id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Chapad0o/Ursos_sem_curso | Chapad0o | 2023-10-26T12:26:38Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-10-26T12:26:38Z | 2023-10-26T12:23:48.000Z | 2023-10-26T12:23:48 | ---
license: openrail
---
| [
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faizalnf1800/yelp_review_full_preprocessed | faizalnf1800 | 2023-10-26T13:18:27Z | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
] | 2023-10-26T13:18:27Z | 2023-10-26T12:42:41.000Z | 2023-10-26T12:42:41 | ---
license: afl-3.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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download_size: 174987714
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---
This Dataset Preprocessed Using NLTK Library. Tokenize,Lemmatize,Lower,Stopword for easier to analyze by ML Model
| [
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Ryan20/hotel_dataset_pushed | Ryan20 | 2023-10-27T08:50:48Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T08:50:48Z | 2023-10-26T12:50:32.000Z | 2023-10-26T12:50:32 | ---
configs:
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data_files:
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path: data/train-*
dataset_info:
features:
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sequence: string
- name: context
dtype: string
- name: questions
sequence: string
splits:
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download_size: 7932
dataset_size: 4634
---
# Dataset Card for "hotel_dataset_pushed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Back-up/UIT-Squad-test | Back-up | 2023-10-26T13:11:07Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T13:11:07Z | 2023-10-26T13:11:05.000Z | 2023-10-26T13:11:05 | ---
dataset_info:
features:
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- name: question
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struct:
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sequence: int64
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splits:
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download_size: 613301
dataset_size: 4654487
configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "UIT-Squad-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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varshil27/Symtoms-Disease-LLama2-Format | varshil27 | 2023-10-26T13:21:40Z | 0 | 0 | null | [
"license:other",
"region:us"
] | 2023-10-26T13:21:40Z | 2023-10-26T13:20:37.000Z | 2023-10-26T13:20:37 | ---
license: other
license_name: other
license_link: LICENSE
---
| [
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Facico/test | Facico | 2023-10-27T06:48:02Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T06:48:02Z | 2023-10-26T13:20:59.000Z | 2023-10-26T13:20:59 | Entry not found | [
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othorizedshogun/poems_dataset | othorizedshogun | 2023-10-26T13:37:06Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T13:37:06Z | 2023-10-26T13:37:02.000Z | 2023-10-26T13:37:02 | ---
dataset_info:
features:
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configs:
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data_files:
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path: data/train-*
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path: data/test-*
---
| [
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enakilci/fi_corpora_parliament_processed | enakilci | 2023-10-26T13:40:10Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T13:40:10Z | 2023-10-26T13:39:56.000Z | 2023-10-26T13:39:56 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
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num_bytes: 309048227
num_examples: 1969624
download_size: 175929435
dataset_size: 309048227
configs:
- config_name: default
data_files:
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path: data/train-*
---
# Dataset Card for "fi_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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0.0509160198271274... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
JFaces/threads-squeez | JFaces | 2023-10-26T14:16:21Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T14:16:21Z | 2023-10-26T13:40:50.000Z | 2023-10-26T13:40:50 | Entry not found | [
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carnival13/nci_nq_t5_tokenized | carnival13 | 2023-10-26T13:53:37Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T13:53:37Z | 2023-10-26T13:53:14.000Z | 2023-10-26T13:53:14 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
- split: eval_zero_shot
path: data/eval_zero_shot-*
- split: eval_normal
path: data/eval_normal-*
dataset_info:
features:
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sequence: int64
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sequence: int32
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splits:
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download_size: 61636686
dataset_size: 140490128
---
# Dataset Card for "nci_nq_t5_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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0.1537854224443435... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
zion84006/tencent_data_encodec | zion84006 | 2023-10-26T21:43:04Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T21:43:04Z | 2023-10-26T14:18:56.000Z | 2023-10-26T14:18:56 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
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path: data/valid-*
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path: data/test-*
dataset_info:
features:
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sequence: int64
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sequence: int64
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sequence: int64
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sequence: int64
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sequence: int64
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sequence: int64
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sequence: int64
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splits:
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num_bytes: 508453304
num_examples: 7620
download_size: 472185815
dataset_size: 19622961669
---
# Dataset Card for "tencent_data_encodec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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0.046291392296552... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pglo/NeurIPS_llm_challenge | pglo | 2023-10-26T14:38:20Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T14:38:20Z | 2023-10-26T14:27:18.000Z | 2023-10-26T14:27:18 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
emi429/humansleepproject-small-individuals | emi429 | 2023-10-26T18:18:10Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T18:18:10Z | 2023-10-26T14:31:15.000Z | 2023-10-26T14:31:15 | ---
dataset_info:
features:
- name: rr_intervals
dtype: int64
- name: sleep_stage
dtype: int64
- name: patient_id
dtype: int64
splits:
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num_bytes: 12096
num_examples: 504
- name: train
num_bytes: 49680
num_examples: 2070
download_size: 47116
dataset_size: 61776
---
# Dataset Card for "humansleepproject-small-individuals"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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yangcong/sd_config | yangcong | 2023-10-26T14:53:34Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T14:53:34Z | 2023-10-26T14:52:54.000Z | 2023-10-26T14:52:54 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
JWBickel/StrongsChunked_English_Phrase_Counts | JWBickel | 2023-10-26T15:53:22Z | 0 | 0 | null | [
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | 2023-10-26T15:53:22Z | 2023-10-26T15:14:50.000Z | 2023-10-26T15:14:50 | ---
language:
- en
size_categories:
- 10K<n<100K
---
These are KJV phrases and their counts, chunked by Strong's.
It's a CSV file, delimited by carats.
-------------------------------------
RowID ^ StrongsChunkedPhrase ^ Count
_____________________________________
Note that the first record is nonsense - it's just a space. Taking it out would have thrown off the Row IDs. Don't overlook it (but overlook my flaw). | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mesolitica/translated-router-switch-instruct | mesolitica | 2023-10-26T15:17:36Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T15:17:36Z | 2023-10-26T15:17:11.000Z | 2023-10-26T15:17:11 | Entry not found | [
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0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
JWBickel/KJVWordCounts | JWBickel | 2023-10-26T15:34:15Z | 0 | 0 | null | [
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | 2023-10-26T15:34:15Z | 2023-10-26T15:19:53.000Z | 2023-10-26T15:19:53 | ---
language:
- en
pretty_name: KJV Word Counts
size_categories:
- 10K<n<100K
---
# Dataset Card for KJVWordCounts
This is a simple list of every word and their total counts in the King James version of the Bible.
## Dataset Structure
It's a CSV file, delimited with a carat.
Word ^ Count
## Dataset Card Author
Jeremy Bickel
## Dataset Card Contact
JeremyWBickel@gmail.com | [
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0.03219321742653... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Maxlinn/dalle3-paper-images | Maxlinn | 2023-10-26T15:24:55Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T15:24:55Z | 2023-10-26T15:22:53.000Z | 2023-10-26T15:22:53 | # DALLE3 Paper Images
Extracted examples shown in dalle-3 paper without any compression: https://cdn.openai.com/papers/dall-e-3.pdf
Used https://pdfcandy.com/extract-images.html to extract images from dalle-3 paper.
Do not include three images that caption may not be correct. | [
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-0.47602546215057373,
0.262494802474975... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ParisNeo/lollms_aware_dataset | ParisNeo | 2023-10-27T20:43:37Z | 0 | 1 | null | [
"task_categories:conversational",
"language:en",
"license:apache-2.0",
"LoLLMs",
"QnA",
"region:us"
] | 2023-10-27T20:43:37Z | 2023-10-26T15:25:07.000Z | 2023-10-26T15:25:07 | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- LoLLMs
- QnA
---
# LoLLMs-QNA Dataset
## Dataset Description
The LoLLMs-QNA dataset was created by ParisNeo. The dataset is based on the documentation and knowledge base developed for LoLLMs. It aims to provide a comprehensive collection of questions and corresponding answers related to LoLLMs and its functionalities.
The dataset is structured as a JSON file, with each entry consisting of a question and its corresponding answer. The questions cover various aspects of LoLLMs, including installation, features, functionalities, system requirements, and comparisons with other similar tools. The answers provide detailed information and instructions to assist users in understanding and utilizing LoLLMs effectively.
It is important to note that the dataset also contains some generic thoughts and reflections about AI and its potential uses and threats. These thoughts represent ParisNeo's personal views and should not be considered as a universally accepted truth.
## Dataset Creation Process
The LoLLMs-QNA dataset was created using a two-step process outlined in ParisNeo's white paper titled "From Text to Interactive Knowledge: Building Chat-Style Databases for AI Training." The process involves extracting questions from raw text and then utilizing a vectorized version of the raw data along with an LLM to generate answers.
The raw text used for question extraction includes the documentation and knowledge base developed for LoLLMs, along with ParisNeo's personal insights and expertise in the field of AI. The questions were then manually crafted from this raw text to cover a wide range of topics related to LoLLMs.
To generate the answers, a vectorized version of the raw data was created, along with an LLM model trained on the specific domain of LoLLMs. The LLM model was then used to generate accurate and informative answers to the extracted questions.
## Used Models
This database was built using Database Maker on LoLLMs.
Database Maker implements the algorithm presented in the white paper [From Text to Interactive Knowledge: Building Chat-Style Databases for AI Training](https://huggingface.co/datasets/ParisNeo/lollms_aware_dataset/resolve/main/lollms_db_building_strategy.pdf)
To do the LLM tasks required to generate the questions and answers, I used the [airoboros-l2-70b-2.2.1](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1) model by [jondurbin](https://huggingface.co/jondurbin).
Updates to this database will come as LoLLMs documentation evolve and new functionalities are added constantly. So stay tuned.
## Dataset Format
The LoLLMs-QNA dataset is provided as a JSON file. Each entry in the dataset consists of a dictionary with two key-value pairs:
- "question": The question posed by the user.
- "answer": The corresponding answer to the question.
Example entry:
```
{
"question": "What are the features of Lollms-webui?",
"answer": "The features of Lollms-webui include:..."
}
```
## Usage and Disclaimer
The LoLLMs-QNA dataset is intended to be used for various tasks, including training AI models, developing chatbots, and assisting users in understanding and utilizing LoLLMs. However, it is important to note that the dataset reflects ParisNeo's personal vision and perspectives about AI and LoLLMs. The answers provided in the dataset should not be considered as universally accepted truths, but rather as ParisNeo's personal insights and instructions.
It is recommended to use the dataset in conjunction with other sources of information and to verify the accuracy and relevance of the answers provided. Users should exercise critical thinking and consider the specific context and requirements of their own applications and use cases.
## Acknowledgments
ParisNeo would like to express gratitude to the open-source community and contributors who have supported the development and improvement of LoLLMs. The dataset is provided as a contribution back to the community and aims to facilitate the understanding and utilization of LoLLMs.
## Special thanks
Special Thanks to [jondurbin](https://huggingface.co/jondurbin) for his advices and for providing the LLM that was used to build this dataset.
Also special thanks to [Tom Jobbins](https://huggingface.co/TheBloke) for quantizing the model that was used to build this database.
## Licence
Apache 2.0. | [
-0.40610742568969727,
-0.9484795928001404,
0.32503917813301086,
0.015689896419644356,
-0.08390024304389954,
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0.0137354210... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
anlp/anno_augmented | anlp | 2023-10-26T17:33:41Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T17:33:41Z | 2023-10-26T15:31:37.000Z | 2023-10-26T15:31:37 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: sentences
sequence: string
- name: ner_tags
sequence: string
splits:
- name: train
num_bytes: 1227934
num_examples: 247
download_size: 0
dataset_size: 1227934
---
# Dataset Card for "anno_augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7647762894630432,
-0.5203880071640015,
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-0.5811387300491333,
0.000765057862736... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Kishore05/Kan | Kishore05 | 2023-10-26T15:57:28Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T15:57:28Z | 2023-10-26T15:57:25.000Z | 2023-10-26T15:57:25 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 19721.78947368421
num_examples: 17
- name: validation
num_bytes: 2320.2105263157896
num_examples: 2
download_size: 25309
dataset_size: 22042.0
---
# Dataset Card for "Kan"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.4221488833427429,
-0.3241894841194153,
0.31893211603164673,
0.24686606228351593,
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0.16511520743370056,
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0.5146880745887756,
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-0.9568521976470947,
-0.5610563158988953,
-0.19137316942214966... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
laurievb/open-lid-dataset | laurievb | 2023-11-10T10:12:56Z | 0 | 1 | null | [
"task_categories:text-classification",
"size_categories:100M<n<1B",
"license:other",
"region:us"
] | 2023-11-10T10:12:56Z | 2023-10-26T16:00:52.000Z | 2023-10-26T16:00:52 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: language
dtype:
class_label:
names:
'0': plt_Latn
'1': sun_Latn
'2': ukr_Cyrl
'3': spa_Latn
'4': por_Latn
'5': mya_Mymr
'6': mkd_Cyrl
'7': war_Latn
'8': nso_Latn
'9': wol_Latn
'10': kam_Latn
'11': mal_Mlym
'12': gle_Latn
'13': ayr_Latn
'14': rus_Cyrl
'15': pbt_Arab
'16': pag_Latn
'17': twi_Latn
'18': als_Latn
'19': lit_Latn
'20': amh_Ethi
'21': tur_Latn
'22': tel_Telu
'23': vec_Latn
'24': zsm_Latn
'25': ckb_Arab
'26': tgk_Cyrl
'27': tha_Thai
'28': hye_Armn
'29': deu_Latn
'30': tat_Cyrl
'31': swh_Latn
'32': kac_Latn
'33': tuk_Latn
'34': lvs_Latn
'35': tso_Latn
'36': fao_Latn
'37': tpi_Latn
'38': umb_Latn
'39': mlt_Latn
'40': cym_Latn
'41': ben_Beng
'42': hat_Latn
'43': ron_Latn
'44': tir_Ethi
'45': ewe_Latn
'46': ind_Latn
'47': snd_Arab
'48': nld_Latn
'49': urd_Arab
'50': vie_Latn
'51': mar_Deva
'52': fra_Latn
'53': lug_Latn
'54': pol_Latn
'55': ban_Latn
'56': est_Latn
'57': srp_Cyrl
'58': kin_Latn
'59': nno_Latn
'60': fur_Latn
'61': kmr_Latn
'62': bho_Deva
'63': fin_Latn
'64': mri_Latn
'65': ilo_Latn
'66': fij_Latn
'67': slk_Latn
'68': knc_Arab
'69': guj_Gujr
'70': kor_Hang
'71': tum_Latn
'72': kab_Latn
'73': afr_Latn
'74': eng_Latn
'75': acq_Arab
'76': som_Latn
'77': tgl_Latn
'78': epo_Latn
'79': bjn_Arab
'80': mni_Beng
'81': sot_Latn
'82': nob_Latn
'83': kat_Geor
'84': ory_Orya
'85': arb_Arab
'86': heb_Hebr
'87': ibo_Latn
'88': asm_Beng
'89': uzn_Latn
'90': sna_Latn
'91': mos_Latn
'92': fuv_Latn
'93': hne_Deva
'94': apc_Arab
'95': hun_Latn
'96': ita_Latn
'97': bem_Latn
'98': slv_Latn
'99': ssw_Latn
'100': szl_Latn
'101': nya_Latn
'102': kir_Cyrl
'103': hrv_Latn
'104': pap_Latn
'105': kik_Latn
'106': knc_Latn
'107': lmo_Latn
'108': hau_Latn
'109': eus_Latn
'110': ltz_Latn
'111': grn_Latn
'112': lus_Latn
'113': taq_Latn
'114': scn_Latn
'115': kmb_Latn
'116': azj_Latn
'117': isl_Latn
'118': swe_Latn
'119': uig_Arab
'120': jpn_Jpan
'121': sag_Latn
'122': xho_Latn
'123': ast_Latn
'124': kan_Knda
'125': sin_Sinh
'126': acm_Arab
'127': tzm_Tfng
'128': dan_Latn
'129': zho_Hant
'130': zho_Hans
'131': pes_Arab
'132': fon_Latn
'133': tam_Taml
'134': yor_Latn
'135': run_Latn
'136': arz_Arab
'137': awa_Deva
'138': pan_Guru
'139': gaz_Latn
'140': lao_Laoo
'141': bos_Latn
'142': ces_Latn
'143': bam_Latn
'144': crh_Latn
'145': ltg_Latn
'146': bul_Cyrl
'147': gla_Latn
'148': ell_Grek
'149': prs_Arab
'150': smo_Latn
'151': ajp_Arab
'152': tsn_Latn
'153': bak_Cyrl
'154': srd_Latn
'155': ace_Arab
'156': kas_Arab
'157': lua_Latn
'158': taq_Tfng
'159': jav_Latn
'160': cat_Latn
'161': kon_Latn
'162': hin_Deva
'163': lin_Latn
'164': khk_Cyrl
'165': cjk_Latn
'166': mag_Deva
'167': dik_Latn
'168': bug_Latn
'169': bjn_Latn
'170': yue_Hant
'171': zul_Latn
'172': npi_Deva
'173': kas_Deva
'174': dzo_Tibt
'175': ary_Arab
'176': bel_Cyrl
'177': kbp_Latn
'178': khm_Khmr
'179': ace_Latn
'180': nus_Latn
'181': ceb_Latn
'182': mai_Deva
'183': san_Deva
'184': dyu_Latn
'185': quy_Latn
'186': lim_Latn
'187': min_Latn
'188': oci_Latn
'189': kaz_Cyrl
'190': luo_Latn
'191': sat_Olck
'192': ydd_Hebr
'193': shn_Mymr
'194': ars_Arab
'195': lij_Latn
'196': aeb_Arab
'197': bod_Tibt
'198': glg_Latn
'199': kea_Latn
'200': azb_Arab
- name: dataset_source
dtype: string
splits:
- name: train
num_bytes: 21749592609
num_examples: 118296182
download_size: 16568412828
dataset_size: 21749592609
license: other
task_categories:
- text-classification
size_categories:
- 100M<n<1B
---
# Dataset Card for "open-lid-dataset"
## Dataset Description
- **Repository:** [https://github.com/laurieburchell/open-lid-dataset]()
- **Paper:** [An Open Dataset and Model for Language Identification](https://aclanthology.org/2023.acl-short.75/)
- **Point of Contact:** laurie.burchell AT ed.ac.uk
### Dataset Summary
The OpenLID dataset covers 201 languages and is designed for training language identification models. The majority of the source datasets were derived from news sites, Wikipedia, or religious text, though some come from other domains (e.g. transcribed conversations, literature, or social media). A sample of each language in each source was manually audited to check it was in the attested language (see [the paper](https://aclanthology.org/2023.acl-short.75/)) for full details.
### Supported tasks
This dataset is intended for training high-coverage language identification models (e.g. [OpenLID](https://huggingface.co/laurievb/OpenLID)). It is compatible with the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) evaluation benchmark.
### Languages
There are 201 languages included in the dataset with varying amounts of data: the largest class (English) contains 7.5 million lines of data, and the smallest (South Azerbaijani) contains 532 lines of data. The mean number of lines per language is 602,812. A full breakdown of lines of data per language is available [on the repo](https://github.com/laurieburchell/open-lid-dataset/blob/main/languages.md).
## Dataset Structure
### Data Instances
Each entry in the dataset consists of a line of data, a language label included script information, and a tag indicating the source.
```json
{
"text": "¿Serás exaltada hasta el cielo?",
"language": "spa_Latn",
"dataset_source": "lti"
}
```
### Data Splits
Only a train split is provided. The dataset is designed to be compatible with the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) evaluation benchmark.
## Dataset Creation
### Curation Rationale
Recent work has found that existing language identification algorithms perform poorly in practice compared to test performance. The problem is particularly acute for low-resource languages: [Kreutzer et al. (2022)](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00447/109285/Quality-at-a-Glance-An-Audit-of-Web-Crawled) found a positive Spearman rank correlation between quality of data and size of language for all of the \ac{lid}-filtered multilingual datasets they studied. In addition, for a significant fraction of the language corpora they studied, less than half of the sentences were in the correct language. They point out that such low-quality data not only leads to poor performance in downstream tasks, but that it also contributes to `representation washing', where the community is given a false view of the actual progress of low-resource natural language processing.
There are several open language identification models offering quick classification and high language coverage (e.g. CLD3, No Language Left Behind). However, to the best of our knowledge, none of the commonly-used scalable language identificaiton systems make their training data public.
This dataset aims to address that gap by curating and combining sources of open training data for language identification and by auditing a sample of all languages in each source to check reliability.
### Source Data
The majority of the source datasets were derived from news sites, Wikipedia, or religious text, though some come from other domains (e.g. transcribed conversations, literature, or social media). We provide a full list at the end of this model card along with the licensing information for each source.
#### Initial Data Collection and Normalisation
Our initial aim was to cover the same languages present in the FLORES-200 Evaluation Benchmark so that we could use this dataset for evaluation. However, during the curation process, we decided to exclude three languages. Firstly, though Akan and Twi are both included as separate languages in FLORES-200, Akan is actually a macrolanguage covering a language continuum which includes Twi. Given the other languages in FLORES-200 are individual languages, we decided to exclude Akan. Secondly, FLORES-200 includes Modern Standard Arabic (MSA) written in Latin script. It is true that Arabic dialects are often written in Latin characters in informal situations (e.g. social media). However, MSA is a form of standardised Arabic which is not usually used in informal situations. Since we could not any find naturally-occurring training data, we excluded MSA from the dataset. Finally, we excluded Minangkabau in Arabic script because it is now rarely written this way, making it difficult to find useful training data.
The first step in our manual audit was to check and standardise language labels, as these are often inconsistent or idiosyncratic. We chose to copy the language codes in FLORES-200 and reassign macrolanguage or ambiguous language codes in the data sources we found to the dominant individual language. Whilst this resulted in more useful data for some languages, for other languages we had to be more conservative. For example, we originally reassigned text labelled as the macrolanguage Malay (msa_Latn) to Standard Malay, but this led to a large drop in performance as the former covers a very diverse set of languages.
Two of the authors then carried out a manual audit of a random sample of all data sources and languages: one a native Bulgarian speaker (able to read Cyrillic and Latin scripts and Chinese characters), and the other a native English speaker (able to read Latin, Arabic and Hebrew scripts). For languages we knew, we checked the language was what we expected. For unfamiliar languages in a script we could read, we compared the sample to the Universal Declaration of Human Rights or failing that, to a sample of text on Wikipedia. We compared features of the text which are common in previous language identification algorithms and could be identified easily by humans: similar diacritics, word lengths, common words, loan words matching the right cultural background, similar suffixes and prefixes, and vowel/consonant patterns. For scripts we could not read, we checked that all lines of the sample matched the script in the Universal Declaration of Human Rights.
We kept preprocessing minimal so that the process was as language agnostic as possible. We used the scripts provided with Moses to remove non-printing characters and detokenise the data where necessary. We then filtered the data so that each line contained at least one character in the expected script (as defined by Perl) to allow for borrowings. Finally, we sampled proportionally to $ p_l^{0.3} $, where $ p_l $ is the fraction of lines in the dataset which are in language $ l $. This aims to ameliorate class skew issues.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset covers a number of low-resourced languages. This makes it a potentially useful resource, but due to the limited amount of data and domains, care must be taken not to overclaim performance or coverage.
### Discussion of Biases
Our work aims to broaden natural language processing coverage by allowing practitioners to identify relevant data in more languages. However, we note that language identification is inherently a normative activity that risks excluding minority dialects, scripts, or entire microlanguages from a macrolanguage. Choosing which languages to cover may reinforce power imbalances, as only some groups gain access to language processing technologies.
In addition, errors in language identification can have a significant impact on downstream performance, particularly (as is often the case) when a system is used as a `black box'. The performance of our classifier is not equal across languages which could lead to worse downstream performance for particular groups. We mitigate this by providing metrics by class.
## Additional information
The dataset was curated from the sources listed below by Laurie Burchell and Nikolay Bogoychev.
### Licensing Information
License considerations for each source are given below. Open use for non-commercial purposes is covered by all licences.
If you view any part of this dataset as a violation of intellectual property rights, please let us know and we will remove it.
| Source | Description | License |
|---|---|---|
|[Arabic Dialects Dataset](https://www.lancaster.ac.uk/staff/elhaj/corpora.html)| Dataset of Arabic dialects for Gulf, Egyptian, Levantine, and Tunisian Arabic dialects plus MSA|No explicit license; website describes data as "some free and useful Arabic corpora that I have created for researchers working on Arabic Natural Language Processing, Corpus and Computational Linguistics."|
|[BLTR](https://github.com/shashwatup9k/bho-resources)|Monolingual Bhojpuri corpus|[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)|
|[Global Voices](https://opus.nlpl.eu/GlobalVoices-v2015.php)|A parallel corpus of news stories from the web site Global Voices|The website for [Global Voices](https://globalvoices.org/) is licensed as [Creative Commons Attribution 3.0](https://creativecommons.org/licenses/by/3.0/). There is no explicit additional license accompanying the dataset.|
|[Guaraní Parallel Set](https://github.com/sgongora27/giossa-gongora-guarani-2021)|Parallel Guaraní-Spanish news corpus sourced from Paraguyan websites|No explicit license|
|[HKCanCor](https://github.com/fcbond/hkcancor)|Transcribed conversations in Hong Kong Cantonese|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)|
|[IADD](https://github.com/JihadZa/IADD)|Arabic dialect identification dataset covering 5 regions (Maghrebi, Levantine, Egypt, Iraq, and Gulf) and 9 countries (Algeria, Morocco, Tunisia, Palestine, Jordan, Syria, Lebanon, Egypt and Iraq). It is created from five corpora: [DART](http://qufaculty.qu.edu.qa/telsay), [SHAMI](https://github.com/GU-CLASP/shami-corpus), [TSAC](https://github.com/fbougares/TSAC), [PADIC](https://sourceforge.net/projects/padic/), and [AOC](https://www.cs.jhu.edu/data-archive/AOC-2010/). | Multiple licenses: [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) (SHAMI); [GNU Lesser General Public License v3.0](https://github.com/fbougares/TSAC/blob/master/LICENSE) (TSAC); [GNU General Public License v3](https://www.gnu.org/licenses/gpl-3.0.en.html) (PADIC). DART and AOC had no explicit license.|
|[Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download)|A collection of corpora in different languages with an identical format.|The [Terms of Usage](https://wortschatz.uni-leipzig.de/en/usage) states "Permission for use is granted free of charge solely for non-commercial personal and scientific purposes licensed under the [Creative Commons License CC BY-NC](https://creativecommons.org/licenses/by-nc/4.0/)."|
|[LTI](https://www.cs.cmu.edu/~ralf/langid.html)|Training data for language identification|From the README: "With the exception of the contents of the Europarl/, ProjectGutenberg/, and PublicDomain/ directories, all code and text in this corpus are copyrighted. However, they may be redistributed under the terms of various Creative Commons licenses and the GNU GPL. Copying the unmodified archive noncommercially is permitted by all of the licenses. For commercial redistribution or redistribution of modified versions, please consult the individual licenses."|
|[MADAR Shared Task 2019, subtask 1](https://camel.abudhabi.nyu.edu/madar-shared-task-2019/)|Dialectal Arabic in the travel domain|The MADAR Corpus has a custom license, the text of which can be found in this repo.|
|[EM corpus](http://lepage-lab.ips.waseda.ac.jp/en/projects/meiteilon-manipuri-language-resources/)|Parallel Manipuri-English sentences crawled from [The Sangai Express](https://www.thesangaiexpress.com/)|[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)|
|[MIZAN](https://github.com/omidkashefi/Mizan)|Parallel Persian-English corpus from literature domain|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)|
|[MT560 v1](https://opus.nlpl.eu/MT560.php)|A machine translation dataset for over 500 languages to English. We have filtered out data from OPUS-100, Europarl, Open Subtitles, Paracrawl, Wikimedia, Wikimatrix, Wikititles, and Common Crawl due to issues with the fidelity of the language labels. |[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)|
|[NLLB Seed](https://github.com/facebookresearch/flores/blob/main/nllb_seed/README.md)|Around 6000 sentences in 39 languages sampled from Wikipedia, intended to cover languages lacking training data.|[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)|
|[SETIMES](https://opus.nlpl.eu/SETIMES.php)|A parallel corpus of news articles in the Balkan languages|[CC-BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/)|
|[Tatoeba](https://opus.nlpl.eu/Tatoeba.php)|Collaborative sentence translations|[CC BY 2.0 FR](https://creativecommons.org/licenses/by/2.0/fr/)|
|[Tehran English-Persian parallel corpus (TEP)](https://opus.nlpl.eu/TEP.php)|Parallel Persian-English sentences sourced from subtitles|[GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html)|
|[Turkic Interlingua (TIL) Corpus](https://github.com/turkic-interlingua/til-mt)|A large-scale parallel corpus combining most of the public datasets for 22 Turkic languages|[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)|
|[WiLI-2018](https://zenodo.org/record/841984)|Wikipedia language identification benchmark containing 235K paragraphs of 235 languages|[Open Data Commons Open Database License (ODbL) v1.0](https://opendatacommons.org/licenses/odbl/1-0/)|
|[XL-Sum](https://github.com/csebuetnlp/xl-sum)|Summarisation dataset covering 44 languages, sourced from BBC News|[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)|
### Citation Information
If you use this dataset, please cite all the authors [in the citation file](https://github.com/laurieburchell/open-lid-dataset/blob/main/citations.bib) who compiled the source datasets, plus the OpenLID paper:
```bibtex
@inproceedings{burchell-etal-2023-open,
title = "An Open Dataset and Model for Language Identification",
author = "Burchell, Laurie and
Birch, Alexandra and
Bogoychev, Nikolay and
Heafield, Kenneth",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.75",
doi = "10.18653/v1/2023.acl-short.75",
pages = "865--879",
abstract = "Language identification (LID) is a fundamental step in many natural language processing pipelines. However, current LID systems are far from perfect, particularly on lower-resource languages. We present a LID model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033{\%} across 201 languages, outperforming previous work. We achieve this by training on a curated dataset of monolingual data, which we audit manually to ensure reliability. We make both the model and the dataset available to the research community. Finally, we carry out detailed analysis into our model{'}s performance, both in comparison to existing open models and by language class.",
}
```
### Contributions
Thanks to @hac541309 and @davanstrien for adding this dataset. | [
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royson/train_splits_helm | royson | 2023-10-26T16:27:29Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-26T16:27:29Z | 2023-10-26T16:17:46.000Z | 2023-10-26T16:17:46 | ---
license: apache-2.0
---
Contains the following train split from datasets in [helm](https://github.com/stanford-crfm/helm):
- big bench
- mmlu
- TruthfulQA
- cnn/dm
- gsm
- bbq
- boolq
- NarrativeQA
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- math
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Each prompt has <= 5 in-context samples along with a sample, all of which from the train set of the respective datasets. | [
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sabman/llmrouter | sabman | 2023-11-08T19:32:26Z | 0 | 1 | null | [
"license:cc-by-4.0",
"region:us"
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license: cc-by-4.0
---
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vishnusr/code_searchnet_reduced | vishnusr | 2023-10-26T16:57:30Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T16:57:30Z | 2023-10-26T16:57:00.000Z | 2023-10-26T16:57:00 | ---
configs:
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path: data/train-*
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download_size: 440777
dataset_size: 992068
---
# Dataset Card for "code_searchnet_reduced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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CJWeiss/lcr | CJWeiss | 2023-10-26T17:10:08Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T17:10:08Z | 2023-10-26T17:09:59.000Z | 2023-10-26T17:09:59 | ---
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---
# Dataset Card for "lcr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Aliquip/sd-prompttastic | Aliquip | 2023-11-02T19:06:03Z | 0 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2023-11-02T19:06:03Z | 2023-10-26T17:19:38.000Z | 2023-10-26T17:19:38 | ---
license: cc0-1.0
---
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Kishore05/kan100 | Kishore05 | 2023-10-26T17:40:48Z | 0 | 0 | null | [
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Th3ro/nRadioWaveDataset | Th3ro | 2023-10-26T18:15:07Z | 0 | 0 | null | [
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"license:wtfpl",
"region:us"
] | 2023-10-26T18:15:07Z | 2023-10-26T18:01:47.000Z | 2023-10-26T18:01:47 | ---
license: wtfpl
task_categories:
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language:
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size_categories:
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--- | [
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SummerSigh/AncientMNIST | SummerSigh | 2023-10-26T18:06:58Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T18:06:58Z | 2023-10-26T18:03:37.000Z | 2023-10-26T18:03:37 | ---
configs:
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path: data/train-*
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splits:
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---
# Dataset Card for "AncientMNIST"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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vira-chatbot/vira-intents-mod | vira-chatbot | 2023-10-31T03:54:15Z | 0 | 0 | null | [
"region:us"
] | 2023-10-31T03:54:15Z | 2023-10-26T18:04:54.000Z | 2023-10-26T18:04:54 | ---
dataset_info:
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---
# Dataset Card for "vira-intents-mod"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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varshil27/1mg-train-data-LLama2-formatted | varshil27 | 2023-10-26T18:30:30Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-10-26T18:30:30Z | 2023-10-26T18:29:24.000Z | 2023-10-26T18:29:24 | ---
license: mit
---
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BossBossNJb/cifar100_dataset_th_en | BossBossNJb | 2023-10-28T09:14:29Z | 0 | 0 | null | [
"license:apache-2.0",
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] | 2023-10-28T09:14:29Z | 2023-10-26T18:35:54.000Z | 2023-10-26T18:35:54 | ---
license: apache-2.0
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Kateway/Thursday | Kateway | 2023-10-26T18:42:34Z | 0 | 0 | null | [
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JasperHG90/neurips-efficiency-challenge-2023 | JasperHG90 | 2023-10-27T05:09:24Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-27T05:09:24Z | 2023-10-26T18:46:50.000Z | 2023-10-26T18:46:50 | ---
license: apache-2.0
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fia24/filtered_lemma41kV0.0.1 | fia24 | 2023-10-26T18:59:05Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T18:59:05Z | 2023-10-26T18:58:59.000Z | 2023-10-26T18:58:59 | ---
configs:
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data_files:
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dataset_info:
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---
# Dataset Card for "filtered_lemma41kV0.0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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leffff/south-park-character-png-dataset-old | leffff | 2023-10-26T19:12:34Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-10-26T19:12:34Z | 2023-10-26T19:09:39.000Z | 2023-10-26T19:09:39 | ---
license: mit
---
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jessica-ecosia/gpdr-dpr-dataset | jessica-ecosia | 2023-10-26T20:27:22Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T20:27:22Z | 2023-10-26T20:27:04.000Z | 2023-10-26T20:27:04 | ---
dataset_info:
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---
# Dataset Card for "gpdr-dpr-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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DuckyPolice/Mark-Rober-Voice | DuckyPolice | 2023-10-27T20:14:24Z | 0 | 1 | null | [
"language:en",
"license:wtfpl",
"region:us"
] | 2023-10-27T20:14:24Z | 2023-10-26T20:40:52.000Z | 2023-10-26T20:40:52 | ---
license: wtfpl
language:
- en
pretty_name: Mark Rober Voice Dataset
---
this is where i put mark rober voice clips | [
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---
# Dataset Card for "es-2610-no-demoji-m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Cam1234K/caze | Cam1234K | 2023-10-26T21:01:03Z | 0 | 0 | null | [
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MaxReynolds/TestUpload | MaxReynolds | 2023-10-26T21:10:24Z | 0 | 0 | null | [
"region:us"
] | 2023-10-26T21:10:24Z | 2023-10-26T21:10:22.000Z | 2023-10-26T21:10:22 | ---
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---
# Dataset Card for "TestUpload"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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faizalnf1800/scifi-book | faizalnf1800 | 2023-10-27T11:43:33Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T11:43:33Z | 2023-10-26T21:49:13.000Z | 2023-10-26T21:49:13 | ---
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# Dataset Card for "scifi-book"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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shanchen/OncQA | shanchen | 2023-10-30T14:24:19Z | 0 | 1 | null | [
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"license:cc-by-sa-4.0",
"medical",
"arxiv:2310.17703",
"region:us"
] | 2023-10-30T14:24:19Z | 2023-10-26T21:56:33.000Z | 2023-10-26T21:56:33 | ---
license: cc-by-sa-4.0
task_categories:
- conversational
- text2text-generation
language:
- en
tags:
- medical
---
## OncQA: The Impact of Using an AI Chatbot to Respond to Patient Messages
### Importance
Documentation burden is a major factor contributing to clinician burnout, which is increasing across the country and threatens our capacity to provide patient care in the U.S. While AI chatbots show potential in reducing this burden by aiding in documentation and are being incorporated into electronic health record systems, their influence on clinical decision-making remains understudied for this purpose.
### Objective
Investigate the acceptability, safety, and potential human factors issues when utilizing an AI-powered chatbot to draft responses to patients' inquiries.
### Design
- A 2-stage cross-sectional study was designed around 100 synthetic cancer patient scenarios couples with patient messages.
- These questions emulate realistic oncology scenarios.
- **Stage 1: Manual Reponse**: Six oncologists were randomly allocated 26 questions for response.
- **Stage 2: AI-Assisted Response**: The same oncologists received 26 new questions, alongside GPT-4 generated responses for editing.
- Informed consent was obtained.
- Participants were blinded to the source of the drafts.
- Surveys were undertaken for every scenario/response.
### About this repo
The dataset shows here is the complete stage2 parsed data with all physician edits.
If you wish to see the full data for stage1 and others please visit https://github.com/AIM-Harvard/OncQA/
### Settings
This research was conducted at the Brigham and Women’s Hospital, Boston, MA in 2023.
### Participants
Six board-certified oncologists participated.
### Intervention
Employment of GPT-4, an AI chatbot, for drafting responses to patient inquiries.
### Main Outcomes & Measures
- Evaluate the impact and utility of an AI chatbot in assisting responses to patient messages.
- Impact was determined by comparing response length and readability, using the Flesch reading ease score, and content.
- Utility was ascertained through physician feedback on surveys regarding acceptability, potential harm, and efficiency of chatbot-crafted drafts.
### Results
- On average, manual responses were more concise than those by GPT-4 or AI-assisted (34 vs. 169 vs. 160 words, p<0.001).
- Manual responses were more readable than GPT-4 or AI-assisted messages (Flesch score 67 vs. 45 vs. 46, p<0.001).
- About 58% of GPT-4 drafts were immediately acceptable, with 82% posing a low risk of harm.
- Utilizing the GPT-4 draft enhanced documentation efficiency in 77% of replies.
- Surprisingly, 31% of GPT-4 responses were perceived to be human-written, despite being AI-generated.
- 7.7% of survey responses felt unedited GPT-4 drafts could lead to severe harm or death.
- Among 56 dual-annotated responses, annotation agreement was low for manual responses (Cohen's kappa 0.10), but improved for AI-assisted responses (Cohen's kappa 0.52).
- AI-assistance led to differences in clinical content in the responses (p=0.001).
- Manual replies were more likely to advise direct clinical actions, while GPT-4 drafts often provided educational and self-management suggestions.
- AI-aided replies closely mirrored GPT-4 drafts but introduced some direct clinical actions.
### Conclusions & Relevance
AI-generated chatbot responses, while lengthier and less accessible, were overall safe and improved efficiency. AI-assistance altered the nature of physician feedback and reduced variability. AI chatbots are a promising avenue to address physician burnout and could improve patient care, however interactions between humans and AI might affect clinical decisions in unexpected ways. Addressing these interactions is vital for the safe incorporation of such technologies.
**Note**: It's imperative to delve deeper into human-AI interactions and their potential impact on outcomes.
# Citation:
```
@misc{chen2023impact,
title={The impact of using an AI chatbot to respond to patient messages},
author={Shan Chen and Marco Guevara and Shalini Moningi and Frank Hoebers and Hesham Elhalawani and Benjamin H. Kann and Fallon E. Chipidza and Jonathan Leeman and Hugo J. W. L. Aerts and Timothy Miller and Guergana K. Savova and Raymond H. Mak and Maryam Lustberg and Majid Afshar and Danielle S. Bitterman},
year={2023},
eprint={2310.17703},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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ajdesh2000/combined_train_dataset | ajdesh2000 | 2023-10-27T04:51:13Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T04:51:13Z | 2023-10-26T22:12:50.000Z | 2023-10-26T22:12:50 | ---
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---
# Dataset Card for "combined_train_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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marcus2000/new_sentiment | marcus2000 | 2023-10-27T11:45:52Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T11:45:52Z | 2023-10-26T22:21:23.000Z | 2023-10-26T22:21:23 | ---
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# Dataset Card for "new_sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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CLMBR/mSCAN | CLMBR | 2023-09-05T05:27:21Z | 0 | 1 | null | [
"license:bsd",
"region:us"
] | 2023-09-05T05:27:21Z | 2023-10-26T23:03:04.000Z | 2023-10-26T23:03:04 | ---
license: bsd
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thanhduycao/soict_train_dataset_filter | thanhduycao | 2023-10-27T01:02:51Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T01:02:51Z | 2023-10-27T01:01:46.000Z | 2023-10-27T01:01:46 | ---
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---
# Dataset Card for "soict_train_dataset_filter"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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NissrineH/AIS-Australian_Institute_of_Sports | NissrineH | 2023-10-27T01:53:38Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T01:53:38Z | 2023-10-27T01:38:06.000Z | 2023-10-27T01:38:06 | ---
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rmadrig/docubot0.3 | rmadrig | 2023-10-27T02:03:58Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T02:03:58Z | 2023-10-27T02:03:11.000Z | 2023-10-27T02:03:11 | Entry not found | [
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hudssntao/prompt_learning_paper | hudssntao | 2023-10-27T02:12:10Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T02:12:10Z | 2023-10-27T02:07:27.000Z | 2023-10-27T02:07:27 | ---
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---
# Dataset Card for "prompt_learning_paper"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hudssntao/test_dataset | hudssntao | 2023-10-27T03:27:15Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T03:27:15Z | 2023-10-27T02:43:19.000Z | 2023-10-27T02:43:19 | ---
dataset_info:
features:
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splits:
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num_examples: 2
download_size: 1227
dataset_size: 40
configs:
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data_files:
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---
# Dataset Card for "test_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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zhongshupeng/dataset_4090_2 | zhongshupeng | 2023-10-27T03:01:33Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T03:01:33Z | 2023-10-27T02:47:49.000Z | 2023-10-27T02:47:49 | # Disclaimer:
this dataset is curated for NeurIPS 2023 LLM efficiency challange, and currently work in progress. Please use at your own risk.
# Data composition:
All data were derived from the training set portion of the open source dataset.
**gsm2k_dolly15k_cnnadd6k_mmlulog1.7w_bbqabc8k.json**：
-gsm8k_2000: https://huggingface.co/datasets/gsm8k
-dolly_15000: https://huggingface.co/datasets/databricks/databricks-dolly-15k
-cnn_dailymail_6000: https://huggingface.co/datasets/cnn_dailymail
-mmlu_17000: https://huggingface.co/datasets/cais/mmlu
-bbq_8000: https://huggingface.co/datasets/tasksource/bigbench
**lima_4kall.json**
-lima_1000: https://huggingface.co/datasets/GAIR/lima
-3000 of gsm8k_dolly15k_cnnadd8k_mmlulog1.7w_bbqabc8k.json: https://huggingface.co/datasets/zhongshupeng/dataset_4090_1 | [
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"region:us"
] | 2023-10-27T03:04:31Z | 2023-10-27T03:02:27.000Z | 2023-10-27T03:02:27 | # Disclaimer:
this dataset is curated for NeurIPS 2023 LLM efficiency challange, and currently work in progress. Please use at your own risk.
# Data composition:
All data were derived from the training set portion of the open source dataset.
**gsm2k_dolly12k_cnnadd4k_mmlulog1.7w_bbqabc8k.json**：
-gsm8k_2000: https://huggingface.co/datasets/gsm8k
-dolly_12000: https://huggingface.co/datasets/databricks/databricks-dolly-15k
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gaurav16/temples_dataset | gaurav16 | 2023-10-27T03:22:25Z | 0 | 1 | null | [
"task_categories:question-answering",
"size_categories:1M<n<10M",
"language:en",
"license:apache-2.0",
"art",
"region:us"
] | 2023-10-27T03:22:25Z | 2023-10-27T03:15:21.000Z | 2023-10-27T03:15:21 | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- art
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name: Indian Temple Destruction Dataset
## Dataset Details
### Dataset Description
The Indian Temple Destruction Dataset provides information about historical temples that were destroyed in the past in India, including details on the locations of these temples, the entities responsible for their destruction, and contact information for inquiries.
- **Curated by:** Gaurav Sinha
- **Funded by [optional]:** [Information Not Available]
- **Shared by [optional]:** [Information Not Available]
- **Language(s) (NLP):** English
- **License:** [Information Not Available]
### Dataset Sources [optional]
- **Repository:** [Link to the dataset repository]
- **Paper [optional]:** [Link to any associated research paper]
- **Demo [optional]:** [Link to a demo or usage example]
## Uses
### Direct Use
This dataset can be used for historical research, cultural preservation efforts, and to understand the history of temple destruction in India.
### Out-of-Scope Use
Misuse of this dataset for promoting hatred, violence, or discrimination is strictly out of scope.
## Dataset Structure
[Information Not Available]
## Dataset Creation
### Curation Rationale
The dataset was created to document the historical information about the destruction of temples in India for research, education, and preservation purposes. It includes data from books authored by Sir Sita Ram Goel and contributions by Gaurav Sinha.
### Source Data
#### Data Collection and Processing
The data for this dataset was collected from historical records, scholarly research, and reputable sources, including books authored by Sir Sita Ram Goel. It also includes contributions by Gaurav Sinha. The dataset was carefully compiled to provide accurate and valuable information.
#### Who are the source data producers?
The source data was produced by historians, researchers, and scholars, including Sir Sita Ram Goel, who documented the destruction of temples in India. Contributions by Gaurav Sinha are also part of the dataset.
### Annotations [optional]
[Information Not Available]
## Bias, Risks, and Limitations
This dataset may contain historical events that could be sensitive to some communities. It is essential to use this data responsibly and with cultural sensitivity.
### Recommendations
Users should exercise caution when using this dataset to ensure that it is used for educational and research purposes and not for promoting hatred or discrimination.
## Citation [optional]
**BibTeX:**
[Information Not Available]
**APA:**
[Information Not Available]
## Glossary [optional]
[Information Not Available]
## More Information [optional]
[Information Not Available]
## Dataset Card Authors [optional]
Gaurav Sinha
## Dataset Card Contact
For inquiries related to this dataset, please contact [Your Email Address]. | [
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hudssntao/test2 | hudssntao | 2023-10-30T05:11:50Z | 0 | 0 | null | [
"region:us"
] | 2023-10-30T05:11:50Z | 2023-10-27T03:32:52.000Z | 2023-10-27T03:32:52 | ---
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# Dataset Card for "test2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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GalacticV/Aria_3 | GalacticV | 2023-10-27T03:45:47Z | 0 | 0 | null | [
"license:openrail",
"region:us"
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license: openrail
---
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Deojoandco/capstone_hal_without_gold | Deojoandco | 2023-10-27T04:03:05Z | 0 | 0 | null | [
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# Dataset Card for "capstone_hal_without_gold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Deojoandco/capstone_hal_with_gold | Deojoandco | 2023-10-27T04:03:41Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T04:03:41Z | 2023-10-27T04:03:31.000Z | 2023-10-27T04:03:31 | ---
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# Dataset Card for "capstone_hal_with_gold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ycchen/oasst_lima_arc | ycchen | 2023-10-27T04:18:07Z | 0 | 0 | null | [
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# Dataset Card for "oasst_lima_arc"
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atmallen/quirky_math_bob_grader_last_1.0e_0.0p_finetuning | atmallen | 2023-10-27T04:33:48Z | 0 | 0 | null | [
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# Dataset Card for "quirky_math_bob_grader_last_1.0e_0.0p_finetuning"
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anonymouse03052002/kan-ds-mini | anonymouse03052002 | 2023-10-27T06:34:59Z | 0 | 0 | null | [
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# Dataset Card for "kan-ds-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ajdesh2000/exodus_combined_general_train_dataset_v2 | ajdesh2000 | 2023-10-27T05:15:33Z | 0 | 0 | null | [
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tianyang/repo_dedup_sep2023 | tianyang | 2023-10-27T05:26:00Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T05:26:00Z | 2023-10-27T05:21:30.000Z | 2023-10-27T05:21:30 | ---
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path: data/train-*
---
# Dataset Card for "repo_dedup_sep2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ayushtues/scalecrafter | ayushtues | 2023-10-27T06:35:59Z | 0 | 0 | null | [
"region:us"
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anlp/anno1_w_elimination | anlp | 2023-10-27T05:53:10Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T05:53:10Z | 2023-10-27T05:53:09.000Z | 2023-10-27T05:53:09 | ---
configs:
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features:
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splits:
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dataset_size: 1239484
---
# Dataset Card for "anno1_w_elimination"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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fia24/filtered_lemma41kV0.0.2 | fia24 | 2023-10-27T06:08:22Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T06:08:22Z | 2023-10-27T06:08:14.000Z | 2023-10-27T06:08:14 | ---
configs:
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---
# Dataset Card for "filtered_lemma41kV0.0.2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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anonymouse03052002/final | anonymouse03052002 | 2023-10-27T06:19:06Z | 0 | 0 | null | [
"region:us"
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Adminhuggingface/LORA_ONE_DATA | Adminhuggingface | 2023-10-27T06:18:33Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T06:18:33Z | 2023-10-27T06:18:32.000Z | 2023-10-27T06:18:32 | ---
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---
# Dataset Card for "LORA_ONE_DATA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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yuyijiong/Chinese_Paper_QA | yuyijiong | 2023-11-21T05:56:27Z | 0 | 4 | null | [
"size_categories:1K<n<10K",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-11-21T05:56:27Z | 2023-10-27T06:26:05.000Z | 2023-10-27T06:26:05 | ---
license: cc-by-nc-4.0
language:
- zh
size_categories:
- 1K<n<10K
---
# 中文论文问答数据集
* 来自知网的论文数据，版权受限，不能直接公开。下载后请勿上传到公开场合。
* 包括 为论文写摘要、基于论文内容的问答 两个任务。论文摘要任务已经迁移到[论文摘要数据集](https://huggingface.co/datasets/yuyijiong/Chinese_Paper_Abstract/settings)中。
## 改进版
* 此数据集中筛选出较长的论文，并为每篇论文设计多个任务，形成新数据集：[中文论文多任务数据集](https://huggingface.co/datasets/yuyijiong/Paper_mutli_QA_Chinese) | [
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Facico/test2 | Facico | 2023-10-27T06:48:29Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T06:48:29Z | 2023-10-27T06:33:47.000Z | 2023-10-27T06:33:47 | Entry not found | [
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Facico/test3 | Facico | 2023-10-27T06:49:10Z | 0 | 0 | null | [
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SergioSCA/Structures | SergioSCA | 2023-10-27T07:07:46Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-27T07:07:46Z | 2023-10-27T07:06:26.000Z | 2023-10-27T07:06:26 | ---
license: apache-2.0
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **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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sanak/bergains_conversatation | sanak | 2023-10-27T08:05:05Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-27T08:05:05Z | 2023-10-27T08:03:15.000Z | 2023-10-27T08:03:15 | ---
license: apache-2.0
---
| [
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anlp/relabel_SciERC | anlp | 2023-10-27T18:37:16Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T18:37:16Z | 2023-10-27T08:13:15.000Z | 2023-10-27T08:13:15 | ---
configs:
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data_files:
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path: data/train-*
dataset_info:
features:
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sequence: string
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sequence: string
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splits:
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num_bytes: 2267323
num_examples: 3238
download_size: 312123
dataset_size: 2267323
---
# Dataset Card for "relabel_SciERC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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anlp/sentence_w_elimination | anlp | 2023-10-27T08:43:53Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T08:43:53Z | 2023-10-27T08:22:29.000Z | 2023-10-27T08:22:29 | ---
configs:
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data_files:
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dataset_info:
features:
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sequence: string
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sequence: string
splits:
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num_bytes: 1201528
num_examples: 990
download_size: 244599
dataset_size: 1201528
---
# Dataset Card for "sentence_w_elimination"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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AudioDecBenchmark/superb_ks | AudioDecBenchmark | 2023-10-27T08:43:49Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T08:43:49Z | 2023-10-27T08:42:59.000Z | 2023-10-27T08:42:59 | ---
configs:
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data_files:
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path: data/original-*
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path: data/descript_audio_codec-*
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path: data/encodec_hf-*
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path: data/speech_tokenizer-*
dataset_info:
features:
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dtype:
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splits:
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num_examples: 3081
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num_examples: 3081
download_size: 544447448
dataset_size: 618061932.704
---
# Dataset Card for "superb_ks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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anlp/sentence_anno | anlp | 2023-10-27T08:49:02Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T08:49:02Z | 2023-10-27T08:49:00.000Z | 2023-10-27T08:49:00 | ---
configs:
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data_files:
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path: data/train-*
dataset_info:
features:
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sequence: string
splits:
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download_size: 244599
dataset_size: 1201528
---
# Dataset Card for "sentence_anno"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ManuBansal/33param_snp500_validationSet | ManuBansal | 2023-10-27T09:17:41Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T09:17:41Z | 2023-10-27T09:16:52.000Z | 2023-10-27T09:16:52 | Entry not found | [
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MrDontKnowWhatToDo/Intent_Recognition | MrDontKnowWhatToDo | 2023-10-27T11:01:55Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T11:01:55Z | 2023-10-27T09:19:22.000Z | 2023-10-27T09:19:22 | {
"intents": [
{
"tag": "busy",
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"व्यस्त हु",
"बिजी हु",
"बात नई कर सकता",
"व्यस्त",
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"पूरी तरह व्यस्त, कोई विश्राम नहीं",
"एक के बाद एक मीटिंगों में व्यस्त",
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"वापस कॉल करो",
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"कॉल करो",
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"थोड़ी देर बाद कॉल करो",
"अभी बात नई कर सकता",
"नहीं"
],
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]
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"हेल",
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"हई",
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"हाइ"
],
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]
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]
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"नई",
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]
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"tag": "miscellaneous question",
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"क्या लोन",
"मैंने कब लोन लिया",
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"किसका मेम",
"किस चीज का पैसा मेम"
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"ये कॉल आपने जो सोनाटा के साथ लोन लिया था उसके संबंध मे किया गया है । आपकी पिछली <due_installments> किश्त और <emi_amount> रुपए बकाया है।"
]
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{
"tag": "Agree to Pay",
"pattern": [
"ठीक है",
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"आजाओ",
"दे सकता हु",
"दे दूंगा",
"हा कर दूंगा",
"कर दूंगा",
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"हाँ",
"हा कर दूंगा",
"कर दूंगा",
"आज अजाऊँगा ब्रांच",
"ब्रांच मे अजाऊँगा",
"का आ जाऊंगा",
"जी",
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"ठीक है",
"हा कर सकते हैं",
"जी कर सकते हैं",
"आज नई हो पाएगा",
"आज नई हो सकता",
"आज बिजी हु",
"कल करता हु",
"आज मुश्किल है",
"आज नहीं हो पाएगा कल कर देंगे",
"नहीं हो पाएगा जी आज",
"कल देंगे कल"
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"कृपया आप <emi_amount> तैयार रखें हमारा एजेंट पैसे लेने के लिए आपके पास आएगा,धन्यवाद."
]
},
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"tag": "insist to pay tomorrow",
"pattern": [
"बाद मे देता हु",
"नहीं कर सकता",
"नई कर",
"नहीं",
"नहीं दे सकता",
"आज नहीं हो पाएगा",
"नहीं कर पाऊंगा",
"नई कर सकता",
"नई हो पाएगा",
"नई हो पाएगा",
"नहीं"
],
"response": [
"कृपया आप <emi_amount> तैयार रखें हमारा एजेंट पैसे लेने के लिए आपके पास आएगा,धन्यवाद."
]
}
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fia24/filtered_lemma41kV0.0.3 | fia24 | 2023-10-27T10:04:32Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T10:04:32Z | 2023-10-27T10:04:26.000Z | 2023-10-27T10:04:26 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
dataset_info:
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num_examples: 3558
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num_examples: 3557
download_size: 1196489
dataset_size: 2236466.0
---
# Dataset Card for "filtered_lemma41kV0.0.3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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makram93/accepted_pairs_50 | makram93 | 2023-10-27T12:18:50Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T12:18:50Z | 2023-10-27T10:15:10.000Z | 2023-10-27T10:15:10 | ---
dataset_info:
features:
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dtype: string
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dtype: string
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sequence: string
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dtype: string
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num_examples: 100
download_size: 78941
dataset_size: 88447.0623234648
configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "accepted_pairs_50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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makram93/rejected_pairs_50 | makram93 | 2023-10-27T12:18:53Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T12:18:53Z | 2023-10-27T10:15:13.000Z | 2023-10-27T10:15:13 | ---
dataset_info:
features:
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sequence: string
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dtype: string
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splits:
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num_examples: 100
download_size: 85583
dataset_size: 88447.0623234648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rejected_pairs_50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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0.103414706885814... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rohit901/nlp_proj_llm_hallucination | rohit901 | 2023-10-27T11:58:09Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T11:58:09Z | 2023-10-27T10:49:03.000Z | 2023-10-27T10:49:03 | ---
configs:
- config_name: default
data_files:
- split: gpt3_5
path: "gpt3_5_hallucination.json"
- split: gpt4
path: "gpt4_hallucination.json"
---
| [
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cellfabrik/algae | cellfabrik | 2023-10-27T11:10:28Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-27T11:10:28Z | 2023-10-27T11:08:53.000Z | 2023-10-27T11:08:53 | ---
license: apache-2.0
---
| [
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omgbobbyg/Zip-Code-to-Timezone | omgbobbyg | 2023-10-27T11:29:34Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T11:29:34Z | 2023-10-27T11:14:09.000Z | 2023-10-27T11:14:09 | ---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Zip Code to Timezone Offset Mapping
<!-- Provide a quick summary of the dataset. -->
This dataset maps Zip Codes and Postal Codes for the USA and Canada to the relevant timezone offset.
## Dataset Details
### Dataset Description
In addition to providing a mapping from a Zip Code or Postal Code to timezone offset, it also contains the timezone offset for DST (if observed).
- **Curated by:** Bobby Gill, BlueLabel
### Acknowledgements
<!-- Provide the basic links for the dataset. -->
- **Based off the Work Here:** [https://www.kaggle.com/datasets/joeleichter/us-zip-codes-with-lat-and-long]
| [
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capjamesg/taylor-swift-records | capjamesg | 2023-10-27T11:37:16Z | 0 | 1 | null | [
"license:mit",
"region:us"
] | 2023-10-27T11:37:16Z | 2023-10-27T11:34:24.000Z | 2023-10-27T11:34:24 | ---
license: mit
---
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qgyd2021/writing_a_novel | qgyd2021 | 2023-10-27T12:00:25Z | 0 | 0 | null | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100M<n<1B",
"language:zh",
"license:apache-2.0",
"region:us"
] | 2023-10-27T12:00:25Z | 2023-10-27T11:47:10.000Z | 2023-10-27T11:47:10 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
- text2text-generation
language:
- zh
size_categories:
- 100M<n<1B
---
## Writing a Novel
写小说：
（1）下载小说。
（2）利用大模型将小说重新整理成一句一行。
（3）利用大模型对小说的各段落写一个摘要。
（4）摘要做 query，小说内容做 response，训练模型。
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philschmid/mt-bench | philschmid | 2023-10-27T12:27:18Z | 0 | 1 | null | [
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AudreyYZY/demo | AudreyYZY | 2023-10-27T13:31:25Z | 0 | 0 | null | [
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makram93/rejected_pairs_base | makram93 | 2023-10-27T15:03:14Z | 0 | 0 | null | [
"region:us"
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path: data/train-*
---
# Dataset Card for "rejected_pairs_base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5597395896911621,
-0.38346073031425476,
0.1865488588809967,
0.472183495759964,
-0.35733017325401306,
0.10772011429071426,
0.21217410266399384,
0.17023241519927979,
0.7932339310646057,
0.5946432948112488,
-0.790540874004364,
-0.7697962522506714,
-0.5360861420631409,
-0.09114068001508713,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mponty/web_crawl_docs | mponty | 2023-10-27T13:18:47Z | 0 | 0 | null | [
"region:us"
] | 2023-10-27T13:18:47Z | 2023-10-27T13:18:10.000Z | 2023-10-27T13:18:10 | ---
dataset_info:
features:
- name: text
dtype: string
- name: lang
dtype: string
- name: source
dtype: string
- name: url
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1157430740
num_examples: 87370
download_size: 492924255
dataset_size: 1157430740
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "web_crawl_docs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.6027529239654541,
-0.12610092759132385,
0.37284764647483826,
0.10937613993883133,
-0.21105289459228516,
-0.03408947214484215,
0.2258095145225525,
0.06755482405424118,
0.76572585105896,
0.19517964124679565,
-0.8539220094680786,
-1.0619927644729614,
-0.5854493379592896,
-0.092741869390010... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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