04-20T08:50:41+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #license-cc #isolet #tabular_classification #binary_classification #multiclass_classification #UCI #region-us
| Isolet
======
The Isolet dataset from the UCI ML repository.
Configurations and tasks
========================
Configuration: isolet, Task: Multiclass classification, Description: What letter was uttered?
Usage
=====
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #license-cc #isolet #tabular_classification #binary_classification #multiclass_classification #UCI #region-us \n"
]
| [
62
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #license-cc #isolet #tabular_classification #binary_classification #multiclass_classification #UCI #region-us \n"
]
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|
1d64f1adc7a7e1f3c517b5e2729dbf13f2c80f88 | ---
---
The dataset contains images of people collected from the web by typing common given names into Google Image Search.
The coordinates of the eyes, the nose and the center of the mouth for each frontal face are provided in a ground truth file.
This information can be used to align and crop the human faces or as a ground truth for a face detection algorithm.
The dataset has 10,524 human faces of various resolutions and in different settings, e.g. portrait images, groups of people, etc.
Profile faces or very low resolution faces are not labeled.
The ground truth contains the following information:
image-name Leye-x Leye-y Reye-x Reye-y nose-x nose-y mouth-x mouth-y
Citation:
Fink, M., & Perona, P. (2022). Caltech 10k Web Faces (1.0) [Data set]. CaltechDATA. https://doi.org/10.22002/D1.20132
| syedashfaq/Caltech_Webfaces | [
"region:us"
]
| 2023-04-02T16:35:45+00:00 | {} | 2023-04-02T16:52:09+00:00 | []
| []
| TAGS
#region-us
| ---
---
The dataset contains images of people collected from the web by typing common given names into Google Image Search.
The coordinates of the eyes, the nose and the center of the mouth for each frontal face are provided in a ground truth file.
This information can be used to align and crop the human faces or as a ground truth for a face detection algorithm.
The dataset has 10,524 human faces of various resolutions and in different settings, e.g. portrait images, groups of people, etc.
Profile faces or very low resolution faces are not labeled.
The ground truth contains the following information:
image-name Leye-x Leye-y Reye-x Reye-y nose-x nose-y mouth-x mouth-y
Citation:
Fink, M., & Perona, P. (2022). Caltech 10k Web Faces (1.0) [Data set]. CaltechDATA. URL
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
8b7f6a74a199a2a2196d4d471b47ff4330782727 | Motorbikes (Side) dataset. Collected by undergrads at Caltech (Feb '01) from the web. 826 images of motorbikes from the side. JPEG format.
ImageData.mat is a MATLAB file containing the variable SubDir_Data which is an 8 x 826 matrix.
Each column of this matrix hold the coordinates of the bike within the image, in the form: [x_bot_left y_bot_left x_top_left y_top_left x_top_right y_top_right x_bot_right y_bot_right].
Please cite the below wherever required.
Perona, P. (2022). Caltech Motorcycles 2001 (1.0) [Data set]. CaltechDATA. https://doi.org/10.22002/D1.20088 | syedashfaq/Motorcycles | [
"region:us"
]
| 2023-04-02T16:39:13+00:00 | {} | 2023-04-02T16:43:35+00:00 | []
| []
| TAGS
#region-us
| Motorbikes (Side) dataset. Collected by undergrads at Caltech (Feb '01) from the web. 826 images of motorbikes from the side. JPEG format.
URL is a MATLAB file containing the variable SubDir_Data which is an 8 x 826 matrix.
Each column of this matrix hold the coordinates of the bike within the image, in the form: [x_bot_left y_bot_left x_top_left y_top_left x_top_right y_top_right x_bot_right y_bot_right].
Please cite the below wherever required.
Perona, P. (2022). Caltech Motorcycles 2001 (1.0) [Data set]. CaltechDATA. URL | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
0002186e8dd9af47ba7e4076cd6b7a8a206ed055 | # Dataset Card for "upslash"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 8glabs/upslash | [
"region:us"
]
| 2023-04-02T16:43:47+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20988.0, "num_examples": 1}], "download_size": 22146, "dataset_size": 20988.0}} | 2023-04-02T16:45:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "upslash"
More Information needed | [
"# Dataset Card for \"upslash\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"upslash\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"upslash\"\n\nMore Information needed"
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|
04cedbbb9acc9c2efe5150e1f0381346444cb8c3 | # Dataset Card for "QGG-RACE Dataset"
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
- GitHub Repository: N/A
- Paper: N/A
- Leaderboard: N/A
- Point of Contact: N/A
## Dataset Summary
QGG-RACE Dataset is a subset of RACE, containing three types of questions: Factoid, Cloze, and Summarization.
Dataset Download: [GitHub Release](https://github.com/p208p2002/QGG-RACE-dataset/releases/download/v1.0/qgg-dataset.zip)
Data Statistics:
Types | Examples | Train | Dev | Test
------------- | ------------------------------------------ | ----- | ---- | ----
Cloze | Yingying is Wangwang's _ . | 43167 | 2405 | 2462
Factiod | What can Mimi do? | 18405 | 1030 | 944
Summarization | According to this passage we know that _ . | 3004 | 175 | 184
## Supported Tasks and Leaderboards
- Question Generation
- Reading Comprehension
- Text Summarization
## Languages
The dataset is in English.
## Dataset Structure
### Data Instances
An example data instance from the dataset is shown below:
```json
{
"answers": [
"D",
"A",
"B",
"C"
],
"options": [
[
"States",
"Doubts",
"Confirms",
"Removes"
],
[
"shows the kind of male birds females seek out.",
"indicates the wandering albatross is the most faithful.",
"is based on Professor Stutchbury's 20 years' research.",
"suggests that female birds select males near their home."
],
[
"young birds' quality depends on their feather.",
"some male birds care for others' young as their own.",
"female birds go to find males as soon as autumn comes.",
"female birds are responsible for feeding the hungry babies."
],
[
"A book about love-birds.",
"Birds' living habits and love life",
"The fact that birds don't love their mates forever.",
"The factors that influence birds to look for another mate."
]
],
"questions": [
"What does the underline word \"dispels\" mean?",
"The book The Private Lives of Birds _ .",
"According to the passage, we can infer that _ .",
"What is the passage mainly about?"
],
"article": "Birds are not as loyal to their partners as you might think ...",
"id": "high11327.txt",
"factoid_questions": [
"What does the underline word \"dispels\" mean?"
],
"cloze_questions": [
"The book The Private Lives of Birds _ ."
],
"summarization_questions": [
"According to the passage, we can infer that _ ."
]
}
```
## Data Fields
- id: Unique identifier for the example.
- article: The main text passage.
- questions: List of questions related to the passage.
- options: List of answer options for each question.
- answers: Indexes of the correct answers for each question.
- factoid_questions: List of factoid questions.
- cloze_questions: List of cloze questions.
- summarization_questions: List of summarization questions.
### Data Splits
- Train: Contains 65,576 examples.
- Dev: Contains 3,610 examples.
- Test: Contains 3,590 examples.
## Dataset Creation
### Curation Rationale
QGG-RACE dataset is created as a subset of RACE, focusing on three types of questions: Factoid, Cloze, and Summarization. This dataset is intended to facilitate research in question generation and reading comprehension.
### Source Data
#### Initial Data Collection and Normalization
QGG-RACE dataset is derived from RACE dataset.
#### Who are the source language producers?
The source language producers are the authors of the RACE dataset.
### Annotations
#### Annotation process
The dataset is annotated with questions and their corresponding answer options.
#### Who are the annotators?
The annotators are the authors of the RACE dataset.
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
The QGG-RACE dataset can be used for research in question generation and reading comprehension, leading to improvements in these fields.
### Discussion of Biases
The dataset may inherit some biases from the RACE dataset as it is a subset of it.
### Other Known Limitations
No other known limitations.
## Additional Information
### Dataset Curators
The QGG-RACE dataset is curated by the authors of the QGG-RACE dataset GitHub repository.
### Licensing Information
The dataset is released under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
No citation information is available for the QGG-RACE dataset.
### Contributions
Thanks to @p208p2002 for creating the QGG-RACE dataset. | voidful/EQG-RACE-PLUS | [
"region:us"
]
| 2023-04-02T17:39:25+00:00 | {"dataset_info": {"features": [{"name": "questions", "list": [{"name": "answer", "struct": [{"name": "answer_index", "dtype": "int64"}, {"name": "answer_text", "dtype": "string"}]}, {"name": "options", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_type", "dtype": "string"}]}, {"name": "article", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train_all", "num_bytes": 63952721, "num_examples": 25137}, {"name": "train_middle", "num_bytes": 12480455, "num_examples": 6409}, {"name": "dev_high", "num_bytes": 2790766, "num_examples": 1021}, {"name": "dev_middle", "num_bytes": 712198, "num_examples": 368}, {"name": "test_middle", "num_bytes": 714595, "num_examples": 362}, {"name": "train_high", "num_bytes": 51472267, "num_examples": 18728}, {"name": "test_high", "num_bytes": 2850894, "num_examples": 1045}], "download_size": 33312158, "dataset_size": 134973896}} | 2023-05-24T07:16:18+00:00 | []
| []
| TAGS
#region-us
| Dataset Card for "QGG-RACE Dataset"
===================================
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
-------------------
* GitHub Repository: N/A
* Paper: N/A
* Leaderboard: N/A
* Point of Contact: N/A
Dataset Summary
---------------
QGG-RACE Dataset is a subset of RACE, containing three types of questions: Factoid, Cloze, and Summarization.
Dataset Download: GitHub Release
Data Statistics:
Supported Tasks and Leaderboards
--------------------------------
* Question Generation
* Reading Comprehension
* Text Summarization
Languages
---------
The dataset is in English.
Dataset Structure
-----------------
### Data Instances
An example data instance from the dataset is shown below:
Data Fields
-----------
* id: Unique identifier for the example.
* article: The main text passage.
* questions: List of questions related to the passage.
* options: List of answer options for each question.
* answers: Indexes of the correct answers for each question.
* factoid\_questions: List of factoid questions.
* cloze\_questions: List of cloze questions.
* summarization\_questions: List of summarization questions.
### Data Splits
* Train: Contains 65,576 examples.
* Dev: Contains 3,610 examples.
* Test: Contains 3,590 examples.
Dataset Creation
----------------
### Curation Rationale
QGG-RACE dataset is created as a subset of RACE, focusing on three types of questions: Factoid, Cloze, and Summarization. This dataset is intended to facilitate research in question generation and reading comprehension.
### Source Data
#### Initial Data Collection and Normalization
QGG-RACE dataset is derived from RACE dataset.
#### Who are the source language producers?
The source language producers are the authors of the RACE dataset.
### Annotations
#### Annotation process
The dataset is annotated with questions and their corresponding answer options.
#### Who are the annotators?
The annotators are the authors of the RACE dataset.
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information.
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
The QGG-RACE dataset can be used for research in question generation and reading comprehension, leading to improvements in these fields.
### Discussion of Biases
The dataset may inherit some biases from the RACE dataset as it is a subset of it.
### Other Known Limitations
No other known limitations.
Additional Information
----------------------
### Dataset Curators
The QGG-RACE dataset is curated by the authors of the QGG-RACE dataset GitHub repository.
### Licensing Information
The dataset is released under the CC BY 4.0 License.
No citation information is available for the QGG-RACE dataset.
### Contributions
Thanks to @p208p2002 for creating the QGG-RACE dataset.
| [
"### Data Instances\n\n\nAn example data instance from the dataset is shown below:\n\n\nData Fields\n-----------\n\n\n* id: Unique identifier for the example.\n* article: The main text passage.\n* questions: List of questions related to the passage.\n* options: List of answer options for each question.\n* answers: Indexes of the correct answers for each question.\n* factoid\\_questions: List of factoid questions.\n* cloze\\_questions: List of cloze questions.\n* summarization\\_questions: List of summarization questions.",
"### Data Splits\n\n\n* Train: Contains 65,576 examples.\n* Dev: Contains 3,610 examples.\n* Test: Contains 3,590 examples.\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nQGG-RACE dataset is created as a subset of RACE, focusing on three types of questions: Factoid, Cloze, and Summarization. This dataset is intended to facilitate research in question generation and reading comprehension.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nQGG-RACE dataset is derived from RACE dataset.",
"#### Who are the source language producers?\n\n\nThe source language producers are the authors of the RACE dataset.",
"### Annotations",
"#### Annotation process\n\n\nThe dataset is annotated with questions and their corresponding answer options.",
"#### Who are the annotators?\n\n\nThe annotators are the authors of the RACE dataset.",
"### Personal and Sensitive Information\n\n\nThe dataset does not contain any personal or sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe QGG-RACE dataset can be used for research in question generation and reading comprehension, leading to improvements in these fields.",
"### Discussion of Biases\n\n\nThe dataset may inherit some biases from the RACE dataset as it is a subset of it.",
"### Other Known Limitations\n\n\nNo other known limitations.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe QGG-RACE dataset is curated by the authors of the QGG-RACE dataset GitHub repository.",
"### Licensing Information\n\n\nThe dataset is released under the CC BY 4.0 License.\n\n\nNo citation information is available for the QGG-RACE dataset.",
"### Contributions\n\n\nThanks to @p208p2002 for creating the QGG-RACE dataset."
]
| [
"TAGS\n#region-us \n",
"### Data Instances\n\n\nAn example data instance from the dataset is shown below:\n\n\nData Fields\n-----------\n\n\n* id: Unique identifier for the example.\n* article: The main text passage.\n* questions: List of questions related to the passage.\n* options: List of answer options for each question.\n* answers: Indexes of the correct answers for each question.\n* factoid\\_questions: List of factoid questions.\n* cloze\\_questions: List of cloze questions.\n* summarization\\_questions: List of summarization questions.",
"### Data Splits\n\n\n* Train: Contains 65,576 examples.\n* Dev: Contains 3,610 examples.\n* Test: Contains 3,590 examples.\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nQGG-RACE dataset is created as a subset of RACE, focusing on three types of questions: Factoid, Cloze, and Summarization. This dataset is intended to facilitate research in question generation and reading comprehension.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nQGG-RACE dataset is derived from RACE dataset.",
"#### Who are the source language producers?\n\n\nThe source language producers are the authors of the RACE dataset.",
"### Annotations",
"#### Annotation process\n\n\nThe dataset is annotated with questions and their corresponding answer options.",
"#### Who are the annotators?\n\n\nThe annotators are the authors of the RACE dataset.",
"### Personal and Sensitive Information\n\n\nThe dataset does not contain any personal or sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe QGG-RACE dataset can be used for research in question generation and reading comprehension, leading to improvements in these fields.",
"### Discussion of Biases\n\n\nThe dataset may inherit some biases from the RACE dataset as it is a subset of it.",
"### Other Known Limitations\n\n\nNo other known limitations.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe QGG-RACE dataset is curated by the authors of the QGG-RACE dataset GitHub repository.",
"### Licensing Information\n\n\nThe dataset is released under the CC BY 4.0 License.\n\n\nNo citation information is available for the QGG-RACE dataset.",
"### Contributions\n\n\nThanks to @p208p2002 for creating the QGG-RACE dataset."
]
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"passage: TAGS\n#region-us \n### Data Instances\n\n\nAn example data instance from the dataset is shown below:\n\n\nData Fields\n-----------\n\n\n* id: Unique identifier for the example.\n* article: The main text passage.\n* questions: List of questions related to the passage.\n* options: List of answer options for each question.\n* answers: Indexes of the correct answers for each question.\n* factoid\\_questions: List of factoid questions.\n* cloze\\_questions: List of cloze questions.\n* summarization\\_questions: List of summarization questions.### Data Splits\n\n\n* Train: Contains 65,576 examples.\n* Dev: Contains 3,610 examples.\n* Test: Contains 3,590 examples.\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nQGG-RACE dataset is created as a subset of RACE, focusing on three types of questions: Factoid, Cloze, and Summarization. This dataset is intended to facilitate research in question generation and reading comprehension.### Source Data#### Initial Data Collection and Normalization\n\n\nQGG-RACE dataset is derived from RACE dataset.#### Who are the source language producers?\n\n\nThe source language producers are the authors of the RACE dataset.### Annotations#### Annotation process\n\n\nThe dataset is annotated with questions and their corresponding answer options.#### Who are the annotators?\n\n\nThe annotators are the authors of the RACE dataset.### Personal and Sensitive Information\n\n\nThe dataset does not contain any personal or sensitive information.\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\nThe QGG-RACE dataset can be used for research in question generation and reading comprehension, leading to improvements in these fields.### Discussion of Biases\n\n\nThe dataset may inherit some biases from the RACE dataset as it is a subset of it.### Other Known Limitations\n\n\nNo other known limitations.\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\nThe QGG-RACE dataset is curated by the authors of the QGG-RACE dataset GitHub repository."
]
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|
9e427d7c48ddb79e7b7cceee0fa226e936153d79 | # Dataset Card for "procedural_gen_3operands"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sethapun/procedural_gen_3operands | [
"region:us"
]
| 2023-04-02T18:19:44+00:00 | {"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 71608, "num_examples": 2000}, {"name": "validation", "num_bytes": 14332, "num_examples": 400}], "download_size": 35255, "dataset_size": 85940}} | 2023-04-02T18:19:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "procedural_gen_3operands"
More Information needed | [
"# Dataset Card for \"procedural_gen_3operands\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"procedural_gen_3operands\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"procedural_gen_3operands\"\n\nMore Information needed"
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|
69cacc1df7b02f9fcceae4481d8878d4e9b330bc | # Dataset Card for "procedural_gen_4operands"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sethapun/procedural_gen_4operands | [
"region:us"
]
| 2023-04-02T18:20:28+00:00 | {"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 85550, "num_examples": 2000}, {"name": "validation", "num_bytes": 17072, "num_examples": 400}], "download_size": 41332, "dataset_size": 102622}} | 2023-04-02T18:20:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "procedural_gen_4operands"
More Information needed | [
"# Dataset Card for \"procedural_gen_4operands\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"procedural_gen_4operands\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"procedural_gen_4operands\"\n\nMore Information needed"
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|
98dbd50f76d9a5bef2752fde79a3ab9122f0e837 | This dataset contains random objects from home. The objects are taken mostly from kitchen, bathroom and living-room environments. Each directory ("Train" and "Test") contains a subdirectory named 'Gtruth/', containing ground truth .mat files. The .mat files contain a variable 'outline'. For training images, 'outline' contains the corners of a bounding box defining the model in the training image. For test images, 'outline' contains the names of the models present in the image along with a bounding box for each of them. If a same training object is defined by several training images, 'outline' references all these training images, with the same bounding box. You can also find a tar archive of the whole dataset in "HomeObjects06.tar".
Citation:
Moreels, P., & Perona, P. (2022). Caltech Home Objects 2006 (1.0) [Data set]. CaltechDATA. https://doi.org/10.22002/D1.20089
| syedashfaq/Home_Objects | [
"region:us"
]
| 2023-04-02T18:37:26+00:00 | {} | 2023-04-02T18:40:34+00:00 | []
| []
| TAGS
#region-us
| This dataset contains random objects from home. The objects are taken mostly from kitchen, bathroom and living-room environments. Each directory ("Train" and "Test") contains a subdirectory named 'Gtruth/', containing ground truth .mat files. The .mat files contain a variable 'outline'. For training images, 'outline' contains the corners of a bounding box defining the model in the training image. For test images, 'outline' contains the names of the models present in the image along with a bounding box for each of them. If a same training object is defined by several training images, 'outline' references all these training images, with the same bounding box. You can also find a tar archive of the whole dataset in "URL".
Citation:
Moreels, P., & Perona, P. (2022). Caltech Home Objects 2006 (1.0) [Data set]. CaltechDATA. URL
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
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|
0128bbc51a202b500778aba4f5e30e87c2f8a2bf | # AutoTrain Dataset for project: arp_summ_1
## Dataset Description
This dataset has been automatically processed by AutoTrain for project arp_summ_1.
### 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": " eat , grass , horse ",
"target": " The old horse ate grass all day. "
},
{
"text": " lay , dog , rug ",
"target": " Brown dog chews on bone while laying on the rug. "
}
]
```
### 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 | 197 |
| valid | 50 |
| AryaParikh/autotrain-data-arp_summ_1 | [
"task_categories:summarization",
"region:us"
]
| 2023-04-02T18:40:43+00:00 | {"task_categories": ["summarization"]} | 2023-04-02T22:19:55+00:00 | []
| []
| TAGS
#task_categories-summarization #region-us
| AutoTrain Dataset for project: arp\_summ\_1
===========================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project arp\_summ\_1.
### 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-summarization #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:"
]
| [
16,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-summarization #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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|
b4d3d29faaee2ca5acb9ba872323be06677a4a7d |
# Camoscio instruction-tuning dataset
This repository contains the dataset used to train [Camoscio](https://huggingface.co/teelinsan/camoscio-7b-llama).
This dataset is an Italian translation with ChatGPT of the [Stanford Alpaca dataset](https://github.com/tatsu-lab/stanford_alpaca).
Please refer to the [Camoscio repo](https://github.com/teelinsan/camoscio) for more info.
| teelinsan/camoscio | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:it",
"license:openrail",
"llama",
"instruction-tuning",
"region:us"
]
| 2023-04-02T19:12:37+00:00 | {"language": ["it"], "license": "openrail", "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "tags": ["llama", "instruction-tuning"]} | 2023-04-02T19:18:52+00:00 | []
| [
"it"
]
| TAGS
#task_categories-conversational #size_categories-10K<n<100K #language-Italian #license-openrail #llama #instruction-tuning #region-us
|
# Camoscio instruction-tuning dataset
This repository contains the dataset used to train Camoscio.
This dataset is an Italian translation with ChatGPT of the Stanford Alpaca dataset.
Please refer to the Camoscio repo for more info.
| [
"# Camoscio instruction-tuning dataset\n\nThis repository contains the dataset used to train Camoscio.\n\nThis dataset is an Italian translation with ChatGPT of the Stanford Alpaca dataset.\n\nPlease refer to the Camoscio repo for more info."
]
| [
"TAGS\n#task_categories-conversational #size_categories-10K<n<100K #language-Italian #license-openrail #llama #instruction-tuning #region-us \n",
"# Camoscio instruction-tuning dataset\n\nThis repository contains the dataset used to train Camoscio.\n\nThis dataset is an Italian translation with ChatGPT of the Stanford Alpaca dataset.\n\nPlease refer to the Camoscio repo for more info."
]
| [
47,
58
]
| [
"passage: TAGS\n#task_categories-conversational #size_categories-10K<n<100K #language-Italian #license-openrail #llama #instruction-tuning #region-us \n# Camoscio instruction-tuning dataset\n\nThis repository contains the dataset used to train Camoscio.\n\nThis dataset is an Italian translation with ChatGPT of the Stanford Alpaca dataset.\n\nPlease refer to the Camoscio repo for more info."
]
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|
3f4efe36090e20c22ab814c31ca220b5b48d0027 | Overview
This data set contains Beluga whales (Delphinapterus leucas) with pre-cropped images and individual animal identifications. This dataset represents a collaborative effort based on the data collection and population modeling efforts conducted in the Cook Inlet off the cost of Alaska from 2016-2019. The photos (5,902) and metadata from 1,617 unique encounters (within 1 hour) were collected from boat-based cameras and a camera looking down from above on an aerial drone. Images are annotated with full-image bounding boxes and viewpoints were labeled (top, left, right). A total of 788 individual Beluga whales were identified by hand by trained experts using scarring patterns and other visual markings. This dataset is being released in tandem with the “Where’s Whale-do?” ID competition hosted by DrivenData and is identical to the public training set used in that competition.
Data format
The training dataset is released in the Microsoft COCO .json format. We have collapsed the entire dataset into a single “train” label and have left “val” and “test” empty; we do this as an invitation to researchers to experiment with their own novel approaches for dealing with the unbalanced and chaotic distribution on the number of sightings per individual. All of the images in the dataset have been resized to have a maximum dimension of 1,200 pixels. The metadata for all animal sightings is defined by an axis-aligned bounding box and includes information on the viewpoint of the animal, a species (category) ID, a source image ID, an individual string ID name, and other miscellaneous values. The temporal ordering of the images can be determined from the metadata for each image.
Test data was added later, after the competition, and is thus in a different format. Contact the dataset owner for questions about the test data.
Citation, license, and contact information
For research or press contact, please direct all correspondence to Wild Me at [email protected]. Wild Me is a registered 501(c)(3) not-for-profit based in Portland, Oregon, USA and brings state-of-the-art computer vision tools to ecology researchers working around the globe on wildlife conservation. | syedashfaq/Beluga | [
"region:us"
]
| 2023-04-02T19:22:55+00:00 | {} | 2023-04-02T19:33:51+00:00 | []
| []
| TAGS
#region-us
| Overview
This data set contains Beluga whales (Delphinapterus leucas) with pre-cropped images and individual animal identifications. This dataset represents a collaborative effort based on the data collection and population modeling efforts conducted in the Cook Inlet off the cost of Alaska from 2016-2019. The photos (5,902) and metadata from 1,617 unique encounters (within 1 hour) were collected from boat-based cameras and a camera looking down from above on an aerial drone. Images are annotated with full-image bounding boxes and viewpoints were labeled (top, left, right). A total of 788 individual Beluga whales were identified by hand by trained experts using scarring patterns and other visual markings. This dataset is being released in tandem with the “Where’s Whale-do?” ID competition hosted by DrivenData and is identical to the public training set used in that competition.
Data format
The training dataset is released in the Microsoft COCO .json format. We have collapsed the entire dataset into a single “train” label and have left “val” and “test” empty; we do this as an invitation to researchers to experiment with their own novel approaches for dealing with the unbalanced and chaotic distribution on the number of sightings per individual. All of the images in the dataset have been resized to have a maximum dimension of 1,200 pixels. The metadata for all animal sightings is defined by an axis-aligned bounding box and includes information on the viewpoint of the animal, a species (category) ID, a source image ID, an individual string ID name, and other miscellaneous values. The temporal ordering of the images can be determined from the metadata for each image.
Test data was added later, after the competition, and is thus in a different format. Contact the dataset owner for questions about the test data.
Citation, license, and contact information
For research or press contact, please direct all correspondence to Wild Me at info@URL. Wild Me is a registered 501(c)(3) not-for-profit based in Portland, Oregon, USA and brings state-of-the-art computer vision tools to ecology researchers working around the globe on wildlife conservation. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
ef1fb068893045d537280ad3e8fa988c14a944db | # Dataset Card for "pokemon-512"
A cleaned + upsampled-to-512px-square version of https://www.kaggle.com/datasets/djilax/pkmn-image-dataset, suitable for training high-resolution unconditional image generators.
| madebyollin/pokemon-512 | [
"region:us"
]
| 2023-04-02T20:58:27+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1136778591.55, "num_examples": 6930}], "download_size": 1115147878, "dataset_size": 1136778591.55}} | 2023-04-02T21:25:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pokemon-512"
A cleaned + upsampled-to-512px-square version of URL suitable for training high-resolution unconditional image generators.
| [
"# Dataset Card for \"pokemon-512\"\n\nA cleaned + upsampled-to-512px-square version of URL suitable for training high-resolution unconditional image generators.\n\n"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pokemon-512\"\n\nA cleaned + upsampled-to-512px-square version of URL suitable for training high-resolution unconditional image generators.\n\n"
]
| [
6,
56
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pokemon-512\"\n\nA cleaned + upsampled-to-512px-square version of URL suitable for training high-resolution unconditional image generators.\n\n"
]
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]
|
95f232ae4b3fcf5ccd301d55f25209718cba7122 |
09/04/2023 Update:
New instructions added from: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
Original Version: https://github.com/tatsu-lab/stanford_alpaca#data-release
AI BASED TRANSLATION RESULTS OF STANFORD ALPACA EN TO TR
For academic only, please cite before you use it.
Taşar, D. E. T. (2023). stanford-alpaca-cleaned-turkish-translated [Dataset]. In Stanford Alpaca TR (1.0.1.a). https://huggingface.co/datasets/emre/stanford-alpaca-cleaned-turkish-translated
### Citation
Please cite the repo if you use the data or code in this repo.
```
@misc{alpaca-tr,tasar-2023
author = {Taşar, Davut Emre},
title = {stanford-alpaca-cleaned-turkish-translated},
year = {2023},
publisher = {Huggingface},
journal = {Huggingface repository},
howpublished = {\url{https://huggingface.co/datasets/emre/stanford-alpaca-cleaned-turkish-translated}},
}
``` | emre/stanford-alpaca-cleaned-turkish-translated | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:tr",
"license:afl-3.0",
"region:us"
]
| 2023-04-02T21:25:08+00:00 | {"language": ["tr"], "license": "afl-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"]} | 2023-04-08T20:28:43+00:00 | []
| [
"tr"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-Turkish #license-afl-3.0 #region-us
|
09/04/2023 Update:
New instructions added from: URL
Original Version: URL
AI BASED TRANSLATION RESULTS OF STANFORD ALPACA EN TO TR
For academic only, please cite before you use it.
Taşar, D. E. T. (2023). stanford-alpaca-cleaned-turkish-translated [Dataset]. In Stanford Alpaca TR (1.0.1.a). URL
Please cite the repo if you use the data or code in this repo.
| []
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Turkish #license-afl-3.0 #region-us \n"
]
| [
43
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Turkish #license-afl-3.0 #region-us \n"
]
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|
8b9efd736aa328573868f6cc5c88436ba345ce13 | ~10k conversations from Omegle. Scraped using: http://web.archive.org/cdx/search/xd?url=logs.omegle.com/*&fl=timestamp,original,statuscode&output=json. For these logs to have ended up on the cdx, it means the url was posted publicly at some point.
* PII removed by searching for conversations with these words: forbidden_words = ["kik", "telegram", "skype", "wickr", "discord", "dropbox", "insta ", "insta?", "instagram", "snap ", "snapchat"].
* Conversations with racial slurs removed.
* English only.
* Obviously, the dataset still contains a lot of (sometimes extreme) NSFW content. Do not view or use this dataset if you are under 18.
General process for scraping (There are probably other datasets that can be scraped using this method):
1. Go to page in archive.org cdx
2. Check if the page contains a log
3. Download the log image
4. Use OCR to read it
5. Save it to a json file.
This dataset could be useful for training casual conversational AI's but it likely still requires more filtering. Use at your own risk. | anon8231489123/Omegle_logs_dataset | [
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-04-02T22:15:21+00:00 | {"language": ["en"], "license": "apache-2.0"} | 2023-04-02T22:34:21+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-apache-2.0 #region-us
| ~10k conversations from Omegle. Scraped using: URL For these logs to have ended up on the cdx, it means the url was posted publicly at some point.
* PII removed by searching for conversations with these words: forbidden_words = ["kik", "telegram", "skype", "wickr", "discord", "dropbox", "insta ", "insta?", "instagram", "snap ", "snapchat"].
* Conversations with racial slurs removed.
* English only.
* Obviously, the dataset still contains a lot of (sometimes extreme) NSFW content. Do not view or use this dataset if you are under 18.
General process for scraping (There are probably other datasets that can be scraped using this method):
1. Go to page in URL cdx
2. Check if the page contains a log
3. Download the log image
4. Use OCR to read it
5. Save it to a json file.
This dataset could be useful for training casual conversational AI's but it likely still requires more filtering. Use at your own risk. | []
| [
"TAGS\n#language-English #license-apache-2.0 #region-us \n"
]
| [
18
]
| [
"passage: TAGS\n#language-English #license-apache-2.0 #region-us \n"
]
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|
764d5540bcd1da587ec2af4bc299bfe6da1b6d27 |
# Dataset Card for XNLI Parallel Corpus
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
### Supported Tasks and Leaderboards
Binary mode classification (spoken vs written)
### Languages
- English
- German
- French
## Dataset Structure
### Data Instances
{
'text': "And he said , Mama , I 'm home .",
'label': 0
}
### Data Fields
- text: sentence
- label: binary label of text (0: spoken 1: written)
### Data Splits
- train: 830
- test: 1669
### Other Statistics
#### Vocabulary Size
- English
- train: 4363
- test: 7128
- German
- train: 5070
- test: 8601
- French
- train: 4881
- test: 7935
#### Average Sentence Length
- English
- train: 20.689156626506023
- test: 20.75254643499101
- German
- train: 20.367469879518072
- test: 20.639904134212102
- French
- train: 23.455421686746988
- test: 23.731575793888556
#### Label Split
- train:
- 0: 166
- 1: 664
- test:
- 0: 334
- 1: 1335
#### Out-of-vocabulary words in model
- English
- BERT (bert-base-uncased)
- train: 800
- test: 1638
- mBERT (bert-base-multilingual-uncased)
- train: 1347
- test: 2693
- German BERT (bert-base-german-dbmdz-uncased)
- train: 3228
- test: 5581
- flauBERT (flaubert-base-uncased)
- train: 4363
- test: 7128
- German
- BERT (bert-base-uncased)
- train: 4285
- test: 7387
- mBERT (bert-base-multilingual-uncased)
- train: 3126
- test: 5863
- German BERT (bert-base-german-dbmdz-uncased)
- train: 2033
- test: 3938
- flauBERT (flaubert-base-uncased)
- train: 5069
- test: 8600
- French
- BERT (bert-base-uncased)
- train: 3784
- test: 6289
- mBERT (bert-base-multilingual-uncased)
- train: 2847
- test: 5084
- German BERT (bert-base-german-dbmdz-uncased)
- train: 4212
- test: 6964
- flauBERT (flaubert-base-uncased)
- train: 4881
- test: 7935
## Dataset Creation
### Curation Rationale
N/A
### Source Data
https://github.com/facebookresearch/XNLI
Here is the citation for the original XNLI paper.
```
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin",
title = "XNLI: Evaluating Cross-lingual Sentence Representations",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing",
year = "2018",
publisher = "Association for Computational Linguistics",
location = "Brussels, Belgium",
}
```
#### Initial Data Collection and Normalization
N/A
#### Who are the source language producers?
N/A
### Annotations
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
N/A
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
N/A
### Licensing Information
N/A
### Citation Information
### Contributions
N/A | nanakonoda/xnli_parallel | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|xnli",
"language:en",
"language:de",
"language:fr",
"mode classification",
"aligned",
"region:us"
]
| 2023-04-02T23:49:12+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en", "de", "fr"], "license": [], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|xnli"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "XNLI Parallel Corpus", "tags": ["mode classification", "aligned"], "dataset_info": [{"config_name": "en", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "spoken", "1": "written"}}}}], "splits": [{"name": "train", "num_bytes": 92288, "num_examples": 830}, {"name": "test", "num_bytes": 186853, "num_examples": 1669}]}, {"config_name": "de", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "spoken", "1": "written"}}}}], "splits": [{"name": "train", "num_bytes": 105681, "num_examples": 830}, {"name": "test", "num_bytes": 214008, "num_examples": 1669}]}, {"config_name": "fr", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "spoken", "1": "written"}}}}], "splits": [{"name": "train", "num_bytes": 830, "num_examples": 109164}, {"name": "test", "num_bytes": 221286, "num_examples": 1669}]}], "download_size": 1864, "dataset_size": 1840} | 2023-04-18T12:23:10+00:00 | []
| [
"en",
"de",
"fr"
]
| TAGS
#task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|xnli #language-English #language-German #language-French #mode classification #aligned #region-us
|
# Dataset Card for XNLI Parallel Corpus
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
### Supported Tasks and Leaderboards
Binary mode classification (spoken vs written)
### Languages
- English
- German
- French
## Dataset Structure
### Data Instances
{
'text': "And he said , Mama , I 'm home .",
'label': 0
}
### Data Fields
- text: sentence
- label: binary label of text (0: spoken 1: written)
### Data Splits
- train: 830
- test: 1669
### Other Statistics
#### Vocabulary Size
- English
- train: 4363
- test: 7128
- German
- train: 5070
- test: 8601
- French
- train: 4881
- test: 7935
#### Average Sentence Length
- English
- train: 20.689156626506023
- test: 20.75254643499101
- German
- train: 20.367469879518072
- test: 20.639904134212102
- French
- train: 23.455421686746988
- test: 23.731575793888556
#### Label Split
- train:
- 0: 166
- 1: 664
- test:
- 0: 334
- 1: 1335
#### Out-of-vocabulary words in model
- English
- BERT (bert-base-uncased)
- train: 800
- test: 1638
- mBERT (bert-base-multilingual-uncased)
- train: 1347
- test: 2693
- German BERT (bert-base-german-dbmdz-uncased)
- train: 3228
- test: 5581
- flauBERT (flaubert-base-uncased)
- train: 4363
- test: 7128
- German
- BERT (bert-base-uncased)
- train: 4285
- test: 7387
- mBERT (bert-base-multilingual-uncased)
- train: 3126
- test: 5863
- German BERT (bert-base-german-dbmdz-uncased)
- train: 2033
- test: 3938
- flauBERT (flaubert-base-uncased)
- train: 5069
- test: 8600
- French
- BERT (bert-base-uncased)
- train: 3784
- test: 6289
- mBERT (bert-base-multilingual-uncased)
- train: 2847
- test: 5084
- German BERT (bert-base-german-dbmdz-uncased)
- train: 4212
- test: 6964
- flauBERT (flaubert-base-uncased)
- train: 4881
- test: 7935
## Dataset Creation
### Curation Rationale
N/A
### Source Data
URL
Here is the citation for the original XNLI paper.
#### Initial Data Collection and Normalization
N/A
#### Who are the source language producers?
N/A
### Annotations
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
N/A
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
N/A
### Licensing Information
N/A
### Contributions
N/A | [
"# Dataset Card for XNLI Parallel Corpus",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary",
"### Supported Tasks and Leaderboards\n\nBinary mode classification (spoken vs written)",
"### Languages\n- English\n- German\n- French",
"## Dataset Structure",
"### Data Instances\n\n{\n 'text': \"And he said , Mama , I 'm home .\", \n 'label': 0\n}",
"### Data Fields\n- text: sentence\n- label: binary label of text (0: spoken 1: written)",
"### Data Splits\n - train: 830\n - test: 1669",
"### Other Statistics",
"#### Vocabulary Size\n- English\n - train: 4363\n - test: 7128\n \n- German\n - train: 5070\n - test: 8601\n \n- French\n - train: 4881\n - test: 7935",
"#### Average Sentence Length\n- English\n - train: 20.689156626506023\n - test: 20.75254643499101\n \n- German\n - train: 20.367469879518072\n - test: 20.639904134212102\n \n- French\n - train: 23.455421686746988\n - test: 23.731575793888556",
"#### Label Split\n - train:\n - 0: 166\n - 1: 664\n - test:\n - 0: 334\n - 1: 1335",
"#### Out-of-vocabulary words in model\n- English\n - BERT (bert-base-uncased)\n - train: 800\n - test: 1638\n \n - mBERT (bert-base-multilingual-uncased)\n - train: 1347\n - test: 2693\n \n - German BERT (bert-base-german-dbmdz-uncased)\n - train: 3228\n - test: 5581\n \n - flauBERT (flaubert-base-uncased)\n - train: 4363\n - test: 7128\n \n- German\n - BERT (bert-base-uncased)\n - train: 4285\n - test: 7387\n \n - mBERT (bert-base-multilingual-uncased)\n - train: 3126\n - test: 5863\n\n - German BERT (bert-base-german-dbmdz-uncased)\n - train: 2033\n - test: 3938\n \n - flauBERT (flaubert-base-uncased)\n - train: 5069\n - test: 8600\n \n- French\n - BERT (bert-base-uncased)\n - train: 3784\n - test: 6289\n \n - mBERT (bert-base-multilingual-uncased)\n - train: 2847\n - test: 5084\n \n - German BERT (bert-base-german-dbmdz-uncased)\n - train: 4212\n - test: 6964\n \n - flauBERT (flaubert-base-uncased)\n - train: 4881\n - test: 7935",
"## Dataset Creation",
"### Curation Rationale\n\nN/A",
"### Source Data\n\nURL\n\nHere is the citation for the original XNLI paper.",
"#### Initial Data Collection and Normalization\n\nN/A",
"#### Who are the source language producers?\n\nN/A",
"### Annotations",
"#### Annotation process\n\nN/A",
"#### Who are the annotators?\n\nN/A",
"### Personal and Sensitive Information\n\nN/A",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nN/A",
"### Discussion of Biases\n\nN/A",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nN/A",
"### Licensing Information\n\nN/A",
"### Contributions\n\nN/A"
]
| [
"TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|xnli #language-English #language-German #language-French #mode classification #aligned #region-us \n",
"# Dataset Card for XNLI Parallel Corpus",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary",
"### Supported Tasks and Leaderboards\n\nBinary mode classification (spoken vs written)",
"### Languages\n- English\n- German\n- French",
"## Dataset Structure",
"### Data Instances\n\n{\n 'text': \"And he said , Mama , I 'm home .\", \n 'label': 0\n}",
"### Data Fields\n- text: sentence\n- label: binary label of text (0: spoken 1: written)",
"### Data Splits\n - train: 830\n - test: 1669",
"### Other Statistics",
"#### Vocabulary Size\n- English\n - train: 4363\n - test: 7128\n \n- German\n - train: 5070\n - test: 8601\n \n- French\n - train: 4881\n - test: 7935",
"#### Average Sentence Length\n- English\n - train: 20.689156626506023\n - test: 20.75254643499101\n \n- German\n - train: 20.367469879518072\n - test: 20.639904134212102\n \n- French\n - train: 23.455421686746988\n - test: 23.731575793888556",
"#### Label Split\n - train:\n - 0: 166\n - 1: 664\n - test:\n - 0: 334\n - 1: 1335",
"#### Out-of-vocabulary words in model\n- English\n - BERT (bert-base-uncased)\n - train: 800\n - test: 1638\n \n - mBERT (bert-base-multilingual-uncased)\n - train: 1347\n - test: 2693\n \n - German BERT (bert-base-german-dbmdz-uncased)\n - train: 3228\n - test: 5581\n \n - flauBERT (flaubert-base-uncased)\n - train: 4363\n - test: 7128\n \n- German\n - BERT (bert-base-uncased)\n - train: 4285\n - test: 7387\n \n - mBERT (bert-base-multilingual-uncased)\n - train: 3126\n - test: 5863\n\n - German BERT (bert-base-german-dbmdz-uncased)\n - train: 2033\n - test: 3938\n \n - flauBERT (flaubert-base-uncased)\n - train: 5069\n - test: 8600\n \n- French\n - BERT (bert-base-uncased)\n - train: 3784\n - test: 6289\n \n - mBERT (bert-base-multilingual-uncased)\n - train: 2847\n - test: 5084\n \n - German BERT (bert-base-german-dbmdz-uncased)\n - train: 4212\n - test: 6964\n \n - flauBERT (flaubert-base-uncased)\n - train: 4881\n - test: 7935",
"## Dataset Creation",
"### Curation Rationale\n\nN/A",
"### Source Data\n\nURL\n\nHere is the citation for the original XNLI paper.",
"#### Initial Data Collection and Normalization\n\nN/A",
"#### Who are the source language producers?\n\nN/A",
"### Annotations",
"#### Annotation process\n\nN/A",
"#### Who are the annotators?\n\nN/A",
"### Personal and Sensitive Information\n\nN/A",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nN/A",
"### Discussion of Biases\n\nN/A",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nN/A",
"### Licensing Information\n\nN/A",
"### Contributions\n\nN/A"
]
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"passage: TAGS\n#task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|xnli #language-English #language-German #language-French #mode classification #aligned #region-us \n# Dataset Card for XNLI Parallel Corpus## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary### Supported Tasks and Leaderboards\n\nBinary mode classification (spoken vs written)### Languages\n- English\n- German\n- French## Dataset Structure### Data Instances\n\n{\n 'text': \"And he said , Mama , I 'm home .\", \n 'label': 0\n}### Data Fields\n- text: sentence\n- label: binary label of text (0: spoken 1: written)### Data Splits\n - train: 830\n - test: 1669### Other Statistics#### Vocabulary Size\n- English\n - train: 4363\n - test: 7128\n \n- German\n - train: 5070\n - test: 8601\n \n- French\n - train: 4881\n - test: 7935#### Average Sentence Length\n- English\n - train: 20.689156626506023\n - test: 20.75254643499101\n \n- German\n - train: 20.367469879518072\n - test: 20.639904134212102\n \n- French\n - train: 23.455421686746988\n - test: 23.731575793888556#### Label Split\n - train:\n - 0: 166\n - 1: 664\n - test:\n - 0: 334\n - 1: 1335"
]
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|
35d3114dc201d542a3d233a95511561b5be46ccf | # Dataset Card for "flint_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Kevin-M-Smith/flint_images | [
"region:us"
]
| 2023-04-02T23:53:18+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "clutter", "1": "email", "2": "email-squished", "3": "handwritten-document", "4": "spreadsheet", "5": "typeset-document"}}}}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 178391248.0, "num_examples": 4965}, {"name": "test", "num_bytes": 42819947.0, "num_examples": 1242}], "download_size": 221040943, "dataset_size": 221211195.0}} | 2023-04-02T23:55:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "flint_images"
More Information needed | [
"# Dataset Card for \"flint_images\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"flint_images\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"flint_images\"\n\nMore Information needed"
]
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|
35a7ec787213a8bd2eacbe3c589cf04140ff23f9 | # Dataset Card for "flores200_eng_input_scaffolding_large_mt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hlillemark/flores200_eng_input_scaffolding_large_mt5 | [
"region:us"
]
| 2023-04-03T00:46:40+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 19412563611, "num_examples": 20480000}, {"name": "val", "num_bytes": 3827042, "num_examples": 5000}, {"name": "test", "num_bytes": 7670994, "num_examples": 10000}], "download_size": 9319509109, "dataset_size": 19424061647}} | 2023-04-03T01:01:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "flores200_eng_input_scaffolding_large_mt5"
More Information needed | [
"# Dataset Card for \"flores200_eng_input_scaffolding_large_mt5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"flores200_eng_input_scaffolding_large_mt5\"\n\nMore Information needed"
]
| [
6,
30
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"flores200_eng_input_scaffolding_large_mt5\"\n\nMore Information needed"
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|
3707e889631dce6ae81b36a09bda0bdf21a4f558 | # Dataset Card for "wikipedia_stage2_coverage_20230402"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MartinKu/wikipedia_stage2_coverage_20230402 | [
"region:us"
]
| 2023-04-03T00:51:56+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": 113325298194, "num_examples": 3295240}], "download_size": 33360668694, "dataset_size": 113325298194}} | 2023-04-06T10:09:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikipedia_stage2_coverage_20230402"
More Information needed | [
"# Dataset Card for \"wikipedia_stage2_coverage_20230402\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia_stage2_coverage_20230402\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia_stage2_coverage_20230402\"\n\nMore Information needed"
]
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|
7c958249387ed68e62459ac0c8b1dcf95dfa73b8 | This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5
Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora | gbharti/wealth-alpaca_lora | [
"language:en",
"region:us"
]
| 2023-04-03T01:17:27+00:00 | {"language": ["en"]} | 2023-04-07T08:12:57+00:00 | []
| [
"en"
]
| TAGS
#language-English #region-us
| This dataset is a combination of Stanford's Alpaca (URL and FiQA (URL with another 1.3k pairs custom generated using GPT3.5
Script for tuning through Kaggle's (URL) free resources using PEFT/LoRa: URL | []
| [
"TAGS\n#language-English #region-us \n"
]
| [
10
]
| [
"passage: TAGS\n#language-English #region-us \n"
]
| [
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|
fe03c5c96bba491966c1a687a6b58f36c0288b58 | # Mtet
- Num examples:
- 5,072 (test)
- 6,212 (validation)
- Language: English, Vietnamese
## Prompts
"Translate the following sentence into <target>: ",
"What is the <target> translation for: ",
"What is the <target> equivalent of: ",
"What does the following sentence means in <target>: ",
"Interpret the following sentence into <target>: ",
"What is the <target> interpretation for: ",
"The <target> translation of the following sentence: ",
"What is the <target> meaning of the following sentence: ",
"What is the <target> meaning of this sentence: ",
"Please translate the following sentence to <target>: "
"Dịch câu sau sang tiếng <target>: ",
"Nghĩa tiếng <target> của câu sau: ",
"Dịch câu tiếng <from> sau sang tiếng <target>: ",
"Thông dịch câu tiếng <from> sau tiếng <target>: ",
"Chuyển câu tiếng <from> sang tiếng <target>: ",
"Chuyển nghĩa câu tiếng <from> sang tiếng <target>: ",
"Câu tiếng <from> có nghĩa là gì trong tiếng <target>: ",
"Câu sau có nghĩa tiếng <target> là gì: ",
"Hãy dịch câu sau sang tiếng <target>: ",
"Giải thích nghĩa câu sau sang tiếng <target>: ",
"Giải thích nghĩa câu tiếng <from> sang tiếng <target>"
| H13u/mtet-test | [
"task_categories:translation",
"size_categories:1M<n<10M",
"language:en",
"language:vi",
"LM",
"region:us"
]
| 2023-04-03T02:46:52+00:00 | {"language": ["en", "vi"], "size_categories": ["1M<n<10M"], "task_categories": ["translation"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "test"}, {"name": "validation"}]}, "tags": ["LM"]} | 2023-04-06T02:22:12+00:00 | []
| [
"en",
"vi"
]
| TAGS
#task_categories-translation #size_categories-1M<n<10M #language-English #language-Vietnamese #LM #region-us
| # Mtet
- Num examples:
- 5,072 (test)
- 6,212 (validation)
- Language: English, Vietnamese
## Prompts
"Translate the following sentence into <target>: ",
"What is the <target> translation for: ",
"What is the <target> equivalent of: ",
"What does the following sentence means in <target>: ",
"Interpret the following sentence into <target>: ",
"What is the <target> interpretation for: ",
"The <target> translation of the following sentence: ",
"What is the <target> meaning of the following sentence: ",
"What is the <target> meaning of this sentence: ",
"Please translate the following sentence to <target>: "
"Dịch câu sau sang tiếng <target>: ",
"Nghĩa tiếng <target> của câu sau: ",
"Dịch câu tiếng <from> sau sang tiếng <target>: ",
"Thông dịch câu tiếng <from> sau tiếng <target>: ",
"Chuyển câu tiếng <from> sang tiếng <target>: ",
"Chuyển nghĩa câu tiếng <from> sang tiếng <target>: ",
"Câu tiếng <from> có nghĩa là gì trong tiếng <target>: ",
"Câu sau có nghĩa tiếng <target> là gì: ",
"Hãy dịch câu sau sang tiếng <target>: ",
"Giải thích nghĩa câu sau sang tiếng <target>: ",
"Giải thích nghĩa câu tiếng <from> sang tiếng <target>"
| [
"# Mtet\n- Num examples: \n - 5,072 (test)\n - 6,212 (validation)\n- Language: English, Vietnamese",
"## Prompts\n \"Translate the following sentence into <target>: \",\n \"What is the <target> translation for: \",\n \"What is the <target> equivalent of: \",\n \"What does the following sentence means in <target>: \",\n \"Interpret the following sentence into <target>: \",\n \"What is the <target> interpretation for: \",\n \"The <target> translation of the following sentence: \",\n \"What is the <target> meaning of the following sentence: \",\n \"What is the <target> meaning of this sentence: \",\n \"Please translate the following sentence to <target>: \"\n \"Dịch câu sau sang tiếng <target>: \",\n \"Nghĩa tiếng <target> của câu sau: \",\n \"Dịch câu tiếng <from> sau sang tiếng <target>: \",\n \"Thông dịch câu tiếng <from> sau tiếng <target>: \",\n \"Chuyển câu tiếng <from> sang tiếng <target>: \",\n \"Chuyển nghĩa câu tiếng <from> sang tiếng <target>: \",\n \"Câu tiếng <from> có nghĩa là gì trong tiếng <target>: \",\n \"Câu sau có nghĩa tiếng <target> là gì: \",\n \"Hãy dịch câu sau sang tiếng <target>: \",\n \"Giải thích nghĩa câu sau sang tiếng <target>: \",\n \"Giải thích nghĩa câu tiếng <from> sang tiếng <target>\""
]
| [
"TAGS\n#task_categories-translation #size_categories-1M<n<10M #language-English #language-Vietnamese #LM #region-us \n",
"# Mtet\n- Num examples: \n - 5,072 (test)\n - 6,212 (validation)\n- Language: English, Vietnamese",
"## Prompts\n \"Translate the following sentence into <target>: \",\n \"What is the <target> translation for: \",\n \"What is the <target> equivalent of: \",\n \"What does the following sentence means in <target>: \",\n \"Interpret the following sentence into <target>: \",\n \"What is the <target> interpretation for: \",\n \"The <target> translation of the following sentence: \",\n \"What is the <target> meaning of the following sentence: \",\n \"What is the <target> meaning of this sentence: \",\n \"Please translate the following sentence to <target>: \"\n \"Dịch câu sau sang tiếng <target>: \",\n \"Nghĩa tiếng <target> của câu sau: \",\n \"Dịch câu tiếng <from> sau sang tiếng <target>: \",\n \"Thông dịch câu tiếng <from> sau tiếng <target>: \",\n \"Chuyển câu tiếng <from> sang tiếng <target>: \",\n \"Chuyển nghĩa câu tiếng <from> sang tiếng <target>: \",\n \"Câu tiếng <from> có nghĩa là gì trong tiếng <target>: \",\n \"Câu sau có nghĩa tiếng <target> là gì: \",\n \"Hãy dịch câu sau sang tiếng <target>: \",\n \"Giải thích nghĩa câu sau sang tiếng <target>: \",\n \"Giải thích nghĩa câu tiếng <from> sang tiếng <target>\""
]
| [
40,
33,
339
]
| [
"passage: TAGS\n#task_categories-translation #size_categories-1M<n<10M #language-English #language-Vietnamese #LM #region-us \n# Mtet\n- Num examples: \n - 5,072 (test)\n - 6,212 (validation)\n- Language: English, Vietnamese## Prompts\n \"Translate the following sentence into <target>: \",\n \"What is the <target> translation for: \",\n \"What is the <target> equivalent of: \",\n \"What does the following sentence means in <target>: \",\n \"Interpret the following sentence into <target>: \",\n \"What is the <target> interpretation for: \",\n \"The <target> translation of the following sentence: \",\n \"What is the <target> meaning of the following sentence: \",\n \"What is the <target> meaning of this sentence: \",\n \"Please translate the following sentence to <target>: \"\n \"Dịch câu sau sang tiếng <target>: \",\n \"Nghĩa tiếng <target> của câu sau: \",\n \"Dịch câu tiếng <from> sau sang tiếng <target>: \",\n \"Thông dịch câu tiếng <from> sau tiếng <target>: \",\n \"Chuyển câu tiếng <from> sang tiếng <target>: \",\n \"Chuyển nghĩa câu tiếng <from> sang tiếng <target>: \",\n \"Câu tiếng <from> có nghĩa là gì trong tiếng <target>: \",\n \"Câu sau có nghĩa tiếng <target> là gì: \",\n \"Hãy dịch câu sau sang tiếng <target>: \",\n \"Giải thích nghĩa câu sau sang tiếng <target>: \",\n \"Giải thích nghĩa câu tiếng <from> sang tiếng <target>\""
]
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|
7d380c2e1ad63da086fe7af1de1dd409662eb1eb | # Dataset Card for "avatarkorra_lite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/avatarkorra_lite | [
"region:us"
]
| 2023-04-03T03:12:49+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": 696084068.125, "num_examples": 3063}], "download_size": 695784657, "dataset_size": 696084068.125}} | 2023-04-03T03:13:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "avatarkorra_lite"
More Information needed | [
"# Dataset Card for \"avatarkorra_lite\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"avatarkorra_lite\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"avatarkorra_lite\"\n\nMore Information needed"
]
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|
39cbf9acae21e3abe570356ac430d5ca8060305c | # Dataset Card for "VALUE_mrpc_been_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_mrpc_been_done | [
"language:en",
"region:us"
]
| 2023-04-03T03:18:17+00:00 | {"language": "en", "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 157072, "num_examples": 552}, {"name": "train", "num_bytes": 351111, "num_examples": 1250}, {"name": "validation", "num_bytes": 35340, "num_examples": 126}], "download_size": 356711, "dataset_size": 543523}} | 2023-08-02T23:08:25+00:00 | []
| [
"en"
]
| TAGS
#language-English #region-us
| # Dataset Card for "VALUE_mrpc_been_done"
More Information needed | [
"# Dataset Card for \"VALUE_mrpc_been_done\"\n\nMore Information needed"
]
| [
"TAGS\n#language-English #region-us \n",
"# Dataset Card for \"VALUE_mrpc_been_done\"\n\nMore Information needed"
]
| [
10,
20
]
| [
"passage: TAGS\n#language-English #region-us \n# Dataset Card for \"VALUE_mrpc_been_done\"\n\nMore Information needed"
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|
2d80ad5616ed0d247af4b62dc2f916dd768566e9 | # Dataset Card for "VALUE_mrpc_dey_it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_mrpc_dey_it | [
"region:us"
]
| 2023-04-03T03:18:23+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 10261, "num_examples": 40}, {"name": "train", "num_bytes": 18467, "num_examples": 74}, {"name": "validation", "num_bytes": 3254, "num_examples": 13}], "download_size": 32889, "dataset_size": 31982}} | 2023-04-03T03:18:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_mrpc_dey_it"
More Information needed | [
"# Dataset Card for \"VALUE_mrpc_dey_it\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_mrpc_dey_it\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_mrpc_dey_it\"\n\nMore Information needed"
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80df7ba7720273938a47864c4387885cabd7b161 | # Dataset Card for "VALUE_mrpc_null_genetive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_mrpc_null_genetive | [
"region:us"
]
| 2023-04-03T03:18:24+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 120404, "num_examples": 436}, {"name": "train", "num_bytes": 262071, "num_examples": 933}, {"name": "validation", "num_bytes": 34673, "num_examples": 122}], "download_size": 277759, "dataset_size": 417148}} | 2023-04-03T03:18:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_mrpc_null_genetive"
More Information needed | [
"# Dataset Card for \"VALUE_mrpc_null_genetive\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_mrpc_null_genetive\"\n\nMore Information needed"
]
| [
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20
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_mrpc_null_genetive\"\n\nMore Information needed"
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|
fa8434b5ad56f0d8db875a82cf295472e3df1b79 | # Dataset Card for "VALUE_mrpc_drop_aux"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_mrpc_drop_aux | [
"region:us"
]
| 2023-04-03T03:18:26+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 30798, "num_examples": 119}, {"name": "train", "num_bytes": 64928, "num_examples": 245}, {"name": "validation", "num_bytes": 5706, "num_examples": 24}], "download_size": 79747, "dataset_size": 101432}} | 2023-04-03T03:18:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_mrpc_drop_aux"
More Information needed | [
"# Dataset Card for \"VALUE_mrpc_drop_aux\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_mrpc_drop_aux\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_mrpc_drop_aux\"\n\nMore Information needed"
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|
7fc4f8891cf5be948eb37ad6cbc42cb50d7e88ab | # Dataset Card for "VALUE_mrpc_negative_concord"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_mrpc_negative_concord | [
"region:us"
]
| 2023-04-03T03:18:26+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 17986, "num_examples": 70}, {"name": "train", "num_bytes": 39506, "num_examples": 150}, {"name": "validation", "num_bytes": 6781, "num_examples": 26}], "download_size": 53966, "dataset_size": 64273}} | 2023-04-03T03:18:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_mrpc_negative_concord"
More Information needed | [
"# Dataset Card for \"VALUE_mrpc_negative_concord\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_mrpc_negative_concord\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_mrpc_negative_concord\"\n\nMore Information needed"
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|
bb9af39c4ccb53c56be44eacf39ba12a5bbb43d1 | # Dataset Card for "VALUE_mrpc_negative_inversion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_mrpc_negative_inversion | [
"region:us"
]
| 2023-04-03T03:18:26+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1353, "num_examples": 5}], "download_size": 4790, "dataset_size": 1353}} | 2023-04-03T03:18:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_mrpc_negative_inversion"
More Information needed | [
"# Dataset Card for \"VALUE_mrpc_negative_inversion\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_mrpc_negative_inversion\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_mrpc_negative_inversion\"\n\nMore Information needed"
]
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|
9744b48908bef93671b4c231b841e71b5f52558f | # Dataset Card for "VALUE_mrpc_null_relcl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_mrpc_null_relcl | [
"region:us"
]
| 2023-04-03T03:18:33+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 56055, "num_examples": 196}, {"name": "train", "num_bytes": 128874, "num_examples": 447}, {"name": "validation", "num_bytes": 12223, "num_examples": 44}], "download_size": 140882, "dataset_size": 197152}} | 2023-04-03T03:18:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_mrpc_null_relcl"
More Information needed | [
"# Dataset Card for \"VALUE_mrpc_null_relcl\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_mrpc_null_relcl\"\n\nMore Information needed"
]
| [
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20
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_mrpc_null_relcl\"\n\nMore Information needed"
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|
6f973fe001b3b211eb5490ec75e4893e7cdbc820 | # Dataset Card for "VALUE_mrpc_uninflect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/VALUE_mrpc_uninflect | [
"region:us"
]
| 2023-04-03T03:18:38+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 156118, "num_examples": 566}, {"name": "train", "num_bytes": 346454, "num_examples": 1256}, {"name": "validation", "num_bytes": 35575, "num_examples": 131}], "download_size": 363040, "dataset_size": 538147}} | 2023-04-03T03:18:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VALUE_mrpc_uninflect"
More Information needed | [
"# Dataset Card for \"VALUE_mrpc_uninflect\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VALUE_mrpc_uninflect\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VALUE_mrpc_uninflect\"\n\nMore Information needed"
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|
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