text-classification bool 2 classes | text stringlengths 0 664k |
|---|---|
true |
# Dataset Card for MultiRC_TH_drop
### Dataset Description
This dataset is Thai translated version of [multirc](https://huggingface.co/datasets/super_glue/viewer/multirc) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
The score was penalized by the length of original text compare to translated text. The row that any score < 0.66 was droped. |
false |
## DreamEditBench for Subject Replacement task and Subject Addition task.
## Dataset Description
- **Homepage:** https://dreameditbenchteam.github.io
- **Repository:** https://github.com/DreamEditBenchTeam/DreamEdit
<!-- **Paper:** https://arxiv.org/abs/2306.12624 -->
The goal of subject replacement is to replace a subject from a source image with a customized subject. In contrast, the aim of the subject addition task is to add a customized
subject to a desired position in the source image. To standardize the evaluation of the two proposed tasks, we curate a new benchmark, i.e. DreamEditBench, consisting of 22 subjects in alignment with DreamBooth with 20 images for each subject correspondingly. For the subject replacement task, we collect 10 images for each type, which include same-typed source subjects in diverse environments. The images are retrieved from the
internet with the search query “a photo of [Class name]”, and the source subject should be the main subject in
the image which dominates a major part of the photo. For the subject addition task, we collect 10 reasonable
backgrounds for each type of subject. In the meantime, we manually designate the
specific location the target subject should be placed with a bounding box in the background. To collect the
specific backgrounds for each subject, we first brainstorm and list the possible common environments of the
subjects, then we search the listed keywords from the internet to retrieve and pick the backgrounds
## Data Structure
There are 22 subject folders in each task folder respectively. In each subject folder, there are 10 source images. For Subject Addition task, there is an additional bbox.json file recording the manually labeled bounding box for each background.
The replacement_subset.csv and addition_subset.csv record the easy/hard subset division for each task correspondingly.
## Citation Information
If you find this dataset useful, please consider citing our paper:
```
@misc{li2023dreamedit,
title={DreamEdit: Subject-driven Image Editing},
author={Tianle Li and Max Ku and Cong Wei and Wenhu Chen},
year={2023},
eprint={2306.12624},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
true | # AutoTrain Dataset for project: based-in-fact
## Dataset Description
This dataset has been automatically processed by AutoTrain for project based-in-fact.
### 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": "\u4e0a\u4e2a\u5927\u5b66\u771f\u7684\u662f\u4ec0\u4e48\u4eba\u90fd\u70b8\u51fa\u6765\u4e86",
"target": 0
},
{
"text": "\u5982\u679c\u4e00\u4e2aHIV\u611f\u67d3\u8005\u5bf9\u4e8e\u6297\u9006\u8f6c\u5f55\u75c5\u6bd2\u836f\u7269\u5341\u5206\u8010\u53d7\uff0c\u90a3\u4e48\u4ed6\u7684\u6cbb\u7597\u4f1a\u53d8\u5f97\u5341\u5206\u590d\u6742\uff0c\u75c5\u60c5\u5c06\u6076\u5316",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['emotion', 'fact'], 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 | 30932 |
| valid | 3000 | |
false |
# DEplain-web-sent: A corpus for German Sentence Simplification
DEplain-web-sent is a subcorpus of DEplain [Stodden et al., 2023]((https://arxiv.org/abs/2305.18939)) for evaluation of sentence simplification.
The corpus consists of 1846 sentence pairs of 147 parallel documents crawled from the web in standard German and plain German (or easy-to-read German). All documents are either published under an open license, or the copyright holders gave us permission to share the data.
Human annotators sentence-wise aligned the 147 documents of the test set to build a corpus for sentence simplification. For the document-level version of this corpus, please see [https://huggingface.co/datasets/DEplain/DEplain-web-doc](https://huggingface.co/datasets/DEplain/DEplain-web-doc).
Due to the small size of the sentence pairs, we only provide a test set for evaluation of text simplification models.
If you are interested in a larger corpus, please check our paper and the provided web crawler and alignment methods to extend the corpus. You can find this data here: [https://github.com/rstodden/DEPlain/](https://github.com/rstodden/DEPlain/tree/main/E__Sentence-level_Corpus/DEplain-web-sent/auto/open).
If you use the automatically aligned data, please use it cautiously, as the alignment quality might be error-prone.
# Dataset Card for DEplain-web-sent
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [DEplain-web GitHub repository](https://github.com/rstodden/DEPlain)
- **Paper:** ["DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification."](https://arxiv.org/abs/2305.18939)
- **Point of Contact:** [Regina Stodden](regina.stodden@hhu.de)
### Dataset Summary
[DEplain-web](https://github.com/rstodden/DEPlain) [(Stodden et al., 2023)](https://arxiv.org/abs/2305.18939) is a dataset for the evaluation of sentence and document simplification in German. All texts of this dataset are scraped from the web. All documents were licenced with an open license. The simple-complex sentence pairs are manually aligned.
This dataset only contains a test set. For additional training and development data, please scrape more data from the web using a [web scraper for text simplification data](https://github.com/rstodden/data_collection_german_simplification) and align the sentences of the documents automatically using, for example, [MASSalign](https://github.com/ghpaetzold/massalign) by [Paetzold et al. (2017)](https://www.aclweb.org/anthology/I17-3001/).
### Supported Tasks and Leaderboards
The dataset supports the evaluation of `text-simplification` systems. Success in this task is typically measured using the [SARI](https://huggingface.co/metrics/sari) and [FKBLEU](https://huggingface.co/metrics/fkbleu) metrics described in the paper [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029.pdf).
### Languages
The texts in this dataset are written in German (de-de). The texts are in German plain language variants, e.g., plain language (Einfache Sprache) or easy-to-read language (Leichte Sprache).
### Domains
The texts are from 6 different domains: fictional texts (literature and fairy tales), bible texts, health-related texts, texts for language learners, texts for accessibility, and public administration texts.
## Dataset Structure
### Data Access
- The dataset is licensed with different open licenses dependent on the subcorpora.
### Data Instances
- `document-simplification` configuration: an instance consists of an original document and one reference simplification.
- `sentence-simplification` configuration: an instance consists of an original sentence and one manually aligned reference simplification. Please see [https://huggingface.co/datasets/DEplain/DEplain-web-sent](https://huggingface.co/datasets/DEplain/DEplain-web-sent).
- `sentence-wise alignment` configuration: an instance consists of original and simplified documents and manually aligned sentence pairs. In contrast to the sentence-simplification configurations, this configuration contains also sentence pairs in which the original and the simplified sentences are exactly the same. Please see [https://github.com/rstodden/DEPlain](https://github.com/rstodden/DEPlain/tree/main/C__Alignment_Algorithms)
### Data Fields
| data field | data field description |
|-------------------------------------------------|-------------------------------------------------------------------------------------------------------|
| `original` | an original text from the source dataset |
| `simplification` | a simplified text from the source dataset |
| `pair_id` | document pair id |
| `complex_document_id ` (on doc-level) | id of complex document (-1) |
| `simple_document_id ` (on doc-level) | id of simple document (-0) |
| `original_id ` (on sent-level) | id of sentence(s) of the original text |
| `simplification_id ` (on sent-level) | id of sentence(s) of the simplified text |
| `domain ` | text domain of the document pair |
| `corpus ` | subcorpus name |
| `simple_url ` | origin URL of the simplified document |
| `complex_url ` | origin URL of the simplified document |
| `simple_level ` or `language_level_simple ` | required CEFR language level to understand the simplified document |
| `complex_level ` or `language_level_original ` | required CEFR language level to understand the original document |
| `simple_location_html ` | location on hard disk where the HTML file of the simple document is stored |
| `complex_location_html ` | location on hard disk where the HTML file of the original document is stored |
| `simple_location_txt ` | location on hard disk where the content extracted from the HTML file of the simple document is stored |
| `complex_location_txt ` | location on hard disk where the content extracted from the HTML file of the simple document is stored |
| `alignment_location ` | location on hard disk where the alignment is stored |
| `simple_author ` | author (or copyright owner) of the simplified document |
| `complex_author ` | author (or copyright owner) of the original document |
| `simple_title ` | title of the simplified document |
| `complex_title ` | title of the original document |
| `license ` | license of the data |
| `last_access ` or `access_date` | data origin data or data when the HTML files were downloaded |
| `rater` | id of the rater who annotated the sentence pair |
| `alignment` | type of alignment, e.g., 1:1, 1:n, n:1 or n:m |
### Data Splits
DEplain-web contains a training set, a development set and a test set.
The dataset was split based on the license of the data. All manually-aligned sentence pairs with an open license are part of the test set. The document-level test set, also only contains the documents which are manually aligned. For document-level dev and test set the documents which are not aligned or not public available are used. For the sentence-level, the alingment pairs can be produced by automatic alignments (see [Stodden et al., 2023](https://arxiv.org/abs/2305.18939)).
Document-level:
| | Train | Dev | Test | Total |
|-------------------------|-------|-----|------|-------|
| DEplain-web-manual-open | - | - | 147 | 147 |
| DEplain-web-auto-open | 199 | 50 | - | 279 |
| DEplain-web-auto-closed | 288 | 72 | - | 360 |
| in total | 487 | 122 | 147 | 756 |
Sentence-level:
| | Train | Dev | Test | Total |
|-------------------------|-------|-----|------|-------|
| DEplain-web-manual-open | - | - | 1846 | 1846 |
| DEplain-web-auto-open | 514 | 138 | - | 652 |
| DEplain-web-auto-closed | 767 | 175 | - | 942 |
| in total | 1281 | 313 | 1846 | |
| **subcorpus** | **simple** | **complex** | **domain** | **description** | **\ doc.** |
|----------------------------------|------------------|------------------|------------------|-------------------------------------------------------------------------------|------------------|
| **EinfacheBücher** | Plain German | Standard German / Old German | fiction | Books in plain German | 15 |
| **EinfacheBücherPassanten** | Plain German | Standard German / Old German | fiction | Books in plain German | 4 |
| **ApothekenUmschau** | Plain German | Standard German | health | Health magazine in which diseases are explained in plain German | 71 |
| **BZFE** | Plain German | Standard German | health | Information of the German Federal Agency for Food on good nutrition | 18 |
| **Alumniportal** | Plain German | Plain German | language learner | Texts related to Germany and German traditions written for language learners. | 137 |
| **Lebenshilfe** | Easy-to-read German | Standard German | accessibility | | 49 |
| **Bibel** | Easy-to-read German | Standard German | bible | Bible texts in easy-to-read German | 221 |
| **NDR-Märchen** | Easy-to-read German | Standard German / Old German | fiction | Fairytales in easy-to-read German | 10 |
| **EinfachTeilhaben** | Easy-to-read German | Standard German | accessibility | | 67 |
| **StadtHamburg** | Easy-to-read German | Standard German | public authority | Information of and regarding the German city Hamburg | 79 |
| **StadtKöln** | Easy-to-read German | Standard German | public authority | Information of and regarding the German city Cologne | 85 |
: Documents per Domain in DEplain-web.
| domain | avg. | std. | interpretation | \ sents | \ docs |
|------------------|---------------|---------------|-------------------------|-------------------|------------------|
| bible | 0.7011 | 0.31 | moderate | 6903 | 3 |
| fiction | 0.6131 | 0.39 | moderate | 23289 | 3 |
| health | 0.5147 | 0.28 | weak | 13736 | 6 |
| language learner | 0.9149 | 0.17 | almost perfect | 18493 | 65 |
| all | 0.8505 | 0.23 | strong | 87645 | 87 |
: Inter-Annotator-Agreement per Domain in DEplain-web-manual.
| operation | documents | percentage |
|-----------|-------------|------------|
| rehphrase | 863 | 11.73 |
| deletion | 3050 | 41.47 |
| addition | 1572 | 21.37 |
| identical | 887 | 12.06 |
| fusion | 110 | 1.5 |
| merge | 77 | 1.05 |
| split | 796 | 10.82 |
| in total | 7355 | 100 |
: Information regarding Simplification Operations in DEplain-web-manual.
## Dataset Creation
### Curation Rationale
Current German text simplification datasets are limited in their size or are only automatically evaluated.
We provide a manually aligned corpus to boost text simplification research in German.
### Source Data
#### Initial Data Collection and Normalization
The parallel documents were scraped from the web using a [web scraper for text simplification data](https://github.com/rstodden/data_collection_german_simplification).
The texts of the documents were manually simplified by professional translators.
The data was split into sentences using a German model of SpaCy.
Two German native speakers have manually aligned the sentence pairs by using the text simplification annotation tool [TS-ANNO](https://github.com/rstodden/TS_annotation_tool) by [Stodden & Kallmeyer (2022)](https://aclanthology.org/2022.acl-demo.14/).
#### Who are the source language producers?
The texts of the documents were manually simplified by professional translators. See for an extensive list of the scraped URLs see Table 10 in [Stodden et al. (2023)](https://arxiv.org/abs/2305.18939).
### Annotations
#### Annotation process
The instructions given to the annotators are available [here](https://github.com/rstodden/TS_annotation_tool/tree/master/annotation_schema).
#### Who are the annotators?
The annotators are two German native speakers, who are trained in linguistics. Both were at least compensated with the minimum wage of their country of residence.
They are not part of any target group of text simplification.
### Personal and Sensitive Information
No sensitive data.
## Considerations for Using the Data
### Social Impact of Dataset
Many people do not understand texts due to their complexity. With automatic text simplification methods, the texts can be simplified for them. Our new training data can benefit in training a TS model.
### Discussion of Biases
no bias is known.
### Other Known Limitations
The dataset is provided under different open licenses depending on the license of each website were the data is scraped from. Please check the dataset license for additional information.
## Additional Information
### Dataset Curators
DEplain-APA was developed by researchers at the Heinrich-Heine-University Düsseldorf, Germany. This research is part of the PhD-program ``Online Participation'', supported by the North Rhine-Westphalian (German) funding scheme ``Forschungskolleg''.
### Licensing Information
The corpus includes the following licenses: CC-BY-SA-3, CC-BY-4, and CC-BY-NC-ND-4. The corpus also include a "save_use_share" license, for these documents the data provider permitted us to share the data for research purposes.
### Citation Information
```
@inproceedings{stodden-etal-2023-deplain,
title = "{DE}-plain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification",
author = "Stodden, Regina and
Momen, Omar and
Kallmeyer, Laura",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
notes = "preprint: https://arxiv.org/abs/2305.18939",
}
```
This dataset card uses material written by [Juan Diego Rodriguez](https://github.com/juand-r) and [Yacine Jernite](https://github.com/yjernite). |
true | |
false | # Dataset Card for "xglm_enth2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
true | # Dataset Card for "articles_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
false | # MF Rocket Paraphrase Corpus (MFRPC) - A State of the Art Paraphrasing Solution
## Dataset Description
MF Rocket Paraphrase Corpus (MFRPC) ) is a corpus consisting of 10,000 sentence pairs. Each sentence pair contains a source sentence and the paraphrased version of the source sentence. The source sentences are created manually and are intended to represent typical sentences found in online articles. They are limited to general topics and are not restricted to a specific domain. The paraphrased sentences were created partly using GPT-3 and partly manually. In this way, we hope to investigate the performance of GPT-3 in a typical real-world setting and improve the quality of the paraphrased sentences through manual corrections.
By finetuning a model we Pegasus with this data, we create a paraphraser that performs very well. The results are indistinguishable from human parahrased sentences in a blind test.
We are currently working on a data set with complete paragraphs or articles.
For more information, our Contact form can be used at https://mf-rocket.de.
### 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": "To overcome these difficulties, you must select an activity or goal that you are enthusiastic about [...]",
"target": "To overcome these challenges, you need to find an activity or goal that you are passionate about and[...]"
},
{
"text": "If you are unsure about what to do next, seek advice from a close friend or family member you can tr[...]",
"target": "If you are feeling lost, ask a trusted friend or family member for their opinion about what you shou[...]"
}
]
```
### 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 | 8000 |
| valid | 2000 |
|
false |
This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `test` split have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `related_work` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==20`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5270 | 0.2005 | 0.0573 | 0.3785 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5310 | 0.2026 | 0.059 | 0.3831 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5229 | 0.2081 | 0.058 | 0.3794 | |
false |
This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `train`, `validation` and `test` splits have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `related_work` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==4`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5270 | 0.2005 | 0.1551 | 0.2357 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5310 | 0.2026 | 0.1603 | 0.2432 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5229 | 0.2081 | 0.1612 | 0.2440 | |
false |
This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of the `train`, `validation`, and `test` splits have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `related_work` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5270 | 0.2005 | 0.2005 | 0.2005 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5310 | 0.2026 | 0.2026 | 0.2026 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5229 | 0.2081 | 0.2081 | 0.2081 | |
false |
This is a copy of the [Cochrane](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `target` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==25`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.7790 | 0.4487 | 0.1959 | 0.6268 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.7856 | 0.4424 | 0.1995 | 0.6433 |
Retrieval results on the `test` set:
N/A. Test set is blind so we do not have any queries. |
false |
This is a copy of the [Cochrane](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of the `train`, `validation`, and `test` splits have been replaced by a __dense__ retriever.
- __query__: The `target` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.7790 | 0.4487 | 0.4487 | 0.4487 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.7856 | 0.4424 | 0.4424 | 0.4424 |
Retrieval results on the `test` set:
N/A. Test set is blind so we do not have any queries. |
false |
This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==25`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4764 | 0.2395 | 0.1932 | 0.2895 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4364 | 0.2125 | 0.1823 | 0.2524 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4481 | 0.2224 | 0.1943 | 0.2567 | |
false |
This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of the `train`, `validation`, and `test` splits have been replaced by a __dense__ retriever.
- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4764 | 0.2395 | 0.2395 | 0.2395 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4364 | 0.2125 | 0.2125 | 0.2125 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4481 | 0.2224 | 0.2224 | 0.2224 | |
false |
This is a copy of the [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of its `test` split have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==10`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8590 | 0.6490 | 0.5967 | 0.6631 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8578 | 0.6326 | 0.6040 | 0.6401 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8678 | 0.6631 | 0.6301 | 0.6740 | |
false |
This is a copy of the [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of the `train`, `validation`, and `test` splits have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8590 | 0.6490 | 0.6490 | 0.6490 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8578 | 0.6326 | 0.6326 | 0.6326 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8678 | 0.6631 | 0.6631 | 0.6631 | |
false |
This is a copy of the [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of its `train`, `validation, and `test` splits have been have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==9`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8590 | 0.6490 | 0.6239 | 0.6271 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8578 | 0.6326 | 0.6301 | 0.6031 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8678 | 0.6631 | 0.6564 | 0.6338 | |
false |
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==10`
Retrieval results on the `train` set:
Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8661 | 0.6867 | 0.2118 | 0.7966 |
Retrieval results on the `validation` set:
Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8626 | 0.6859 | 0.2083 | 0.7949 |
Retrieval results on the `test` set:
Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8625 | 0.6927 | 0.2096 | 0.7971 | |
false |
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `train`, `validation` and `test` splits have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==3`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8661 | 0.6867 | 0.5936 | 0.6917 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8626 | 0.6859 | 0.5874 | 0.6925 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8625 | 0.6927 | 0.5938 | 0.6993 | |
false |
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of the `train`, `validation`, and `test` splits have been replaced by a __dense__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8661 | 0.6867 | 0.6867 | 0.6867 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8626 | 0.6859 | 0.6859 | 0.6859 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8625 | 0.6927 | 0.6927 | 0.6927 | |
false |
# Dataset Card for [for-ULPGL-Dissertation]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** krm/for-ULPGL-Dissertation
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Ce dataset est essentiellement basé sur le dataset *GEM/Orange_sum* dédié à la synthèse d'articles en français. Il est constitué des données abstract de ce dataset (Orange_sum) auxquelles a été ajouté un certain nombre de synthèses générées par le système **Mon Résumeur** de **David Krame**.
### Supported Tasks and Leaderboards
Synthèse automatique
### Languages
Français
## Dataset Structure
### Data Fields
*summary* et *text* sont les champs du dataset avec :
**text** contient les textes et
**summary** les synthèses correspondantes.
### Data Splits
Pour le moment (le 16 Octobre 2022), le dataset est constitué de :
> **21721** données d'entraînement (split dénommé **train**)
> **1545** données de validation (split dénommé **validation**)
> **1581** données de test (split dénommé **test**)
## 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
|
false |
# Dataset Card for Racó Forums Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Point of Contact:** [blanca.calvo@bsc.es](blanca.calvo@bsc.es)
### Dataset Summary
The Racó Forums Corpus is a 19-million-sentence corpus of Catalan user-generated text built from the forums of [Racó Català](https://www.racocatala.cat/forums).
Since the existing available corpora in Catalan lacked conversational data, we searched for a major source of such data for Catalan, and we found Racó Català, a popular multitopic online forum. We obtained a database dump and we transformed all the threads so that we obtained documents that traversed all the existing paths from the root (initial comment) to the leaves (last comment with no reply). In other words, if T is a tree such that T = {A,B,C,D} and the first comment is A that is replied by B and C independently, and, then, C is replied by D, we obtain two different documents A,B and A,C,D in the fairseq language modeling format.
### Supported Tasks and Leaderboards
This corpus is mainly intended to pretrain language models and word representations.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
The sentences are ordered to preserve the forum structure of comments and answers. T is a tree such that T = {A,B,C,D} and the first comment is A that is replied by B and C independently, and, then, C is replied by D, we obtain two different documents A,B and A,C,D in the fairseq language modeling format.
### Data Instances
```
Ni la Paloma, ni la Razz, ni Bikini, ni res: la cafeteria Slàvia, a Les borges Blanques. Quin concertàs el d'ahir de Pomada!!! Fuà!!! va ser tan tan tan tan tan tan tan bo!!! Flipant!!! Irrepetible!!
És cert, l'Slàvia mola màxim.
```
### Data Splits
The dataset contains two splits: `train` and `valid`.
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. The data was structured to preserve the dialogue structure of forums.
### Source Data
#### Initial Data Collection and Normalization
The data was structured and anonymized by the BSC.
#### Who are the source language producers?
The data was provided by Racó Català.
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The data was annonymised to remove user names and emails, which were changed to random Catalan names. The mentions to the chat itself have also been changed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that, since the data comes from user-generated forums, this will contain biases, hate speech and toxic content. We have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons Attribution Non-commercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
```
### Contributions
Thanks to Racó Català for sharing their data.
|
false |
# About Dataset
The dataset consists of data from a bunch of youtube videos ranging from videos from fastai lessons, FSDL lesson to random videos teaching something.
In total this dataset contains 600 chapter markers in youtube and contains 25, 000 lesson transcript.
This dataset can be used for NLP tasks like summarization, topic segmentation etc. You can refer to some of the models we have trained with this dataset
in [github repo link](https://github.com/ohmeow/fsdl_2022_course_project) for Full stack deep learning 2022 projects.
|
false |
# Dataset Card for "CompanyWeb"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [PLACEHOLDER]()
- **Repository:** [PLACEHOLDER]()
- **Paper:** [PLACEHOLDER]()
- **Leaderboard:** [PLACEHOLDER]()
- **Point of Contact:** [PLACEHOLDER]()
### Dataset Summary
The dataset contains textual content extracted from 1,788,413 company web pages of 393,542 companies. The companies included in the dataset are small, medium and large international enterprises including publicly listed companies. Additional company information is provided in form of the corresponding Standard Industry Classification (SIC) label `sic4`.
The text includes all textual information contained on the website with a timeline ranging from 2014 to 2021. The search includes all subsequent pages with links from the homepage containing the company domain name.
We filter the resulting textual data to only include English text utilizing the FastText language detection API [(Joulin et al., 2016)](https://aclanthology.org/E17-2068/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- en
## Dataset Structure
### Data Instances
- **#Instances:** 1789413
- **#Companies:** 393542
- **#Timeline:** 2014-2021
### Data Fields
- `id`: instance identifier `(string)`
- `cid`: company identifier `(string)`
- `text`: website text `(string)`
- `sic4`: 4-digit SIC `(string)`
### 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
```bibtex
@misc{title_year,
title={TITLE},
author={AUTHORS},
year={2022},
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
false | |
false |
# laion2b_multi_korean_subset_with_image
## Dataset Description
- **Download Size** 342 GB
img2dataset을 통해 다운로드에 성공한 [Bingsu/laion2B-multi-korean-subset](https://huggingface.co/datasets/Bingsu/laion2B-multi-korean-subset) 이미지를 정리한 데이터셋입니다.
이미지는 9,800,137장입니다.
이미지는 짧은 쪽 길이가 256이 되도록 리사이즈 되었으며, 품질 100인 webp파일로 다운로드 되었습니다.
## Usage
### 1. datasets
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/laion2b_multi_korean_subset_with_image", streaming=True, split="train")
>>> dataset.features
{'image': Image(decode=True, id=None),
'text': Value(dtype='string', id=None),
'width': Value(dtype='int32', id=None),
'height': Value(dtype='int32', id=None)}
>>> next(iter(dataset))
{'image': <PIL.WebPImagePlugin.WebPImageFile image mode=RGB size=256x256>,
'text': '소닉기어 에어폰5 휴대용 스테레오 블루투스 헤드폰',
'width': 256,
'height': 256}
```
### 2. webdataset
이 데이터셋은 [webdataset](https://github.com/webdataset/webdataset)으로 사용할 수 있도록 구성되어있습니다. 데이터를 다운로드하지 않고 스트리밍으로 처리한다면 1번 방법보다 훨씬 빠릅니다.
!! 아래 방법은 Windows에서는 에러가 발생합니다.
```python
>>> import webdataset as wds
>>> url = "https://huggingface.co/datasets/Bingsu/laion2b_multi_korean_subset_with_image/resolve/main/data/{00000..02122}.tar"
>>> dataset = wds.WebDataset(url).shuffle(1000).decode("pil").to_tuple("webp", "json")
```
```python
>>> next(iter(dataset))
...
```
이 글을 작성하는 현재(22-10-18), webp이미지의 자동 디코딩을 지원하지 않고 있기 때문에([PR #215](https://github.com/webdataset/webdataset/pull/215)), 직접 디코딩해야 합니다.
```python
import io
import webdataset as wds
from PIL import Image
def preprocess(data):
webp, jsn = data
img = Image.open(io.BytesIO(webp))
out = {
"image": img,
"text": jsn["caption"],
"width": jsn["width"],
"height": jsn["height"]
}
return out
url = "https://huggingface.co/datasets/Bingsu/laion2b_multi_korean_subset_with_image/resolve/main/data/{00000..02122}.tar"
dataset = wds.WebDataset(url).shuffle(1000).decode("pil").to_tuple("webp", "json").map(preprocess)
```
```python
>>> next(iter(dataset))
{'image': <PIL.WebPImagePlugin.WebPImageFile image mode=RGB size=427x256>,
'text': '[따블리에]유아동 미술가운, 미술 전신복',
'width': 427,
'height': 256}
```
## Note
각각의 tar 파일은 위 처럼 구성되어 있습니다.
다운로드에 실패한 이미지는 건너뛰어져있기 때문에 파일 이름은 완전히 연속적이지는 않습니다.
각각의 json 파일은 다음처럼 되어있습니다.
```json
{
"caption": "\ub514\uc790\uc778 \uc53d\ud0b9\uacfc \ub514\uc9c0\ud138 \ud2b8\ub79c\uc2a4\ud3ec\uba54\uc774\uc158",
"url": "https://image.samsungsds.com/kr/insights/dt1.jpg?queryString=20210915031642",
"key": "014770069",
"status": "success",
"error_message": null,
"width": 649,
"height": 256,
"original_width": 760,
"original_height": 300,
"exif": "{}"
}
```
txt파일은 json파일의 "caption"을 담고 있습니다.
|
true |
# Dataset Card for SloIE
### Dataset Summary
SloIE is a manually labelled dataset of Slovene idiomatic expressions. It contains 29399 sentences with 75 different expressions that can occur with either a literal or an idiomatic meaning, with appropriate manual annotations for each token. The idiomatic expressions were selected from the [Slovene Lexical Database]( (http://hdl.handle.net/11356/1030). Only expressions that can occur with both a literal and an idiomatic meaning were selected. The sentences were extracted from the Gigafida corpus.
For a more detailed description of the dataset, please see the paper Škvorc et al. (2022) - see below.
### Supported Tasks and Leaderboards
Idiom detection.
### Languages
Slovenian.
## Dataset Structure
### Data Instances
A sample instance from the dataset:
```json
{
'sentence': 'Fantje regljajo v enem kotu, deklice pa svoje obrazke barvajo s pisanimi barvami.',
'expression': 'barvati kaj s črnimi barvami',
'word_order': [11, 10, 12, 13, 14],
'sentence_words': ['Fantje', 'regljajo', 'v', 'enem', 'kotu,', 'deklice', 'pa', 'svoje', 'obrazke', 'barvajo', 's', 'pisanimi', 'barvami.'],
'is_idiom': ['*', '*', '*', '*', '*', '*', '*', '*', 'NE', 'NE', 'NE', 'NE', 'NE']
}
```
In this `sentence`, the words of the expression "barvati kaj s črnimi barvami" are used in a literal sense, as indicated by the "NE" annotations inside `is_idiom`. The "*" annotations indicate the words are not part of the expression.
### Data Fields
- `sentence`: raw sentence in string form - **WARNING**: this is at times slightly different from the words inside `sentence_words` (e.g., "..." here could be "." in `sentence_words`);
- `expression`: the annotated idiomatic expression;
- `word_order`: numbers indicating the positions of tokens that belong to the expression;
- `sentence_words`: words in the sentence;
- `is_idiom`: a string denoting whether each word has an idiomatic (`"DA"`), literal (`"NE"`), or ambiguous (`"NEJASEN ZGLED"`) meaning. `"*"` means that the word is not part of the expression.
## Additional Information
### Dataset Curators
Tadej Škvorc, Polona Gantar, Marko Robnik-Šikonja.
### Licensing Information
CC BY-NC-SA 4.0.
### Citation Information
```
@article{skvorc2022mice,
title = {MICE: Mining Idioms with Contextual Embeddings},
journal = {Knowledge-Based Systems},
volume = {235},
pages = {107606},
year = {2022},
doi = {https://doi.org/10.1016/j.knosys.2021.107606},
url = {https://www.sciencedirect.com/science/article/pii/S0950705121008686},
author = {{\v S}kvorc, Tadej and Gantar, Polona and Robnik-{\v S}ikonja, Marko},
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
true | # AutoTrain Dataset for project: ethos-sentiments
## Dataset Description
This dataset has been automatically processed by AutoTrain for project ethos-sentiments.
### 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": "well it's clear now why europeans can't differ niggers and shitskins from human",
"target": 0
},
{
"text": "These boys will then grow up with people making fun of them and they will then hate their parents for ruining their lives.",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['hate_speech', 'no_hate_speech'], 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 | 798 |
| valid | 200 |
|
true |
# Dataset Card for `reviews_with_drift`
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [language](#language)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### language
Text is mainly written in english.
## 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
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset. |
true | # Dataset Card for Kill-Me-Please Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
## Dataset Description
- **Repository:** [github pet project repo](https://github.com/takiholadi/generative-kill-me-please)
### Dataset Summary
It is an Russian-language dataset containing just over 30k unique stories as written as users of https://killpls.me as of period from March 2009 to October 2022. This resource was blocked by Roskomnadzor so consider text-generation task if you want more stories.
### Languages
ru-RU
## Dataset Structure
### Data Instances
Here is an example of instance:
```
{'text': 'По глупости удалил всю 10 летнюю базу. Восстановлению не подлежит. Мне конец. КМП!'
'tags': 'техника'
'votes': 2914
'url': 'https://killpls.me/story/616'
'datetime': '4 июля 2009, 23:20'}
```
### Data Fields
- `text`: a string containing the body of the story
- `tags`: a string containing a comma-separated tags in a multi-label setup, fullset of tags (except of one empty-tagged record): `внешность`, `деньги`, `друзья`, `здоровье`, `отношения`, `работа`, `разное`, `родители`, `секс`, `семья`, `техника`, `учеба`
- `votes`: an integer sum of upvotes/downvotes
- `url`: a string containing the url where the story was web-scraped from
- `datetime`: a string containing with the datetime the story was written
### Data Splits
The has 2 multi-label stratified splits: train and test.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 27,321 |
| Test | 2,772 |
|
false | # AutoTrain Dataset for project: swin-muppet
## Dataset Description
This dataset has been automatically processed by AutoTrain for project swin-muppet.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<286x286 RGB PIL image>",
"target": 7
},
{
"image": "<169x170 RGB PIL image>",
"target": 13
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(num_classes=24, names=['Animal', 'Beaker', 'Bert', 'BigBird', 'Bunsen', 'Camilla', 'CookieMonster', 'Elmo', 'Ernie', 'Floyd', 'Fozzie', 'Gonzo', 'Grover', 'Kermit', 'Oscar', 'Pepe', 'Piggy', 'Rowlf', 'Scooter', 'Statler', 'SwedishChef', 'TheCount', 'Waldorf', 'Zoot'], 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 | 599 |
| valid | 162 |
|
false |
# Dataset Card for Thesaurus of Modern Slovene 1.0
Also known as "Sopomenke 1.0". Available in application form online: https://viri.cjvt.si/sopomenke/slv/.
### Dataset Summary
This is an automatically created Slovene thesaurus from Slovene data available in a comprehensive English–Slovenian dictionary, a monolingual dictionary, and a corpus. A network analysis on the bilingual dictionary word co-occurrence graph was used, together with additional information from the distributional thesaurus data available as part of the Sketch Engine tool and extracted from the 1.2 billion word Gigafida corpus and the monolingual dictionary.
For a detailed description of the data, please see the paper Krek et al. (2017).
### Supported Tasks and Leaderboards
Other (the data is a knowledge base).
### Languages
Slovenian.
## Dataset Structure
### Data Instances
Each entry is stored in its own instance. The following instance contains the metadata for the `headword` "abeceda" (EN: "alphabet").
```
{
'id_headword': 'th.12',
'headword': 'abeceda',
'groups_core': [],
'groups_near': [
{
'id_words': ['th.12.1', 'th.12.2'],
'words': ['pisava', 'črkopis'],
'scores': [0.3311710059642792, 0.3311710059642792],
'domains': [['jezikoslovje'], ['jezikoslovje']]
}
]
}
```
### Data Fields
- `id_headword`: a string ID of the word;
- `headword`: the word whose synonyms are grouped in the instance;
- `groups_core`: groups of likely synonyms - each group contains the IDs of the words (`id_words`), the synonyms (`words`), and how strong the synonym relation (`scores`) is. Some groups also have domains annotated (`domains`, >= 1 per word, i.e. `domains` is a list of lists);
- `groups_near`: same as `groups_near`, but the synonyms here are typically less likely to be exact synonyms and more likely to be otherwise similar.
## Additional Information
### Dataset Curators
Simon Krek; et al. (please see http://hdl.handle.net/11356/1166 for the full list).
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@article{krek2017translation,
title={From translation equivalents to synonyms: creation of a Slovene thesaurus using word co-occurrence network analysis},
author={Krek, Simon and Laskowski, Cyprian and Robnik-{\v{S}}ikonja, Marko},
journal={Proceedings of eLex},
pages={93--109},
year={2017}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
false |
# Dataset Card for FB15k-237
## Table of Contents
- [Dataset Card for FB15k-237](#dataset-card-for-fb15k-237)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://deepai.org/dataset/fb15k-237](https://deepai.org/dataset/fb15k-237)
- **Repository:**
- **Paper:** [More Information Needed](https://paperswithcode.com/dataset/fb15k-237)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
FB15k-237 is a link prediction dataset created from FB15k. While FB15k consists of 1,345 relations, 14,951 entities, and 592,213 triples, many triples are inverses that cause leakage from the training to testing and validation splits. FB15k-237 was created by Toutanova and Chen (2015) to ensure that the testing and evaluation datasets do not have inverse relation test leakage. In summary, FB15k-237 dataset contains 310,079 triples with 14,505 entities and 237 relation types.
### Supported Tasks and Leaderboards
Supported Tasks: link prediction task on knowledge graphs.
Leaderboads:
[More Information Needed](https://paperswithcode.com/sota/link-prediction-on-fb15k-237)
### 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
```
@inproceedings{schlichtkrull2018modeling,
title={Modeling relational data with graph convolutional networks},
author={Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter and Berg, Rianne van den and Titov, Ivan and Welling, Max},
booktitle={European semantic web conference},
pages={593--607},
year={2018},
organization={Springer}
}
```
### Contributions
Thanks to [@pp413](https://github.com/pp413) for adding this dataset. |
false |
# Dataset Card for SloWNet
### Dataset Summary
sloWNet is the Slovene WordNet developed in the expand approach: it contains the complete Princeton WordNet 3.0 and over 70 000 Slovene literals. These literals have been added automatically using different types of existing resources, such as bilingual dictionaries, parallel corpora and Wikipedia. 33 000 literals have been subsequently hand-validated.
For a detailed description of the data, please see the paper Fišer et al. (2012).
### Supported Tasks and Leaderboards
Other (the data is a knowledge base).
### Languages
Slovenian.
## Dataset Structure
### Data Instances
Each synset is stored in its own instance. The following instance represents a synset containing the English synonyms `{'able'}` and Slovene synonyms `{'sposoben', 'zmožen'}`:
```
{
'id': 'eng-30-00001740-a',
'pos': 'a',
'bcs': 3,
'en_synonyms': {
'words': ['able'],
'senses': [1],
'pwnids': ['able%3:00:00::']
},
'sl_synonyms': {
'words': ['sposoben', 'zmožen'],
'is_validated': [False, False]
},
'en_def': "(usually followed by `to') having the necessary means or skill or know-how or authority to do something",
'sl_def': 'N/A',
'en_usages': [
'able to swim',
'she was able to program her computer',
'we were at last able to buy a car',
'able to get a grant for the project'
],
'sl_usages': [],
'ilrs': {
'types': ['near_antonym', 'be_in_state', 'be_in_state', 'eng_derivative', 'eng_derivative'],
'id_synsets': ['eng-30-00002098-a', 'eng-30-05200169-n', 'eng-30-05616246-n', 'eng-30-05200169-n', 'eng-30-05616246-n']
},
'semeval07_cluster': 'able',
'domains': ['quality']
}
```
### Data Fields
- `id`: a string ID of the synset;
- `pos`: part of speech tag of the synset;
- `bcs`: Base Concept Set index (`-1` if not present);
- `en_synonyms`: the English synonyms in the synset - synonym `i` is described with its form (`words[i]`), sense (`senses[i]`), and Princeton WordNet ID (`pwnids[i]`);
- `sl_synonyms`: the Slovene synonyms in the synset - synonym `i` is described with its form (`words[i]`) and a flag marking if its correctness has been manually validated (`is_validated[i]`);
- `en_def`: the English definition (`"N/A"` if not present);
- `sl_def`: the Slovene definition (`"N/A"` if not present);
- `en_usages`: the English examples of usage;
- `sl_usages`: the Slovene examples of usage;
- `ilrs`: internal language relations - relation `i` is described by its type (`types[i]`) and the target synset (`id_synsets[i]`);
- `semeval07_cluster`: string cluster (`"N/A"` if not present);
- `domains`: domains of the synset.
## Additional Information
### Dataset Curators
Darja Fišer.
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@inproceedings{fiser2012slownet,
title={sloWNet 3.0: development, extension and cleaning},
author={Fi{\v{s}}er, Darja and Novak, Jernej and Erjavec, Toma{\v{z}}},
booktitle={Proceedings of 6th International Global Wordnet Conference (GWC 2012)},
pages={113--117},
year={2012}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
false |
# Dataset Card for CrossWOZ
- **Repository:** https://github.com/thu-coai/CrossWOZ
- **Paper:** https://aclanthology.org/2020.tacl-1.19/
- **Leaderboard:** None
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
```
from convlab.util import load_dataset, load_ontology, load_database
dataset = load_dataset('crosswoz')
ontology = load_ontology('crosswoz')
database = load_database('crosswoz')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
### Dataset Summary
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides. We also provide a user simulator and several benchmark models for pipelined taskoriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus.
- **How to get the transformed data from original data:**
- Run `python preprocess.py` in the current directory. Need `../../crosswoz/` as the original data.
- **Main changes of the transformation:**
- Add simple description for domains, slots, and intents.
- Switch intent&domain of `General` dialog acts => domain == 'General' and intent in ['thank','bye','greet','welcome']
- Binary dialog acts include: 1) domain == 'General'; 2) intent in ['NoOffer', 'Request', 'Select']; 3) slot in ['酒店设施']
- Categorical dialog acts include: slot in ['酒店类型', '车型', '车牌']
- Non-categorical dialogue acts: others. assert intent in ['Inform', 'Recommend'] and slot != 'none' and value != 'none'
- Transform original user goal to list of `{domain: {'inform': {slot: [value, mentioned/not mentioned]}, 'request': {slot: [value, mentioned/not mentioned]}}}`, stored as `user_state` of user turns.
- Transform `sys_state_init` (first API call of system turns) without `selectedResults` as belief state in user turns.
- Transform `sys_state` (last API call of system turns) to `db_query` with domain states that contain non-empty `selectedResults`. The `selectedResults` are saved as `db_results` (only contain entity name). Both stored in system turns.
- **Annotations:**
- user goal, user state, dialogue acts, state, db query, db results.
- Multiple values in state are separated by spaces, meaning all constraints should be satisfied.
### Supported Tasks and Leaderboards
NLU, DST, Policy, NLG, E2E, User simulator
### Languages
Chinese
### Data Splits
| split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) |
|------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------|
| train | 5012 | 84674 | 16.89 | 20.55 | 3.02 | 99.67 | - | 100 | 94.39 |
| validation | 500 | 8458 | 16.92 | 20.53 | 3.04 | 99.62 | - | 100 | 94.36 |
| test | 500 | 8476 | 16.95 | 20.51 | 3.08 | 99.61 | - | 100 | 94.85 |
| all | 6012 | 101608 | 16.9 | 20.54 | 3.03 | 99.66 | - | 100 | 94.43 |
6 domains: ['景点', '餐馆', '酒店', '地铁', '出租', 'General']
- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage.
- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage.
### Citation
```
@article{zhu2020crosswoz,
author = {Qi Zhu and Kaili Huang and Zheng Zhang and Xiaoyan Zhu and Minlie Huang},
title = {Cross{WOZ}: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset},
journal = {Transactions of the Association for Computational Linguistics},
year = {2020}
}
```
### Licensing Information
Apache License, Version 2.0 |
false |
# Dataset Card for WSDMCup2023
## Dataset Description
- **Homepage:** [Toloka Visual Question Answering Challenge](https://toloka.ai/challenges/wsdm2023)
- **Repository:** [WSDM Cup 2023 Starter Pack](https://github.com/Toloka/WSDMCup2023)
- **Paper:**
- **Leaderboard:** [CodaLab Competition Leaderboard](https://codalab.lisn.upsaclay.fr/competitions/7434#results)
- **Point of Contact:** research@toloka.ai
| Question | Image and Answer |
| --- | --- |
| What do you use to hit the ball? | <img src="https://tlkfrontprod.azureedge.net/portal-production/static/uploaded/images/KUsGAc_eqdMcNxkBXzzl/KUsGAc_eqdMcNxkBXzzl_webp_1280_x2.webp" width="228" alt="What do you use to hit the ball?"> |
| What do people use for cutting? | <img src="https://tlkfrontprod.azureedge.net/portal-production/static/uploaded/images/brXEVYckNLfQKcfNu4DF/brXEVYckNLfQKcfNu4DF_webp_1280_x2.webp" width="228" alt="What do people use for cutting?"> |
| What do we use to support the immune system and get vitamin C? | <img src="https://tlkfrontprod.azureedge.net/portal-production/static/uploaded/images/HQ0A-ZvZCGCmYfTs83K7/HQ0A-ZvZCGCmYfTs83K7_webp_1280_x2.webp" width="228" alt="What do we use to support the immune system and get vitamin C?"> |
### Dataset Summary
The WSDMCup2023 Dataset consists of images associated with textual questions.
One entry (instance) in our dataset is a question-image pair labeled with the ground truth coordinates of a bounding box containing
the visual answer to the given question. The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in
Context dataset, [MS COCO](https://cocodataset.org/). All data labeling was performed on the [Toloka crowdsourcing platform](https://toloka.ai/).
Our dataset has 45,199 instances split among three subsets: train (38,990 instances), public test (1,705 instances),
and private test (4,504 instances). The entire train dataset was available for everyone since the start of the challenge.
The public test dataset was available since the evaluation phase of the competition but without any ground truth labels.
After the end of the competition, public and private sets were released.
## Dataset Citation
Please cite the challenge results or dataset description as follows.
- Ustalov D., Pavlichenko N., Likhobaba D., and Smirnova A. [WSDM Cup 2023 Challenge on Visual Question Answering](http://ceur-ws.org/Vol-3357/invited1.pdf). *Proceedings of the 4th Crowd Science Workshop on Collaboration of Humans and Learning Algorithms for Data Labeling.* Singapore, 2023, pp. 1–7.
```bibtex
@inproceedings{TolokaWSDMCup2023,
author = {Ustalov, Dmitry and Pavlichenko, Nikita and Likhobaba, Daniil and Smirnova, Alisa},
title = {{WSDM~Cup 2023 Challenge on Visual Question Answering}},
year = {2023},
booktitle = {Proceedings of the 4th Crowd Science Workshop on Collaboration of Humans and Learning Algorithms for Data Labeling},
pages = {1--7},
address = {Singapore},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-3357/invited1.pdf},
language = {english},
}
```
### Supported Tasks and Leaderboards
The Visual Question Answering.
### Language
English
## Dataset Structure
### Data Instances
A data instance contains a URL to the picture, information about the image size - width and height, information about the ground truth bounding box - left top and right bottom dots, and contains the question related to the picture.
```
{'image': https://toloka-cdn.azureedge.net/wsdmcup2023/000000000013.jpg,
'width': 640,
'height': 427,
'left': 129,
'top': 192,
'right': 155,
'bottom': 212,
'question': What does it use to breath?}
```
### Data Fields
* image: contains URL to the image
* width: value in pixels of image width
* height: value in pixels of image height
* left: the x coordinate in pixels to determine the left-top dot of the bounding box
* top: the y coordinate in pixels to determine the left-top dot of the bounding box
* right: the x coordinate in pixels to determine the right-bottom dot of the bounding box
* bottom: the y coordinate in pixels to determine the right-bottom dot of the bounding box
* question: a question related to the picture
### Data Splits
There are four splits in the data: train, train_sample, test_public, and test_private. 'train' split contains the full pull for model training.
The 'train-sample' split contains the part of the 'train' split. The 'test_public' split contains public data to test the model.
The 'test_private' split contains private data for the final model test.
### Source Data
The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in
Context dataset, [MS COCO](https://cocodataset.org/).
### Annotations
All data labeling was performed on the [Toloka crowdsourcing platform](https://toloka.ai/).
Only annotators who self-reported the knowledge of English had access to the annotation task.
### Citation Information
* Competition: https://toloka.ai/challenges/wsdm2023
* CodaLab: https://codalab.lisn.upsaclay.fr/competitions/7434
* Dataset: https://doi.org/10.5281/zenodo.7057740 |
false | |
true |
# Dataset Card for VUA Metaphor Corpus
**Important note#1**: This is a slightly simplified but mostly complete parse of the corpus. What is missing are lemmas and some metadata that was not important at the time of writing the parser. See the section `Simplifications` for more information on this.
**Important note#2**: The dataset contains metadata - to ignore it and correctly remap the annotations, see the section `Discarding metadata`.
### Dataset Summary
VUA Metaphor Corpus (VUAMC) contains a selection of excerpts from BNC-Baby files that have been annotated for metaphor. There are four registers, each comprising about 50 000 words: academic texts, news texts, fiction, and conversations.
Words have been separately labelled as participating in multi-word expressions (about 1.5%) or as discarded for metaphor analysis (0.02%). Main categories include words that are related to metaphor (MRW), words that signal metaphor (MFlag), and words that are not related to metaphor. For metaphor-related words, subdivisions have been made between clear cases of metaphor versus borderline cases (WIDLII, When In Doubt, Leave It In). Another parameter of metaphor-related words makes a distinction between direct metaphor, indirect metaphor, and implicit metaphor.
### Supported Tasks and Leaderboards
Metaphor detection, metaphor type classification.
### Languages
English.
## Dataset Structure
### Data Instances
A sample instance from the dataset:
```
{
'document_name': 'kcv-fragment42',
'words': ['', 'I', 'think', 'we', 'should', 'have', 'different', 'holidays', '.'],
'pos_tags': ['N/A', 'PNP', 'VVB', 'PNP', 'VM0', 'VHI', 'AJ0', 'NN2', 'PUN'],
'met_type': [
{'type': 'mrw/met', 'word_indices': [5]}
],
'meta': ['vocal/laugh', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A']
}
```
### Data Fields
The instances are ordered as they appear in the corpus.
- `document_name`: a string containing the name of the document in which the sentence appears;
- `words`: words in the sentence (`""` when the word represents metadata);
- `pos_tags`: POS tags of the words, encoded using the BNC basic tagset (`"N/A"` when the word does not have an associated POS tag);
- `met_type`: metaphors in the sentence, marked by their type and word indices;
- `meta`: selected metadata tags providing additional context to the sentence. Metadata may not correspond to a specific word. In this case, the metadata is represented with an empty string (`""`) in `words` and a `"N/A"` tag in `pos_tags`.
## Dataset Creation
For detailed information on the corpus, please check out the references in the `Citation Information` section or contact the dataset authors.
## Simplifications
The raw corpus is equipped with rich metadata and encoded in the TEI XML format. The textual part is fully parsed except for the lemmas, i.e. all the sentences in the raw corpus are present in the dataset.
However, parsing the metadata fully is unnecessarily tedious, so certain simplifications were made:
- paragraph information is not preserved as the dataset is parsed at sentence level;
- manual corrections (`<corr>`) of incorrectly written words are ignored, and the original, incorrect form of the words is used instead;
- `<ptr>` and `<anchor>` tags are ignored as I cannot figure out what they represent;
- the attributes `rendition` (in `<hi>` tags) and `new` (in `<shift>` tags) are not exposed.
## Discarding metadata
The dataset contains rich metadata, which is stored in the `meta` attribute. To keep data aligned, empty words or `"N/A"`s are inserted into the other attributes. If you want to ignore the metadata and correct the metaphor type annotations, you can use code similar to the following snippet:
```python3
data = datasets.load_dataset("matejklemen/vuamc")["train"]
data = data.to_pandas()
for idx_ex in range(data.shape[0]):
curr_ex = data.iloc[idx_ex]
idx_remap = {}
for idx_word, word in enumerate(curr_ex["words"]):
if len(word) != 0:
idx_remap[idx_word] = len(idx_remap)
# Note that lists are stored as np arrays by datasets, while we are storing new data in a list!
# (unhandled for simplicity)
words, pos_tags, met_type = curr_ex[["words", "pos_tags", "met_type"]].tolist()
if len(idx_remap) != len(curr_ex["words"]):
words = list(filter(lambda _word: len(_word) > 0, curr_ex["words"]))
pos_tags = list(filter(lambda _pos: _pos != "N/A", curr_ex["pos_tags"]))
met_type = []
for met_info in curr_ex["met_type"]:
met_type.append({
"type": met_info["type"],
"word_indices": list(map(lambda _i: idx_remap[_i], met_info["word_indices"]))
})
```
## Additional Information
### Dataset Curators
Gerard Steen; et al. (please see http://hdl.handle.net/20.500.12024/2541 for the full list).
### Licensing Information
Available for non-commercial use on condition that the terms of the [BNC Licence](http://www.natcorp.ox.ac.uk/docs/licence.html) are observed and that this header is included in its entirety with any copy distributed.
### Citation Information
```
@book{steen2010method,
title={A method for linguistic metaphor identification: From MIP to MIPVU},
author={Steen, Gerard and Dorst, Lettie and Herrmann, J. and Kaal, Anna and Krennmayr, Tina and Pasma, Trijntje},
volume={14},
year={2010},
publisher={John Benjamins Publishing}
}
```
```
@inproceedings{leong-etal-2020-report,
title = "A Report on the 2020 {VUA} and {TOEFL} Metaphor Detection Shared Task",
author = "Leong, Chee Wee (Ben) and
Beigman Klebanov, Beata and
Hamill, Chris and
Stemle, Egon and
Ubale, Rutuja and
Chen, Xianyang",
booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
year = "2020",
url = "https://aclanthology.org/2020.figlang-1.3",
doi = "10.18653/v1/2020.figlang-1.3",
pages = "18--29"
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
true |
# Dataset Card for GuiaCat
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Point of Contact:** [blanca.calvo@bsc.es](blanca.calvo@bsc.es)
### Dataset Summary
GuiaCat is a dataset consisting of 5.750 restaurant reviews in Catalan, with 5 associated scores and a label of sentiment. The data was provided by [GuiaCat](https://guiacat.cat) and curated by the BSC.
### Supported Tasks and Leaderboards
This corpus is mainly intended for sentiment analysis.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
The dataset consists of restaurant reviews labelled with 5 scores: service, food, price-quality, environment, and average. Reviews also have a sentiment label, derived from the average score, all stored as a csv file.
### Data Instances
```
7,7,7,7,7.0,"Aquest restaurant té una llarga història. Ara han tornat a canviar d'amos i aquest canvi s'ha vist molt repercutit en la carta, preus, servei, etc. Hi ha molta varietat de menjar, i tot boníssim, amb especialitats molt ben trobades. El servei molt càlid i agradable, dóna gust que et serveixin així. I la decoració molt agradable també, bastant curiosa. En fi, pel meu gust, un bon restaurant i bé de preu.",bo
8,9,8,7,8.0,"Molt recomanable en tots els sentits. El servei és molt atent, pulcre i gens agobiant; alhora els plats també presenten un aspecte acurat, cosa que fa, juntament amb l'ambient, que t'oblidis de que, malauradament, està situat pròxim a l'autopista.Com deia, l'ambient és molt acollidor, té un menjador principal molt elegant, perfecte per quedar bé amb tothom!Tot i això, destacar la bona calitat / preu, ja que aquest restaurant té una carta molt extensa en totes les branques i completa, tant de menjar com de vins. Pel qui entengui de vins, podriem dir que tot i tenir una carta molt rica, es recolza una mica en els clàssics.",molt bo
```
### Data Fields
- service: a score from 0 to 10 grading the service
- food: a score from 0 to 10 grading the food
- price-quality: a score from 0 to 10 grading the relation between price and quality
- environment: a score from 0 to 10 grading the environment
- avg: average of all the scores
- text: the review
- label: it can be "molt bo", "bo", "regular", "dolent", "molt dolent"
### Data Splits
* dev.csv: 500 examples
* test.csv: 500 examples
* train.csv: 4,750 examples
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
The data of this dataset has been provided by [GuiaCat](https://guiacat.cat).
#### Initial Data Collection and Normalization
[N/A]
#### Who are the source language producers?
The language producers were the users from GuiaCat.
### Annotations
The annotations are automatically derived from the scores that the users provided while reviewing the restaurants.
#### Annotation process
The mapping between average scores and labels is:
- Higher than 8: molt bo
- Between 8 and 6: bo
- Between 6 and 4: regular
- Between 4 and 2: dolent
- Less than 2: molt dolent
#### Who are the annotators?
Users
### Personal and Sensitive Information
No personal information included, although it could contain hate or abusive language.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that this data might contain biases. We have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons Attribution Non-commercial No-Derivatives 4.0 International](https://creativecommons.org/licenses/by-nc-nd/4.0/).
### Citation Information
```
```
### Contributions
We want to thank GuiaCat for providing this data.
|
false |
# Dataset Card for Quasimodo
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/commonsense/quasimodo
- **Repository:** https://github.com/Aunsiels/CSK
- **Paper:** Romero et al., Commonsense Properties from Query Logs and Question Answering Forums, CIKM, 2019
### Dataset Summary
A commonsense knowledge base constructed automatically from question-answering forums and query logs.
### Supported Tasks and Leaderboards
Can be useful for tasks requiring external knowledge such as question answering.
### Languages
English
## Dataset Structure
### Data Instances
```python
{
"subject": "elephant",
"predicate": "has_body_part"
"object": "trunk",
"modality": "TBC[so long trunks] x#x2 // TBC[long trunks] x#x9 // TBC[big trunks] x#x6 // TBC[long trunk] x#x1 // TBC[such big trunks] x#x1 0 0.9999667967035647 elephants have trunks x#x34 x#xGoogle Autocomplete, Bing Autocomplete, Yahoo Questions, Answers.com Questions, Reddit Questions // a elephants have trunks x#x2 x#xGoogle Autocomplete // a elephant have a trunk x#x2 x#xGoogle Autocomplete // elephants have so long trunks x#x2 x#xGoogle Autocomplete // elephants have long trunks x#x8 x#xGoogle Autocomplete, Yahoo Questions, Answers.com Questions // elephants have big trunks x#x6 x#xGoogle Autocomplete, Answers.com Questions, Reddit Questions // elephants have trunk x#x3 x#xGoogle Autocomplete, Yahoo Questions // elephant have long trunks x#x1 x#xGoogle Autocomplete // elephant has a trunk x#x1 x#xGoogle Autocomplete // elephants have a trunk x#x2 x#xAnswers.com Questions // an elephant has a long trunk x#x1 x#xAnswers.com Questions // elephant have trunks x#x1 x#xAnswers.com Questions // elephants have such big trunks x#x1 x#xReddit Questions",
"score": 0.9999667967668732,
"local_sigma": 1.0
}
```
### Data Fields
- subject: The subject of the triple
- predicate: The predicate of the triple
- object: The object of the triple
- modality: Modalities associated with the triples with their counts. TBC means the object can be further refined to the listed objects
- is_negative: 1 if the statement was negated
- score: salience score of the supervised scoring model
- local sigma: strict conditional probability of observing a (predicate, object) with a specific subject. I.e., a measure of how unique a statement is. E.g., local_sigma(lawyers, defend, serial_killers) = 1, local_sigma(lawyers, make, money) = 0.01, even though both statements have a similar score of 0.99.
## Dataset Creation
See original paper.
## Additional Information
### Licensing Information
CC-BY 2.0
### Citation Information
Romero et al., Commonsense Properties from Query Logs and Question Answering Forums, CIKM, 2019
|
true | |
false |
# Dataset Card for Wine Recognition dataset
## Dataset Description
- **Homepage:** https://archive.ics.uci.edu/ml/datasets/wine
- **Papers:**
1. S. Aeberhard, D. Coomans and O. de Vel,
Comparison of Classifiers in High Dimensional Settings,
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
2. S. Aeberhard, D. Coomans and O. de Vel,
"THE CLASSIFICATION PERFORMANCE OF RDA"
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
- **Point of Contact:** stefan'@'coral.cs.jcu.edu.au
### Dataset Summary
These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. In a classification context, this is a well posed problem with "well behaved" class structures. A good data set for first testing of a new classifier, but not very challenging.
### Supported Tasks and Leaderboards
Classification (cultivar) from continuous variables (all other variables)
## Dataset Structure
### Data Instances
178 wines
### Data Fields
1. Wine category (cultivar)
2. Alcohol
3. Malic acid
4. Ash
5. Alcalinity of ash
6. Magnesium
7. Total phenols
8. Flavanoids
9. Nonflavanoid phenols
10. Proanthocyanins
11. Color intensity
12. Hue
13. OD280/OD315 of diluted wines
14. Proline
### Data Splits
None
## Dataset Creation
### Source Data
https://archive.ics.uci.edu/ml/datasets/wine
#### Initial Data Collection and Normalization
Original Owners:
Forina, M. et al, PARVUS -
An Extendible Package for Data Exploration, Classification and Correlation.
Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno,
16147 Genoa, Italy.
## Additional Information
### Dataset Curators
Stefan Aeberhard
### Licensing Information
No information found on the original website |
false | # Dataset Card for OLM May 2017 Common Crawl
Cleaned and deduplicated pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from 16% of the May 2017 Common Crawl snapshot.
Note: `last_modified_timestamp` was parsed from whatever a website returned in it's `Last-Modified` header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with `last_modified_timestamp`. |
false |
# Dataset Card for V4Design Europeana style dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
> 1614 paintings belonging to the categories Baroque, Rococo, and Other. The images were obtained using the Europeana Search API, selecting open objects from the art thematic collection. 24k images were obtained, from which the current dataset was derived. The labels were added by the V4Design team, using a custom annotation tool. As described in the project documentation, other categories were used besides Baroque and Rococo. But for the sake of training a machine learning model we have retained only the categories with a significant number of annotations [source](https://zenodo.org/record/4896487)
This version of the dataset is generated using the [CSV file](https://zenodo.org/record/4896487) hosted on Zenodo. This CSV file contains the labels with URLs for the relevant images. Some of these URLs no longer resolve to an image. For consitency with the original dataset and if these URLs become valid again, these rows of the data are preserved here. If you want only successfully loaded images in your dataset, you can filter out the missing images as follows.
```python
ds = ds.filter(lambda x: x['image'] is not None)
```
### Supported Tasks and Leaderboards
This dataset is primarily intended for `image-classification`.
### 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
```
@dataset{europeana_2021_4896487,
author = {Europeana and
V4Design},
title = {V4Design/Europeana style dataset},
month = jun,
year = 2021,
publisher = {Zenodo},
doi = {10.5281/zenodo.4896487},
url = {https://doi.org/10.5281/zenodo.4896487}
}
```
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
|
false |
# Dataset Card for Early Printed Books Font Detection Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**https://doi.org/10.5281/zenodo.3366686
- **Paper:**: https://doi.org/10.1145/3352631.3352640
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
> This dataset is composed of photos of various resolution of 35'623 pages of printed books dating from the 15th to the 18th century. Each page has been attributed by experts from one to five labels corresponding to the font groups used in the text, with two extra-classes for non-textual content and fonts not present in the following list: Antiqua, Bastaπrda, Fraktur, Gotico Antiqua, Greek, Hebrew, Italic, Rotunda, Schwabacher, and Textura.
[More Information Needed]
### Supported Tasks and Leaderboards
The primary use case for this datasets is
- `multi-label-image-classification`: This dataset can be used to train a model for multi label image classification where each image can have one, or more labels.
- `image-classification`: This dataset could also be adapted to only predict a single label for each image
### Languages
The dataset includes books from a range of libraries (see below for further details). The paper doesn't provide a detailed overview of language breakdown. However, the books are from the 15th-18th century and appear to be dominated by European languages from that time period. The dataset also includes Hebrew.
[More Information Needed]
## Dataset Structure
This dataset has a single configuration.
### Data Instances
An example instance from this dataset:
```python
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x3840 at 0x7F6AC192D850>,
'labels': [5]}
```
### Data Fields
This dataset contains two fields:
- `image`: the image of the book page
- `labels`: one or more labels for the font used in the book page depicted in the `image`
### Data Splits
The dataset is broken into a train and test split with the following breakdown of number of examples:
- train: 24,866
- test: 10,757
## Dataset Creation
### Curation Rationale
The dataset was created to help train and evaluate automatic methods for font detection. The paper describing the paper also states that:
>data was cherry-picked, thus it is not statistically representative of what can be found in libraries. For example, as we had a small amount of Textura at the start, we specifically looked for more pages containing this font group, so we can expect that less than 3.6 % of randomly selected pages from libraries would contain Textura.
### Source Data
#### Initial Data Collection and Normalization
The images in this dataset are from books held by the British Library (London), Bayerische Staatsbibliothek München, Staatsbibliothek zu Berlin, Universitätsbibliothek Erlangen, Universitätsbibliothek Heidelberg, Staats- und Universitäatsbibliothek Göttingen, Stadt- und Universitätsbibliothek Köln, Württembergische Landesbibliothek Stuttgart and Herzog August Bibliothek Wolfenbüttel.
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
false | # AutoTrain Dataset for project: lojban-translation
## Dataset Description
This dataset has been automatically processed by AutoTrain for project lojban-translation.
### Languages
The BCP-47 code for the dataset's language is en2jb.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"source": "I read the poem for my child.",
"target": "mi tcidu lo pemci te cu'u le panzi be mi"
},
{
"source": "Jim is learning how to drive a car.",
"target": "la jim cilre fi lo nu klasazri lo karce"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"source": "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 | 8000 |
| valid | 2000 |
|
false |
# Dataset Card for [naacl2022]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a named entity recognition dataset annotated for the science entity recognition task, a [project](https://github.com/neubig/nlp-from-scratch-assignment-2022) from the CMU 11-711 course.
### Supported Tasks and Leaderboards
NER task.
### Languages
English
## Dataset Structure
### Data Instances
A sample of the dataset
{'id': '0',
'tokens': ['We', 'sample', '50', 'negative', 'cases', 'from', 'T5LARGE', '+', 'GenMC', 'for', 'each', 'dataset'],
'ner_tags':['O', 'O', 'O', 'O', 'O', 'O', 'B-MethodName', 'O', 'B-MethodName', 'O', 'O', 'O']}
### Data Fields
id,tokens,ner_tags
- `id`: a `string` feature give the sample index.
- `tokens`: a `list` of `string` features give the sequence.
- `ner_tags`: a `list` of classification labels for each token in the sentence, with possible values including
`O` (0), `B-MethodName` (1), `I-MethodName` (2), `B-HyperparameterName` (3),`I-HyperparameterName` (4),`B-HyperparameterValue` (5),`I-HyperparameterValue` (6),`B-MetricName` (7),`I-MetricName` (8),`B-MetricValue` (9),`I-MetricValue` (10),`B-TaskName` (11),`I-TaskName` (12),`B-DatasetName` (13),`I-DatasetName` (14).
### Data Splits
Data split into
train.txt
dev.txt
test.txt
## 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
The data is annotated by using labelstudio, the papers are collected from TACL and ACL 2022 conferences.
#### Who are the annotators?
Xiaoyue Cui and Haotian Teng annotated the datasets.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@xcui297](https://github.com/xcui297); [@haotianteng](https://github.com/haotianteng) for adding this dataset.
|
false | # AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages
- [GitHub Repository of the Paper](https://github.com/bonaventuredossou/MLM_AL)
This repository contains the dataset for our paper [`AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages`](https://arxiv.org/pdf/2211.03263.pdf) which will appear at the third Simple and Efficient Natural Language Processing, at EMNLP 2022.
## Our self-active learning framework
## Languages Covered
AfroLM has been pretrained from scratch on 23 African Languages: Amharic, Afan Oromo, Bambara, Ghomalá, Éwé, Fon, Hausa, Ìgbò, Kinyarwanda, Lingala, Luganda, Luo, Mooré, Chewa, Naija, Shona, Swahili, Setswana, Twi, Wolof, Xhosa, Yorùbá, and Zulu.
## Evaluation Results
AfroLM was evaluated on MasakhaNER1.0 (10 African Languages) and MasakhaNER2.0 (21 African Languages) datasets; on text classification and sentiment analysis. AfroLM outperformed AfriBERTa, mBERT, and XLMR-base, and was very competitive with AfroXLMR. AfroLM is also very data efficient because it was pretrained on a dataset 14x+ smaller than its competitors' datasets. Below the average F1-score performances of various models, across various datasets. Please consult our paper for more language-level performance.
Model | MasakhaNER | MasakhaNER2.0* | Text Classification (Yoruba/Hausa) | Sentiment Analysis (YOSM) | OOD Sentiment Analysis (Twitter -> YOSM) |
|:---: |:---: |:---: | :---: |:---: | :---: |
`AfroLM-Large` | **80.13** | **83.26** | **82.90/91.00** | **85.40** | **68.70** |
`AfriBERTa` | 79.10 | 81.31 | 83.22/90.86 | 82.70 | 65.90 |
`mBERT` | 71.55 | 80.68 | --- | --- | --- |
`XLMR-base` | 79.16 | 83.09 | --- | --- | --- |
`AfroXLMR-base` | `81.90` | `84.55` | --- | --- | --- |
- (*) The evaluation was made on the 11 additional languages of the dataset.
- Bold numbers represent the performance of the model with the **smallest pretrained data**.
## Pretrained Models and Dataset
**Models:**: [AfroLM-Large](https://huggingface.co/bonadossou/afrolm_active_learning) and **Dataset**: [AfroLM Dataset](https://huggingface.co/datasets/bonadossou/afrolm_active_learning_dataset)
## HuggingFace usage of AfroLM-large
```python
from transformers import XLMRobertaModel, XLMRobertaTokenizer
model = XLMRobertaModel.from_pretrained("bonadossou/afrolm_active_learning")
tokenizer = XLMRobertaTokenizer.from_pretrained("bonadossou/afrolm_active_learning")
tokenizer.model_max_length = 256
```
`Autotokenizer` class does not successfully load our tokenizer. So we recommend using directly the `XLMRobertaTokenizer` class. Depending on your task, you will load the according mode of the model. Read the [XLMRoberta Documentation](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)
## Reproducing our result: Training and Evaluation
- To train the network, run `python active_learning.py`. You can also wrap it around a `bash` script.
- For the evaluation:
- NER Classification: `bash ner_experiments.sh`
- Text Classification & Sentiment Analysis: `bash text_classification_all.sh`
## Citation
``@inproceedings{dossou-etal-2022-afrolm,
title = "{A}fro{LM}: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 {A}frican Languages",
author = "Dossou, Bonaventure F. P. and
Tonja, Atnafu Lambebo and
Yousuf, Oreen and
Osei, Salomey and
Oppong, Abigail and
Shode, Iyanuoluwa and
Awoyomi, Oluwabusayo Olufunke and
Emezue, Chris",
booktitle = "Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sustainlp-1.11",
pages = "52--64",}``
## Reach out
Do you have a question? Please create an issue and we will reach out as soon as possible |
false |
# Dataset Card for mt_en_it
## Table of Contents
- [Dataset Card for mt_en_it](#dataset-card-for-mt-en-it)
- [Table of Contents](#table-of-contents)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
### Dataset Summary
This dataset comprises traditional Neapolitan songs from [napoligrafia](https://www.napoligrafia.it) translated into Italian.
### Languages
- italian-to-neapolitan
### Data Instances
A sample from the dataset.
```python
{
'url': "url",
'napoletano': "o, quacche ghiuorno, 'a frennesia mme piglia",
'italiano': "o, qualche giorno, la rabbia mi prende"
}
```
The text is provided without further preprocessing or tokenization.
### Data Fields
- `url`: source URL.
- `napoletano`: Neapolitan text.
- `italiano`: Italian text.
### Dataset Creation
The dataset was created by scraping [napoligrafia](https://www.napoligrafia.it) songs. |
true |
# Dataset Card for GLUE
## Table of Contents
- [Dataset Card for GLUE](#dataset-card-for-glue)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [ax](#ax)
- [cola](#cola)
- [mnli](#mnli)
- [mnli_matched](#mnli_matched)
- [mnli_mismatched](#mnli_mismatched)
- [mrpc](#mrpc)
- [qnli](#qnli)
- [qqp](#qqp)
- [rte](#rte)
- [sst2](#sst2)
- [stsb](#stsb)
- [wnli](#wnli)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [ax](#ax-1)
- [cola](#cola-1)
- [mnli](#mnli-1)
- [mnli_matched](#mnli_matched-1)
- [mnli_mismatched](#mnli_mismatched-1)
- [mrpc](#mrpc-1)
- [qnli](#qnli-1)
- [qqp](#qqp-1)
- [rte](#rte-1)
- [sst2](#sst2-1)
- [stsb](#stsb-1)
- [wnli](#wnli-1)
- [Data Fields](#data-fields)
- [ax](#ax-2)
- [cola](#cola-2)
- [mnli](#mnli-2)
- [mnli_matched](#mnli_matched-2)
- [mnli_mismatched](#mnli_mismatched-2)
- [mrpc](#mrpc-2)
- [qnli](#qnli-2)
- [qqp](#qqp-2)
- [rte](#rte-2)
- [sst2](#sst2-2)
- [stsb](#stsb-2)
- [wnli](#wnli-2)
- [Data Splits](#data-splits)
- [ax](#ax-3)
- [cola](#cola-3)
- [mnli](#mnli-3)
- [mnli_matched](#mnli_matched-3)
- [mnli_mismatched](#mnli_mismatched-3)
- [mrpc](#mrpc-3)
- [qnli](#qnli-3)
- [qqp](#qqp-3)
- [rte](#rte-3)
- [sst2](#sst2-3)
- [stsb](#stsb-3)
- [wnli](#wnli-3)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://nyu-mll.github.io/CoLA/](https://nyu-mll.github.io/CoLA/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 955.33 MB
- **Size of the generated dataset:** 229.68 MB
- **Total amount of disk used:** 1185.01 MB
### Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
### Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found [at this address](https://gluebenchmark.com/). It comprises the following tasks:
#### ax
A manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.
#### cola
The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.
#### mnli
The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.
#### mnli_matched
The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mnli_mismatched
The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mrpc
The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
#### qnli
The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.
#### qqp
The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.
#### rte
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.
#### sst2
The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.
#### stsb
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.
#### wnli
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).
### Languages
The language data in GLUE is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
#### ax
- **Size of downloaded dataset files:** 0.21 MB
- **Size of the generated dataset:** 0.23 MB
- **Total amount of disk used:** 0.44 MB
An example of 'test' looks as follows.
```
{
"premise": "The cat sat on the mat.",
"hypothesis": "The cat did not sit on the mat.",
"label": -1,
"idx: 0
}
```
#### cola
- **Size of downloaded dataset files:** 0.36 MB
- **Size of the generated dataset:** 0.58 MB
- **Total amount of disk used:** 0.94 MB
An example of 'train' looks as follows.
```
{
"sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
"label": 1,
"id": 0
}
```
#### mnli
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 78.65 MB
- **Total amount of disk used:** 376.95 MB
An example of 'train' looks as follows.
```
{
"premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
"hypothesis": "Product and geography are what make cream skimming work.",
"label": 1,
"idx": 0
}
```
#### mnli_matched
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 3.52 MB
- **Total amount of disk used:** 301.82 MB
An example of 'test' looks as follows.
```
{
"premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.",
"hypothesis": "Hierbas is a name worth looking out for.",
"label": -1,
"idx": 0
}
```
#### mnli_mismatched
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 3.73 MB
- **Total amount of disk used:** 302.02 MB
An example of 'test' looks as follows.
```
{
"premise": "What have you decided, what are you going to do?",
"hypothesis": "So what's your decision?,
"label": -1,
"idx": 0
}
```
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
#### ax
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### cola
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `unacceptable` (0), `acceptable` (1).
- `idx`: a `int32` feature.
#### mnli
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_matched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_mismatched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Splits
#### ax
| |test|
|---|---:|
|ax |1104|
#### cola
| |train|validation|test|
|----|----:|---------:|---:|
|cola| 8551| 1043|1063|
#### mnli
| |train |validation_matched|validation_mismatched|test_matched|test_mismatched|
|----|-----:|-----------------:|--------------------:|-----------:|--------------:|
|mnli|392702| 9815| 9832| 9796| 9847|
#### mnli_matched
| |validation|test|
|------------|---------:|---:|
|mnli_matched| 9815|9796|
#### mnli_mismatched
| |validation|test|
|---------------|---------:|---:|
|mnli_mismatched| 9832|9847|
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{warstadt2018neural,
title={Neural Network Acceptability Judgments},
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
journal={arXiv preprint arXiv:1805.12471},
year={2018}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
Note that each GLUE dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
```
### Contributions
Thanks to [@patpizio](https://github.com/patpizio), [@jeswan](https://github.com/jeswan), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. |
false | |
false |
# Dataset Card for Pokémon BLIP captions with English and Japanese.
Dataset used to train Pokémon text to image model, add a Japanese Column of [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions)
BLIP generated captions for Pokémon images from Few Shot Pokémon dataset introduced by Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis (FastGAN). Original images were obtained from FastGAN-pytorch and captioned with the pre-trained BLIP model.
For each row the dataset contains image en_text (caption in English) and ja_text (caption in Japanese) keys. image is a varying size PIL jpeg, and text is the accompanying text caption. Only a train split is provided.
The Japanese captions are translated by [Deepl](https://www.deepl.com/translator) |
true | |
false | # AutoTrain Dataset for project: oaoqoqkaksk
## Dataset Description
This dataset has been automatically processed by AutoTrain for project oaoqoqkaksk.
### Languages
The BCP-47 code for the dataset's language is en2nl.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"target": "\u00de\u00e6t Sunnanrastere onl\u00edcnescynn",
"source": "The Sun raster image format"
},
{
"target": "Lundon",
"source": "Gordon"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "Value(dtype='string', id=None)",
"source": "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 | 1528 |
| valid | 383 |
|
true |
# Dataset Card for openpi_v2
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Open PI is the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. Our solution is a new task formulation in which just the text is provided, from which a set of state changes (entity, attribute, before, after) is generated for each step, where the entity, attribute, and values must all be predicted from an open vocabulary.
### Supported Tasks and Leaderboards
- `Task 1`: Given paragraph (e.g., with 5 steps), identify entities that change (challenge: implicit entities, some explicit entities that don’t change)
- `Task 3`: Given paragraph, identify the attributes of entity that change (challenge: implicit entities, attributes & many combinations)
- `Task 4`: Given paragraph & an entity, identify the sequence of attribute value changes (challenge: implicit attributes)
- `Task 7`: Given image url, identify the visual attributes of entity and non-visual attributes of entity that change
### Languages
English
## Dataset Structure
### Data Instances
A typical instance in the dataset:
```
{
"goal": "goal1_text",
"steps": [
"step1_text",
"step2_text",
...
],
"topics": "topic1_annotation",
"image_urls": [
"step1_url_text",
"step2_url_text",
...
],
"states": [
{
"answers_openpiv1_metadata": {
"entity": "entity1 | entity2 | ...",
"attribute": "attribute1 | attribute2 | ...",
"answers": [
"before: step1_entity1_before | step1_entity2_before, after: step1_entity1_after | step1_entity2_after",
...
],
"modality": [
"step1_entity1_modality_id | step1_entity2_modality_id",
...
]
},
"entity": "entity1 | entity2 | ...",
"attribute": "attribute1 | attribute2 | ...",
"answers": [
"before: step1_entity1_before_merged | step1_entity2_before_merged, after: step1_entity1_after_merged | step1_entity2_after_merged",
...
]
}
]
}
```
### Data Fields
The following is an excerpt from the dataset README:
Within "goal", "steps", "topics", and "image_urls", the fields should be self-explanatory. Listed below is an explanation about those within "states":
#### Fields specific to questions:
### Data Splits
Train, Valid, Dev
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
true |
# MNLI Norwegian
The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that it covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalisation evaluation. There is also a [HuggingFace version](https://huggingface.co/datasets/multi_nli) of the dataset available.
This dataset is machine translated using Google Translate. From this translation different version of the dataset where created. Included in the repo is a version that is specifically suited for training sentence-BERT-models. This version include the triplet: base-entailment-contradiction. It also includes a version that mixes English and Norwegian, as well as both csv and json-verions. The script for generating the datasets are included in this repo.
Please note that there is no test dataset for MNLI, since this is closed. The authors of MNLI informs us that they selected 7500 new contexts in the same way as the original MNLI contexts. That means the English part of the XNLI test sets is highly comparable. For each genre, the text is generally in-domain with the original MNLI test set (it's from the same source and selected by me in the same way). In most cases the XNLI test set can therefore be used.
### The following datasets are available in the repo:
* mnli_no_en_for_simcse.csv
* mnli_no_en_small_for_simcse.csv
* mnli_no_for_simcse.csv
* multinli_1.0_dev_matched_no_mt.jsonl
* multinli_1.0_dev_mismatched_no_mt.jsonl
* multinli_1.0_train_no_mt.jsonl
* nli_for_simcse.csv
* xnli_dev_no_mt.jsonl
* xnli_test_no_mt.jsonl
### Licensing Information
The majority of the corpus is released under the OANC’s license, which allows all content to be freely used, modified, and shared under permissive terms. The data in the FICTION section falls under several permissive licenses; Seven Swords is available under a Creative Commons Share-Alike 3.0 Unported License, and with the explicit permission of the author, Living History and Password Incorrect are available under Creative Commons Attribution 3.0 Unported Licenses; the remaining works of fiction are in the public domain in the United States (but may be licensed differently elsewhere). The translation and compilation of the Norwegian part is released under the Creative Commons Attribution 3.0 Unported Licenses.
### Citation Information
The datasets are compiled and machine translated by the AiLab at the Norwegian National Library. However, the vast majority of the work related to this dataset is compiling the English version. We therefore suggest that you also cite the original work:
```
@InProceedings{N18-1101,
author = "Williams, Adina
and Nangia, Nikita
and Bowman, Samuel",
title = "A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference",
booktitle = "Proceedings of the 2018 Conference of
the North American Chapter of the
Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long
Papers)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1112--1122",
location = "New Orleans, Louisiana",
url = "http://aclweb.org/anthology/N18-1101"
}
|
false | # AutoTrain Dataset for project: testtextexists
## Dataset Description
This dataset has been automatically processed by AutoTrain for project testtextexists.
### 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": "According to the National Soft Drink Association, the annual consumption of soda by the U.S. citizens is 600 cans",
"target": 66.0
},
{
"text": "Experts say new vaccines are fake!",
"target": 50.0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='float32', 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 | 19 |
| valid | 18 |
|
false |
# Dataset Card for "lmqg/qa_squadshifts"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2004.14444](https://arxiv.org/abs/2004.14444)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is SQuADShifts dataset with custom split of training/validation/test following [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts).
### Supported Tasks and Leaderboards
* `question-answering`
### Languages
English (en)
## Dataset Structure
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature of id
- `title`: a `string` feature of title of the paragraph
- `context`: a `string` feature of paragraph
- `question`: a `string` feature of question
- `answers`: a `json` feature of answers
### Data Splits
| name |train | valid | test |
|-------------|------:|------:|-----:|
|default (all)|9209|6283 |18,844|
| amazon |3295|1648|4942|
| new_wiki |2646|1323|3969|
| nyt |3355|1678|5032|
| reddit |3268|1634|4901|
## Citation Information
```
@inproceedings{miller2020effect,
title={The effect of natural distribution shift on question answering models},
author={Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
booktitle={International Conference on Machine Learning},
pages={6905--6916},
year={2020},
organization={PMLR}
}
``` |
false | # Dataset Card for "eclassQuery"
This Dataset consists of paraphrases of ECLASS-standard pump-properties. It can be used to evaluate models on the task of matching these paraphrases to the actual ECLASS-standard pump-properties based on their semantics. |
true | |
true | # Dataset Card for PubMed
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** : [https://www.nlm.nih.gov/databases/download/pubmed_medline.html]()
- **Documentation:** : [https://www.nlm.nih.gov/databases/download/pubmed_medline_documentation.html]()
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
NLM produces a baseline set of MEDLINE/PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year. Each day, NLM produces update files that include new, revised and deleted citations. See our documentation page for more information.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- English
## Dataset Structure
Bear in mind the data comes from XML that have various tags that are hard to reflect
in a concise JSON format. Tags and list are kind of non "natural" to XML documents
leading this library to make some choices regarding data. "Journal" info was dropped
altogether as it would have led to many fields being empty all the time.
The hierarchy is also a bit unnatural but the choice was made to keep as close as
possible to the original data for future releases that may change schema from NLM's side.
Author has been kept and contains either "ForeName", "LastName", "Initials", or "CollectiveName".
(All the fields will be present all the time, but only some will be filled)
### Data Instances
```json
{
"MedlineCitation": {
"PMID": 0,
"DateCompleted": {"Year": 0, "Month": 0, "Day": 0},
"NumberOfReferences": 0,
"DateRevised": {"Year": 0, "Month": 0, "Day": 0},
"Article": {
"Abstract": {"AbstractText": "Some abstract (can be missing)" },
"ArticleTitle": "Article title",
"AuthorList": {"Author": [
{"FirstName": "John", "ForeName": "Doe", "Initials": "JD", "CollectiveName": ""}
{"CollectiveName": "The Manhattan Project", "FirstName": "", "ForeName": "", "Initials": ""}
]},
"Language": "en",
"GrantList": {
"Grant": [],
},
"PublicationTypeList": {"PublicationType": []},
},
"MedlineJournalInfo": {"Country": "France"},
"ChemicalList": {"Chemical": [{
"RegistryNumber": "XX",
"NameOfSubstance": "Methanol"
}]},
"CitationSubset": "AIM",
"MeshHeadingList": {
"MeshHeading": [],
},
},
"PubmedData": {
"ArticleIdList": {"ArticleId": "10.1002/bjs.1800650203"},
"PublicationStatus": "ppublish",
"History": {"PubMedPubDate": [{"Year": 0, "Month": 0, "Day": 0}]},
"ReferenceList": [{"Citation": "Somejournal", "CitationId": 01}],
},
}
```
### Data Fields
Main Fields will probably interest people are:
- "MedlineCitation" > "Article" > "AuthorList" > "Author"
- "MedlineCitation" > "Article" > "Abstract" > "AbstractText"
- "MedlineCitation" > "Article" > "Article Title"
- "MedlineCitation" > "ChemicalList" > "Chemical"
- "MedlineCitation" > "NumberOfReferences"
### Data Splits
There are no splits in this dataset. It is given as is.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html]()
#### 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
[https://www.nlm.nih.gov/databases/download/terms_and_conditions.html]()
### Citation Information
[Courtesy of the U.S. National Library of Medicine](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html).
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset. |
true |
# Dataset Card for Machine Paraphrase Dataset (MPC)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rat1.ionale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** https://ieeexplore.ieee.org/document/9651895
- **Total size:** 2.23 GB
- **Train size:** 1.52 GB
- **Test size:** 861 MB
### Dataset Summary
The Autoencoder Paraphrase Corpus (APC) consists of ~200k examples of original, and paraphrases using three neural language models.
It uses three models (BERT, RoBERTa, Longformer) on three source texts (Wikipedia, arXiv, student theses).
The examples are aligned, i.e., we sample the same paragraphs for originals and paraphrased versions.
### How to use it
You can load the dataset using the `load_dataset` function:
```python
from datasets import load_dataset
ds = load_dataset("jpwahle/autoencoder-paraphrase-dataset")
print(ds[0])
#OUTPUT:
{
'text': 'War memorial formally unveiled on Whit Monday 16 May 1921 by the Prince of Wales later King Edward VIII with Lutyens in attendance At the unveiling ceremony Captain Fortescue gave a speech during wherein he announced that 11 600 men and women from Devon had been inval while serving in imperialist war He later stated that some 63 700 8 000 regulars 36 700 volunteers 19 000 conscripts had served in the armed forces The heroism of the dead are recorded on a roll of honour of which three copies were made one for Exeter Cathedral one To be held by Tasman county council and another honoring the Prince of Wales placed in a hollow in bedrock base of the war memorial The princes visit generated considerable excitement in the area Thousands of spectators lined the street to greet his motorcade and shops on Market High Street hung out banners with welcoming messages After the unveiling Edward spent ten days touring the local area',
'label': 1,
'dataset': 'wikipedia',
'method': 'longformer'
}
```
### Supported Tasks and Leaderboards
Paraphrase Identification
### Languages
English
## Dataset Structure
### Data Instances
```json
{
'text': 'War memorial formally unveiled on Whit Monday 16 May 1921 by the Prince of Wales later King Edward VIII with Lutyens in attendance At the unveiling ceremony Captain Fortescue gave a speech during wherein he announced that 11 600 men and women from Devon had been inval while serving in imperialist war He later stated that some 63 700 8 000 regulars 36 700 volunteers 19 000 conscripts had served in the armed forces The heroism of the dead are recorded on a roll of honour of which three copies were made one for Exeter Cathedral one To be held by Tasman county council and another honoring the Prince of Wales placed in a hollow in bedrock base of the war memorial The princes visit generated considerable excitement in the area Thousands of spectators lined the street to greet his motorcade and shops on Market High Street hung out banners with welcoming messages After the unveiling Edward spent ten days touring the local area',
'label': 1,
'dataset': 'wikipedia',
'method': 'longformer'
}
```
### Data Fields
| Feature | Description |
| --- | --- |
| `text` | The unique identifier of the paper. |
| `label` | Whether it is a paraphrase (1) or the original (0). |
| `dataset` | The source dataset (Wikipedia, arXiv, or theses). |
| `method` | The method used (bert, roberta, longformer). |
### Data Splits
- train (Wikipedia x [bert, roberta, longformer])
- test ([Wikipedia, arXiv, theses] x [bert, roberta, longformer])
## Dataset Creation
### Curation Rationale
Providing a resource for testing against autoencoder paraprhased plagiarism.
### Source Data
#### Initial Data Collection and Normalization
- Paragraphs from `featured articles` from the English Wikipedia dump
- Paragraphs from full-text pdfs of arXMLiv
- Paragraphs from full-text pdfs of Czech student thesis (bachelor, master, PhD).
#### 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
[Jan Philip Wahle](https://jpwahle.com/)
### Licensing Information
The Autoencoder Paraphrase Dataset is released under CC BY-NC 4.0. By using this corpus, you agree to its usage terms.
### Citation Information
```bib
@inproceedings{9651895,
title = {Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection},
author = {Wahle, Jan Philip and Ruas, Terry and Meuschke, Norman and Gipp, Bela},
year = 2021,
booktitle = {2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
volume = {},
number = {},
pages = {226--229},
doi = {10.1109/JCDL52503.2021.00065}
}
```
### Contributions
Thanks to [@jpwahle](https://github.com/jpwahle) for adding this dataset. |
false | # Dataset Card for DBLP Discovery Dataset (D3)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/jpwahle/lrec22-d3-dataset
- **Paper:** https://aclanthology.org/2022.lrec-1.283/
- **Total size:** 8.71 GB
### Dataset Summary
DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than 6 million publications from DBLP and extracted pertinent metadata (e.g., abstracts, author affiliations, citations) from the publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research. We present an initial analysis focused on the volume of computer science research (e.g., number of papers, authors, research activity), trends in topics of interest, and citation patterns. Our findings show that computer science is a growing research field (15% annually), with an active and collaborative researcher community. While papers in recent years present more bibliographical entries in comparison to previous decades, the average number of citations has been declining. Investigating papers’ abstracts reveals that recent topic trends are clearly reflected in D3. Finally, we list further applications of D3 and pose supplemental research questions. The D3 dataset, our findings, and source code are publicly available for research purposes.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
Total size: 8.71 GB
Papers size: 8.13 GB
Authors size: 0.58 GB
### Data Fields
#### Papers
| Feature | Description |
| --- | --- |
| `corpusid` | The unique identifier of the paper. |
| `externalids` | The same paper in other repositories (e.g., DOI, ACL). |
| `title` | The title of the paper. |
| `authors` | The authors of the paper with their `authorid` and `name`. |
| `venue` | The venue of the paper. |
| `year` | The year of the paper publication. |
| `publicationdate` | A more precise publication date of the paper. |
| `abstract` | The abstract of the paper. |
| `outgoingcitations` | The number of references of the paper. |
| `ingoingcitations` | The number of citations of the paper. |
| `isopenaccess` | Whether the paper is open access. |
| `influentialcitationcount` | The number of influential citations of the paper according to SemanticScholar. |
| `s2fieldsofstudy` | The fields of study of the paper according to SemanticScholar. |
| `publicationtypes` | The publication types of the paper. |
| `journal` | The journal of the paper. |
| `updated` | The last time the paper was updated. |
| `url` | A url to the paper in SemanticScholar. |
#### Authors
| Feature | Description |
| --- | --- |
| `authorid` | The unique identifier of the author. |
| `externalids` | The same author in other repositories (e.g., ACL, PubMed). This can include `ORCID` |
| `name` | The name of the author. |
| `affiliations` | The affiliations of the author. |
| `homepage` | The homepage of the author. |
| `papercount` | The number of papers the author has written. |
| `citationcount` | The number of citations the author has received. |
| `hindex` | The h-index of the author. |
| `updated` | The last time the author was updated. |
| `email` | The email of the author. |
| `s2url` | A url to the author in SemanticScholar. |
### Data Splits
- `papers`
- `authors`
## Dataset Creation
### Curation Rationale
Providing a resource to analyze the state of computer science research statistically and semantically.
### Source Data
#### Initial Data Collection and Normalization
DBLP and from v2.0 SemanticScholar
## Additional Information
### Dataset Curators
[Jan Philip Wahle](https://jpwahle.com/)
### Licensing Information
The DBLP Discovery Dataset is released under the CC BY-NC 4.0. By using this corpus, you are agreeing to its usage terms.
### Citation Information
If you use the dataset in any way, please cite:
```bib
@inproceedings{Wahle2022c,
title = {D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research},
author = {Wahle, Jan Philip and Ruas, Terry and Mohammad, Saif M. and Gipp, Bela},
year = {2022},
month = {July},
booktitle = {Proceedings of The 13th Language Resources and Evaluation Conference},
publisher = {European Language Resources Association},
address = {Marseille, France},
doi = {},
}
```
Also make sure to cite the following papers if you use SemanticScholar data:
```bib
@inproceedings{ammar-etal-2018-construction,
title = "Construction of the Literature Graph in Semantic Scholar",
author = "Ammar, Waleed and
Groeneveld, Dirk and
Bhagavatula, Chandra and
Beltagy, Iz",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
month = jun,
year = "2018",
address = "New Orleans - Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-3011",
doi = "10.18653/v1/N18-3011",
pages = "84--91",
}
```
```bib
@inproceedings{lo-wang-2020-s2orc,
title = "{S}2{ORC}: The Semantic Scholar Open Research Corpus",
author = "Lo, Kyle and Wang, Lucy Lu and Neumann, Mark and Kinney, Rodney and Weld, Daniel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.447",
doi = "10.18653/v1/2020.acl-main.447",
pages = "4969--4983"
}
```### Contributions
Thanks to [@jpwahle](https://github.com/jpwahle) for adding this dataset.
|
false |
# Dataset Card for laion2B-multi-turkish-subset
## Dataset Description
- **Homepage:** [laion-5b](https://laion.ai/blog/laion-5b/)
- **Huggingface:** [laion/laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi)
- **Point of Contact:** [mcemilg](mailto:mcg@mcemilg.dev)
### Dataset Summary
[LAION-5B](https://laion.ai/blog/laion-5b/) is a large scale openly accessible image-text dataset contains text from multiple languages. This is a Turkish subset data of [laion/laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi). It's compatible to be used with [image2dataset](https://github.com/rom1504/img2dataset) to fetch the images at scale.
### Data Structure
```python
DatasetDict({
train: Dataset({
features: ['SAMPLE_ID', 'URL', 'TEXT', 'HEIGHT', 'WIDTH', 'LICENSE', 'LANGUAGE', 'NSFW', 'similarity'],
num_rows: 34638627
})
})
```
```python
{
'SAMPLE_ID': Value(dtype='int64', id=None),
'URL': Value(dtype='string', id=None),
'TEXT': Value(dtype='string', id=None),
'HEIGHT': Value(dtype='int64', id=None),
'WIDTH': Value(dtype='int64', id=None),
'LICENSE': Value(dtype='string', id=None),
'LANGUAGE': Value(dtype='string', id=None),
'NSFW': Value(dtype='string', id=None),
'similarity': Value(dtype='float64', id=None)
}
```
### Notes
The data was basically processed to drop non-Turkish and irrelevant texts before published. Both [FastText](https://fasttext.cc/docs/en/language-identification.html) and [langdetect](https://pypi.org/project/langdetect/) libraries were used to identify if the text is Turkish or not. The cleaning process can be summarized as follows:
- replace \"\"\" with empty str
- remove URLs in texts
- Drop if both FastText and LangDetect are highly confident with there is no Turkish in text.
- Drop empty text fields.
### License
CC-BY-4.0
|
false | # AutoTrain Dataset for project: led-samsum-dialogsum
## Dataset Description
This dataset has been automatically processed by AutoTrain for project led-samsum-dialogsum.
### 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
[
{
"feat_Unnamed: 0": 0,
"feat_id": 0,
"text": "Amanda: I baked cookies. Do you want some?\nJerry: Sure!\nAmanda: I'll bring you tomorrow :-)",
"target": "Amanda baked cookies and will bring Jerry some tomorrow."
},
{
"feat_Unnamed: 0": 1,
"feat_id": 1,
"text": "Olivia: Who are you voting for in this election? \nOliver: Liberals as always.\nOlivia: Me too!!\nOliver: Great",
"target": "Olivia and Olivier are voting for liberals in this election. "
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_Unnamed: 0": "Value(dtype='int64', id=None)",
"feat_id": "Value(dtype='int64', id=None)",
"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 | 27191 |
| valid | 1318 |
|
false | Over 20,000 256x256 mel spectrograms of 5 second samples of music from my Spotify liked playlist. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models.
```
x_res = 1024
y_res = 1024
sample_rate = 44100
n_fft = 2048
hop_length = 512
``` |
false |
# Dataset Card for "bill_summarization"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/dreamproit/BillML
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Dataset for summarization of summarization of US Congressional bills (bill_summarization).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
English
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 186 MB
- **Total amount of disk used:** 177 MB
### Data Fields
- id: id of the bill.
- sections: list of bill sections with section_id and text.
- text: bill text.
- text_len: number of characters in the text.
- summary: summary of the bill.
- summary_len: number of characters in the summary.
- title: official title of the bill.
### Data Splits
No splits.
## Dataset Creation
### Curation Rationale
Bills (proposed laws) are specialized, structured documents with great public significance. Often, the language of a bill may not directly explain the potential impact of the legislation. For bills in the U.S. Congress, the Congressional Research Service of the Library of Congress provides professional, non-partisan summaries of bills. These are valuable for public understanding of the bills and are serve as an essential part of the lawmaking process to understand the meaning and potential legislative impact.
This dataset collects the text of bills, some metadata, as well as the CRS summaries. In order to build more accurate ML models for bill summarization it is important to have a clean dataset, alongside the professionally-written CRS summaries. ML summarization models built on generic data are bound to produce less accurate results (sometimes creating summaries that describe the opposite of a bill's actual effect). In addition, models that attempt to summarize all bills (some of which may reach 4000 pages long) may also be inaccurate due to the current limitations of summarization on long texts.
As a result, this dataset collects bill and summary information for only small bills (10 sections or fewer). It is meant as a starting point for community-driven development of ML models for bill summarization. In the future, we may expand or enhance the dataset in a number of ways-- adding metadata, including larger bills, and providing feedback from expert legislative analysts on any automated summaries that are produced.
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
The data consists of the US congress bills that were collected from the [Govinfo](https://github.com/unitedstates/congress) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license.
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed]
#### 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
dreamproit.com
### Licensing Information
Bill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under CC0 (https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@BorodaUA](https://github.com/BorodaUA), [@alexbojko](https://github.com/alexbojko) for adding this dataset. |
false |
# Dataset Card for "lmqg/qa_harvesting_from_wikipedia_pseudo"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over [`lmqg/qa_harvesting_from_wikipedia`](https://huggingface.co/datasets/lmqg/qa_harvesting_from_wikipedia), 1 million paragraph and answer pairs collected in [Du and Cardie, 2018](https://aclanthology.org/P18-1177/), made for question-answering based evaluation (QAE) for question generation model proposed by [Zhang and Bansal, 2019](https://aclanthology.org/D19-1253/).
The `train` split is the synthetic data and the `validation` split is the original validation set of [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), where the model should be evaluate on.
This contains synthetic QA datasets created with the following QG models:
- [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad)
- [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad)
- [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad)
- [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad)
- [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad)
See more detail about the QAE at [https://github.com/asahi417/lm-question-generation/tree/master/misc/qa_based_evaluation](https://github.com/asahi417/lm-question-generation/tree/master/misc/emnlp_2022/qa_based_evaluation).
### Supported Tasks and Leaderboards
* `question-answering`
### Languages
English (en)
## Dataset Structure
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature of id
- `title`: a `string` feature of title of the paragraph
- `context`: a `string` feature of paragraph
- `question`: a `string` feature of question
- `answers`: a `json` feature of answers
### Data Splits
|train |validation|
|--------:|---------:|
|1,092,142| 10,570 |
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
``` |
false | Dataset contains queries for Problog database of facts about USA geography. Taken from [this source](https://www.cs.utexas.edu/users/ml/nldata/geoquery.html) |
false | # Dataset Card for "saf_micro_job_german"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotation process](#annotation-process)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022)
### Dataset Summary
Short Answer Feedback (SAF) dataset is a short answer dataset introduced in [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 8 German questions used in micro-job training on the crowd-worker platform appJobber - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the [saf_communication_networks_english](https://huggingface.co/datasets/Short-Answer-Feedback/saf_communication_networks_english) dataset to examine the English subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at [saf_legal_domain_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_legal_domain_german).
### Supported Tasks and Leaderboards
- `short_answer_feedback`: The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.
### Languages
The questions, reference answers, provided answers and the answer feedback in the dataset are written in German.
## Dataset Structure
### Data Instances
An example of an entry of the training split looks as follows.
```
{
"id": "1",
"question": "Frage 1: Ist das eine Frage?",
"reference_answer": "Ja, das ist eine Frage.",
"provided_answer": "Ich bin mir sicher, dass das eine Frage ist.",
"answer_feedback": "Korrekt!",
"verification_feedback": "Correct",
"score": 1
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature (UUID4 in HEX format).
- `question`: a `string` feature representing a question.
- `reference_answer`: a `string` feature representing a reference answer to the question.
- `provided_answer`: a `string` feature representing an answer that was provided for a particular question.
- `answer_feedback`: a `string` feature representing the feedback given to the provided answers.
- `verification_feedback`: a `string` feature representing an automatic labeling of the score. It can be `Correct` (`score` = 1), `Incorrect` (`score` = 0) or `Partially correct` (all intermediate scores).
- `score`: a `float64` feature (between 0 and 1) representing the score given to the provided answer.
### Data Splits
The dataset is comprised of four data splits.
- `train`: used for training, contains a set of questions and the provided answers to them.
- `validation`: used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).
- `test_unseen_answers`: used for testing, contains unseen answers to the questions present in the `train` split.
- `test_unseen_questions`: used for testing, contains unseen questions that do not appear in the `train` split.
| Split |train|validation|test_unseen_answers|test_unseen_questions|
|-------------------|----:|---------:|------------------:|--------------------:|
|Number of instances| 1226| 308| 271| 602|
## Dataset Creation
### Annotation Process
Two experienced appJobber employees were selected to evaluate the crowd-worker platform’s answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.
## Additional Information
### Citation Information
```
@inproceedings{filighera-etal-2022-answer,
title = "Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset",
author = "Filighera, Anna and
Parihar, Siddharth and
Steuer, Tim and
Meuser, Tobias and
Ochs, Sebastian",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.587",
doi = "10.18653/v1/2022.acl-long.587",
pages = "8577--8591",
}
```
### Contributions
Thanks to [@JohnnyBoy2103](https://github.com/JohnnyBoy2103) for adding this dataset. |
true | # TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages
This repo contains the code and data for the EMNLP 2022 findings paper TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages which can be found [here](https://aclanthology.org/2022.findings-emnlp.420/).
## Data
The TyDiP dataset is licensed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
The `data` folder contains the different files we release as part of the TyDiP dataset. The TyDiP dataset comprises of an English train set and English test set that are adapted from the Stanford Politeness Corpus, and test data in 9 more languages (Hindi, Korean, Spanish, Tamil, French, Vietnamese, Russian, Afrikaans, Hungarian) that we annotated.
```
data/
├── all
├── binary
└── unlabelled_train_sets
```
`data/all` consists of the complete train and test sets.
`data/binary` is a filtered version of the above where sentences from the top and bottom 25 percentile of scores is only present. This is the data that we used for training and evaluation in the paper.
`data/unlabelled_train_sets`
If you use the English train or test data, please cite the Stanford Politeness Dataset
```
@inproceedings{danescu-niculescu-mizil-etal-2013-computational,
title = "A computational approach to politeness with application to social factors",
author = "Danescu-Niculescu-Mizil, Cristian and
Sudhof, Moritz and
Jurafsky, Dan and
Leskovec, Jure and
Potts, Christopher",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-1025",
pages = "250--259",
}
```
If you use the test data from the 9 target languages, please cite our paper
```
@inproceedings{srinivasan-choi-2022-tydip,
title = "{T}y{D}i{P}: A Dataset for Politeness Classification in Nine Typologically Diverse Languages",
author = "Srinivasan, Anirudh and
Choi, Eunsol",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.420",
pages = "5723--5738",
}
```
## Code
`politeness_regresor.py` is used for training and evaluation of transformer models
To train a model
```
python politeness_regressor.py --train_file data/binary/en_train_binary.csv --test_file data/binary/en_test_binary.csv --model_save_location model.pt --pretrained_model xlm-roberta-large --gpus 1 --batch_size 4 --accumulate_grad_batches 8 --max_epochs 5 --checkpoint_callback False --logger False --precision 16 --train --test --binary --learning_rate 5e-6
```
To test this trained model on $lang
```
python politeness_regressor.py --test_file data/binary/${lang}_test_binary.csv --load_model model.pt --gpus 1 --batch_size 32 --test --binary
```
## Politeness Strategies
`strategies` contains the processed strategy lexicon for different languages. `strategies/learnt_strategies.xlsx` contains the human edited strategies for 4 langauges
## Annotation Interface
`annotation.html` contains the UI used for conducting data annotation |
true | # AutoTrain Dataset for project: tweet-es-sent
## Dataset Description
This dataset has been automatically processed by AutoTrain for project tweet-es-sent.
### 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
[
{
"target": 1,
"text": "1sola vuelta! arauz presidente! 1sola vuelta! todo 1 1sola la 1 es ecdor! por ti!1 por 1 los tuyos!1 por nosotros juntos1 mas de 45 d apoyo popular el 7 se vota 1por la vida por el futuro,por la esperanza guayaquil ec dor es 1"
},
{
"target": 1,
"text": "excelente decisi\u00f3n , las mujeres son importantes y por esa raz\u00f3n, a productos de primera necesidad hay que quitarles el iva "
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "ClassLabel(num_classes=3, names=['0', '1', '2'], id=None)",
"text": "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 | 12400 |
| valid | 3685 |
|
true | # AutoTrain Dataset for project: compliance
## Dataset Description
This dataset has been automatically processed by AutoTrain for project compliance.
### 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": "Welcome back Abhishek! What can I do to help? ",
"target": 0
},
{
"text": "Hi , I am calling from ABC finance. I would like to inform you that you are eligible for a Personal Loan",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['Negative', 'Positive'], 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 | 31 |
| valid | 9 |
|
true | # AutoTrain Dataset for project: demo_compliance
## Dataset Description
This dataset has been automatically processed by AutoTrain for project demo_compliance.
### 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": "Welcome back Abhishek! What can I do to help? ",
"target": 0
},
{
"text": "Hi , I am calling from ABC finance. I would like to inform you that you are eligible for a Personal Loan",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=2, names=['Negative', 'Positive'], 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 | 31 |
| valid | 9 |
|
false |
# Dataset Card for Leipzig Corpora Swiss German
## Dataset Description
- **Homepage:** https://wortschatz.uni-leipzig.de/en/download/Swiss%20German
- **Repository:** https://huggingface.co/datasets/statworx/leipzip-swiss
### Dataset Summary
Swiss German Wikipedia corpus based on material from 2021.
The corpus gsw_wikipedia_2021 is a Swiss German Wikipedia corpus based on material from 2021. It contains 232,933 sentences and 3,824,547 tokens.
### Languages
Swiss-German
## Dataset Structure
### Data Instances
Single sentences.
### Data Fields
`sentence`: Text as string.
### Data Splits
[More Information Needed]
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
https://corpora.uni-leipzig.de/en?corpusId=gsw_wikipedia_2021
## Additional Information
### Licensing Information
Creative-Commons-Lizenz CC BY-NC
### Citation Information
Leipzig Corpora Collection: Swiss German Wikipedia corpus based on material from 2021. Leipzig Corpora Collection. Dataset. https://corpora.uni-leipzig.de?corpusId=gsw_wikipedia_2021
|
false |
# Dataset Card for Danish WIT
## Dataset Description
- **Repository:** <https://gist.github.com/saattrupdan/bb6c9c52d9f4b35258db2b2456d31224>
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk)
- **Size of downloaded dataset files:** 7.5 GB
- **Size of the generated dataset:** 7.8 GB
- **Total amount of disk used:** 15.3 GB
### Dataset Summary
Google presented the Wikipedia Image Text (WIT) dataset in [July
2021](https://dl.acm.org/doi/abs/10.1145/3404835.3463257), a dataset which contains
scraped images from Wikipedia along with their descriptions. WikiMedia released
WIT-Base in [September
2021](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/),
being a modified version of WIT where they have removed the images with empty
"reference descriptions", as well as removing images where a person's face covers more
than 10% of the image surface, along with inappropriate images that are candidate for
deletion. This dataset is the Danish portion of the WIT-Base dataset, consisting of
roughly 160,000 images with associated Danish descriptions. We release the dataset
under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/), in
accordance with WIT-Base's [identical
license](https://huggingface.co/datasets/wikimedia/wit_base#licensing-information).
### Supported Tasks and Leaderboards
Training machine learning models for caption generation, zero-shot image classification
and text-image search are the intended tasks for this dataset. No leaderboard is active
at this point.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 7.5 GB
- **Size of the generated dataset:** 7.8 GB
- **Total amount of disk used:** 15.3 GB
An example from the `train` split looks as follows.
```
{
"image": [PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x409 at 0x7FE4384E2190],
"image_url": "https://upload.wikimedia.org/wikipedia/commons/4/45/Bispen_-_inside.jpg",
"embedding": [2.8568285, 2.9562542, 0.33794892, 8.753725, ...],
"metadata_url": "http://commons.wikimedia.org/wiki/File:Bispen_-_inside.jpg",
"original_height": 3161,
"original_width": 2316,
"mime_type": "image/jpeg",
"caption_attribution_description": "Kulturhuset Bispen set indefra. Biblioteket er til venstre",
"page_url": "https://da.wikipedia.org/wiki/Bispen",
"attribution_passes_lang_id": True,
"caption_alt_text_description": None,
"caption_reference_description": "Bispen set indefra fra 1. sal, hvor ....",
"caption_title_and_reference_description": "Bispen [SEP] Bispen set indefra ...",
"context_page_description": "Bispen er navnet på det offentlige kulturhus i ...",
"context_section_description": "Bispen er navnet på det offentlige kulturhus i ...",
"hierarchical_section_title": "Bispen",
"is_main_image": True,
"page_changed_recently": True,
"page_title": "Bispen",
"section_title": None
}
```
### Data Fields
The data fields are the same among all splits.
- `image`: an `Image` feature.
- `image_url`: a `str` feature.
- `embedding`: a `list` feature.
- `metadata_url`: a `str` feature.
- `original_height`: an `int` or `NaN` feature.
- `original_width`: an `int` or `NaN` feature.
- `mime_type`: a `str` or `None` feature.
- `caption_attribution_description`: a `str` or `None` feature.
- `page_url`: a `str` feature.
- `attribution_passes_lang_id`: a `bool` or `None` feature.
- `caption_alt_text_description`: a `str` or `None` feature.
- `caption_reference_description`: a `str` or `None` feature.
- `caption_title_and_reference_description`: a `str` or `None` feature.
- `context_page_description`: a `str` or `None` feature.
- `context_section_description`: a `str` or `None` feature.
- `hierarchical_section_title`: a `str` feature.
- `is_main_image`: a `bool` or `None` feature.
- `page_changed_recently`: a `bool` or `None` feature.
- `page_title`: a `str` feature.
- `section_title`: a `str` or `None` feature.
### Data Splits
Roughly 2.60% of the WIT-Base dataset comes from the Danish Wikipedia. We have split
the resulting 168,740 samples into a training set, validation set and testing set of
the following sizes:
| split | samples |
|---------|--------:|
| train | 167,460 |
| val | 256 |
| test | 1,024 |
## Dataset Creation
### Curation Rationale
It is quite cumbersome to extract the Danish portion of the WIT-Base dataset,
especially as the dataset takes up 333 GB of disk space, so the curation of Danish-WIT
is purely to make it easier to work with the Danish portion of it.
### Source Data
The original data was collected from WikiMedia's
[WIT-Base](https://huggingface.co/datasets/wikimedia/wit_base) dataset, which in turn
comes from Google's [WIT](https://huggingface.co/datasets/google/wit) dataset.
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) curated this dataset.
### Licensing Information
The dataset is licensed under the [CC BY-SA 4.0
license](https://creativecommons.org/licenses/by-sa/4.0/).
|
false |
# Dataset Card for MMCRSC
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [MAGICDATA Mandarin Chinese Read Speech Corpus](https://openslr.org/68/)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MAGICDATA Mandarin Chinese Read Speech Corpus was developed by MAGIC DATA Technology Co., Ltd. and freely published for non-commercial use.
The contents and the corresponding descriptions of the corpus include:
The corpus contains 755 hours of speech data, which is mostly mobile recorded data.
1080 speakers from different accent areas in China are invited to participate in the recording.
The sentence transcription accuracy is higher than 98%.
Recordings are conducted in a quiet indoor environment.
The database is divided into training set, validation set, and testing set in a ratio of 51: 1: 2.
Detail information such as speech data coding and speaker information is preserved in the metadata file.
The domain of recording texts is diversified, including interactive Q&A, music search, SNS messages, home command and control, etc.
Segmented transcripts are also provided.
The corpus aims to support researchers in speech recognition, machine translation, speaker recognition, and other speech-related fields. Therefore, the corpus is totally free for academic use.
The corpus is a subset of a much bigger data ( 10566.9 hours Chinese Mandarin Speech Corpus ) set which was recorded in the same environment. Please feel free to contact us via business@magicdatatech.com for more details.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
zh-CN
## Dataset Structure
### Data Instances
```json
{
'file': '14_3466_20170826171404.wav',
'audio': {
'path': '14_3466_20170826171404.wav',
'array': array([0., 0., 0., ..., 0., 0., 0.]),
'sampling_rate': 16000
},
'text': '请搜索我附近的超市',
'speaker_id': 143466,
'id': '14_3466_20170826171404.wav'
}
```
### Data Fields
- file: A path to the downloaded audio file in .wav format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
### 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
Please cite the corpus as "Magic Data Technology Co., Ltd., "http://www.imagicdatatech.com/index.php/home/dataopensource/data_info/id/101", 05/2019".
|
false |
# Gold standards and outputs
## Dataset Description
- MapReader’s GitHub: https://github.com/Living-with-machines/MapReader
- MapReader paper: https://dl.acm.org/doi/10.1145/3557919.3565812
- Zenodo link for gold standards and outputs: https://doi.org/10.5281/zenodo.7147906
- Contacts: Katherine McDonough, The Alan Turing Institute, kmcdonough at turing.ac.uk; Kasra Hosseini, The Alan Turing Institute, k.hosseinizad at gmail.com
### Dataset Summary
Here we share gold standard annotations and outputs from early experiments using MapReader. MapReader creates datasets for humanities research using historical map scans and metadata as inputs.
Using maps provided by the National Library of Scotland, these annotations and outputs reflect labeling tasks relevant to historical research on the [Living with Machines](https://livingwithmachines.ac.uk/) project.
Data shared here is derived from maps printed in nineteenth-century Britain by the Ordnance Survey, Britain's state mapping agency. These maps cover England, Wales, and Scotland from 1888 to 1913.
## Directory structure
The gold standards and outputs are stored on [Zenodo](https://doi.org/10.5281/zenodo.7147906). It contains the following directories/files:
```
MapReader_Data_SIGSPATIAL_2022
├── README
├── annotations
│ ├── maps
│ │ ├── map_100942121.png
│ │ ├── ...
│ │ └── map_99383316.png
│ ├── slice_meters_100_100
│ │ ├── test
│ │ │ ├── patch-...PNG
│ │ │ ├── ...
│ │ │ └── patch-...PNG
│ │ ├── train
│ │ │ ├── patch-...PNG
│ │ │ ├── ...
│ │ │ └── patch-...PNG
│ │ └── val
│ │ ├── patch-...PNG
│ │ ├── ...
│ │ └── patch-...PNG
│ ├── test.csv
│ ├── train.csv
│ └── valid.csv
└── outputs
├── label_01_03
│ ├── pred_01_03_all.csv
│ ├── pred_01_03_keep_01_0250.csv
│ ├── pred_01_03_keep_05_0500.csv
│ └── pred_01_03_keep_10_1000.csv
├── label_02
│ ├── pred_02_all.csv
│ ├── pred_02_keep_01_0250.csv
│ ├── pred_02_keep_05_0500.csv
│ └── pred_02_keep_10_1000.csv
├── patches_all.csv
├── percentage
│ └── pred_02_keep_1_250_01_03_keep_1_250_percentage.csv
└── resources
├── StopsGB4paper.csv
└── six_inch4paper.json
```
## annotations
The `annotations` directory is as follows:
```
├── annotations
│ ├── maps
│ │ ├── map_100942121.png
│ │ ├── ...
│ │ └── map_99383316.png
│ ├── slice_meters_100_100
│ │ ├── test
│ │ │ ├── patch-...PNG
│ │ │ ├── ...
│ │ │ └── patch-...PNG
│ │ ├── train
│ │ │ ├── patch-...PNG
│ │ │ ├── ...
│ │ │ └── patch-...PNG
│ │ └── val
│ │ ├── patch-...PNG
│ │ ├── ...
│ │ └── patch-...PNG
│ ├── test.csv
│ ├── train.csv
│ └── valid.csv
```
### annotations/train.csv, valid.csv and test.csv
In the `MapReader_Data_SIGSPATIAL_2022/annotations` directory, there are three CSV files, namely `train.csv`, `valid.csv` and `test.csv`. These files have two columns:
```
image_id,label
slice_meters_100_100/train/patch-1390-3892-1529-4031-#map_101590193.png#.PNG,0
slice_meters_100_100/train/patch-1716-3960-1848-4092-#map_101439245.png#.PNG,0
...
```
in which:
- `image_id`: path to each labelled patch. For example in `slice_meters_100_100/train/patch-1390-3892-1529-4031-#map_101590193.png#.PNG`:
- `slice_meters_100_100/train`: directory where the patch is stored. (in this example, it is a patch used for training)
- `patch-1390-3892-1529-4031-#map_101590193.png#.PNG` has two parts itself: `patch-1390-3892-1529-4031` is the patch ID, and the patch itself is extracted from `map_101590193.png` map sheet.
- `label`: label assigned to each patch by an annotator.
- Labels: 0: no [building or railspace]; 1: railspace; 2: building; and 3: railspace and [non railspace] building.
### annotations/slice_meters_100_100
Patches used for training, validation, and test in PNG format.
```
├── annotations
│ ├── slice_meters_100_100
│ │ ├── test
│ │ │ ├── patch-...PNG
│ │ │ ├── ...
│ │ │ └── patch-...PNG
│ │ ├── train
│ │ │ ├── patch-...PNG
│ │ │ ├── ...
│ │ │ └── patch-...PNG
│ │ └── val
│ │ ├── patch-...PNG
│ │ ├── ...
│ │ └── patch-...PNG
```
### annotations/maps
Map sheets retrieved from the National Library of Scotland via webservers. These maps were later sliced into patches which can be found in `annotations/slice_meters_100_100`.
```
├── annotations
│ ├── maps
│ │ ├── map_100942121.png
│ │ ├── ...
│ │ └── map_99383316.png
```
## outputs
The `outputs` directory is as follows:
```
└── outputs
├── label_01_03
│ ├── pred_01_03_all.csv
│ ├── pred_01_03_keep_01_0250.csv
│ ├── pred_01_03_keep_05_0500.csv
│ └── pred_01_03_keep_10_1000.csv
├── label_02
│ ├── pred_02_all.csv
│ ├── pred_02_keep_01_0250.csv
│ ├── pred_02_keep_05_0500.csv
│ └── pred_02_keep_10_1000.csv
├── patches_all.csv
├── percentage
│ └── pred_02_keep_1_250_01_03_keep_1_250_percentage.csv
└── resources
├── StopsGB4paper.csv
└── six_inch4paper.json
```
### outputs/label_01_03
Starting with:
```
└── outputs
├── label_01_03
│ ├── pred_01_03_all.csv
│ ├── pred_01_03_keep_01_0250.csv
│ ├── pred_01_03_keep_05_0500.csv
│ └── pred_01_03_keep_10_1000.csv
```
The file `pred_01_03_all.csv` contains the following columns:
```
,center_lon,center_lat,pred,conf,mean_pixel_RGB,std_pixel_RGB,mean_pixel_A,image_id,parent_id,pub_date,url,x,y,z,opening_year_quicks,closing_year_quicks,dist2quicks
0,-0.4011055106547341,52.61260776720805,1,0.9898980855941772,0.8450341820716858,0.1668068021535873,1.0,patch-3014-0-3151-137-#map_100890251.png#.PNG,map_100890251.png,1902,https://maps.nls.uk/view/100890251,3880925.8529841416,-27169.29919979412,5044483.051365171,1867,1929,1121.9150481268305
1,-0.399645312864389,52.61260776720805,1,0.9999995231628418,0.823089599609375,0.1925655305385589,1.0,patch-3151-0-3288-137-#map_100890251.png#.PNG,map_100890251.png,1902,https://maps.nls.uk/view/100890251,3880926.544140446,-27070.392789791513,5044483.051365171,1867,1929,1113.0714735200893
...
```
- **center_lon**: longitude of the patch center
- **center_lat**: latitude of the patch center
- **pred**: predicted label for the patch
- **conf**: model confidence
- **mean_pixel_RGB**: mean pixel intensities, using all three channels
- **std_pixel_RGB**: standard deviations of pixel intensities, using all three channels
- **mean_pixel_A**: mean pixel intensities of alpha channel
- **image_id**: patch ID
- **parent_id**: ID of the map sheet that the patch belongs to
- **pub_date**: publication date of the map sheet that the patch belongs to
- **url**: URL of the map sheet that the patch belongs to
- **x, y, z**: to compute distances (using k-d tree)
- **opening_year_quicks**: Date when the railway station first opened
- **closing_year_quicks**: Date when the railway station last closed,
- **dist2quicks**: distance to the closest StopsGB in meters.
NB: See `outputs/resources` below for description of the StopsGB (railway station) data and links to related publications.
---
The other files in `outputs/label_01_03` have the same columns as `pred_01_03_all.csv` (described above). The difference is:
- `pred_01_03_all.csv`: all patches predicted as labels 1 (railspace) or 3 (railspace and [non railspace] building).
- `pred_01_03_keep_01_0250.csv`: similar to `pred_01_03_all.csv` except that we removed those patches that had no other neighboring patches with the same label within a radius of 250 meters. Note 01 and 0250 in the name. 01 means one neighboring patch and 0250 means 250 meters.
- `pred_01_03_keep_05_0500.csv`: similar to `pred_01_03_all.csv` except that we removed those patches that had less than five neighboring patches with the same label within a radius of 500 meters.
- `pred_01_03_keep_10_1000.csv`: similar to `pred_01_03_all.csv` except that we removed those patches that had less than ten neighboring patches with the same label within a radius of 1000 meters.
### outputs/label_02
Next, these files:
```
├── label_02
│ ├── pred_02_all.csv
│ ├── pred_02_keep_01_0250.csv
│ ├── pred_02_keep_05_0500.csv
│ └── pred_02_keep_10_1000.csv
```
Are the same as the files described above for `label_01_03` except for label 02 (i.e., building).
### outputs/patches_all.csv
And last:
```
└── outputs
├── patches_all.csv
```
The file `patches_all.csv` has the following columns:
⚠️ this file contains the results for 30,490,411 patches used in the MapReader paper.
```
center_lat,center_lon,pred
52.61260776720805,-0.4332298620423274,0
52.61260776720805,-0.4317696642519822,0
...
```
in which:
- **center_lon**: longitude of the patch center
- **center_lat**: latitude of the patch center
- **pred**: predicted label for the patch
### outputs/percentage
We have added one file in `outputs/percentage`:
```
└── outputs
├── percentage
│ └── pred_02_keep_1_250_01_03_keep_1_250_percentage.csv
```
This file has the following columns:
```
,center_lon,center_lat,pred,conf,mean_pixel_RGB,std_pixel_RGB,mean_pixel_A,image_id,parent_id,pub_date,url,x,y,z,dist2rail,dist2quicks,dist2quicks_km,dist2rail_km,dist2rail_minus_station,dist2quicks_km_quantized,dist2rail_km_quantized,dist2rail_minus_station_quantized,perc_neigh_rails,perc_neigh_builds,harmonic_mean_rail_build
0,-0.4040259062354244,52.61260776720805,2,0.9999010562896729,0.8095282316207886,0.1955385357141494,1.0,patch-2740-0-2877-137-#map_100890251.png#.PNG,map_100890251.png,1902,https://maps.nls.uk/view/100890251,3880924.4631095687,-27367.11196679585,5044483.051365171,197.8176497186437,1164.8640633870857,1.1648640633870857,0.1978176497186437,0.9670464136684418,1.0,0.0,0.5,7.198443579766536,4.669260700389105,5.664349046373668
1,-0.4054861040257695,52.61171342293056,2,0.9999876022338868,0.8741853833198547,0.1160899400711059,1.0,patch-2603-137-2740-274-#map_100890251.png#.PNG,map_100890251.png,1902,https://maps.nls.uk/view/100890251,3881002.836728637,-27466.57793328472,5044422.621073416,296.73252022623865,1290.9640259717814,1.2909640259717814,0.2967325202262386,0.9942315057455428,1.0,0.0,0.5,7.050092764378478,4.452690166975881,5.45813633371237
...
```
in which:
- **center_lon**: longitude of the patch center
- **center_lat**: latitude of the patch center
- **pred**: predicted label for the patch
- **conf**: model confidence
- **mean_pixel_RGB**: mean pixel intensities, using all three channels
- **std_pixel_RGB**: standard deviations of pixel intensities, using all three channels
- **mean_pixel_A**: mean pixel intensities of alpha channel
- **image_id**: patch ID
- **parent_id**: ID of the map sheet that the patch belongs to
- **pub_date**: publication date of the map sheet that the patch belongs to
- **url**: URL of the map sheet that the patch belongs to
- **x, y, z**: to compute distances (using k-d tree)
- **dist2rail**: distance to the closest railspace patch (i.e., the patch that is classified as 1: railspace or 3: railspace and [non railspace] building)
- **dist2quicks**: distance to the closest StopsGB station in meters.
- **dist2quicks_km**: distance to the closest StopsGB station in km.
- **dist2rail_km**: similar to **dist2rail** except in km.
- **dist2rail_minus_station**: | dist2rail_km - dist2quicks_km |
- **dist2quicks_km_quantized**: discrete version of **dist2quicks_km**, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf).
- **dist2rail_km_quantized**: discrete version of **dist2rail_km**, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf).
- **dist2rail_minus_station_quantized**: discrete version of **dist2rail_minus_station**, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf).
- **perc_neigh_rails**: what is the percentage of neighboring patches predicted as rail (labels 01 and 03).
- **perc_neigh_builds**: what is the percentage of neighboring patches predicted as building (label 02).
- **harmonic_mean_rail_build**: Harmonic mean of *perc_neigh_rails* and **perc_neigh_builds**.
These additional `percentage` attributes shed light on the relationship between 'railspace' and stations, something we explore in further Living with Machines research.
### outputs/resources
Finally, we have the following files:
```
└── outputs
└── resources
├── StopsGB4paper.csv
└── six_inch4paper.json
```
- `StopsGB4paper.csv`: this is a trimmed down version of StopsGB, a dataset documenting passenger railway stations in Great Britain (see [this link](https://bl.iro.bl.uk/concern/datasets/0abea1b1-2a43-4422-ba84-39b354c8bb09?locale=en) for the complete dataset). We filtered the stations as follows:
- Keep only stations for which "ghost_entry" and "cross_ref" columns are "False". (These two fields help remove records in the StopsGB dataset that are not actually stations, but relics of the original publication formatting.)
- "Opening" was NOT "unknown".
- The map sheet was surveyed during a year when the station was operational (i.e., "opening_year_quicks" <= survey_date_of_map_sheet <= "closing_year_quicks").
You can learn more about the StopsGB dataset and how it was created from this paper:
```
Mariona Coll Ardanuy, Kaspar Beelen, Jon Lawrence, Katherine McDonough, Federico Nanni, Joshua Rhodes, Giorgia Tolfo, and Daniel C.S. Wilson. "Station to Station: Linking and Enriching Historical British Railway Data." In Computational Humanities Research (CHR2021). 2021.
```
```bibtex
@inproceedings{lwm-station-to-station-2021,
title = "Station to Station: Linking and Enriching Historical British Railway Data",
author = "Coll Ardanuy, Mariona and
Beelen, Kaspar and
Lawrence, Jon and
McDonough, Katherine and
Nanni, Federico and
Rhodes, Joshua and
Tolfo, Giorgia and
Wilson, Daniel CS",
booktitle = "Computational Humanities Research",
year = "2021",
}
```
- `six_inch4paper.json`: similar to [metadata_OS_Six_Inch_GB_WFS_light.json](https://github.com/Living-with-machines/MapReader/blob/main/mapreader/persistent_data/metadata_OS_Six_Inch_GB_WFS_light.json) on MapReader's GitHub with some minor changes.
## Dataset Creation
### Curation Rationale
These annotations of map patches are part of a research project to develop humanistic methods for structuring visual information on digitized historical maps. Dividing thousands of nineteenth-century map sheets into 100m x 100m patches and labeling those patches with historically-meaningful concepts diverges from traditional methods for creating data from maps, both in terms of scale (the number of maps being examined), and of type (raster-style patches vs. pixel-level vector data). For more on the rationale for this approach, see the following paper:
```
Kasra Hosseini, Katherine McDonough, Daniel van Strien, Olivia Vane, Daniel C S Wilson, Maps of a Nation? The Digitized Ordnance Survey for New Historical Research, *Journal of Victorian Culture*, Volume 26, Issue 2, April 2021, Pages 284–299.
```
```bibtex
@article{hosseini_maps_2021,
title = {Maps of a Nation? The Digitized Ordnance Survey for New Historical Research},
volume = {26},
rights = {All rights reserved},
issn = {1355-5502},
url = {https://doi.org/10.1093/jvcult/vcab009},
doi = {10.1093/jvcult/vcab009},
shorttitle = {Maps of a Nation?},
pages = {284--299},
number = {2},
journaltitle = {Journal of Victorian Culture},
author = {Hosseini, Kasra and {McDonough}, Katherine and van Strien, Daniel and Vane, Olivia and Wilson, Daniel C S},
urldate = {2021-05-19},
date = {2021-04-01},
}
```
### Source Data
#### Initial Data Access
Data was accessed via the National Library of Scotland's Historical Maps API: https://maps.nls.uk/projects/subscription-api/
The data shared here is derived from the six-inch to one mile sheets printed between 1888-1913: https://maps.nls.uk/projects/subscription-api/#gb6inch
### Annotations and Outputs
The annotations and output datasets collected here are related to experiments to identify the 'footprint' of rail infrastructure in the UK, a concept we call 'railspace'. We also created a dataset to identify buildings on the maps.
#### Annotation process
The custom annotation interface built into MapReader is designed specifically to assist researchers in labeling patches relevant to concepts of interest to their research questions.
Our **guidelines** for the data shared here were:
- for any non-null label (railspace, building, or railspace + building), if a patch contains any visual signal for that label (e.g. 'railspace'), it should be assigned the relevant label. For example, if it is possible for an annotator to see a railway track passing through the corner of a patch, that patch is labeled as 'railspace'.
- the context around the patch should not be used as an aid in extreme cases where it is nearly impossible to determine whether a patch contains a non-null label
- however, the patch context shown in the annotation interface can be used to quickly distinguish between different content types, particularly where the contiguity of a type across patches is useful in determining what label to assign
- for 'railspace': use this label for any type of rail infrastructure as determined by expert labelers. This includes, for example, single-track mining railroads; larger double-track passenger routes; sidings and embankments; etc. It excludes urban trams.
- for 'building': use this label for any size building
- for 'building + railspace': use this label for patches combining these two types of content
Because 'none' (e.g. null) patches made up the vast majority of patches in the total dataset from these map sheets, we ordered patches to annotate based on their pixel intensity. This allowed us to focus first on patches containing more visual content printed on the map sheet, and later to move more quickly through the patches that captured parts of the map with little to no printed features.
#### Who are the annotators?
Data shared here was annotated by Kasra Hosseini and Katherine McDonough.
Members of the Living with Machines research team contributed early annotations during the development of MapReader: Ruth Ahnert, Kaspar Beelen, Mariona Coll-Ardanuy, Emma Griffin, Tim Hobson, Jon Lawrence, Giorgia Tolfo, Daniel van Strien, Olivia Vane, and Daniel C.S. Wilson.
## Credits and re-use terms
### MapReader outputs
The files shared here (other than ```resources```) under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/) (CC-BY-NC-SA) licence.
If you are interested in working with OS maps used to create these results, please also note the re-use terms of the original map images and metadata detailed below.
### Digitized maps
MapReader can retrieve maps from NLS (National Library of Scotland) via webservers. For all the digitized maps (retrieved or locally stored), please note the re-use terms:
Use of the digitised maps for commercial purposes is currently restricted by contract. Use of these digitised maps for non-commercial purposes is permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/) (CC-BY-NC-SA) licence. Please refer to https://maps.nls.uk/copyright.html#exceptions-os for details on copyright and re-use license.
### Map metadata
We have provided some metadata files in on MapReader’s GitHub page (https://github.com/Living-with-machines/MapReader/tree/main/mapreader/persistent_data). For all these file, please note the re-use terms:
Use of the digitised maps for commercial purposes is currently restricted by contract. Use of these digitised maps for non-commercial purposes is permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/) (CC-BY-NC-SA) licence. Please refer to https://maps.nls.uk/copyright.html#exceptions-os for details on copyright and re-use license.
## Acknowledgements
This work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1).
Living with Machines, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund, is a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library and the Universities of Cambridge, East Anglia, Exeter, and Queen Mary University of London. |
false | # Dataset Card for "diana_uribe"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
false | # AutoTrain Dataset for project: mm
## Dataset Description
This dataset has been automatically processed by AutoTrain for project mm.
### 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": "Email from attorney A Dutkanych regarding executed Settlement Agreement",
"target": "Email from attorney A Dutkanych regarding executed Settlement Agreement"
},
{
"text": "Telephone conference with A Royer regarding additional factual background information relating to O Stapletons Charge of Discrimination allegations",
"target": "Telephone conference with A Royer regarding additional factual background information as to O Stapletons Charge of Discrimination allegations"
}
]
```
### 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 | 88 |
| valid | 22 |
|
false | # Romanian paraphrase dataset
This data set was created by me, special for paraphrase
[t5-small-paraphrase-ro](https://huggingface.co/BlackKakapo/t5-small-paraphrase-ro)
[t5-small-paraphrase-ro-v2](https://huggingface.co/BlackKakapo/t5-small-paraphrase-ro-v2)
[t5-base-paraphrase-ro](https://huggingface.co/BlackKakapo/t5-base-paraphrase-ro)
[t5-base-paraphrase-ro-v2](https://huggingface.co/BlackKakapo/t5-base-paraphrase-ro-v2)
Here you can find ~100k examples of paraphrase. |
false | # Romanian grammar dataset
This data set was created by me, special for grammar
Here you can find:
~1600k examples of grammar (TRAIN).
~220k examples of grammar (TEST). |
false | # Dataset Card for "whisper-transcripts-linustechtips"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Whispering-GPT](https://github.com/matallanas/whisper_gpt_pipeline)
- **Repository:** [whisper_gpt_pipeline](https://github.com/matallanas/whisper_gpt_pipeline)
- **Paper:** [whisper](https://cdn.openai.com/papers/whisper.pdf) and [gpt](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
- **Point of Contact:** [Whispering-GPT organization](https://huggingface.co/Whispering-GPT)
### Dataset Summary
This dataset is created by applying whisper to the videos of the Youtube channel [Linus Tech Tips](https://www.youtube.com/channel/UCXuqSBlHAE6Xw-yeJA0Tunw). The dataset was created a medium size whisper model.
### Languages
- **Language**: English
## Dataset Structure
The dataset
### Data Fields
The dataset is composed by:
- **id**: Id of the youtube video.
- **channel**: Name of the channel.
- **channel\_id**: Id of the youtube channel.
- **title**: Title given to the video.
- **categories**: Category of the video.
- **description**: Description added by the author.
- **text**: Whole transcript of the video.
- **segments**: A list with the time and transcription of the video.
- **start**: When started the trancription.
- **end**: When the transcription ends.
- **text**: The text of the transcription.
### Data Splits
- Train split.
## Dataset Creation
### Source Data
The transcriptions are from the videos of [Linus Tech Tips Channel](https://www.youtube.com/channel/UCXuqSBlHAE6Xw-yeJA0Tunw)
### Contributions
Thanks to [Whispering-GPT](https://huggingface.co/Whispering-GPT) organization for adding this dataset. |
true | # Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/venelink/ETPC/
- **Repository:**
- **Paper:** [ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation](http://www.lrec-conf.org/proceedings/lrec2018/pdf/661.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
We present the Extended Paraphrase Typology (EPT) and the Extended Typology Paraphrase Corpus (ETPC). The EPT typology addresses several practical limitations of existing paraphrase typologies: it is the first typology that copes with the non-paraphrase pairs in the paraphrase identification corpora and distinguishes between contextual and habitual paraphrase types. ETPC is the largest corpus to date annotated with atomic paraphrase types. It is the first corpus with detailed annotation of both the paraphrase and the non-paraphrase pairs and the first corpus annotated with paraphrase and negation. Both new resources contribute to better understanding the paraphrase phenomenon, and allow for studying the relationship between paraphrasing and negation. To the developers of Paraphrase Identification systems ETPC corpus offers better means for evaluation and error analysis. Furthermore, the EPT typology and ETPC corpus emphasize the relationship with other areas of NLP such as Semantic Similarity, Textual Entailment, Summarization and Simplification.
### Supported Tasks and Leaderboards
- `text-classification`
### Languages
The text in the dataset is in English (`en`).
## Dataset Structure
### Data Fields
- `idx`: Monotonically increasing index ID.
- `sentence1`: Complete sentence expressing an opinion about a film.
- `sentence2`: Complete sentence expressing an opinion about a film.
- `etpc_label`: Whether the text-pair is a paraphrase, either "yes" (1) or "no" (0) according to etpc annotation schema.
- `mrpc_label`: Whether the text-pair is a paraphrase, either "yes" (1) or "no" (0) according to mrpc annotation schema.
- `negation`: Whether on sentence is a negation of another, either "yes" (1) or "no" (0).
### Data Splits
train: 5801
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Rotten Tomatoes reviewers.
### 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
Unknown.
### Citation Information
```bibtex
@inproceedings{kovatchev-etal-2018-etpc,
title = "{ETPC} - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation",
author = "Kovatchev, Venelin and
Mart{\'\i}, M. Ant{\`o}nia and
Salam{\'o}, Maria",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1221",
}
```
### Contributions
Thanks to [@jpwahle](https://github.com/jpwahle) for adding this dataset. |
false |
# Dataset Card for Danish WIT
## Dataset Description
- **Repository:** <https://gist.github.com/saattrupdan/bb6c9c52d9f4b35258db2b2456d31224>
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk)
- **Size of downloaded dataset files:** 7.5 GB
- **Size of the generated dataset:** 7.8 GB
- **Total amount of disk used:** 15.3 GB
### Dataset Summary
Google presented the Wikipedia Image Text (WIT) dataset in [July
2021](https://dl.acm.org/doi/abs/10.1145/3404835.3463257), a dataset which contains
scraped images from Wikipedia along with their descriptions. WikiMedia released
WIT-Base in [September
2021](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/),
being a modified version of WIT where they have removed the images with empty
"reference descriptions", as well as removing images where a person's face covers more
than 10% of the image surface, along with inappropriate images that are candidate for
deletion. This dataset is the Danish portion of the WIT-Base dataset, consisting of
roughly 160,000 images with associated Danish descriptions. We release the dataset
under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/), in
accordance with WIT-Base's [identical
license](https://huggingface.co/datasets/wikimedia/wit_base#licensing-information).
### Supported Tasks and Leaderboards
Training machine learning models for caption generation, zero-shot image classification
and text-image search are the intended tasks for this dataset. No leaderboard is active
at this point.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 7.5 GB
- **Size of the generated dataset:** 7.8 GB
- **Total amount of disk used:** 15.3 GB
An example from the `train` split looks as follows.
```
{
"image": {
"bytes": b"\xff\xd8\xff\xe0\x00\x10JFIF...",
"path": None
},
"image_url": "https://upload.wikimedia.org/wikipedia/commons/4/45/Bispen_-_inside.jpg",
"embedding": [2.8568285, 2.9562542, 0.33794892, 8.753725, ...],
"metadata_url": "http://commons.wikimedia.org/wiki/File:Bispen_-_inside.jpg",
"original_height": 3161,
"original_width": 2316,
"mime_type": "image/jpeg",
"caption_attribution_description": "Kulturhuset Bispen set indefra. Biblioteket er til venstre",
"page_url": "https://da.wikipedia.org/wiki/Bispen",
"attribution_passes_lang_id": True,
"caption_alt_text_description": None,
"caption_reference_description": "Bispen set indefra fra 1. sal, hvor ....",
"caption_title_and_reference_description": "Bispen [SEP] Bispen set indefra ...",
"context_page_description": "Bispen er navnet på det offentlige kulturhus i ...",
"context_section_description": "Bispen er navnet på det offentlige kulturhus i ...",
"hierarchical_section_title": "Bispen",
"is_main_image": True,
"page_changed_recently": True,
"page_title": "Bispen",
"section_title": None
}
```
### Data Fields
The data fields are the same among all splits.
- `image`: a `dict` feature.
- `image_url`: a `str` feature.
- `embedding`: a `list` feature.
- `metadata_url`: a `str` feature.
- `original_height`: an `int` or `NaN` feature.
- `original_width`: an `int` or `NaN` feature.
- `mime_type`: a `str` or `None` feature.
- `caption_attribution_description`: a `str` or `None` feature.
- `page_url`: a `str` feature.
- `attribution_passes_lang_id`: a `bool` or `None` feature.
- `caption_alt_text_description`: a `str` or `None` feature.
- `caption_reference_description`: a `str` or `None` feature.
- `caption_title_and_reference_description`: a `str` or `None` feature.
- `context_page_description`: a `str` or `None` feature.
- `context_section_description`: a `str` or `None` feature.
- `hierarchical_section_title`: a `str` feature.
- `is_main_image`: a `bool` or `None` feature.
- `page_changed_recently`: a `bool` or `None` feature.
- `page_title`: a `str` feature.
- `section_title`: a `str` or `None` feature.
### Data Splits
Roughly 2.60% of the WIT-Base dataset comes from the Danish Wikipedia. We have split
the resulting 168,740 samples into a training set, validation set and testing set of
the following sizes:
| split | samples |
|---------|--------:|
| train | 167,460 |
| val | 256 |
| test | 1,024 |
## Dataset Creation
### Curation Rationale
It is quite cumbersome to extract the Danish portion of the WIT-Base dataset,
especially as the dataset takes up 333 GB of disk space, so the curation of Danish-WIT
is purely to make it easier to work with the Danish portion of it.
### Source Data
The original data was collected from WikiMedia's
[WIT-Base](https://huggingface.co/datasets/wikimedia/wit_base) dataset, which in turn
comes from Google's [WIT](https://huggingface.co/datasets/google/wit) dataset.
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) curated this dataset.
### Licensing Information
The dataset is licensed under the [CC BY-SA 4.0
license](https://creativecommons.org/licenses/by-sa/4.0/).
|
false |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
false | # Dataset Card for "ChristmasClaymation-blip-captions"
All captions end with the suffix ", Christmas claymation style" |
false |
# IMaSC: ICFOSS Malayalam Speech Corpus
**IMaSC** is a Malayalam text and speech corpus made available by [ICFOSS](https://icfoss.in/) for the purpose of developing speech technology for Malayalam, particularly text-to-speech. The corpus contains 34,473 text-audio pairs of Malayalam sentences spoken by 8 speakers, totalling in approximately 50 hours of audio.
## Dataset Description
- **Paper:** [IMaSC — ICFOSS Malayalam Speech Corpus](https://arxiv.org/abs/2211.12796)
- **Point of Contact:** [Thennal D K](mailto:thennal10@gmail.com)
## Dataset Structure
The dataset consists of 34,473 instances with fields `text`, `speaker`, and `audio`. The audio is mono, sampled at 16kH. The transcription is normalized and only includes Malayalam characters and common punctuation. The table given below specifies how the 34,473 instances are split between the speakers, along with some basic speaker info:
| Speaker | Gender | Age | Time (HH:MM:SS) | Sentences |
| --- | --- | --- | --- | --- |
| Joji | Male | 28 | 06:08:55 | 4,332 |
| Sonia | Female | 43 | 05:22:39 | 4,294 |
| Jijo | Male | 26 | 05:34:05 | 4,093 |
| Greeshma | Female | 22 | 06:32:39 | 4,416 |
| Anil | Male | 48 | 05:58:34 | 4,239 |
| Vidhya | Female | 23 | 04:21:56 | 3,242 |
| Sonu | Male | 25 | 06:04:43 | 4,219 |
| Simla | Female | 24 | 09:34:21 | 5,638 |
| **Total** | | | **49:37:54** | **34,473** |
### Data Instances
An example instance is given below:
```json
{'text': 'സർവ്വകലാശാല വൈസ് ചാൻസലർ ഡോ. ചന്ദ്രബാബുവിനും സംഭവം തലവേദനയാവുകയാണ്',
'speaker': 'Sonia',
'audio': {'path': None,
'array': array([ 0.00921631, 0.00930786, 0.00939941, ..., -0.00497437,
-0.00497437, -0.00497437]),
'sampling_rate': 16000}}
```
### Data Fields
- **text** (str): Transcription of the audio file
- **speaker** (str): The name of the speaker
- **audio** (dict): Audio object including loaded audio array, sampling rate and path to audio (always None)
### Data Splits
We provide all the data in a single `train` split. The loaded dataset object thus looks like this:
```json
DatasetDict({
train: Dataset({
features: ['text', 'speaker', 'audio'],
num_rows: 34473
})
})
```
### Dataset Creation
The text is sourced from [Malayalam Wikipedia](https://ml.wikipedia.org), and read by our speakers in studio conditions. Extensive error correction was conducted to provide a clean, accurate database. Further details are given in our paper, accessible at [https://arxiv.org/abs/2211.12796](https://arxiv.org/abs/2211.12796).
## Additional Information
### Licensing
The corpus is made available under the [Creative Commons license (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation
```
@misc{gopinath2022imasc,
title={IMaSC -- ICFOSS Malayalam Speech Corpus},
author={Deepa P Gopinath and Thennal D K and Vrinda V Nair and Swaraj K S and Sachin G},
year={2022},
eprint={2211.12796},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
|
false | |
false |
# Dataset Summary
AfriCLIRMatrix is a test collection for cross-lingual information retrieval research in 15 diverse African languages. This resource comprises English queries with query–document relevance judgments in 15 African languages automatically mined from Wikipedia
This dataset stores documents of AfriCLIRMatrix. To access the queries and judgments, please refer to [castorini/africlirmatrix](https://github.com/castorini/africlirmatrix).
# Dataset Structure
The only configuration here is the `language`.
An example of document data entry looks as follows:
```
{
'id': '62443',
'contents': 'Acyloin condensation jẹ́ ìyọkúrò àsopọ̀ àwọn carboxylic ester pẹ̀lú lílò metalic sodium lati ṣèdá α-hydroxyketone, tí wọ́n tún mọ̀ sí. Àdàpọ̀ ṣisẹ́ yìí jẹ́ èyí tó ...'
}
```
# Load Dataset
An example to load the dataset:
```
language = 'yoruba'
dataset = load_dataset('castorini/africlirmatrix', language, 'train')
```
# Citation Information
```
coming soon
``` |
false |
# Dataset Card for MultiLegalPile_Wikipedia_Filtered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
The Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models.
It spans over 24 languages and four legal text types.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask.
### Languages
The following languages are supported:
bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
It is structured in the following format: {language}_{text_type}_{shard}.jsonl.xz
text_type is one of the following:
- caselaw
- contracts
- legislation
- other
- wikipedia
Use the dataset like this:
```python
from datasets import load_dataset
config = 'en_contracts' # {language}_{text_type}
dataset = load_dataset('joelito/Multi_Legal_Pile', config, split='train', streaming=True)
```
'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'.
To load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., '
all_legislation').
### Data Instances
The file format is jsonl.xz and there is a `train` and `validation` split available.
Since some configurations are very small or non-existent, they might not contain a train split or not be present at all.
The complete dataset consists of five large subsets:
- [Native Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile)
- [Eurlex Resources](https://huggingface.co/datasets/joelito/eurlex_resources)
- [MC4 Legal](https://huggingface.co/datasets/joelito/mc4_legal)
- [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law)
- [EU Wikipedias](https://huggingface.co/datasets/joelito/EU_Wikipedias)
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
This dataset has been created by combining the following datasets:
Native Multi Legal Pile, Eurlex Resources, MC4 Legal, Pile of Law, EU Wikipedias.
It has been filtered to remove short documents (less than 64 whitespace-separated tokens) and
documents with more than 30% punctuation or numbers (see prepare_legal_data.py for more details).
### 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
```
TODO add citation
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
|
false |
**Homepage:** https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-56
Used lydfiler_16_1.tar.gz and metadata_se_csv.zip |
false |
# Dataset Card for "Reddit Haiku"
This dataset contains haikus from the subreddit [/r/haiku](https://www.reddit.com/r/haiku/) scraped and filtered between October 19th and 10th 2022, combined with a [previous dump](https://zissou.infosci.cornell.edu/convokit/datasets/subreddit-corpus/corpus-zipped/hackintosh_ja~-~hamsters/) of that same subreddit packaged by [ConvoKit](https://convokit.cornell.edu/documentation/subreddit.html) as part of the Subreddit Corpus, which is itself a subset of [pushshift.io](https://pushshift.io/)'s big dump.
A main motivation for this dataset was to collect an alternative haiku dataset for evaluation, in particular for evaluating Fabian Mueller's Deep Haiku [model](fabianmmueller/deep-haiku-gpt-j-6b-8bit) which was trained on the Haiku datasets of [hjhalani30](https://www.kaggle.com/datasets/hjhalani30/haiku-dataset) and [bfbarry](https://www.kaggle.com/datasets/bfbarry/haiku-dataset), which are also available on [huggingface hub](https://huggingface.co/datasets/statworx/haiku).
## Fields
The fields are post id (`id`), the content of the haiku (`processed_title`), upvotes (`ups`), and topic keywords (`keywords`). Topic keywords for each haiku have been extracted with the [KeyBERT library](https://maartengr.github.io/KeyBERT/guides/quickstart.html) and truncated to top-5 keywords.
## Usage
This dataset is intended for evaluation, hence there is only one split which is `test`.
```python
from datasets import load_dataset
d=load_dataset('huanggab/reddit_haiku', data_files='test':'merged_with_keywords.csv'}) # use data_files or it will result in error
>>> print(d['train'][0])
#{'Unnamed: 0': 0, 'id': '1020ac', 'processed_title': "There's nothing inside/There is nothing outside me/I search on in hope.", 'ups': 5, 'keywords': "[('inside', 0.5268), ('outside', 0.3751), ('search', 0.3367), ('hope', 0.272)]"}
```
There is code for scraping and processing in `processing_code`, and a subset of the data with more fields such as author Karma, downvotes and posting time at `processing_code/reddit-2022-10-20-dump.csv`. |
false | # Dataset Card for "Yannic-Kilcher"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
false | # Dataset Card for "azure"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
false |
_The Dataset Teaser is now enabled instead! Isn't this better?_
# TD 01: Natural Ground Textures
This dataset contains multi-photo texture captures in outdoor nature scenes — all focusing on the ground. Each set has different photos that showcase texture variety, making them ideal for training a domain-specific image generator!
Overall information about this dataset:
* **Format** — JPEG-XL, lossless RGB
* **Resolution** — 4032 × 2268
* **Device** — mobile camera
* **Technique** — hand-held
* **Orientation** — portrait or landscape
* **Author**: Alex J. Champandard
* **Configurations**: 4K, 2K (default), 1K
To load the medium- and high-resolution images of the dataset, you'll need to install `jxlpy` from [PyPI](https://pypi.org/project/jxlpy/) with `pip install jxlpy`:
```python
# Recommended use, JXL at high-quality.
from jxlpy import JXLImagePlugin
from datasets import load_dataset
d = load_dataset('texturedesign/td01_natural-ground-textures', 'JXL@4K')
print(len(d['train']), len(d['test']))
```
The lowest-resolution images are available as PNG with a regular installation of `pillow`:
```python
# Alternative use, PNG at low-quality.
from datasets import load_dataset
d = load_dataset('texturedesign/td01_natural-ground-textures', 'PNG@1K')
# EXAMPLE: Discard all other sets except Set #1.
dataset = dataset.filter(lambda s: s['set'] == 1)
# EXAMPLE: Only keep images with index 0 and 2.
dataset = dataset.select([0, 2])
```
Use built-in dataset `filter()` and `select()` to narrow down the loaded dataset for training, or to ease with development.
## Set #1: Rock and Gravel
* **Description**:
- surface rocks with gravel and coarse sand
- strong sunlight from the left, sharp shadows
* **Number of Photos**:
- 7 train
- 2 test
* **Edits**:
- rotated photos to align sunlight
- removed infrequent objects
* **Size**: 77.8 Mb
## Set #2: Dry Grass with Pine Needles
* **Description**:
- field of dry grass and pine needles
- sunlight from the top right, some shadows
* **Number of Photos**:
- 6 train
- 1 test
* **Edits**:
- removed dry leaves and large plants
- removed sticks, rocks and sporadic daisies
* **Size**: 95.2 Mb
## Set #3: Chipped Stones, Broken Leaves and Twiglets
* **Description**:
- autumn path with chipped stones and dry broken leaves
- diffuse light on a cloudy day, very soft shadows
* **Number of Photos**:
- 9 train
- 3 test
* **Edits**:
- removed anything that looks green, fresh leaves
- removed long sticks and large/odd stones
* **Size**: 126.9 Mb
## Set #4: Grass Clumps and Cracked Dirt
* **Description**:
- clumps of green grass, clover and patches of cracked dirt
- diffuse light on cloudy day, shadows under large blades of grass
* **Number of Photos**:
- 9 train
- 2 test
* **Edits**:
- removed dry leaves, sporadic dandelions, and large objects
- histogram matching for two of the photos so the colors look similar
* **Size**: 126.8 Mb
## Set #5: Dirt, Stones, Rock, Twigs...
* **Description**:
- intricate micro-scene with grey dirt, surface rock, stones, twigs and organic debris
- diffuse light on cloudy day, soft shadows around the larger objects
* **Number of Photos**:
- 9 train
- 3 test
* **Edits**:
- removed odd objects that felt out-of-distribution
* **Size**: 102.1 Mb
## Set #6: Plants with Flowers on Dry Leaves
* **Description**:
- leafy plants with white flowers on a bed of dry brown leaves
- soft diffuse light, shaded areas under the plants
* **Number of Photos**:
- 9 train
- 2 test
* **Edits**:
- none yet, inpainting doesn't work well enough
- would remove long sticks and pieces of wood
* **Size**: 105.1 Mb
|
true | # AutoTrain Dataset for project: autotrain_goodreads_string
## Dataset Description
This dataset has been automatically processed by AutoTrain for project autotrain_goodreads_string.
### 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
[
{
"target": 5,
"text": "This book was absolutely ADORABLE!!!!!!!!!!! It was an awesome, light and FUN read. \n I loved the characters but I absolutely LOVED Cam!!!!!!!!!!!! Major Swoooon Worthy! J \n \"You've been checking me out, haven't you? In-between your flaming insults? I feel like man candy.\" \n Seriously, between being HOT, FUNNY and OH SO VERY ADORABLE, Cam was the perfect catch!! \n \" I'm not going out with you Cam.\" \n \" I didn't ask you at this moment, now did I\" One side of his lips curved up. \" But you will eventually.\" \n \"You're delusional\" \n \"I'm determined.\" \n \" More like annoying.\" \n \" Most would say amazing.\" \n Cam and Avery's relationship is tough due to the secrets she keeps but he is the perfect match for breaking her out of her shell and facing her fears. \n This book is definitely a MUST READ. \n Trust me when I say this YOU will not regret it! \n www.Jenreadit.com"
},
{
"target": 4,
"text": "I FINISHED!!! This book took me FOREVER to read! But I am so glad I stuck with it, I really loved it. It took me a while to get into: this book has a TON of characters and storylines. But once I hit about the 100-page mark, I became very invested in the story and couldn't wait to see what would happen with Lizzie, Lane, Edward, Gin and the rest of the family. Oh, and Samuel T. There's a little bit of sex but mostly this is a sweeping romance novel, much like Dynasty and Dallas from the 1980's. If you loved those series, you will love this book. There's betrayal, unrequited love, family fortunes, and much scheming. \n There are many characters to love here and many to hate. Some are over-the-top but I loved the central storyline involving Lane and Lizzie. \n The author really gets the Southern mannerisms right, and the backdrop of the Kentucky Bourbon industry is fascinating. This book ends not so much on a cliffhanger but with many, many loose ends, and I will eagerly pick up the next book in this series."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "ClassLabel(num_classes=6, names=['0_stars', '1_stars', '2_stars', '3_stars', '4_stars', '5_stars'], id=None)",
"text": "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 | 2357 |
| valid | 592 |
|
false | # Dataset Card for "two-minute-papers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
false | # Dataset Card for "goog-tech-talks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.