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OpenLLM-France/Claire-Dialogue-French-0.1 | OpenLLM-France | 2023-11-27T21:57:59Z | 0 | 0 | null | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:conversational",
"task_ids:language-modeling",
"task_ids:dialogue-modeling",
"task_ids:dialogue-generation",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:fr",
"license:cc-by-nc-sa-4... | 2023-11-27T21:57:59Z | 2023-11-27T21:16:05.000Z | 2023-11-27T21:16:05 | ---
pretty_name: Claire French Dialogue Dataset (CFDD)
license: cc-by-nc-sa-4.0
language:
- fr
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
task_categories:
- text-generation
- text2text-generation
- conversational
task_ids:
- language-modeling
- dialogue-modeling
- dialogue-generation
tags:
- conversational
- text-generation
- conditional-text-generation
- dialogue-modeling
- dialogue-generation
viewer: true
---
# Claire French Dialogue Dataset (CFDD) <br /> _A collection of French dialogue transcripts and plays_
This is the first packaged version of the datasets used to train the Claire family of large language models
([OpenLLM-France/Claire-7B-0.1](https://huggingface.co/OpenLLM-France/Claire-7B-0.1)).
The Claire French Dialogue Dataset (CFDD) is a collection of theater plays and transcripts of real French dialogues from various sources, including parliamentary proceedings, interviews, debates, meetings, and free conversations.
Each dialogue is split into speech turns, and each speech turn is labeled with the name of the speaker, or a unique identifier if the speaker is unknown.
* [Dataset composition](#dataset-composition)
* [Data sources](#data-sources)
* [Example use (python)](#example-use-python)
* [Important notes](#important-notes)
* [License](#license)
* [Citations](#citations)
* [Contact](#contact)
## Dataset composition
CFDD can be broken down into:
* 37,015 conversations in total (36,731 in train, 284 in test)
* 2,961,116 speech turns in total (2,934,084 in train, 27,032 in test)
* around 150M words
It is a collection of several independent datasets, classified by the types of conversations they contain. This categorization is designed to more evenly balance the influence of different styles of dialogue on model training and to facilitate future applications of CFDD for which certain types of dialogue might be more helpful than others.
Note that this categorization leads to multiple cases in which the original corpus is split into subcorpora. When the smaller sets are included in our corpus, they are clearly indicated, e.g., "ESLO (1/5)". Some portions of the original corpora have been excluded entirely because they did not include dialogue between adults (e.g., monologues, read literature).
For more information, you can look at the following documents:
* [FR/metadata.csv](FR/metadata.csv) contains further statistics on the different subcorpora (broken down by train/test splits).
* [FR/metadata_filter_datasets_regex.json](FR/metadata_filter_datasets_regex.json) contains information about how original datasets were filtered and/or split into sub-categories.
* [FR/metadata_split_testset_list.json](FR/metadata_split_testset_list.json) contains information about which files in the original datasets were chosen to be in the test set.
### Data sources
<table>
<thead>
<tr>
<th>Dataset</th>
<th>Sub-folder(s)</th>
<th>Description</th>
<th>Words</th>
<th>Turns</th>
<th>Conversations</th>
<th>License (and conditions)</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Parliamentary Proceedings</strong></em></u></td></tr>
<tr>
<td><a href="https://www.assemblee-nationale.fr">Assemblée Nationale</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/AssembleeNationale"><code>FR/AssembleeNationale*</code></a></td>
<td>Parliamentary proceedings from the French National Assembly</td>
<td>133M</td>
<td>1.6M</td>
<td>4.5k</td>
<td><a href="https://www.etalab.gouv.fr/wp-content/uploads/2017/04/ETALAB-Licence-Ouverte-v2.0.pdf">Open License 2.0</a></td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Theatre</strong></em></u></td></tr>
<tr>
<td><a href="https://dracor.org/fre">Theatre Classique</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/TheatreClassique"><code>FR/TheatreClassique</code></a></td>
<td>Classic stage plays</td>
<td>12.8M</td>
<td>441k</td>
<td>25k</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://github.com/dracor-org/fredracor#to-cite-fredracor-">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://theatregratuit.com">Theatre Gratuit</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/TheatreGratuit"><code>FR/TheatreGratuit</code></a></td>
<td>Stage plays</td>
<td>2.7M</td>
<td>155k</td>
<td>4k</td>
<td></td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Interviews</strong></em></u></td></tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (1/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_interview"><code>FR/ESLO_interview</code></a></td>
<td>Guided conversations</td>
<td>4.2M</td>
<td>329k</td>
<td>399</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.cnrtl.fr/corpus/tcof/">TCOF</a> (adults)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/TCOF_adults"><code>FR/TCOF_adults</code></a></td>
<td>Guided conversations (between adults)</td>
<td>765k</td>
<td>49k</td>
<td>237</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/2.0/">CC BY-NC-SA 2.0</a> (<a href="https://www.ortolang.fr/market/corpora/tcof">please cite</a>)</td>
</tr>
<tr>
<td><a href="http://cfpp2000.univ-paris3.fr/Presentation.html">CFPP</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/CFPP"><code>FR/CFPP</code></a></td>
<td>Interviews of people in Paris in 2000</td>
<td>608k</td>
<td>48k</td>
<td>42</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (<a href="https://www.ortolang.fr/market/corpora/cfpp2000">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/Valibel</a> (1/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_valibel_interview"><code>FR/ORFEO_valibel_interview</code></a></td>
<td>Guided conversations of Belgian French speakers</td>
<td>458k</td>
<td>19k</td>
<td>67</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/pfc">PFC</a> (1/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/PFC_guided"><code>FR/PFC_guided</code></a></td>
<td>Guided interviews</td>
<td>268k</td>
<td>15k</td>
<td>173</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/pfc">please cite</a>)</td>
</tr>
<tr>
<td><a href="http://ortolang107.inist.fr/?f%5BnomCorpus%5D%5B%5D=ORFEO_cfpb+%28O%29&locale=fr">ORFEO/CFPB</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_cfpb"><code>FR/ORFEO_cfpb</code></a></td>
<td>Interviews of people in Brussels</td>
<td>138k</td>
<td>11k</td>
<td>12</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a></td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/sldr000832">ACSYNT</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ACSYNT"><code>FR/ACSYNT</code></a></td>
<td>Guided interviews from southwestern France</td>
<td>61k</td>
<td>2.7k</td>
<td>144</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/sldr000832">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Free Conversations</strong></em></u></td></tr>
<tr>
<td><a href="https://ofrom.unine.ch/">OFROM</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/OFROM"><code>FR/OFROM</code></a></td>
<td>Conversations in French-speaking Switzerland</td>
<td>590k</td>
<td>44k</td>
<td>151</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (<a href="https://ofrom.unine.ch/index.php?page=citations">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (2/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_free"><code>FR/ESLO_free</code></a></td>
<td>Diverse conversation</td>
<td>480k</td>
<td>47k</td>
<td>98</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/CRFP</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_crfp"><code>FR/ORFEO_crfp</code></a></td>
<td>Diverse conversations</td>
<td>405k</td>
<td>9k</td>
<td>124</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/C-ORAL-ROM</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_coralrom"><code>FR/ORFEO_coralrom</code></a></td>
<td>Diverse conversation</td>
<td>248k</td>
<td>6k</td>
<td>152</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/pfc">PFC</a> (2/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/PFC_free"><code>FR/PFC_free</code></a></td>
<td>Diverse conversation</td>
<td>230k</td>
<td>14k</td>
<td>146</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/pfc">please cite</a>)</td>
</tr>
<tr>
<td><a href="http://clapi.ish-lyon.cnrs.fr/">CLAPI</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/CLAPI"><code>FR/CLAPI</code></a></td>
<td>Diverse conversation</td>
<td>122k</td>
<td>15k</td>
<td>14</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a></td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/sldr000720">CID</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/CID"><code>FR/CID</code></a></td>
<td>Dialogues between two friends</td>
<td>118k</td>
<td>9k</td>
<td>8</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/sldr000720">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://rhapsodie.modyco.fr/">Rhapsodie</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/Rhapsodie"><code>FR/Rhapsodie</code></a></td>
<td>Diverse conversations</td>
<td>28k</td>
<td>1k</td>
<td>41</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (<a href="https://rhapsodie.modyco.fr/propriete-intellectuelle/">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://github.com/surfacesyntacticud/SUD_French-ParisStories">Paris Stories</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ParisStories"><code>FR/ParisStories</code></a></td>
<td>Diverse conversations in Paris</td>
<td>28k</td>
<td>351</td>
<td>54</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a></td>
</tr>
<tr>
<td>LinTO (1/3)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/LINAGORA_free"><code>FR/LINAGORA_free</code></a></td>
<td>Diverse conversation</td>
<td>26k</td>
<td>2k</td>
<td>4</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://aclanthology.org/2021.emnlp-main.104/">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Meetings</strong></em></u></td></tr>
<tr>
<td>SUMM-RE</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/SUMM-RE"><code>FR/SUMM-RE</code></a></td>
<td>Meeting-style conversations (transcribed with Whisper large-v2 ASR)</td>
<td>1.3M</td>
<td>39k</td>
<td>283</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (please cite)</td>
</tr>
<tr>
<td><a href="http://ortolang107.inist.fr/?f%5BnomCorpus%5D%5B%5D=R%C3%A9unions+de+travail+%28O%29&fnomCorpus=Chambers-Rostand+E&locale=fr">ORFEO/Reunions-de-Travail</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_reunions-de-travail"><code>FR/ORFEO_reunions-de-travail</code></a></td>
<td>Real meetings</td>
<td>210k</td>
<td>12k</td>
<td>29</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a></td>
</tr>
<tr>
<td>LinTO (2/3)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/LINAGORA_meetings"><code>FR/LINAGORA_meetings</code></a></td>
<td>Meetings on speech recognition</td>
<td>41k</td>
<td>1.8k</td>
<td>6</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://aclanthology.org/2021.emnlp-main.104/">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Debates</strong></em></u></td></tr>
<tr>
<td><a href="https://github.com/linto-ai/FREDSum">FREDSum</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/FREDSum"><code>FR/FREDSum</code></a></td>
<td>French political debates</td>
<td>406k</td>
<td>7k</td>
<td>144</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (please cite)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (3/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_conf"><code>FR/ESLO_conf</code></a></td>
<td>Conferences</td>
<td>76k</td>
<td>2k</td>
<td>4</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Assistance</strong></em></u></td></tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (4/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_assistance"><code>FR/ESLO_assistance</code></a></td>
<td>In-person assistance and call-centers</td>
<td>95k</td>
<td>11k</td>
<td>143</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/Fleuron</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_fleuron"><code>FR/ORFEO_fleuron</code></a></td>
<td>Interactions created to teach foreign students about university life</td>
<td>33k</td>
<td>2k</td>
<td>51</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.info.univ-tours.fr/~antoine/parole_publique/OTG/index.html">OTG</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/OTG"><code>FR/OTG</code></a></td>
<td>Dialogues in a tourism office</td>
<td>27k</td>
<td>4k</td>
<td>315</td>
<td><a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> (<a href="https://www.info.univ-tours.fr/~antoine/parole_publique/OTG/index.html">contact before usage</a>)</td>
</tr>
<tr>
<td><a href="https://www.info.univ-tours.fr/~antoine/parole_publique/Accueil_UBS/index.html">Accueil UBS</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/UBS"><code>FR/UBS</code></a></td>
<td>University telephone answering service</td>
<td>7.2k</td>
<td>1k</td>
<td>41</td>
<td><a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> (<a href="https://www.info.univ-tours.fr/~antoine/parole_publique/Accueil_UBS/index.html">contact before usage</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Presentation, Formal Address</strong></em></u></td></tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (5/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_discourse"><code>FR/ESLO_discourse</code></a></td>
<td>Conference presentations</td>
<td>43k</td>
<td>120</td>
<td>9</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td>LinTO (3/3)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/LINAGORA_discourse"><code>FR/LINAGORA_discourse</code></a></td>
<td>Technical presentations (AI topics) with Q/A</td>
<td>38k</td>
<td>1.5k</td>
<td>4</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://aclanthology.org/2021.emnlp-main.104/">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/Valibel</a> (2/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_valibel_discourse"><code>FR/ORFEO_valibel_discourse</code></a></td>
<td>Formal university addresses</td>
<td>12k</td>
<td>5</td>
<td>5</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
</tbody>
</table>
## Example use (python)
In the following `sample_by="paragraph"` is important to ensure that each sample corresponds to a full conversation (not just a speech turn).
Load dataset from HuggingFace cache (downloaded under `~/.cache/huggingface/datasets`):
```python
from datasets import load_dataset
dataset = load_dataset("OpenLLM-France/Claire-Dialogue-French-0.1", sample_by="paragraph", streaming=True)
```
Load dataset from raw text files:
```python
from datasets import load_dataset
import glob
path = "path/to/dataset"
train_files = glob.glob(path + "/*/train.txt")
test_files = glob.glob(path + "/*/test.txt")
dataset = load_dataset("text", data_files={"train": train_files, "test": test_files}, sample_by="paragraph", streaming=True)
```
Iterate on the dataset:
```python
for sample in dataset["train"]:
train_conversation = sample["text"]
...
for sample in dataset["test"]:
test_conversation = sample["text"]
...
```
## Important notes
All datasets were normalized in text files so that:
* Conversations are separated by a single blank line.
* Each line corresponds to a single speech turn.
* Each line begins with a speaker label of the form "`[***:]`".
* When speaker names are anonymized or otherwise unknown, speakers are distinguished by numbers in the following format: "**`[speaker001:]`**", "**`[speaker002:]`**", … <br /> Otherwise, speakers are labeled with their names or roles, e.g. "`[Paul:]`", "`[François Mitterrand:]`", "`[M. le président:]`".
* There are no parentheses: special annotations are always between square brackets.
* Commong tags include:
* "**`[PII]`**": Personally Identifiable Information (anonymized name...)
* "`[NOISE]`": distinct ambient noises
* "`[LAUGHTER]`": laughter
<!-- * Truncated words are sometimes marked with "-" (ex: "je suis dé- décidé") -->
* Depending on the data source, data may or may not include punctuation marks and upper case letters.
* The data were normalized in various ways including unicode NFC normalization, conversion of unbreakable spaces to spaces, and standardization of punctuation marks (`…` -> `...`, `«`/`»`/`“`/`”`/`″`/`„` -> `"`). <!-- `’`/`‘`/`‛`/`ʿ` -> `'`, `ᵉ`/`ᵉʳ` -> `e`/`er`, `‚` -> `,` -->
Those details are described in the paper:
_« The Claire French Dialogue Dataset » (2023)_.
## License
Given that some of the corpora used for training are only available under CC-BY-NC-SA licenses,
Claire-Dialogue-French-0.1 is made available under the [CC-BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
## Citations
When using the CFDD corpus, please cite the following paper:
_Julie Hunter, Jérôme Louradour, Virgile Rennard, Ismaïl Harrando, Guokan Shang, Jean-Pierre Lorré_
« The Claire French Dialogue Dataset » (2023)</b> _(to appear soon on arxiv)_
This paper in turn provides the requested citations for all of the original corpora.
The same references are also listed below.
* **Accueil UBS**
* Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). [Towards a large corpus of spoken dialogue in French that will be freely available: the "Parole Publique" project and its first realisations](https://www.info.univ-tours.fr/~antoine/parole_publique/articles/2002_LREC_CORP.pdf). _Third European Conference on Language Resources and Evaluation_ (LREC). Las Palmas de Gran Canaria, Espagne.
* Jean-Yves Antoine, Jerome Goulian, Jeanne Villaneau, Marc le Tallec (2009). [Word Order Phenomena in Spoken French : a Study on Four Corpora of Task-Oriented Dialogue and its Consequences on Language Processing](https://www.info.univ-tours.fr/~antoine/articles/2009_Corpus_Linguistics.pdf). _Corpus Linguistics_, Liverpool, UK.
* **ACSYNT**
* Cognition, Langue, Langages, Ergonomie - UMR 5263 (CLLE) (2013). ACSYNT [Corpus](https://hdl.handle.net/11403/sldr000832/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage).
* **CFPP**
* Branca-Rosoff S., Fleury S., Lefeuvre F., Pires M., 2012, [Discours sur la ville. Présentation du Corpus de Français Parlé Parisien des années 2000](http://cfpp2000.univ-paris3.fr/CFPP2000.pdf) (CFPP2000).
* CLESTHIA - Langage, systèmes, discours - EA 7345 (CLESTHIA) (2018). CFPP2000 [Corpus](https://hdl.handle.net/11403/cfpp2000/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage), v1.
* **CID**
* Roxane Bertrand, Philippe Blache, Robert Espesser, Gaëlle Ferré, Christine Meunier, Béatrice Priego-Valverde, Stéphane Rauzy (2008). [Le CID — Corpus of Interactional Data — Annotation et Exploitation Multimodale de Parole Conversationnelle](https://hal.science/hal-00349893). _Traitement Automatique des Langues_, vol. 49, no. 3.
* Philippe Blache, Roxane Bertrand, Brigitte Bigi et al. (2010). [Multimodal annotation of conversational data](http://portal.acm.org/citation.cfm?id=1868749). _Proceedings of the Fourth Linguistic Annotation Workshop_.
* Laboratoire parole et langage - UMR 7309 (LPL) (2021). [Transcriptions du corpus CID](https://hdl.handle.net/11403/sldr000720/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage).
* **CLAPI**
* CLAPI, [http://clapi.icar.cnrs.fr](http://clapi.icar.cnrs.fr)
* Groupe ICOR (H. Baldauf-Quilliatre, I. Colon de Carvajal, C. Etienne, E. Jouin-Chardon, S. Teston-Bonnard, V. Traverso) (2016). [CLAPI, une base de données multimodale pour la parole en interaction : apports et dilemmes](https://shs.hal.science/halshs-01316283/). In Avanzi M., Béguelin M.-J. & Diémoz F. (eds), _Corpus de français parlés et français parlés des corpus, Corpus_ 15.
* **ESLO**
* Iris Eshkol-Taravella, Olivier Baude, Denis Maurel, Linda Hriba, Céline Dugua, Isabelle Tellier (2012). Un grand corpus oral « disponible » : le corpus d’Orléans 1968-2012, _Ressources linguistiques libres, TAL_. Volume 52 – n° 3/2011, 17-46.
* Laboratoire Ligérien de Linguistique - UMR 7270 (LLL) (2023). ESLO [Corpus](https://hdl.handle.net/11403/eslo/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage), v1.
* **FREDSum**
* Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, Michalis Vazirgiannis (forthcoming). FREDSum: A Dialogue Summarization Corpus for French Political Debates. _Findings of Empirical Methods in Natural Language Processsing_ (EMNLP).
* **LinTO**
* Lila Gravellier, Julie Hunter, Philippe Muller, Thomas Pellegrini, Isabelle Ferrané (2021).
[Weakly Supervised Discourse Segmentation for Multiparty Oral Conversation](https://aclanthology.org/2021.emnlp-main.104/).
_The 2021 Conference on Empirical Methods in Natural Language Processing_ (EMNLP), pp. 1381–1392.
* **OFROM**
* Mathieu Avanzi, Marie-José Béguelin, Gilles Corminboeuf, Federica Diémoz, Laure Anne Johnsen (2012-2023). [Corpus OFROM – Corpus oral de français de Suisse romande](ofrom.unine.ch). Université de Neuchâtel.
* Mathieu Avanzi, Marie-José Béguelin, Federica Diémoz (2016). [Présentation du corpus OFROM – Corpus oral de français de Suisse romande](ofrom.unine.ch/uploads/Documents/AM-MJB-FD_GC_LAJ_OFROM_23.pdf). Université de Neuchâtel.
* Mathieu Avanzi, Marie-José Béguelin, Federica Diémoz (2016). De l’archive de parole au corpus de référence. Le corpus oral de français de Suisse romande (OFROM). _Actes du colloque Corpus de Français Parlés et Français Parlés des Corpus_ (= Corpus 15), 309-342.
* Gilles Corminboeuf, Julie Rothenbühler, Maguelone Sauzet (éds) (2020). [Français parlés et français ‘tout court’](studialinguisticaromanica.org/index.php/slr/issue/view/4). _Studia Linguistica Romanica_ n°4, publication électronique.
* **ORFEO** (pour chaque corpus issu du projet ORFEO)
* Jeanne-Marie Debaisieux, Christophe Benzitoun, Henri-José Deulofeu. Le projet ORFÉO : un corpus d’étude pour le français contemporain, _Corpus 15_, Actes du colloque Corpus de Français Parlés et Français Parlés des Corpus.
* Carruthers, Janice (2008). Annotating an Oral Corpus using the Text Encoding Initiative. Methodology, Problems, Solutions, _Journal of French Language Studies_ 18(1), 103-119.
* A. Tutin, F. Grossmann (eds) (2014). _L’écrit scientifique : du lexique au discours. Autour de Scientext_. Presses de l’Université de Rennes.
* **ORFEO/CFPB**
* Anne Dister, Emmanuelle Labeau (2017). [Le corpus de français parlé à Bruxelles: origines, hypothèses, développements et prédictions](https://www.semanticscholar.org/paper/Le-corpus-de-fran%C3%A7ais-parl%C3%A9-%C3%A0-Bruxelles%3A-origines%2C-Dister-Labeau/0fe858f6b8c1ce49a2e43b34494c2e76922162fa).
* **ORFEO/C-Oral-Rom**
* Cresti Emanuela, Bacelar do Nascimento Fernanda, Moreno Sandoval Antonio, Veronis Jean, Martin Philippe, Kalid Choukri (2005). The C-ORAL-ROM CORPUS: A Multilingual Resource of Spontaneous Speech for Romance Languages. _Studies in Corpus Linguistics_, 15. John Benjamins Publishing Company 304 pp. (incl. DVD).
* **ORFEO/CRFP**
* Équipe Delic (2004). Recherches sur le français parlé n° 18, « Autour du Corpus de référence du français parlé » Publications de l’université de Provence, 265 p.
* **ORFEO/Valibel**
* Anne Dister, Michel Francard, Philippe Hambye, Anne-Catherine Simon (2009). [Du corpus à la banque de données. Du son, des textes et des métadonnées. L'évolution de banque de données textuelles orales VALIBEL (1989-2009)](https://cdn.uclouvain.be/public/Exports%20reddot/valibel/documents/Dister_et_al_2009_Cahiers.pdf), _Cahiers de Linguistique_ 33/2, 113-129.
* **OTG**
* Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). [Towards a large corpus of spoken dialogue in French that will be freely available: the "Parole Publique" project and its first realisations](https://www.info.univ-tours.fr/~antoine/parole_publique/articles/2002_LREC_CORP.pdf). _Third European Conference on Language Resources and Evaluation_ (LREC). Las Palmas de Gran Canaria, Espagne.
* Jean-Yves Antoine, Sabine Letellier-Zarshenas, Pascale Nicolas, Igor Schadle (2002). [Corpus OTG et ECOLE_MASSY : vers la constitution d’un collection de corpus francophones de dialogue oral diffusés librement](https://www.info.univ-tours.fr/~antoine/parole_publique/articles/2002_TALN_CORP.pdf). _Actes TALN_ 2002. Nancy, France.
* **Paris Stories**
* Sylvain Kahane, Bernard Caron, Emmett Strickland, Kim Gerdes. Annotation guidelines of UD and SUD treebanks for spoken corpora: A proposal. _Proceedings of the 20th International Workshop on Treebanks and Linguistic Theories_ (TLT, SyntaxFest 2021).
* **PFC**
* Jacques Durand, Bernard Laks, Chantal Lyche (2009). Le projet PFC: une source de données primaires structurées. In J. Durand, B. Laks et C. Lyche (eds)(2009) _Phonologie, variation et accents du français_. Paris: Hermès. pp. 19-61.
* Modèles, Dynamiques, Corpus - UMR 7114 (MoDyCo), Université de Groningen (RUG) (2017). PFC - Phonologie du Français Contemporain [Corpus](https://hdl.handle.net/11403/pfc/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage), v1.
* **Rhapsodie**
* see [https://rhapsodie.modyco.fr/propriete-intellectuelle/](https://rhapsodie.modyco.fr/propriete-intellectuelle/)
* **SUMM-RE**
* Hiroyoshi Yamasaki, Jérôme Louradour, Julie Hunter, Laurent Prévot (forthcoming). Transcribing And Aligning Conversational Speech: A Hybrid Pipeline Applied To French Conversations. _Workshop on Automatic Speech Recognition and Understanding_ (ASRU).
* **TCOF**
* Analyse et Traitement Informatique de la Langue Française (2020). TCOF : Traitement de Corpus Oraux en Français [Corpus]. _ORTOLANG (Open Resources and TOols for LANGuage)_
* **Theatre Classique**
* French Drama Corpus (FreDraCor): A TEI P5 Version of Paul Fièvre's "Théâtre Classique" Corpus. Edited by Carsten Milling, Frank Fischer and Mathias Göbel. Hosted on GitHub, 2021 – [https://github.com/dracor-org/fredracor](https://github.com/dracor-org/fredracor)
## Contact
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Dpirad007/llama_train | Dpirad007 | 2023-11-27T22:31:20Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T22:31:20Z | 2023-11-27T22:30:37.000Z | 2023-11-27T22:30:37 | Entry not found | [
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biadrivex/biadata | biadrivex | 2023-11-27T22:38:29Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T22:38:29Z | 2023-11-27T22:35:26.000Z | 2023-11-27T22:35:26 | Entry not found | [
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mtrazzi/talk-to-paul | mtrazzi | 2023-11-27T22:56:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T22:56:40Z | 2023-11-27T22:47:24.000Z | 2023-11-27T22:47:24 | # talk-to-paul
Goal: finetune a LLM on conversational data of Paul Christiano, so you can "talk to paul".
Previously on [Github](https://github.com/mtrazzi/talk-to-paul).
Currently, I've made a few datasets, consisting of Lesswrong posts/comments and podcast transcripts.
* The [15M.jsonl](./15M.jsonl) file has one podcast / lesswrong post data per line. Format is {"text": "..."}. Size 15Mb.
* The [15M.txt](./15M.txt) is the same though instead is just a long text file where things are separated by \<eop\> (end of post) instead of the jsonl format above. Size 15Mb.
* The [prompt_completion_podcast_data.jsonl](./prompt_completion_podcast_data.jsonl) has format {"prompt": "...", "completion": "..."} (see below). It does not currently contain the lesswrong data because lesswrong threads are more tricky to put into some prompt / completion format. (I might add it in the future if it turns out that the prompt completion data is superior).
These are concatenations of smaller datasets you can read more about in the [raw_data](./raw_data) README.
## Format
* In [prompt_completion_data](./prompt_completion_data) on github I have the raw files in a form {"prompt": "...", "completion": "..."} where the prompt is the message before paul christiano says something and the completion is what paul says. This is useful for doing more like instruction finetuning thing, or really training a chatbot. There is no "Paul Christiano:" or "Rob Wiblin:" in this, just directly the text that is being said.
* In the other files however, the messages inside the "text": "" double quotes are separated by \<eom\> (end of message), and at the end of a podcast or a lesswrong thread I have a \<eot\> (end of thread) separator.
* Messages / posts / speakers alternate with either "Full Name:" [... their text ...]] or with username: [...] on lesswrong. Same format with lesswrong posts and comments. For convenience, on lesswrong paul is "Paul Christiano: " instead of "paulfchristiano: " to make it easier for the trained model to learn what to say when prompted "Paul Christiano: " (useful for deploying the paul chatbot).
| [
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BangumiBase/cardcaptorsakura1998 | BangumiBase | 2023-11-28T03:36:42Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-28T03:36:42Z | 2023-11-27T22:54:09.000Z | 2023-11-27T22:54:09 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Card Captor Sakura (1998)
This is the image base of bangumi Card Captor Sakura (1998), we detected 59 characters, 8455 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 2737 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 116 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 111 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 75 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 94 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 261 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 37 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 56 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 943 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 77 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 297 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 195 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 316 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 86 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 62 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 111 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 40 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 47 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 132 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 186 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 16 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 25 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 79 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 296 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 373 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 452 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 37 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 32 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 37 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 72 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 32 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 21 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 8 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 66 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 11 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 96 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 18 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 112 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 28 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 30 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 13 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 10 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 21 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 17 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 20 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 15 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 8 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 67 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 9 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 18 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 11 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 6 | [Download](53/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 54 | 11 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 13 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 8 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 5 | [Download](57/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 345 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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Evanls/minha_voz_mixedmp3_2711_191738.zip | Evanls | 2023-11-27T22:57:06Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-27T22:57:06Z | 2023-11-27T22:57:06.000Z | 2023-11-27T22:57:06 | ---
license: mit
---
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biadrivex/quarteto | biadrivex | 2023-11-27T23:03:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T23:03:52Z | 2023-11-27T22:59:13.000Z | 2023-11-27T22:59:13 | Entry not found | [
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ricardosantoss/top12_com_relatorios_de_alta | ricardosantoss | 2023-11-27T23:03:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T23:03:02Z | 2023-11-27T23:00:28.000Z | 2023-11-27T23:00:28 | ---
dataset_info:
features:
- name: Nota Clinica
dtype: string
- name: Sequencia_CID10_Lista
sequence: string
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configs:
- config_name: default
data_files:
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masasain/testing | masasain | 2023-11-27T23:57:59Z | 0 | 0 | null | [
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djhugg/06myvozmasc06 | djhugg | 2023-11-27T23:15:33Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-27T23:15:33Z | 2023-11-27T23:12:47.000Z | 2023-11-27T23:12:47 | ---
license: openrail
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nRuaif/Anime-pretrain-collection | nRuaif | 2023-11-28T00:27:33Z | 0 | 0 | null | [
"region:us"
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Idavidrein/gpqa | Idavidrein | 2023-11-27T23:18:46Z | 0 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-11-27T23:18:46Z | 2023-11-27T23:18:46.000Z | 2023-11-27T23:18:46 | ---
license: cc-by-4.0
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peytonwsmith/lava2llama | peytonwsmith | 2023-11-27T23:30:15Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T23:30:15Z | 2023-11-27T23:24:34.000Z | 2023-11-27T23:24:34 | ---
configs:
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data_files:
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path: data/test-*
dataset_info:
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splits:
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num_examples: 13
download_size: 0
dataset_size: 57320
---
# Dataset Card for "lava2llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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suyuanliu/TANDEM | suyuanliu | 2023-11-27T23:31:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T23:31:56Z | 2023-11-27T23:31:52.000Z | 2023-11-27T23:31:52 | ---
dataset_info:
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splits:
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---
# Dataset Card for "TANDEM_stimuli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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masasain/phone | masasain | 2023-11-27T23:33:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T23:33:16Z | 2023-11-27T23:32:44.000Z | 2023-11-27T23:32:44 | Entry not found | [
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suyuanliu/WinnVOT | suyuanliu | 2023-11-27T23:36:21Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T23:36:21Z | 2023-11-27T23:36:17.000Z | 2023-11-27T23:36:17 | ---
dataset_info:
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download_size: 26349798
dataset_size: 28249935.0
---
# Dataset Card for "WinnVOT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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suyuanliu/Winnf0VOT | suyuanliu | 2023-11-27T23:36:49Z | 0 | 0 | null | [
"region:us"
] | 2023-11-27T23:36:49Z | 2023-11-27T23:36:42.000Z | 2023-11-27T23:36:42 | ---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 100267569.0
num_examples: 1800
download_size: 93892942
dataset_size: 100267569.0
---
# Dataset Card for "Winnf0VOT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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stanmalkinson199/MikeBirch | stanmalkinson199 | 2023-11-27T23:38:59Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-27T23:38:59Z | 2023-11-27T23:38:32.000Z | 2023-11-27T23:38:32 | ---
license: openrail
---
| [
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masasain/particle | masasain | 2023-11-27T23:42:26Z | 0 | 0 | null | [
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] | 2023-11-27T23:42:26Z | 2023-11-27T23:42:24.000Z | 2023-11-27T23:42:24 | Entry not found | [
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BangumiBase/cardcaptorsakuraclearcardhen | BangumiBase | 2023-11-28T03:22:30Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-28T03:22:30Z | 2023-11-27T23:49:26.000Z | 2023-11-27T23:49:26 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Cardcaptor Sakura - Clear Card-hen
This is the image base of bangumi Cardcaptor Sakura - Clear Card-hen, we detected 46 characters, 5120 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1583 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 381 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 26 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 21 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 57 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 47 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 55 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 18 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 24 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 22 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 38 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 381 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 65 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 120 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 81 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 33 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 21 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 19 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 24 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 23 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 14 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 99 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 14 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 46 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 59 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 47 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 129 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 107 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 462 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 64 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 9 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 134 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 90 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 478 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 14 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 21 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 20 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 16 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 29 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 6 | [Download](39/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 40 | 16 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 6 | [Download](41/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 42 | 8 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 23 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 5 | [Download](44/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 165 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
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masasain/pasta | masasain | 2023-11-28T00:25:29Z | 0 | 0 | null | [
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yin001/imdb_dataset_positive_negative | yin001 | 2023-11-28T00:23:35Z | 0 | 0 | null | [
"language:en",
"region:us"
] | 2023-11-28T00:23:35Z | 2023-11-27T23:58:47.000Z | 2023-11-27T23:58:47 | ---
language:
- en
pretty_name: imdb_dataset_positive_negative
---
---
language:
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AdvayK/SFD_7_9010_split | AdvayK | 2023-11-28T00:13:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T00:13:26Z | 2023-11-28T00:04:03.000Z | 2023-11-28T00:04:03 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype: audio
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dtype: string
splits:
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num_examples: 803
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num_examples: 90
download_size: 444464113
dataset_size: 547367713.0
---
# Dataset Card for "SFD_7_9010_split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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cottonnes/Idk | cottonnes | 2023-11-28T00:31:01Z | 0 | 0 | null | [
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fabsss/WESTFAL | fabsss | 2023-11-28T12:08:44Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-28T12:08:44Z | 2023-11-28T00:19:12.000Z | 2023-11-28T00:19:12 | ---
license: apache-2.0
---
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shrop/multinerd_en_filtered | shrop | 2023-11-28T00:27:58Z | 0 | 0 | null | [
"language:en",
"license:unknown",
"region:us"
] | 2023-11-28T00:27:58Z | 2023-11-28T00:22:23.000Z | 2023-11-28T00:22:23 | ---
license: unknown
language:
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---
A filtered version of the [original English subset from the MultiNERD dataset by Babelscape](https://huggingface.co/datasets/Babelscape/multinerd). The version contains only 5 of the original 15 NER categories: PERSON(PER), ORGANIZATION(ORG), LOCATION(LOC), DISEASES(DIS),
ANIMAL(ANIM). Dataset filtered as part of a test.
See https://huggingface.co/datasets/Babelscape/multinerd for information on the dataset structure. | [
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masasain/0 | masasain | 2023-11-28T00:24:48Z | 0 | 0 | null | [
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tianyi0216/MagicBrush_PartOnly | tianyi0216 | 2023-11-28T00:32:34Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T00:32:34Z | 2023-11-28T00:30:02.000Z | 2023-11-28T00:30:02 | Entry not found | [
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Emm9625/oalc-nsw-legislation-gpt2-ctx1024 | Emm9625 | 2023-11-28T00:57:39Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T00:57:39Z | 2023-11-28T00:57:39.000Z | 2023-11-28T00:57:39 | Entry not found | [
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veluribharath/snli | veluribharath | 2023-11-28T09:24:09Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T09:24:09Z | 2023-11-28T01:09:18.000Z | 2023-11-28T01:09:18 | ---
dataset_info:
features:
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dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: id
dtype: int64
splits:
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num_bytes: 70180560
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num_examples: 9842
download_size: 23577753
dataset_size: 72813776
---
# Dataset Card for "snli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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asrar7787/magento2_rest_api | asrar7787 | 2023-11-28T01:12:21Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-28T01:12:21Z | 2023-11-28T01:11:25.000Z | 2023-11-28T01:11:25 | ---
license: mit
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configs:
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RemoPaysandu/eimlk2 | RemoPaysandu | 2023-11-28T03:40:22Z | 0 | 0 | null | [
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hallah/ddpm-butterflies-128 | hallah | 2023-11-28T01:20:54Z | 0 | 0 | null | [
"region:us"
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NeilSy23/raw-padthink | NeilSy23 | 2023-11-28T01:49:21Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-28T01:49:21Z | 2023-11-28T01:48:31.000Z | 2023-11-28T01:48:31 | ---
license: apache-2.0
---
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limberc/LA2018 | limberc | 2023-11-28T01:53:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T01:53:18Z | 2023-11-28T01:48:52.000Z | 2023-11-28T01:48:52 | - Download heart MRI data [MICCAI 2018 Atrial Segmentation Challenge](http://atriaseg2018.cardiacatlas.org/data/).
- Pre-processing data like existing work [UA-MT](https://github.com/yulequan/UA-MT)
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MohametSena/bio_tkcls | MohametSena | 2023-11-28T02:06:25Z | 0 | 0 | null | [
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sordonia/adauni-v1-desc | sordonia | 2023-11-28T02:08:57Z | 0 | 0 | null | [
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] | 2023-11-28T02:08:57Z | 2023-11-28T02:08:54.000Z | 2023-11-28T02:08:54 | ## model_name: gpt-35-turbo-instruct
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sh-zheng/SurfaceRoughness | sh-zheng | 2023-11-28T02:16:57Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-28T02:16:57Z | 2023-11-28T02:15:48.000Z | 2023-11-28T02:15:48 | ---
license: mit
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iliesse/test | iliesse | 2023-11-28T02:18:38Z | 0 | 0 | null | [
"license:apache-2.0",
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license: apache-2.0
---
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tlittle1/test123 | tlittle1 | 2023-11-28T02:27:21Z | 0 | 0 | null | [
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license: apache-2.0
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manishiitg/en-combine-v2 | manishiitg | 2023-11-28T02:28:36Z | 0 | 0 | null | [
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wz2615/cups_image_test | wz2615 | 2023-11-28T03:06:40Z | 0 | 0 | null | [
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automated-research-group/llama2_7b_chat-siqa-results | automated-research-group | 2023-11-28T15:07:13Z | 0 | 0 | null | [
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---
# Dataset Card for Dataset Name
This is a lot of info wow.
## Dataset Details
### Dataset Description
Just a demo
- **Curated by:** Turtles
- **Funded by [optional]:** Turtles
- **Shared by [optional]:** Turtles
- **Language(s) (NLP):** Enlish
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | [
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HAERAE-HUB/K-MMLU-Preview | HAERAE-HUB | 2023-11-28T04:09:18Z | 0 | 0 | null | [
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path: "data/Accounting_dev-00000-of-00001-05b3bff001ffa24f.parquet"
- split: test
path: "data/Accounting_test-00000-of-00001-7d57145e765162ed.parquet"
- config_name: Agricultural_Sciences
data_files:
- split: train
path: "data/Agricultural_Sciences_auxilary_train-00000-of-00001-30c4d038ff2158b8.parquet"
- split: dev
path: "data/Agricultural_Sciences_dev-00000-of-00001-ba75235c6d9d5d21.parquet"
- split: test
path: "data/Agricultural_Sciences_test-00000-of-00001-5c3c95014cb8c4b7.parquet"
- config_name: Aviation_Engineering_and_Maintenance
data_files:
- split: train
path: "data/Aviation_Engineering_and_Maintenance_auxilary_train-00000-of-00001-bbf25a726049c4af.parquet"
- split: dev
path: "data/Aviation_Engineering_and_Maintenance_dev-00000-of-00001-a424d522c60b6d89.parquet"
- split: test
path: "data/Aviation_Engineering_and_Maintenance_test-00000-of-00001-15fb65f7d283a10f.parquet"
- config_name: Biology
data_files:
- split: train
path: "data/Biology_auxilary_train-00000-of-00001-1f921e4d10f4f0e7.parquet"
- split: dev
path: "data/Biology_dev-00000-of-00001-59edd20525a7e741.parquet"
- split: test
path: "data/Biology_test-00000-of-00001-54926098230b27bb.parquet"
- config_name: Chemical_Engineering
data_files:
- split: train
path: "data/Chemical_Engineering_auxilary_train-00000-of-00001-89caa98d0f357ce8.parquet"
- split: dev
path: "data/Chemical_Engineering_dev-00000-of-00001-77608dd799e0cf21.parquet"
- split: test
path: "data/Chemical_Engineering_test-00000-of-00001-11be3a7add188980.parquet"
- config_name: Chemistry
data_files:
- split: train
path: "data/Chemistry_auxilary_train-00000-of-00001-25bd2b9510356855.parquet"
- split: dev
path: "data/Chemistry_dev-00000-of-00001-2d79ca5cddc871bf.parquet"
- split: test
path: "data/Chemistry_test-00000-of-00001-c000a710582220d2.parquet"
- config_name: Civil_Engineering
data_files:
- split: train
path: "data/Civil_Engineering_auxilary_train-00000-of-00001-2cbf6cabd01a78d6.parquet"
- split: dev
path: "data/Civil_Engineering_dev-00000-of-00001-6df5a46d0dd43e9c.parquet"
- split: test
path: "data/Civil_Engineering_test-00000-of-00001-1cfbdc248f9149d0.parquet"
- config_name: Computer_Science
data_files:
- split: train
path: "data/Computer_Science_auxilary_train-00000-of-00001-019023b12e0a73a9.parquet"
- split: dev
path: "data/Computer_Science_dev-00000-of-00001-4cb0efe01b015410.parquet"
- split: test
path: "data/Computer_Science_test-00000-of-00001-a56c1cf867906939.parquet"
- config_name: Construction
data_files:
- split: train
path: "data/Construction_auxilary_train-00000-of-00001-2a938be15f447874.parquet"
- split: dev
path: "data/Construction_dev-00000-of-00001-7ce0e23b89253880.parquet"
- split: test
path: "data/Construction_test-00000-of-00001-3ac10b84c36ac4f7.parquet"
- config_name: Criminal_Law
data_files:
- split: train
path: "data/Criminal_Law_auxilary_train-00000-of-00001-d773da7f55517476.parquet"
- split: dev
path: "data/Criminal_Law_dev-00000-of-00001-e3214582ddc43c90.parquet"
- split: test
path: "data/Criminal_Law_test-00000-of-00001-4caf21ea2cdeaa6f.parquet"
- config_name: Ecology
data_files:
- split: train
path: "data/Ecology_auxilary_train-00000-of-00001-9251bc372fdd7ef9.parquet"
- split: dev
path: "data/Ecology_dev-00000-of-00001-1fb0f9232a7452ba.parquet"
- split: test
path: "data/Ecology_test-00000-of-00001-0f31e43d5e4a9ccc.parquet"
- config_name: Economics
data_files:
- split: train
path: "data/Economics_auxilary_train-00000-of-00001-3e43331de742b50c.parquet"
- split: dev
path: "data/Economics_dev-00000-of-00001-aee7a2b08e9202be.parquet"
- split: test
path: "data/Economics_test-00000-of-00001-4c901cc91827fec4.parquet"
- config_name: Education
data_files:
- split: train
path: "data/Education_auxilary_train-00000-of-00001-aa040eaa77ac991f.parquet"
- split: dev
path: "data/Education_dev-00000-of-00001-e75cd03980ee4aac.parquet"
- split: test
path: "data/Education_test-00000-of-00001-6a622bd350b258e3.parquet"
- config_name: Electrical_Engineering
data_files:
- split: train
path: "data/Electrical_Engineering_auxilary_train-00000-of-00001-28851e98f11d5729.parquet"
- split: dev
path: "data/Electrical_Engineering_dev-00000-of-00001-caf9dbbf8e3d8474.parquet"
- split: test
path: "data/Electrical_Engineering_test-00000-of-00001-38f82359945bb173.parquet"
- config_name: Electronics_Engineering
data_files:
- split: train
path: "data/Electronics_Engineering_auxilary_train-00000-of-00001-f4042d56872a4f5a.parquet"
- split: dev
path: "data/Electronics_Engineering_dev-00000-of-00001-a470b5d76b9f177f.parquet"
- split: test
path: "data/Electronics_Engineering_test-00000-of-00001-3459abdcea69eb51.parquet"
- config_name: Energy_Management
data_files:
- split: train
path: "data/Energy_Management_auxilary_train-00000-of-00001-534a31ee067d9ac2.parquet"
- split: dev
path: "data/Energy_Management_dev-00000-of-00001-8c5cc28f54be048b.parquet"
- split: test
path: "data/Energy_Management_test-00000-of-00001-0f8f54f1b640b106.parquet"
- config_name: Environmental_Science
data_files:
- split: train
path: "data/Environmental_Science_auxilary_train-00000-of-00001-38f7a923b5098fff.parquet"
- split: dev
path: "data/Environmental_Science_dev-00000-of-00001-6cf3bd3f6e39c676.parquet"
- split: test
path: "data/Environmental_Science_test-00000-of-00001-7b29d806b721b2dc.parquet"
- config_name: Fashion
data_files:
- split: train
path: "data/Fashion_auxilary_train-00000-of-00001-689423c991ca9a85.parquet"
- split: dev
path: "data/Fashion_dev-00000-of-00001-f1f459b20b3a2f52.parquet"
- split: test
path: "data/Fashion_test-00000-of-00001-43bda89d5a2ac078.parquet"
- config_name: Food_Processing
data_files:
- split: train
path: "data/Food_Processing_auxilary_train-00000-of-00001-e1cb31086d43df6f.parquet"
- split: dev
path: "data/Food_Processing_dev-00000-of-00001-4f9f8fb59244947f.parquet"
- split: test
path: "data/Food_Processing_test-00000-of-00001-522b68f80847c4c9.parquet"
- config_name: Gas_Technology_and_Engineering
data_files:
- split: train
path: "data/Gas_Technology_and_Engineering_auxilary_train-00000-of-00001-da9b4651af9d007a.parquet"
- split: dev
path: "data/Gas_Technology_and_Engineering_dev-00000-of-00001-98726283cb9bd366.parquet"
- split: test
path: "data/Gas_Technology_and_Engineering_test-00000-of-00001-1a96354cc263a3ae.parquet"
- config_name: General_Physics
data_files:
- split: train
path: "data/General_Physics_auxilary_train-00000-of-00001-e3fea8cbf15289dc.parquet"
- split: dev
path: "data/General_Physics_dev-00000-of-00001-267b780ee548638e.parquet"
- split: test
path: "data/General_Physics_test-00000-of-00001-226ccaebb3786854.parquet"
- config_name: Geomatics
data_files:
- split: train
path: "data/Geomatics_auxilary_train-00000-of-00001-7c6da04991c75a45.parquet"
- split: dev
path: "data/Geomatics_dev-00000-of-00001-0002bd91a6c9e8e8.parquet"
- split: test
path: "data/Geomatics_test-00000-of-00001-324c463a6acde585.parquet"
- config_name: Health
data_files:
- split: train
path: "data/Health_auxilary_train-00000-of-00001-19cef76861fc38ec.parquet"
- split: dev
path: "data/Health_dev-00000-of-00001-9b58a152ba54ac85.parquet"
- split: test
path: "data/Health_test-00000-of-00001-a619adcff8e6869a.parquet"
- config_name: Industrial_Engineer
data_files:
- split: train
path: "data/Industrial_Engineer_auxilary_train-00000-of-00001-db550665254b98b5.parquet"
- split: dev
path: "data/Industrial_Engineer_dev-00000-of-00001-53b17fabf4587cb7.parquet"
- split: test
path: "data/Industrial_Engineer_test-00000-of-00001-2d589719228fa214.parquet"
- config_name: Information_Technology
data_files:
- split: train
path: "data/Information_Technology_auxilary_train-00000-of-00001-d5556cbcd0841b08.parquet"
- split: dev
path: "data/Information_Technology_dev-00000-of-00001-1e9f4c8d3433175a.parquet"
- split: test
path: "data/Information_Technology_test-00000-of-00001-a70963788087200d.parquet"
- config_name: Interior_Architecture_and_Design
data_files:
- split: train
path: "data/Interior_Architecture_and_Design_auxilary_train-00000-of-00001-51cdd5e6f78abaab.parquet"
- split: dev
path: "data/Interior_Architecture_and_Design_dev-00000-of-00001-56a5f72ae01075b0.parquet"
- split: test
path: "data/Interior_Architecture_and_Design_test-00000-of-00001-5c5d18f41d3f5dcf.parquet"
- config_name: Korean_Language
data_files:
- split: train
path: "data/Korean_Language_auxilary_train-00000-of-00001-6ebf099fa041d0cc.parquet"
- split: dev
path: "data/Korean_Language_dev-00000-of-00001-e836d6401120a371.parquet"
- split: test
path: "data/Korean_Language_test-00000-of-00001-83cf45dd810d4941.parquet"
- config_name: Law
data_files:
- split: train
path: "data/Law_auxilary_train-00000-of-00001-cac92f51af30db5d.parquet"
- split: dev
path: "data/Law_dev-00000-of-00001-cede5230c1152a6b.parquet"
- split: test
path: "data/Law_test-00000-of-00001-5aa87ead9c45541e.parquet"
- config_name: Machine_Design_and_Manufacturing
data_files:
- split: train
path: "data/Machine_Design_and_Manufacturing_auxilary_train-00000-of-00001-2cf4f407cf1d3398.parquet"
- split: dev
path: "data/Machine_Design_and_Manufacturing_dev-00000-of-00001-5c1fbfc95b4af775.parquet"
- split: test
path: "data/Machine_Design_and_Manufacturing_test-00000-of-00001-4d0bbc8161a5f17e.parquet"
- config_name: Management
data_files:
- split: train
path: "data/Management_auxilary_train-00000-of-00001-c77d5ba53435899f.parquet"
- split: dev
path: "data/Management_dev-00000-of-00001-4bf33fdf811ea7f3.parquet"
- split: test
path: "data/Management_test-00000-of-00001-8abe8ae91d388d5d.parquet"
- config_name: Maritime_Engineering
data_files:
- split: train
path: "data/Maritime_Engineering_auxilary_train-00000-of-00001-6e62c6ece7c5dea9.parquet"
- split: dev
path: "data/Maritime_Engineering_dev-00000-of-00001-e48be91c76da47e8.parquet"
- split: test
path: "data/Maritime_Engineering_test-00000-of-00001-3ba47630357ecd07.parquet"
- config_name: Marketing
data_files:
- split: train
path: "data/Marketing_auxilary_train-00000-of-00001-b286ebd70a601d1a.parquet"
- split: dev
path: "data/Marketing_dev-00000-of-00001-9409e8da0f63d762.parquet"
- split: test
path: "data/Marketing_test-00000-of-00001-176f1c57c6402c4f.parquet"
- config_name: Materials_Engineering
data_files:
- split: train
path: "data/Materials_Engineering_auxilary_train-00000-of-00001-261c4b081c4dbd7a.parquet"
- split: dev
path: "data/Materials_Engineering_dev-00000-of-00001-9d182a3fbe8fd8ba.parquet"
- split: test
path: "data/Materials_Engineering_test-00000-of-00001-540f6a5e0ab387b3.parquet"
- config_name: Mechanical_Engineering
data_files:
- split: train
path: "data/Mechanical_Engineering_auxilary_train-00000-of-00001-33a3159a8f4ad027.parquet"
- split: dev
path: "data/Mechanical_Engineering_dev-00000-of-00001-ce104b54e3caedea.parquet"
- split: test
path: "data/Mechanical_Engineering_test-00000-of-00001-bdda1c660989ce5b.parquet"
- config_name: Nondestructive_Testing
data_files:
- split: train
path: "data/Nondestructive_Testing_auxilary_train-00000-of-00001-dd571e08916eb447.parquet"
- split: dev
path: "data/Nondestructive_Testing_dev-00000-of-00001-a48e6e0f4f2fdb77.parquet"
- split: test
path: "data/Nondestructive_Testing_test-00000-of-00001-da050d39e503f0f8.parquet"
- config_name: Patent
data_files:
- split: train
path: "data/Patent_auxilary_train-00000-of-00001-0065bcc248fdf5f5.parquet"
- split: dev
path: "data/Patent_dev-00000-of-00001-8a8826893c4794c1.parquet"
- split: test
path: "data/Patent_test-00000-of-00001-298414f6653ca638.parquet"
- config_name: Political_Science_and_Sociology
data_files:
- split: train
path: "data/Political_Science_and_Sociology_auxilary_train-00000-of-00001-acae070877f9e45c.parquet"
- split: dev
path: "data/Political_Science_and_Sociology_dev-00000-of-00001-6794c5810b938841.parquet"
- split: test
path: "data/Political_Science_and_Sociology_test-00000-of-00001-5e71bdf99446c7aa.parquet"
- config_name: Psychology
data_files:
- split: train
path: "data/Psychology_auxilary_train-00000-of-00001-ce8576fbe87f40d5.parquet"
- split: dev
path: "data/Psychology_dev-00000-of-00001-39f505569a7e8f03.parquet"
- split: test
path: "data/Psychology_test-00000-of-00001-21eb15a5ad9b6460.parquet"
- config_name: Public_Safety
data_files:
- split: train
path: "data/Public_Safety_auxilary_train-00000-of-00001-68f12bb516dee813.parquet"
- split: dev
path: "data/Public_Safety_dev-00000-of-00001-ee9804f4c20df679.parquet"
- split: test
path: "data/Public_Safety_test-00000-of-00001-f0ad12378c0d8974.parquet"
- config_name: Railway_and_Automotive_Engineering
data_files:
- split: train
path: "data/Railway_and_Automotive_Engineering_auxilary_train-00000-of-00001-afd0d7a3cba38f62.parquet"
- split: dev
path: "data/Railway_and_Automotive_Engineering_dev-00000-of-00001-8ec5d13cd14b2388.parquet"
- split: test
path: "data/Railway_and_Automotive_Engineering_test-00000-of-00001-44c3ab24b47b84d8.parquet"
- config_name: Real_Estate
data_files:
- split: train
path: "data/Real_Estate_auxilary_train-00000-of-00001-3e0856a259c017eb.parquet"
- split: dev
path: "data/Real_Estate_dev-00000-of-00001-00a1b1ebc1c8b226.parquet"
- split: test
path: "data/Real_Estate_test-00000-of-00001-ce337de62653d6dd.parquet"
- config_name: Refrigerating_Machinery
data_files:
- split: train
path: "data/Refrigerating_Machinery_auxilary_train-00000-of-00001-f42e31d90b8c6c98.parquet"
- split: dev
path: "data/Refrigerating_Machinery_dev-00000-of-00001-1d661416cd54bbb1.parquet"
- split: test
path: "data/Refrigerating_Machinery_test-00000-of-00001-85362a3bcd195536.parquet"
- config_name: Social_Welfare
data_files:
- split: train
path: "data/Social_Welfare_auxilary_train-00000-of-00001-73004f9151455470.parquet"
- split: dev
path: "data/Social_Welfare_dev-00000-of-00001-75fda4dd1f35e996.parquet"
- split: test
path: "data/Social_Welfare_test-00000-of-00001.parquet"
---
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wz2615/cups_images_test | wz2615 | 2023-11-28T03:08:16Z | 0 | 0 | null | [
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dataset_info:
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splits:
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num_bytes: 22549992.0
num_examples: 42
download_size: 22549682
dataset_size: 22549992.0
configs:
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data_files:
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---
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qpzz/function_test | qpzz | 2023-11-28T06:46:33Z | 0 | 0 | null | [
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license: apache-2.0
---
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Raphael04/julio | Raphael04 | 2023-11-28T05:13:27Z | 0 | 0 | null | [
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license: openrail
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nlp-vtcc/brand_identity | nlp-vtcc | 2023-11-28T03:57:22Z | 0 | 0 | null | [
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furry-br/blitz | furry-br | 2023-11-28T04:01:52Z | 0 | 0 | null | [
"license:openrail",
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license: openrail
---
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TobiasKG/DoomverseAI | TobiasKG | 2023-11-28T04:05:09Z | 0 | 0 | null | [
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AmandaBai98/iptacopan_pubmed-dataset | AmandaBai98 | 2023-11-28T04:04:34Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T04:04:34Z | 2023-11-28T04:04:26.000Z | 2023-11-28T04:04:26 | ---
dataset_info:
features:
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dtype: string
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dtype: string
splits:
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num_bytes: 76176
num_examples: 53
download_size: 49812
dataset_size: 76176
configs:
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data_files:
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path: data/train-*
---
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RickyChen-Infinirc/llm2 | RickyChen-Infinirc | 2023-11-28T04:15:17Z | 0 | 0 | null | [
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jiuyuan/hw3 | jiuyuan | 2023-11-28T05:53:54Z | 0 | 0 | null | [
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alwanrahmana/ner_fine-tuning_preview | alwanrahmana | 2023-11-28T04:23:12Z | 0 | 0 | null | [
"task_categories:token-classification",
"size_categories:n<1K",
"language:id",
"license:unknown",
"region:us"
] | 2023-11-28T04:23:12Z | 2023-11-28T04:19:58.000Z | 2023-11-28T04:19:58 | ---
license: unknown
task_categories:
- token-classification
language:
- id
pretty_name: NERFINETUNING
size_categories:
- n<1K
--- | [
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rishiraj/portuguesechat | rishiraj | 2023-11-28T06:19:44Z | 0 | 2 | null | [
"task_categories:conversational",
"task_categories:text-generation",
"language:pt",
"license:cc-by-nc-4.0",
"arxiv:2203.02155",
"region:us"
] | 2023-11-28T06:19:44Z | 2023-11-28T04:30:15.000Z | 2023-11-28T04:30:15 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
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num_bytes: 30628039
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- name: test
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num_examples: 500
download_size: 19873853
dataset_size: 32272489
configs:
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data_files:
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path: data/train-*
- split: test
path: data/test-*
task_categories:
- conversational
- text-generation
language:
- pt
pretty_name: Portuguese Chat
license: cc-by-nc-4.0
---
# Dataset Card for Portuguese Chat
We know that current English-first LLMs don’t work well for many other languages, both in terms of performance, latency, and speed. Building instruction datasets for non-English languages is an important challenge that needs to be solved.
Dedicated towards addressing this problem, I release 3 new datasets [rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/), [rishiraj/bengalichat](https://huggingface.co/datasets/rishiraj/bengalichat/) & [rishiraj/hindichat](https://huggingface.co/datasets/rishiraj/hindichat/) of 10,000 instructions and demonstrations each. This data can be used for supervised fine-tuning (SFT) to make language multilingual models follow instructions better.
### Dataset Summary
[rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/) was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is translated from [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots/) which comprised mostly of single-turn instructions across the following categories:
| Category | Count |
|:-----------|--------:|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
### Languages
The data in [rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/) are in Portuguese (BCP-47 pt).
### Data Fields
The data fields are as follows:
* `prompt`: Describes the task the model should perform.
* `prompt_id`: A unique ID for the prompt.
* `messages`: An array of messages, where each message indicates the role (system, user, assistant) and the content.
* `category`: Which category the example belongs to (e.g. `Chat` or `Coding`).
* `text`: Content of `messages` in a format that is compatible with dataset_text_field of SFTTrainer.
### Data Splits
| | train_sft | test_sft |
|---------------|------:| ---: |
| portuguesechat | 9500 | 500 |
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{portuguesechat,
author = {Rishiraj Acharya},
title = {Portuguese Chat},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/datasets/rishiraj/portuguesechat}}
}
``` | [
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adamjweintraut/eli5_precomputed | adamjweintraut | 2023-11-28T04:34:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T04:34:42Z | 2023-11-28T04:32:56.000Z | 2023-11-28T04:32:56 | ---
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diffusers/benchmarks | diffusers | 2023-11-28T05:21:54Z | 0 | 0 | null | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-11-28T05:21:54Z | 2023-11-28T04:33:41.000Z | 2023-11-28T04:33:41 | ---
license: apache-2.0
language:
- en
pretty_name: Diffusers Benchmarks
---
Welcome to 🤗 Diffusers Benchmarks!
This is dataset where we keep track of the inference latency and memory information of the core pipelines in the `diffusers` library.
Currently, the core pipelines are the following:
* Stable Diffusion and its derivatives such as ControlNet, T2I Adapter, Image-to-Image, Inpainting
* Stable Diffusion XL and its derivatives
* SSD-1B
* Kandinsky
* Würstchen
* LCM
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adaoranjoku/cs6601_np_anonymized | adaoranjoku | 2023-11-28T05:15:02Z | 0 | 0 | null | [
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Imran1/newdatasetdog | Imran1 | 2023-11-28T04:51:03Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T04:51:03Z | 2023-11-28T04:50:43.000Z | 2023-11-28T04:50:43 | ---
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---
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Imran1/newdatasetdog_test | Imran1 | 2023-11-28T04:54:48Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T04:54:48Z | 2023-11-28T04:54:32.000Z | 2023-11-28T04:54:32 | ---
dataset_info:
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phongo/RegEx | phongo | 2023-11-28T05:02:52Z | 0 | 0 | null | [
"region:us"
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caiosoares26/vozdocoxinhz | caiosoares26 | 2023-11-28T05:07:21Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-28T05:07:21Z | 2023-11-28T05:07:21.000Z | 2023-11-28T05:07:21 | ---
license: openrail
---
| [
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caiosoares26/vozdocoxinha | caiosoares26 | 2023-11-28T05:08:43Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-28T05:08:43Z | 2023-11-28T05:07:42.000Z | 2023-11-28T05:07:42 | ---
license: openrail
---
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malaysia-ai/mosaic-yi | malaysia-ai | 2023-11-28T08:15:38Z | 0 | 0 | null | [
"language:ms",
"region:us"
] | 2023-11-28T08:15:38Z | 2023-11-28T05:10:59.000Z | 2023-11-28T05:10:59 | ---
language:
- ms
---
# Mosaic format for filtered combine dataset to finetune Yi models
This repository is to store dataset shards using mosaic format.
1. prepared at https://github.com/malaysia-ai/dedup-text-dataset/blob/main/yi/combine-dataset.ipynb
2. using tokenizer https://huggingface.co/01-ai/Yi-6B
3. 4096 context length.
## how-to
1. git clone,
```bash
git lfs clone https://huggingface.co/datasets/malaysia-ai/mosaic-yi
```
2. load it,
```python
from streaming import LocalDataset
import numpy as np
from streaming.base.format.mds.encodings import Encoding, _encodings
class UInt16(Encoding):
def encode(self, obj) -> bytes:
return obj.tobytes()
def decode(self, data: bytes):
return np.frombuffer(data, np.uint16)
_encodings['uint16'] = UInt16
dataset = LocalDataset('mosaic-yi')
len(dataset)
``` | [
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worldboss/2021-ghana-population-housing-census | worldboss | 2023-11-28T05:14:06Z | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
] | 2023-11-28T05:14:06Z | 2023-11-28T05:14:06.000Z | 2023-11-28T05:14:06 | ---
license: afl-3.0
---
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DianaJin/miniproject_train | DianaJin | 2023-11-28T12:18:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T12:18:55Z | 2023-11-28T05:16:36.000Z | 2023-11-28T05:16:36 | ---
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---
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DianaJin/miniproject_test | DianaJin | 2023-11-28T12:19:06Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T12:19:06Z | 2023-11-28T05:16:45.000Z | 2023-11-28T05:16:45 | ---
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---
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benayas/banking_llm_v2 | benayas | 2023-11-28T05:18:25Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T05:18:25Z | 2023-11-28T05:18:19.000Z | 2023-11-28T05:18:19 | ---
dataset_info:
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---
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benayas/atis_llm_v2 | benayas | 2023-11-28T05:19:00Z | 0 | 0 | null | [
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benayas/snips_llm_v2 | benayas | 2023-11-28T05:20:01Z | 0 | 0 | null | [
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benayas/massive_llm_v2 | benayas | 2023-11-28T05:23:01Z | 0 | 0 | null | [
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adamjweintraut/eli5_precomputed_slice | adamjweintraut | 2023-11-28T05:40:50Z | 0 | 0 | null | [
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NeilSy23/POCdata | NeilSy23 | 2023-11-28T05:43:22Z | 0 | 0 | null | [
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Harshithacj123/CCU_Llama_QandA_full | Harshithacj123 | 2023-11-28T05:52:12Z | 0 | 0 | null | [
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dvijay/databricks-dolly-15k-formatted | dvijay | 2023-11-28T05:55:42Z | 0 | 0 | null | [
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decoy4600/sgm-new-2 | decoy4600 | 2023-11-28T06:11:45Z | 0 | 0 | null | [
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Prakhar1000/api_dataset | Prakhar1000 | 2023-11-28T06:15:37Z | 0 | 0 | null | [
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adamjweintraut/eli5_lfqa_top | adamjweintraut | 2023-11-28T06:19:41Z | 0 | 0 | null | [
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PixArt-alpha/data_toy | PixArt-alpha | 2023-11-28T06:43:26Z | 0 | 0 | null | [
"license:openrail++",
"text-to-image",
"Pixart-α",
"region:us"
] | 2023-11-28T06:43:26Z | 2023-11-28T06:22:46.000Z | 2023-11-28T06:22:46 | ---
license: openrail++
tags:
- text-to-image
- Pixart-α
---
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