datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
sequence | task_categories
sequence | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
sequence |
|---|---|---|---|---|---|---|---|---|---|
ArtifactAI/odd_model_out_benchmark | ArtifactAI | 2023-12-13T21:08:30Z | 37 | 0 | [
"region:us"
] | null | 2023-12-05T23:31:17Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
splits:
- name: train
num_bytes: 162353
num_examples: 946
- name: test
num_bytes: 41365
num_examples: 237
download_size: 38710
dataset_size: 203718
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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Rijgersberg/ultrachat_10k_nl | Rijgersberg | 2023-12-15T15:26:58Z | 37 | 0 | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:nl",
"language:en",
"license:cc-by-nc-4.0",
"GEITje",
"region:us"
] | [
"conversational",
"text-generation"
] | 2023-12-09T01:05:26Z | ---
configs:
- config_name: default
data_files:
- split: test_sft
path: data/test_sft-*
- split: train_sft
path: data/train_sft-*
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages_nl
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: test_sft
num_bytes: 6296981
num_examples: 500
- name: train_sft
num_bytes: 120475850
num_examples: 9500
download_size: 65516955
dataset_size: 126772831
license: cc-by-nc-4.0
language:
- nl
- en
tags:
- GEITje
task_categories:
- conversational
- text-generation
size_categories:
- 10K<n<100K
pretty_name: Ultrachat 10k NL
---
# Dataset Card for "ultrachat_10k_nl"
A translated version of 10k randomly selected examples from [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k).
Automatically translated by GPT-3.5.
## More info
Read more about GEITje-chat, the datasets and the translation code in the [📄 README](https://github.com/Rijgersberg/GEITje/blob/main/README-en.md) on GitHub.
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adamjweintraut/bart-finetuned-eli5_lfqa_best_slice-256_2023-12-10_run | adamjweintraut | 2023-12-11T05:33:12Z | 37 | 0 | [
"region:us"
] | null | 2023-12-10T04:53:46Z | ---
dataset_info:
features:
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dtype: int64
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dtype: string
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splits:
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num_bytes: 8354389
num_examples: 1250
download_size: 5113622
dataset_size: 8354389
configs:
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data_files:
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path: data/train-*
---
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hf-vision/chest-xray-pneumonia | hf-vision | 2023-12-11T13:23:37Z | 37 | 0 | [
"license:cc-by-4.0",
"region:us"
] | null | 2023-12-10T17:21:01Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': NORMAL
'1': PNEUMONIA
splits:
- name: train
num_bytes: 3186635036.504
num_examples: 5216
- name: validation
num_bytes: 3030633
num_examples: 16
- name: test
num_bytes: 79062317
num_examples: 624
download_size: 1230487171
dataset_size: 3268727986.504
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: cc-by-4.0
---
**Dataset Summary**
* The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).
* Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.
* For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.
* Summary taken from [Application of the AI System for Pneumonia Detection Using Chest X-Ray Images](https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867418301545%3Fshowall%3Dtrue)
* [Dataset source](https://data.mendeley.com/datasets/rscbjbr9sj/2)
**Citation
Citation: Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification”, Mendeley Data, V2, doi: 10.17632/rscbjbr9sj.2 | [
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andrewatef/newPRO | andrewatef | 2023-12-17T20:17:00Z | 36 | 0 | [
"region:us"
] | null | 2023-12-05T21:14:45Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 108544
num_examples: 214
download_size: 108544
dataset_size: 108544
configs:
- config_name: default
data_files:
- split: train
path: data/Article1.jsonl
---
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Clarkliu97/Line_Sketch | Clarkliu97 | 2023-12-13T01:32:31Z | 36 | 0 | [
"region:us"
] | null | 2023-12-08T03:30:23Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 19028741.0
num_examples: 99
download_size: 19025835
dataset_size: 19028741.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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andstor/methods2test_small | andstor | 2023-12-18T00:18:14Z | 36 | 0 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"unit test",
"java",
"code",
"arxiv:2203.12776",
"region:us"
] | [
"text-generation"
] | 2023-12-17T20:26:53Z | ---
language:
- en
license: mit
task_categories:
- text-generation
configs:
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data_files:
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path: data/fm/train-*
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path: data/fm/test-*
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path: data/fm/validation-*
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data_files:
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path: data/fm_t/test-*
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path: data/fm_t/validation-*
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data_files:
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path: data/fm_fc/train-*
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path: data/fm_fc/test-*
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path: data/fm_fc/validation-*
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data_files:
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path: data/fm_fc_t_tc/train-*
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data_files:
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path: data/fm_fc_c_t_tc/train-*
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path: data/fm_fc_c_t_tc/test-*
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path: data/fm_fc_c_t_tc/validation-*
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path: data/fm_fc_c_m/test-*
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path: data/fm_fc_c_m/validation-*
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data_files:
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path: data/fm_fc_c_m_t_tc/train-*
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path: data/fm_fc_c_m_t_tc/test-*
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path: data/fm_fc_c_m_t_tc/validation-*
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data_files:
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path: data/fm_fc_c_m_f/train-*
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path: data/fm_fc_c_m_f/test-*
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path: data/fm_fc_c_m_f/validation-*
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data_files:
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path: data/fm_fc_c_m_f_t_tc/train-*
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path: data/fm_fc_c_m_f_t_tc/test-*
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data_files:
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path: data/t_tc/train-*
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tags:
- unit test
- java
- code
---
## Dataset Description
Microsoft created the methods2test dataset, consisting of Java Junit test cases with its corresponding focal methods.
It contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K
Java open source project hosted on GitHub.
This is smaller subset of the assembled version of the methods2test dataset.
It provides convenient access to the different context levels based on the raw source code (e.g. newlines are preserved). The test cases and associated classes are also made available.
The subset is created by randomly selecting only one sample from each of the 91k projects.
The mapping between test case and focal methods are based heuristics rules and Java developer's best practice.
More information could be found here:
- [methods2test Github repo](https://github.com/microsoft/methods2test)
- [Methods2Test: A dataset of focal methods mapped to test cases](https://arxiv.org/pdf/2203.12776.pdf)
## Dataset Schema
```
t: <TEST_CASE>
t_tc: <TEST_CASE> <TEST_CLASS_NAME>
fm: <FOCAL_METHOD>
fm_fc: <FOCAL_CLASS_NAME> <FOCAL_METHOD>
fm_fc_c: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS>
fm_fc_c_m: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES>
fm_fc_c_m_f: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> <FIELDS>
```
## Focal Context
- fm: this representation incorporates exclusively the source
code of the focal method. Intuitively, this contains the most
important information for generating accurate test cases for
the given method.
- fm+fc: this representations adds the focal class name, which
can provide meaningful semantic information to the model.
- fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this
augmentation is that the test case may require instantiating
an object of the focal class in order to properly test the focal
method.
- fm+fc+c+m: this representation adds the signatures of the
other public methods in the focal class. The rationale which
motivated this inclusion is that the test case may need to
invoke other auxiliary methods within the class (e.g., getters,
setters) to set up or tear down the testing environment.
- fm+fc+c+m+f : this representation adds the public fields of
the focal class. The motivation is that test cases may need to
inspect the status of the public fields to properly test a focal
method.
The different levels of focal contexts are the following:
```
T: test case
T_TC: test case + test class name
FM: focal method
FM_FC: focal method + focal class name
FM_FC_C: focal method + focal class name + constructor signatures
FM_FC_C_M: focal method + focal class name + constructor signatures + public method signatures
FM_FC_C_M_F: focal method + focal class name + constructor signatures + public method signatures + public fields
```
## Limitations
The original authors validate the heuristics by inspecting a
statistically significant sample (confidence level of 95% within 10%
margin of error) of 97 samples from the training set. Two authors
independently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct
link between the test case and the corresponding focal method
## Contribution
All thanks to the original authors.
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niltheory/ExistenceTypes | niltheory | 2023-12-15T16:14:23Z | 35 | 0 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:cc-by-sa-4.0",
"complex reasoning",
"creative writing",
"logic puzzle",
"region:us"
] | [
"text-classification",
"question-answering",
"text-generation"
] | 2023-11-22T09:15:11Z | ---
task_categories:
- text-classification
- question-answering
- text-generation
language:
- en
pretty_name: occybyte
size_categories:
- n<1K
license: cc-by-sa-4.0
tags:
- complex reasoning
- creative writing
- logic puzzle
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset is still a work in progress; it's pretty small at the moment.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
The ExistenceTypes dataset is a highly-contextual logic puzzle classifier that is intended to help with contextual understanding. The model will have to pay attention to the surrounding text and not just the keywords. The statements themselves are structured like logic puzzles and some examples requires multi-step reasoning.
This is to help the model deduce and infer based on given premises. The statements are either true, false or ambigious based on the IP's rules and consistency as well as Godless, Godliving or Mixed Domain. The overall idea is for the model to understand and generate content within' the themeatic and conceptual framework of my IP.
- **Curated by:** [niltheory]
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### Direct Use
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## Dataset Structure
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## 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
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#### Who are the source data producers?
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### Annotations [optional]
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#### Annotation process
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#### 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. -->
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[More Information Needed] | [
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lawallanre/hausa_common_voice_audio | lawallanre | 2023-12-17T21:37:50Z | 35 | 0 | [
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:en",
"license:ofl-1.1",
"region:us"
] | [
"audio-classification"
] | 2023-12-13T11:53:35Z | ---
configs:
- config_name: default
data_files:
- split: train
path: "train,tsv"
- split: test
path: "test.tsv"
- split: dev
path: "dev.tsv"
license: ofl-1.1
task_categories:
- audio-classification
language:
- en
pretty_name: lanre
size_categories:
- 1K<n<10K
--- | [
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tadeodonegana/samsum-es | tadeodonegana | 2023-12-16T21:47:18Z | 35 | 0 | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:samsum",
"language:es",
"license:cc-by-nc-nd-4.0",
"conversations-summarization",
"arxiv:1911.12237",
"region:us"
] | [
"summarization"
] | 2023-12-14T16:57:12Z | ---
annotations_creators:
- expert-generated
language_creators:
- translated
language:
- es
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- samsum
task_categories:
- summarization
task_ids: []
pretty_name: SAMSum Corpus (es)
tags:
- conversations-summarization
dataset_info:
features:
- name: text
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 10105743
num_examples: 14730
- name: validation
num_bytes: 559296
num_examples: 818
- name: test
num_bytes: 580074
num_examples: 819
download_size: 7111425
dataset_size: 11245113
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for SAMSum Corpus (es)
## Dataset Description
Translated [samsum](https://huggingface.co/datasets/samsum) dataset to spanish language.
### Links
- **Samsum original dataset repository:** https://huggingface.co/datasets/samsum
- **Paper:** https://arxiv.org/abs/1911.12237v2
### Languages
Spanish (translated from English [samsum](https://huggingface.co/datasets/samsum) using GPT-3.5 Turbo)
## Dataset Structure
### Data Fields
- text: text of dialogue.
- target: human written summary of the dialogue.
### Data Splits
- train: 14730
- validation: 818
- test: 819
## Licensing Information
non-commercial licence: CC BY-NC-ND 4.0
## Citation Information
```
@inproceedings{gliwa-etal-2019-samsum,
title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization",
author = "Gliwa, Bogdan and
Mochol, Iwona and
Biesek, Maciej and
Wawer, Aleksander",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5409",
doi = "10.18653/v1/D19-5409",
pages = "70--79"
}
```
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] |
xinrongzhang2022/InfiniteBench | xinrongzhang2022 | 2023-12-13T04:29:54Z | 34 | 7 | [
"region:us"
] | null | 2023-11-16T09:29:02Z | ---
configs:
- config_name: default
data_files:
- split: passkey
path: "passkey.jsonl"
- split: kv_retrieval
path: "kv_retrieval.jsonl"
- split: number_string
path: "number_string.jsonl"
- split: code_run
path: "code_run.jsonl"
- split: code_debug
path: "code_debug.jsonl"
- split: math_find
path: "math_find.jsonl"
- split: math_calc
path: "math_calc.jsonl"
- split: longdialogue_qa_eng
path: "longdialogue_qa_eng.jsonl"
- split: longbook_qa_eng
path: "longbook_qa_eng.jsonl"
- split: longbook_sum_eng
path: "longbook_sum_eng.jsonl"
- split: longbook_choice_eng
path: "longbook_choice_eng.jsonl"
- split: longbook_qa_chn
path: "longbook_qa_chn.jsonl"
---
---
license: apache-2.0
---
---
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kardosdrur/dawiki_qa_zephyr | kardosdrur | 2023-12-12T08:24:43Z | 34 | 0 | [
"region:us"
] | null | 2023-12-11T11:38:52Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 2912592.195482657
num_examples: 8925
- name: test
num_bytes: 728392.8045173434
num_examples: 2232
download_size: 2485097
dataset_size: 3640985.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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luizlzg/drbyte_longanswer | luizlzg | 2023-12-11T17:49:11Z | 34 | 1 | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:pt",
"medical",
"biology",
"region:us"
] | [
"text-generation"
] | 2023-12-11T17:48:07Z | ---
task_categories:
- text-generation
language:
- pt
tags:
- medical
- biology
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: drbyte_ptbr_teste*
--- | [
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FeiVw/umatest | FeiVw | 2023-12-17T07:40:37Z | 33 | 0 | [
"license:mit",
"region:us"
] | null | 2023-11-08T12:35:52Z | ---
license: mit
---
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b1nch3f/csic-dataset | b1nch3f | 2023-12-12T14:48:03Z | 33 | 0 | [
"region:us"
] | null | 2023-12-12T13:04:56Z | ---
dataset_info:
features:
- name: method
dtype: string
- name: payload
dtype: string
- name: label
dtype: string
- name: payload_length
dtype: int64
splits:
- name: train
num_bytes: 728026.5909017059
num_examples: 5406
- name: validation
num_bytes: 182073.98277822908
num_examples: 1352
- name: test
num_bytes: 381822.6884593519
num_examples: 2849
download_size: 889953
dataset_size: 1291923.2621392868
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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jkarns/hinted_mbpp_llama2_7B_chat | jkarns | 2023-12-12T20:26:19Z | 33 | 0 | [
"region:us"
] | null | 2023-12-12T20:26:14Z | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: prompt
path: data/prompt-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: task_id
dtype: int64
- name: text
dtype: string
- name: code
dtype: string
- name: test_list
sequence: string
- name: test_setup_code
dtype: string
- name: challenge_test_list
sequence: string
- name: hints
sequence: string
splits:
- name: test
num_bytes: 931659
num_examples: 500
- name: train
num_bytes: 678514
num_examples: 374
- name: prompt
num_bytes: 18915
num_examples: 10
- name: validation
num_bytes: 165965
num_examples: 90
download_size: 657926
dataset_size: 1795053
---
# Dataset Card for "hinted_mbpp_llama2_7B_chat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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heegyu/hh-rlhf-ko | heegyu | 2023-12-17T03:37:11Z | 32 | 0 | [
"language:ko",
"license:mit",
"region:us"
] | null | 2023-12-01T04:55:40Z | ---
license: mit
language:
- ko
---
- Original Dataset: [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- Translation by using [maywell/Synatra-7B-v0.3-Translation](https://huggingface.co/maywell/Synatra-7B-v0.3-Translation)
- Translating in progress... | [
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unaidedelf87777/slimorca-sem_deduped | unaidedelf87777 | 2023-12-12T17:46:46Z | 32 | 1 | [
"region:us"
] | null | 2023-12-11T14:27:52Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: system_message
dtype: string
- name: instruction
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: topic_depth_1
dtype: string
- name: topic_depth_2
dtype: string
- name: topic_depth_3
dtype: string
splits:
- name: train
num_bytes: 834398137
num_examples: 477358
download_size: 423106996
dataset_size: 834398137
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
WARNING: EXTREMELY WORK IN PROGRESS. NOT YET USEABLE; HAVENT REMOVED RLHF INSTANCES YET. | [
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Sofoklis/hairpin_sequences_header_12-dataset | Sofoklis | 2023-12-12T13:59:11Z | 32 | 0 | [
"region:us"
] | null | 2023-12-12T13:59:06Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: number
dtype: int64
- name: name
dtype: string
- name: caption
dtype: string
- name: split_caption
dtype: string
- name: matrix
dtype: image
splits:
- name: train
num_bytes: 7924.5
num_examples: 18
- name: test
num_bytes: 880.5
num_examples: 2
- name: valid
num_bytes: 1320.75
num_examples: 3
download_size: 22289
dataset_size: 10125.75
---
# Dataset Card for "hairpin_sequences_header_12-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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vgaraujov/fapesp | vgaraujov | 2023-12-13T04:31:30Z | 32 | 0 | [
"license:cc-by-4.0",
"region:us"
] | null | 2023-12-13T03:53:16Z | ---
license: cc-by-4.0
---
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Sunbird/salt | Sunbird | 2023-12-15T03:07:58Z | 32 | 0 | [
"region:us"
] | null | 2023-12-14T02:35:48Z | ---
dataset_info:
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features:
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dtype: string
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num_examples: 96
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dataset_size: 1863426682
- config_name: multispeaker-eng
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data_files:
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path: multispeaker-nyn/train-*
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- config_name: studio-english
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- config_name: text-all
data_files:
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path: text-all/train-*
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path: text-all/test-*
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mekaneeky/salt-dataset | mekaneeky | 2023-12-11T16:31:47Z | 31 | 0 | [
"region:us"
] | null | 2023-10-19T11:10:18Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: ach_text
dtype: string
- name: lgg_text
dtype: string
- name: lug_text
dtype: string
- name: nyn_text
dtype: string
- name: teo_text
dtype: string
- name: eng_text
dtype: string
- name: ibo_text
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 10107493
num_examples: 22925
- name: dev
num_bytes: 204553
num_examples: 472
- name: test
num_bytes: 212843
num_examples: 476
download_size: 6167721
dataset_size: 10524889
---
# Dataset Card for "salt-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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developerh/test2 | developerh | 2023-12-14T09:50:51Z | 31 | 0 | [
"task_categories:text-classification",
"finance",
"region:us"
] | [
"text-classification"
] | 2023-11-27T10:23:16Z | ---
task_categories:
- text-classification
tags:
- finance
--- | [
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netcat420/MHENN | netcat420 | 2023-12-11T21:08:48Z | 31 | 1 | [
"license:mit",
"region:us"
] | null | 2023-12-11T20:18:52Z | ---
license: mit
---
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Tobius/teric_asr_lab | Tobius | 2023-12-14T13:44:44Z | 31 | 0 | [
"license:cc0-1.0",
"region:us"
] | null | 2023-12-14T12:18:11Z | ---
license: cc0-1.0
---
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sanduntg/vosrt-test | sanduntg | 2023-12-14T17:20:32Z | 31 | 0 | [
"region:us"
] | null | 2023-12-14T17:20:25Z | ---
dataset_info:
features:
- name: vignette
dtype: string
- name: class
dtype: int64
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 16328523.330246914
num_examples: 5369
download_size: 2382723
dataset_size: 16328523.330246914
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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nlplabtdtu/edu_parent_v1 | nlplabtdtu | 2023-12-15T03:36:16Z | 31 | 0 | [
"region:us"
] | null | 2023-12-15T03:36:12Z | ---
dataset_info:
features:
- name: content
dtype: string
- name: metadata
struct:
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dtype: string
- name: id
dtype: string
- name: seq_num
dtype: int64
- name: source
dtype: string
- name: uni_id
dtype: string
splits:
- name: train
num_bytes: 2922520
num_examples: 1226
download_size: 1028710
dataset_size: 2922520
configs:
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data_files:
- split: train
path: data/train-*
---
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A2H0H0R1/Animal-nutrition-pair | A2H0H0R1 | 2023-12-17T06:43:42Z | 31 | 1 | [
"region:us"
] | null | 2023-12-17T06:43:28Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: response_j
dtype: string
- name: response_k
dtype: string
splits:
- name: train
num_bytes: 10252777
num_examples: 5027
download_size: 4012629
dataset_size: 10252777
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ai4privacy/pii-masking-65k | ai4privacy | 2023-12-12T11:22:59Z | 30 | 14 | [
"size_categories:10K<n<100K",
"language:en",
"language:fr",
"language:de",
"language:it",
"legal",
"business",
"psychology",
"privacy",
"region:us"
] | null | 2023-08-07T06:04:08Z | ---
language:
- en
- fr
- de
- it
tags:
- legal
- business
- psychology
- privacy
size_categories:
- 10K<n<100K
---
# Purpose and Features
The purpose of the model and dataset is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.
The model is a fine-tuned version of "Distilled BERT", a smaller and faster version of BERT. It was adapted for the task of token classification based on the largest to our knowledge open-source PII masking dataset, which we are releasing simultaneously. The model size is 62 million parameters. The original encoding of the parameters yields a model size of 268 MB, which is compressed to 43MB after parameter quantization. The models are available in PyTorch, tensorflow, and tensorflow.js
The dataset is composed of ~43’000 observations. Each row starts with a natural language sentence that includes placeholders for PII and could plausibly be written to an AI assistant. The placeholders are then filled in with mocked personal information and tokenized with the BERT tokenizer. We label the tokens that correspond to PII, serving as the ground truth to train our model.
The dataset covers a range of contexts in which PII can appear. The sentences span 58 sensitive data types (~117 token classes), targeting **125 discussion subjects / use cases** split across business, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...).
Key facts:
- Currently 5.6m tokens with 65k PII examples.
- Multiple languages
- Human-in-the-loop validated high quality dataset
- Synthetic data generated using proprietary algorithms
- Adapted from DistilBertForTokenClassification
- Framework PyTorch
- 8 bit quantization
# Token distribution across PII classes
There are 2 dataset releasees:
- Original release:
- [PII43k_original.jsonl](PII43k_original.jsonl)
- New release with balanced token distribution:
- [english_balanced_10k.jsonl](english_balanced_10k.jsonl)
- [french_balanced_5k.jsonl](french_balanced_5k.jsonl)
- [german_balanced_3k.jsonl](german_balanced_3k.jsonl)
- [italian_balanced_3k.jsonl](italian_balanced_3k.jsonl)
The new release **balances the distribution of tokens across the PII classes** covered by the dataset.
This graph shows the distribution of observations across the different PII classes in the new release:
This is an important improvement, because the old release focused on just a few classes of PII and didn't provide enough examples of the other ones.
This graph shows the unbalanced distribution of observations across the different PII classes in the old release:
Current counts of tokens per example:
# Performance evaluation
| Test Precision | Test Recall | Test Accuracy |
|:-:|:-:|:-:|
# Community Engagement:
Newsletter & updates: www.Ai4privacy.com
- Looking for ML engineers, developers, beta-testers, human in the loop validators (all languages)
- Integrations with already existing open source solutions
# Roadmap and Future Development
- Multilingual benchmarking
- Extended integrations
- Continuously increase the training set
- Further optimisation to the model to reduce size and increase generalisability
- Next released major update is planned for the 14th of July (subscribe to newsletter for updates)
# Use Cases and Applications
**Chatbots**: Incorporating a PII masking model into chatbot systems can ensure the privacy and security of user conversations by automatically redacting sensitive information such as names, addresses, phone numbers, and email addresses.
**Customer Support Systems**: When interacting with customers through support tickets or live chats, masking PII can help protect sensitive customer data, enabling support agents to handle inquiries without the risk of exposing personal information.
**Email Filtering**: Email providers can utilize a PII masking model to automatically detect and redact PII from incoming and outgoing emails, reducing the chances of accidental disclosure of sensitive information.
**Data Anonymization**: Organizations dealing with large datasets containing PII, such as medical or financial records, can leverage a PII masking model to anonymize the data before sharing it for research, analysis, or collaboration purposes.
**Social Media Platforms**: Integrating PII masking capabilities into social media platforms can help users protect their personal information from unauthorized access, ensuring a safer online environment.
**Content Moderation**: PII masking can assist content moderation systems in automatically detecting and blurring or redacting sensitive information in user-generated content, preventing the accidental sharing of personal details.
**Online Forms**: Web applications that collect user data through online forms, such as registration forms or surveys, can employ a PII masking model to anonymize or mask the collected information in real-time, enhancing privacy and data protection.
**Collaborative Document Editing**: Collaboration platforms and document editing tools can use a PII masking model to automatically mask or redact sensitive information when multiple users are working on shared documents.
**Research and Data Sharing**: Researchers and institutions can leverage a PII masking model to ensure privacy and confidentiality when sharing datasets for collaboration, analysis, or publication purposes, reducing the risk of data breaches or identity theft.
**Content Generation**: Content generation systems, such as article generators or language models, can benefit from PII masking to automatically mask or generate fictional PII when creating sample texts or examples, safeguarding the privacy of individuals.
(...and whatever else your creative mind can think of)
# Support and Maintenance
AI4Privacy is a project affiliated with [AISuisse SA](https://www.aisuisse.com/). | [
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liyucheng/mmlu_test | liyucheng | 2023-12-13T07:41:11Z | 30 | 0 | [
"region:us"
] | null | 2023-10-16T23:28:24Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: id
dtype: string
- name: in-context examples
dtype: string
- name: testing input
dtype: string
- name: prompt
dtype: string
- name: task
dtype: string
splits:
- name: train
num_bytes: 90449824
num_examples: 13987
download_size: 14673865
dataset_size: 90449824
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mmlu_all_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ChayanM/mimic-QA-llama2-121k | ChayanM | 2023-12-11T15:11:50Z | 30 | 0 | [
"region:us"
] | null | 2023-12-06T15:54:50Z | ---
dataset_info:
features:
- name: QA
dtype: string
splits:
- name: train
num_bytes: 60434804
num_examples: 121244
download_size: 20587606
dataset_size: 60434804
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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bdsaglam/web_nlg-erx-sft-openai | bdsaglam | 2023-12-17T20:13:48Z | 30 | 0 | [
"region:us"
] | null | 2023-12-09T09:11:32Z | ---
dataset_info:
features:
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 46034310
num_examples: 35426
- name: dev
num_bytes: 5802673
num_examples: 4464
download_size: 11932210
dataset_size: 51836983
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
---
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] |
malaysia-ai/malay-conversational-speech-corpus | malaysia-ai | 2023-12-13T14:38:21Z | 30 | 0 | [
"language:ms",
"region:us"
] | null | 2023-12-13T14:36:14Z | ---
dataset_info:
features:
- name: 'Y'
dtype: string
- name: id
dtype: string
- name: gender
dtype: string
- name: filename
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 48785004.736
num_examples: 3241
download_size: 47709555
dataset_size: 48785004.736
language:
- ms
---
# malay-conversational-speech-corpus
Mirror for https://magichub.com/datasets/malay-conversational-speech-corpus/, license is Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License | [
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Raziullah/dv_finetune_common_voice_13 | Raziullah | 2023-12-14T18:17:16Z | 30 | 0 | [
"license:unknown",
"region:us"
] | null | 2023-12-14T16:50:24Z | ---
license: unknown
---
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lhallee/PiNUI_2048 | lhallee | 2023-12-14T19:30:29Z | 30 | 0 | [
"region:us"
] | null | 2023-12-14T19:29:32Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: seqs
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 1476934338
num_examples: 1547918
- name: valid
num_bytes: 5436191
num_examples: 8493
download_size: 1459247798
dataset_size: 1482370529
---
# Dataset Card for "PiNUI_2048"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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zaanind/sinhala_englsih_parrel_corpus | zaanind | 2023-12-14T11:51:28Z | 29 | 2 | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:si",
"language:en",
"license:gpl",
"region:us"
] | [
"translation"
] | 2023-06-06T15:27:51Z | ---
language:
- si
- en
license: gpl
size_categories:
- 10K<n<100K
task_categories:
- translation
pretty_name: Zoom Eng-Si Nmt Dataset
dataset_info:
features:
- name: english
dtype: string
- name: sinhala
dtype: string
splits:
- name: train
num_bytes: 8516909
num_examples: 80684
download_size: 4162589
dataset_size: 8516909
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Follow me on : https://facebook.com/zaanind | https://github.com/zaanind
Contact : [email protected] | https://m.me/zaanind | https://t.me/zaanind
Dataset Name: Eng-Sinhala Translation Dataset
Description: This dataset contains approximately 80,000 lines of English-Sinhala translation pairs. It can be used to train models for machine translation tasks and other natural language processing applications.
Data License: GPL (GNU General Public License). Please ensure that you comply with the terms and conditions of the GPL when using the dataset.
Note: While you mentioned that some sentences in the dataset might be incorrect due to its large size, it is important to ensure the quality and accuracy of the data for training purposes. Consider performing data cleaning and validation to improve the reliability of your model.
Mission
Our mission is to improve the quality of open-source English to Sinhala machine translation. This dataset, consisting of 8,000 translation pairs, is a step in that direction.
Special Thanks:
We extend our gratitude to the data collected and cleared by the Zoom.lk subtitles team, whose contributions have been invaluable in making this dataset possible.
Please feel free to reach out if you have any questions, suggestions, or would like to collaborate on further improving this dataset or machine translation models. Your support is greatly appreciated!
(Contact : [email protected] | https://m.me/zaanind | https://t.me/zaanind) | [
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michaelginn/latent-trees-agreement-GEN | michaelginn | 2023-12-14T03:45:27Z | 29 | 0 | [
"region:us"
] | null | 2023-11-25T20:47:48Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: depth
dtype: int64
splits:
- name: train
num_bytes: 105894.0
num_examples: 2400
- name: eval
num_bytes: 35298.0
num_examples: 800
- name: test
num_bytes: 56229
num_examples: 800
download_size: 63746
dataset_size: 197421.0
---
# Dataset Card for "latent-trees-agreement-GEN"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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NextDayAI/MultipleResponsesChat_FilteredOnLabels_spicy2_20231204_20231211 | NextDayAI | 2023-12-11T20:38:12Z | 29 | 0 | [
"region:us"
] | null | 2023-12-11T20:38:06Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 121825163.58581752
num_examples: 35539
- name: valid
num_bytes: 12854733.207091242
num_examples: 3750
- name: test
num_bytes: 12854733.207091242
num_examples: 3750
download_size: 92900669
dataset_size: 147534630.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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NextDayAI/MultipleResponsesChat_FilteredOnLabels_spicy1_20231128_20231211 | NextDayAI | 2023-12-11T20:45:11Z | 29 | 0 | [
"region:us"
] | null | 2023-12-11T20:45:02Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 268355462.60934162
num_examples: 79594
- name: valid
num_bytes: 12643327.195329184
num_examples: 3750
- name: test
num_bytes: 12643327.195329184
num_examples: 3750
download_size: 184746922
dataset_size: 293642117.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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nhantruongcse/testing_1500_data_08_to_11_December | nhantruongcse | 2023-12-12T13:46:11Z | 29 | 0 | [
"region:us"
] | null | 2023-12-12T13:46:09Z | ---
dataset_info:
features:
- name: Date
dtype: string
- name: url
dtype: string
- name: Title
dtype: string
- name: Summary
dtype: string
- name: Content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 6491911
num_examples: 1500
download_size: 3384833
dataset_size: 6491911
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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tdh87/Stories | tdh87 | 2023-12-15T09:54:02Z | 29 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T09:36:30Z | ---
license: apache-2.0
---
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rwitz2/15thsfractionproblem | rwitz2 | 2023-12-17T22:15:33Z | 29 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-17T21:32:23Z | ---
license: mit
---
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tim9510019/llama2_QA_Economics_230915 | tim9510019 | 2023-12-18T00:34:49Z | 28 | 3 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:mit",
"finance",
"region:us"
] | [
"question-answering",
"text-generation"
] | 2023-09-15T11:09:29Z | ---
language:
- en
license: mit
task_categories:
- question-answering
- text-generation
dataset_info:
features:
- name: Question
dtype: string
- name: input
dtype: string
- name: Answer
dtype: string
- name: Source
dtype: int64
- name: Date
dtype: timestamp[ns]
- name: Type
dtype: int64
- name: Prompt
dtype: int64
- name: QuestionTokenNum
dtype: int64
- name: inputTokenNum
dtype: int64
- name: AnswerTokenNum
dtype: int64
- name: Source.1
dtype: string
splits:
- name: train
num_bytes: 4172357
num_examples: 662
download_size: 1385068
dataset_size: 4172357
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- finance
---
# Dataset Card for "llama2_QA_Economics_230915"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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jherng/rsna-2023-abdominal-trauma-detection | jherng | 2023-12-17T10:01:39Z | 28 | 2 | [
"task_categories:image-classification",
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"license:mit",
"region:us"
] | [
"image-classification",
"image-segmentation"
] | 2023-09-19T10:10:47Z | ---
license: mit
dataset_info:
- config_name: classification
features:
- name: img_path
dtype: string
- name: bowel
dtype:
class_label:
names:
"0": healthy
"1": injury
- name: extravasation
dtype:
class_label:
names:
"0": healthy
"1": injury
- name: kidney
dtype:
class_label:
names:
"0": healthy
"1": low
"2": high
- name: liver
dtype:
class_label:
names:
"0": healthy
"1": low
"2": high
- name: spleen
dtype:
class_label:
names:
"0": healthy
"1": low
"2": high
- name: any_injury
dtype: bool
- name: metadata
struct:
- name: series_id
dtype: int32
- name: patient_id
dtype: int32
- name: incomplete_organ
dtype: bool
- name: aortic_hu
dtype: float32
- name: pixel_representation
dtype: int32
- name: bits_allocated
dtype: int32
- name: bits_stored
dtype: int32
splits:
- name: train
num_bytes: 802231
num_examples: 4239
- name: test
num_bytes: 89326
num_examples: 472
download_size: 96729254048
dataset_size: 891557
- config_name: classification-with-mask
features:
- name: img_path
dtype: string
- name: seg_path
dtype: string
- name: bowel
dtype:
class_label:
names:
"0": healthy
"1": injury
- name: extravasation
dtype:
class_label:
names:
"0": healthy
"1": injury
- name: kidney
dtype:
class_label:
names:
"0": healthy
"1": low
"2": high
- name: liver
dtype:
class_label:
names:
"0": healthy
"1": low
"2": high
- name: spleen
dtype:
class_label:
names:
"0": healthy
"1": low
"2": high
- name: any_injury
dtype: bool
- name: metadata
struct:
- name: series_id
dtype: int32
- name: patient_id
dtype: int32
- name: incomplete_organ
dtype: bool
- name: aortic_hu
dtype: float32
- name: pixel_representation
dtype: int32
- name: bits_allocated
dtype: int32
- name: bits_stored
dtype: int32
splits:
- name: train
num_bytes: 58138
num_examples: 185
- name: test
num_bytes: 6600
num_examples: 21
download_size: 4196738529
dataset_size: 64738
- config_name: segmentation
features:
- name: img_path
dtype: string
- name: seg_path
dtype: string
- name: metadata
struct:
- name: series_id
dtype: int32
- name: patient_id
dtype: int32
- name: incomplete_organ
dtype: bool
- name: aortic_hu
dtype: float32
- name: pixel_representation
dtype: int32
- name: bits_allocated
dtype: int32
- name: bits_stored
dtype: int32
splits:
- name: train
num_bytes: 50714
num_examples: 185
- name: test
num_bytes: 5757
num_examples: 21
download_size: 4196631843
dataset_size: 56471
task_categories:
- image-classification
- image-segmentation
pretty_name: RSNA 2023 Abdominal Trauma Detection (Preprocessed)
size_categories:
- 1K<n<10K
---
# Dataset Card for RSNA 2023 Abdominal Trauma Detection (Preprocessed)
## Dataset Description
- **Homepage:** [https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection](https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection)
- **Source:** [https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data)
### Dataset Summary
This dataset is the preprocessed version of the dataset from [RSNA 2023 Abdominal Trauma Detection Kaggle Competition](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data).
It is tailored for segmentation and classification tasks. It contains 3 different configs as described below:
- **classification**:
- 4711 instances where each instance includes a CT scan in NIfTI format, target labels, and its relevant metadata.
- **segmentation**:
- 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, and its relevant metadata.
- **classification-with-mask**:
- 206 instances where each instance includes a CT scan in NIfTI format, a segmentation mask in NIfTI format, target labels, and its relevant metadata.
All CT scans and segmentation masks had already been resampled with voxel spacing (2.0, 2.0, 3.0) and thus its reduced file size.
### Usage
```python
from datasets import load_dataset
# Classification dataset
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", streaming=True) # "classification" is the default configuration
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=True) # download dataset on-demand and in-memory (no caching)
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=False) # download dataset and cache locally (~90.09 GiB)
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=True, test_size=0.05, random_state=42) # specify split size for train-test split
# Classification dataset with segmentation masks
rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=True) # download dataset on-demand and in-memory (no caching)
rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=False) # download dataset and cache locally (~3.91 GiB)
rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=False, test_size=0.05, random_state=42) # specify split size for train-test split
# Segmentation dataset
rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=True) # download dataset on-demand and in-memory (no caching)
rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=False) # download dataset and cache locally (~3.91 GiB)
rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=True, test_size=0.1, random_state=42) # specify split size for train-test split
# Get the dataset splits
train_rsna_cls_ds = rsna_cls_ds["train"]; test_rsna_cls_ds = rsna_cls_ds["test"]
train_rsna_clsmask_ds = rsna_clsmask_ds["train"]; test_rsna_clsmask_ds = rsna_clsmask_ds["test"]
train_rsna_seg_ds = rsna_seg_ds["train"]; test_rsna_seg_ds = rsna_seg_ds["test"]
# Tip: Download speed up with multiprocessing
rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", streaming=False, num_proc=8) # num_proc: num of cpu core used for loading the dataset
```
## Dataset Structure
### Data Instances
#### Configuration 1: classification
- **Size of downloaded dataset files:** 90.09 GiB
An example of an instance in the 'classification' configuration looks as follows:
```json
{
"img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/25899/21872.nii.gz",
"bowel": 0,
"extravasation": 0,
"kidney": 0,
"liver": 0,
"spleen": 0,
"any_injury": false,
"metadata": {
"series_id": 21872,
"patient_id": 25899,
"incomplete_organ": false,
"aortic_hu": 113.0,
"pixel_representation": 0,
"bits_allocated": 16,
"bits_stored": 12
}
}
```
#### Configuration 2: segmentation
- **Size of downloaded dataset files:** 3.91 GiB
An example of an instance in the 'segmentation' configuration looks as follows:
```json
{
"img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz",
"seg_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4622.nii.gz",
"metadata": {
"series_id": 4622,
"patient_id": 4791,
"incomplete_organ": false,
"aortic_hu": 223.0,
"pixel_representation": 1,
"bits_allocated": 16,
"bits_stored": 16
}
}
```
#### Configuration 3: classification-with-mask
- **Size of downloaded dataset files:** 3.91 GiB
An example of an instance in the 'classification-with-mask' configuration looks as follows:
```json
{
"img_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/train_images/4791/4622.nii.gz",
"seg_path": "https://huggingface.co/datasets/jherng/rsna-2023-abdominal-trauma-detection/resolve/main/segmentations/4622.nii.gz",
"bowel": 0,
"extravasation": 0,
"kidney": 0,
"liver": 1,
"spleen": 1,
"any_injury": true,
"metadata": {
"series_id": 4622,
"patient_id": 4791,
"incomplete_organ": false,
"aortic_hu": 223.0,
"pixel_representation": 1,
"bits_allocated": 16,
"bits_stored": 16
}
}
```
### Data Fields
The data fields for all configurations are as follows:
- `img_path`: a `string` feature representing the path to the CT scan in NIfTI format.
- `seg_path`: a `string` feature representing the path to the segmentation mask in NIfTI format (only for 'segmentation' and 'classification-with-mask' configurations).
- `bowel`, `extravasation`, `kidney`, `liver`, `spleen`: Class label features indicating the condition of respective organs.
- `any_injury`: a `bool` feature indicating the presence of any injury.
- `metadata`: a dictionary feature containing metadata information with the following fields:
- `series_id`: an `int32` feature.
- `patient_id`: an `int32` feature.
- `incomplete_organ`: a `bool` feature.
- `aortic_hu`: a `float32` feature.
- `pixel_representation`: an `int32` feature.
- `bits_allocated`: an `int32` feature.
- `bits_stored`: an `int32` feature.
### Data Splits
Default split:
- 0.9:0.1 with random_state = 42
| Configuration Name | Train (n_samples) | Test (n_samples) |
| ------------------------ | ----------------: | ---------------: |
| classification | 4239 | 472 |
| segmentation | 185 | 21 |
| classification-with-mask | 185 | 21 |
Modify the split proportion:
```python
rsna_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", test_size=0.05, random_state=42)
```
## Additional Information
### Citation Information
- Preprocessed dataset:
```
@InProceedings{huggingface:dataset,
title = {RSNA 2023 Abdominal Trauma Detection Dataset (Preprocessed)},
author={Hong Jia Herng},
year={2023}
}
```
- Original dataset:
```
@misc{rsna-2023-abdominal-trauma-detection,
author = {Errol Colak, Hui-Ming Lin, Robyn Ball, Melissa Davis, Adam Flanders, Sabeena Jalal, Kirti Magudia, Brett Marinelli, Savvas Nicolaou, Luciano Prevedello, Jeff Rudie, George Shih, Maryam Vazirabad, John Mongan},
title = {RSNA 2023 Abdominal Trauma Detection},
publisher = {Kaggle},
year = {2023},
url = {https://kaggle.com/competitions/rsna-2023-abdominal-trauma-detection}
}
```
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ktennyson6/augmented-vsr-v2 | ktennyson6 | 2023-12-11T23:59:31Z | 28 | 0 | [
"region:us"
] | null | 2023-12-11T23:58:46Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: relation
dtype: string
splits:
- name: train
num_bytes: 981790431.631
num_examples: 6237
download_size: 852413763
dataset_size: 981790431.631
---
# Dataset Card for "augmented-vsr-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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deutsche-telekom/ger-backtrans-paraphrase | deutsche-telekom | 2023-12-17T17:42:02Z | 27 | 7 | [
"task_categories:sentence-similarity",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"language:de",
"license:cc-by-sa-4.0",
"arxiv:1907.05791",
"arxiv:2004.09813",
"region:us"
] | [
"sentence-similarity"
] | 2022-11-21T19:24:43Z | ---
license:
- cc-by-sa-4.0
language:
- de
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
task_categories:
- sentence-similarity
---
# German Backtranslated Paraphrase Dataset
This is a dataset of more than 21 million German paraphrases.
These are text pairs that have the same meaning but are expressed with different words.
The source of the paraphrases are different parallel German / English text corpora.
The English texts were machine translated back into German to obtain the paraphrases.
This dataset can be used for example to train semantic text embeddings.
To do this, for example, [SentenceTransformers](https://www.sbert.net/)
and the [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss)
can be used.
## Creator
This data set was compiled and open sourced by [Philip May](https://may.la/)
of [Deutsche Telekom](https://www.telekom.de/).
## Our pre-processing
Apart from the back translation, we have added more columns (for details see below). We have carried out the following pre-processing and filtering:
- We dropped text pairs where one text was longer than 499 characters.
- In the [GlobalVoices v2018q4](https://opus.nlpl.eu/GlobalVoices-v2018q4.php) texts we have removed the `" · Global Voices"` suffix.
## Your post-processing
You probably don't want to use the dataset as it is, but filter it further.
This is what the additional columns of the dataset are for.
For us it has proven useful to delete the following pairs of sentences:
- `min_char_len` less than 15
- `jaccard_similarity` greater than 0.3
- `de_token_count` greater than 30
- `en_de_token_count` greater than 30
- `cos_sim` less than 0.85
## Columns description
- **`uuid`**: a uuid calculated with Python `uuid.uuid4()`
- **`en`**: the original English texts from the corpus
- **`de`**: the original German texts from the corpus
- **`en_de`**: the German texts translated back from English (from `en`)
- **`corpus`**: the name of the corpus
- **`min_char_len`**: the number of characters of the shortest text
- **`jaccard_similarity`**: the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) of both sentences - see below for more details
- **`de_token_count`**: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large)
- **`en_de_token_count`**: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large)
- **`cos_sim`**: the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) of both sentences measured with [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
## Anomalies in the texts
It is noticeable that the [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles-v2018.php) texts have weird dash prefixes. This looks like this:
```
- Hast du was draufgetan?
```
To remove them you could apply this function:
```python
import re
def clean_text(text):
text = re.sub("^[-\s]*", "", text)
text = re.sub("[-\s]*$", "", text)
return text
df["de"] = df["de"].apply(clean_text)
df["en_de"] = df["en_de"].apply(clean_text)
```
## Parallel text corpora used
| Corpus name & link | Number of paraphrases |
|-----------------------------------------------------------------------|----------------------:|
| [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles-v2018.php) | 18,764,810 |
| [WikiMatrix v1](https://opus.nlpl.eu/WikiMatrix-v1.php) | 1,569,231 |
| [Tatoeba v2022-03-03](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php) | 313,105 |
| [TED2020 v1](https://opus.nlpl.eu/TED2020-v1.php) | 289,374 |
| [News-Commentary v16](https://opus.nlpl.eu/News-Commentary-v16.php) | 285,722 |
| [GlobalVoices v2018q4](https://opus.nlpl.eu/GlobalVoices-v2018q4.php) | 70,547 |
| **sum** |. **21,292,789** |
## Back translation
We have made the back translation from English to German with the help of [Fairseq](https://github.com/facebookresearch/fairseq).
We used the `transformer.wmt19.en-de` model for this purpose:
```python
en2de = torch.hub.load(
"pytorch/fairseq",
"transformer.wmt19.en-de",
checkpoint_file="model1.pt:model2.pt:model3.pt:model4.pt",
tokenizer="moses",
bpe="fastbpe",
)
```
## How the Jaccard similarity was calculated
To calculate the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index)
we are using the [SoMaJo tokenizer](https://github.com/tsproisl/SoMaJo)
to split the texts into tokens.
We then `lower()` the tokens so that upper and lower case letters no longer make a difference. Below you can find a code snippet with the details:
```python
from somajo import SoMaJo
LANGUAGE = "de_CMC"
somajo_tokenizer = SoMaJo(LANGUAGE)
def get_token_set(text, somajo_tokenizer):
sentences = somajo_tokenizer.tokenize_text([text])
tokens = [t.text.lower() for sentence in sentences for t in sentence]
token_set = set(tokens)
return token_set
def jaccard_similarity(text1, text2, somajo_tokenizer):
token_set1 = get_token_set(text1, somajo_tokenizer=somajo_tokenizer)
token_set2 = get_token_set(text2, somajo_tokenizer=somajo_tokenizer)
intersection = token_set1.intersection(token_set2)
union = token_set1.union(token_set2)
jaccard_similarity = float(len(intersection)) / len(union)
return jaccard_similarity
```
## Load this dataset
### With Hugging Face Datasets
```python
# pip install datasets
from datasets import load_dataset
dataset = load_dataset("deutsche-telekom/ger-backtrans-paraphrase")
train_dataset = dataset["train"]
```
### With Pandas
If you want to download the csv file and then load it with Pandas you can do it like this:
```python
df = pd.read_csv("train.csv")
```
## Citations, Acknowledgements and Licenses
**OpenSubtitles**
- citation: P. Lison and J. Tiedemann, 2016, [OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles](http://www.lrec-conf.org/proceedings/lrec2016/pdf/947_Paper.pdf). In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
- also see http://www.opensubtitles.org/
- license: no special license has been provided at OPUS for this dataset
**WikiMatrix v1**
- citation: Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://arxiv.org/abs/1907.05791), arXiv, July 11 2019
- license: [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
**Tatoeba v2022-03-03**
- citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
- license: [CC BY 2.0 FR](https://creativecommons.org/licenses/by/2.0/fr/)
- copyright: https://tatoeba.org/eng/terms_of_use
**TED2020 v1**
- citation: Reimers, Nils and Gurevych, Iryna, [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813), In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, November 2020
- acknowledgements to [OPUS](https://opus.nlpl.eu/) for this service
- license: please respect the [TED Talks Usage Policy](https://www.ted.com/about/our-organization/our-policies-terms/ted-talks-usage-policy)
**News-Commentary v16**
- citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
- license: no special license has been provided at OPUS for this dataset
**GlobalVoices v2018q4**
- citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
- license: no special license has been provided at OPUS for this dataset
## Citation
```latex
@misc{ger-backtrans-paraphrase,
title={Deutsche-Telekom/ger-backtrans-paraphrase - dataset at Hugging Face},
url={https://huggingface.co/datasets/deutsche-telekom/ger-backtrans-paraphrase},
year={2022},
author={May, Philip}
}
```
## Licensing
Copyright (c) 2022 [Philip May](https://may.la/),
[Deutsche Telekom AG](https://www.telekom.com/)
This work is licensed under [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
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Noppawat-Rew/mimic3_it_gpt | Noppawat-Rew | 2023-12-11T05:27:49Z | 27 | 0 | [
"region:us"
] | null | 2023-12-09T08:02:55Z | ---
dataset_info:
features:
- name: SUBJECT_ID
dtype: int64
- name: HADM_ID
dtype: int64
- name: TEXT
dtype: string
- name: LABELS
dtype: string
- name: length
dtype: int64
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 20909423
num_examples: 1019
download_size: 10004186
dataset_size: 20909423
configs:
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data_files:
- split: train
path: data/train-*
---
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atmallen/quirky_sciq_pythia-410m_bob_easy | atmallen | 2023-12-12T06:54:07Z | 27 | 0 | [
"region:us"
] | null | 2023-12-12T01:12:21Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
- name: difficulty
dtype: float64
- name: statement
dtype: string
- name: character
dtype: string
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: bob_log_odds
dtype: float64
splits:
- name: train
num_bytes: 3637062.50295402
num_examples: 5838
- name: validation
num_bytes: 304362.045
num_examples: 494
- name: test
num_bytes: 316343.916
num_examples: 504
download_size: 1395650
dataset_size: 4257768.46395402
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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] |
sleiva/xone_copilot_js | sleiva | 2023-12-12T13:50:22Z | 27 | 0 | [
"region:us"
] | null | 2023-12-12T13:50:21Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: project
dtype: string
- name: file
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splits:
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num_bytes: 575314
num_examples: 31
download_size: 191600
dataset_size: 575314
---
# Dataset Card for "xone_copilot_js"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Lez94/take-off-eyeglasses-200 | Lez94 | 2023-12-15T13:19:34Z | 27 | 0 | [
"region:us"
] | null | 2023-12-12T15:40:30Z | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edited_image
dtype: image
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 20494816.0
num_examples: 200
download_size: 20513907
dataset_size: 20494816.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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dputilov/generatemidas24aithreshold | dputilov | 2023-12-13T09:19:54Z | 27 | 0 | [
"region:us"
] | null | 2023-12-13T09:16:40Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: mask
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2368720763.276
num_examples: 8682
download_size: 3246467885
dataset_size: 2368720763.276
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "generatemidas24aithreshold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nhantruongcse/result | nhantruongcse | 2023-12-16T07:12:17Z | 27 | 0 | [
"region:us"
] | null | 2023-12-14T14:40:22Z | ---
dataset_info:
features:
- name: model_name
dtype: string
- name: BLEU_score
dtype: float64
- name: BERT_score_P
dtype: float64
- name: BERT_score_F1
dtype: float64
- name: BERT_score_R
dtype: float64
- name: Rouge_rouge1_P
dtype: float64
- name: Rouge_rouge2_P
dtype: float64
- name: Rouge_rougeL_P
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1324
num_examples: 11
download_size: 6198
dataset_size: 1324
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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hynky/czech_news_dataset_v2 | hynky | 2023-12-17T13:49:47Z | 26 | 0 | [
"arxiv:2307.10666",
"region:us"
] | null | 2023-03-08T23:13:57Z | ---
dataset_info:
features:
- name: url
dtype: string
- name: authors
sequence: string
- name: headline
dtype: string
- name: brief
dtype: string
- name: keywords
sequence: string
- name: category
dtype:
class_label:
names:
'0': None
'1': Zahraniční
'2': Domácí
'3': Sport
'4': Kultura
'5': Revue
'6': Koktejl
'7': Ekonomika
'8': Krimi
'9': Podnikání
'10': Auto
'11': Věda
'12': Komentáře
'13': Cestování
'14': Finance
'15': Technologie
'16': Bydlení
'17': Koronavirus
'18': Byznys
'19': Rozhovory
'20': Podcasty
'21': Životní styl
'22': Literatura
'23': Vánoce
'24': Výtvarné umění
'25': Kolo
- name: content
dtype: large_string
- name: comments_num
dtype: int32
- name: server
dtype:
class_label:
names:
'0': None
'1': seznamzpravy
'2': idnes
'3': aktualne
'4': novinky
'5': denik
'6': irozhlas
- name: category_unclean
dtype: string
- name: authors_gender
sequence:
class_label:
names:
'0': None
'1': MAN
'2': WOMAN
- name: authors_cum_gender
dtype:
class_label:
names:
'0': None
'1': MAN
'2': WOMAN
'3': MIXED
- name: day_of_week
dtype:
class_label:
names:
'0': None
'1': MONDAY
'2': TUESDAY
'3': WEDNESDAY
'4': THURSDAY
'5': FRIDAY
'6': SATURDAY
'7': SUNDAY
- name: date
dtype: timestamp[us]
splits:
- name: train
num_bytes: 5140455719
num_examples: 1641471
- name: validation
num_bytes: 529659351
num_examples: 144836
- name: test
num_bytes: 553381092
num_examples: 144837
download_size: 4133635457
dataset_size: 6223496162
---
# Dataset Card for "czech_news_dataset_v2"
- Dataset containing the news articles from major online news outlets collected from 2000-2022.
- Follow-up paper https://arxiv.org/abs/2307.10666 (v1 of the dataset)
- Changes from v1
- Better contribution of novinky.cz in later stages
- More articles, as a mistake in filtering was fixed.
- Collection was done using CmonCrawl.
- The dataset should be used for Research only purposes as I don't have rights for articles itself.
- If you have any question contact me at [email protected] | [
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atmallen/quirky_sciq_pythia-410m | atmallen | 2023-12-12T06:54:01Z | 26 | 0 | [
"region:us"
] | null | 2023-12-07T08:18:54Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
- name: difficulty
dtype: float64
- name: statement
dtype: string
- name: character
dtype: string
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: bob_log_odds
dtype: float64
splits:
- name: train
num_bytes: 29103976
num_examples: 46716
- name: validation
num_bytes: 2464470
num_examples: 4000
- name: test
num_bytes: 2510666
num_examples: 4000
download_size: 7307630
dataset_size: 34079112
---
# Dataset Card for "quirky_sciq_pythia-410m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mattobaldo/personal_dataset | mattobaldo | 2023-12-12T09:01:49Z | 26 | 0 | [
"annotations_creators:found",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | [
"text"
] | 2023-12-07T09:33:33Z | ---
language:
- en
size_categories:
- 1K<n<10K
annotations_creators:
- found
- crowdsourced
- machine-generated
language_creators:
- expert-generated
- crowdsourced
- found
multilinguality:
- monolingual
pretty_name: personal_dataset
task_categories:
- text
--- | [
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adamjweintraut/bart-finetuned-eli5_precomputed_best_slice-256_2023-12-09_run | adamjweintraut | 2023-12-11T05:33:30Z | 26 | 0 | [
"region:us"
] | null | 2023-12-09T23:52:19Z | ---
dataset_info:
features:
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dtype: string
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splits:
- name: train
num_bytes: 15883851
num_examples: 1250
download_size: 9859508
dataset_size: 15883851
configs:
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data_files:
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path: data/train-*
---
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] |
nikesh66/Slang-Dataset | nikesh66 | 2023-12-11T11:18:32Z | 26 | 1 | [
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | null | 2023-12-11T11:15:40Z | ---
language:
- en
size_categories:
- 1K<n<10K
---
# Slang Dataset
It contains artificially generated slang data along with their label
## Dataset Descripton:
- Number of Rows: 5,000
- Number of Columns: 2
- Column Names: 'Tweet', 'Sarcasm (yes/no)'
- Description: This dataset features tweets labeled for sarcasm. Each tweet is accompanied by a label ('yes' or 'no') indicating whether the tweet is sarcastic.
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argilla/ultrafeedback-multi-binarized-preferences-cleaned | argilla | 2023-12-11T14:21:14Z | 26 | 3 | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"dpo",
"preference",
"ultrafeedback",
"region:us"
] | [
"text-generation"
] | 2023-12-11T14:04:24Z | ---
language:
- en
license: mit
size_categories:
- 100K<n<1M
task_categories:
- text-generation
pretty_name: UltraFeedback Multi-Binarized Preferences Cleaned
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen-rating
dtype: float64
- name: chosen-model
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected-rating
dtype: float64
- name: rejected-model
dtype: string
splits:
- name: train
num_bytes: 738122612
num_examples: 157675
download_size: 196872615
dataset_size: 738122612
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- dpo
- preference
- ultrafeedback
---
# UltraFeedback - Multi-Binarized using the Average of Preference Ratings (Cleaned)
This dataset represents a new iteration on top of [`argilla/ultrafeedback-binarized-preferences-cleaned`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned),
and has been created to explore whether DPO fine-tuning with more than one rejection per chosen response helps the model perform better in the
AlpacaEval, MT-Bench, and LM Eval Harness benchmarks.
Read more about Argilla's approach towards UltraFeedback binarization at [`argilla/ultrafeedback-binarized-preferences/README.md`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences/blob/main/README.md),
and about the parent approach of this one at [`argilla/ultrafeedback-binarized-preferences-cleaned/README.md`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned/blob/main/README.md),
## Differences with `argilla/ultrafeedback-binarized-preferences`
Thanks to the recent issue identified by [AllenAI](https://huggingface.co/allenai) related to the TruthfulQA contamination within the
original UltraFeedback dataset due to some prompts being reused from the TruthfulQA dataset (used for benchmarking
in the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) from HuggingFace H4), we also decided
to follow AllenAI's advice and remove those from the UltraFeedback dataset that we binarized using a completely different approach, which
implied using the average of the preference ratings rather than the critique overall score, as
[`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) did.
Besides that, we also saw that not only the rows with the `source=truthful_qa` were contamined (for obvious reasons), but also some
coming from ShareGPT, so we also removed those doing a left join with both subsets from the [`truthful_qa`](https://huggingface.co/datasets/truthful_qa) dataset.
Finally, we also modified the formatting to be aligned with both [`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized),
and [`allenai/ultrafeedback_binarized_cleaned`](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned) in order to ease
the integration within the [`huggingface/alignment-handbook`](https://github.com/huggingface/alignment-handbook) so that the formatting is standardized.
## Differences with `argilla/ultrafeedback-binarized-preferences-cleaned`
We kept the same pre-processing steps for cleaning the [`openbmb/UltraFeedback`](https://huggingface.co/datasets/openbmb/UltraFeedback) as well as the same preference
rating calculation towards deciding whether to select a response as chosen or rejected, the difference is that this one is using a multi-binarization approach, where each chosen response has its own row with one different row
depending on the number of rejected responses, meaning that for the same prompt we will have the same chosen response and different rejected responses.
## Reproduce
<a target="_blank" href="https://colab.research.google.com/drive/1CTvQq_HmwuUPTuAboGLFDtqcel4xef-g?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
To reproduce the data processing combining both our approach and the suggestions from HuggingFace H4 w.r.t. the formatting and the ones from AllenAI to
remove the TruthfulQA contamination, feel free to run the attached Colab Notebook or just view it at [`notebook.ipynb`](./notebook.ipynb) within this repository.
From Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as
ourselves, as well as the whole community, benefit a lot from open source and we also want to give back.
## Citation
If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: https://huggingface.co/datasets/openbmb/UltraFeedback
Additionally, you may also want to cite our work with Notus 7B, which lead the curation of the UltraFeedback dataset:
```bibtex
@misc{notus2023,
author = {Alvaro Bartolome and Gabriel Martin and Daniel Vila},
title = {Notus},
year = {2023},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/argilla-io/notus}}
}
```
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argilla/ultrafeedback-multi-binarized-quality-preferences-cleaned | argilla | 2023-12-11T15:13:55Z | 26 | 2 | [
"region:us"
] | null | 2023-12-11T15:13:29Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen-rating
dtype: float64
- name: chosen-model
dtype: string
- name: rejected
list:
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dtype: string
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dtype: string
- name: rejected-rating
dtype: float64
- name: rejected-model
dtype: string
splits:
- name: train
num_bytes: 724022562.4845791
num_examples: 154663
download_size: 194977204
dataset_size: 724022562.4845791
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ppower1/hoopers | ppower1 | 2023-12-11T17:53:13Z | 26 | 0 | [
"region:us"
] | null | 2023-12-11T17:44:49Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 17775
num_examples: 100
download_size: 3106
dataset_size: 17775
---
# Dataset Card for "hoopers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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infinex/LaMini-en-id-sampled | infinex | 2023-12-12T08:32:37Z | 26 | 1 | [
"region:us"
] | null | 2023-12-12T07:23:45Z | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 28634119.980589807
num_examples: 69999
- name: test
num_bytes: 12271941.019410195
num_examples: 30000
download_size: 24029254
dataset_size: 40906061.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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ctam8736/papi_asr | ctam8736 | 2023-12-14T20:09:43Z | 26 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-14T19:41:24Z | ---
license: mit
dataset_info:
features:
- name: audio
dtype: audio
- name: transcript
dtype: string
splits:
- name: train
num_bytes: 2069046327.3903358
num_examples: 9291
- name: test
num_bytes: 512170761.8897977
num_examples: 2322
download_size: 2483932273
dataset_size: 2581217089.2801332
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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PedroCJardim/QASports | PedroCJardim | 2023-12-15T17:01:32Z | 25 | 2 | [
"task_categories:question-answering",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"sports",
"open-domain-qa",
"extractive-qa",
"region:us"
] | [
"question-answering"
] | 2023-07-14T17:28:19Z | ---
configs:
- config_name: all
data_files:
- split: train
path:
- "trainBask.csv"
- "trainSocc.csv"
- "trainFoot.csv"
- split: test
path:
- "testBask.csv"
- "testSocc.csv"
- "testFoot.csv"
- split: validation
path:
- "validationBask.csv"
- "validationSocc.csv"
- "validationFoot.csv"
default: true
- config_name: basketball
data_files:
- split: train
path: "trainBask.csv"
- split: test
path: "testBask.csv"
- split: validation
path: "validationBask.csv"
- config_name: football
data_files:
- split: train
path: "trainFoot.csv"
- split: test
path: "testFoot.csv"
- split: validation
path: "validationFoot.csv"
- config_name: soccer
data_files:
- split: train
path: "trainSocc.csv"
- split: test
path: "testSocc.csv"
- split: validation
path: "validationSocc.csv"
license: mit
task_categories:
- question-answering
language:
- en
tags:
- sports
- open-domain-qa
- extractive-qa
size_categories:
- 1M<n<10M
pretty_name: QASports
---
### Dataset Summary
QASports is the first large sports-themed question answering dataset counting over 1.5 million questions and answers about 54k preprocessed wiki pages, using as documents the wiki of 3 of the most popular sports in the world, Soccer, American Football and Basketball. Each sport can be downloaded individually as a subset, with the train, test and validation splits, or all 3 can be downloaded together.
- 🎲 Complete dataset: https://osf.io/n7r23/
- 🔧 Processing scripts: https://github.com/leomaurodesenv/qasports-dataset-scripts/
### Supported Tasks and Leaderboards
Extractive Question Answering.
### Languages
English.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"answer": {
"offset": [42,44],
"text": "16"
},
"context": "The following is a list of squads for all 16 national teams competing at the Copa América Centenario. Each national team had to submit a squad of 23 players, 3 of whom must be goalkeepers. The provisional squads were announced on 4 May 2016. A final selection was provided to the organisers on 20 May 2016." ,
"qa_id": "61200579912616854316543272456523433217",
"question": "How many national teams competed at the Copa América Centenario?",
"context_id": "171084087809998484545703642399578583178",
"context_title": "Copa América Centenario squads | Football Wiki | Fandom",
"url": "https://football.fandom.com/wiki/Copa_Am%C3%A9rica_Centenario_squads"
}
```
### Data Fields
The data fields are the same among all splits.
- '': int
- `id_qa`: a `string` feature.
- `context_id`: a `string` feature.
- `context_title`: a `string` feature.
- `url`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `offset`: a list feature containing:
- 2 `int32` features for start and end.
### Citation
```
@inproceedings{jardim:2023:qasports-dataset,
author={Pedro Calciolari Jardim and Leonardo Mauro Pereira Moraes and Cristina Dutra Aguiar},
title = {{QASports}: A Question Answering Dataset about Sports},
booktitle = {Proceedings of the Brazilian Symposium on Databases: Dataset Showcase Workshop},
address = {Belo Horizonte, MG, Brazil},
url = {https://github.com/leomaurodesenv/qasports-dataset-scripts},
publisher = {Brazilian Computer Society},
pages = {1-12},
year = {2023}
}
```
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AndriyBilinskiy/ukr-news | AndriyBilinskiy | 2023-12-11T14:22:21Z | 25 | 1 | [
"size_categories:10K<n<100K",
"language:uk",
"license:unknown",
"region:us"
] | null | 2023-12-08T17:55:15Z | ---
license: unknown
language:
- uk
size_categories:
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oraul/orca_small_splitted | oraul | 2023-12-11T07:15:55Z | 25 | 1 | [
"region:us"
] | null | 2023-12-11T06:58:53Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3842159531.8209243
num_examples: 851442
- name: test
num_bytes: 1152649664.5591998
num_examples: 255433
- name: valid
num_bytes: 493995936.6198757
num_examples: 109472
download_size: 2964895612
dataset_size: 5488805133.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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qazisaad/news_recommendations_vectorized | qazisaad | 2023-12-17T12:31:59Z | 25 | 0 | [
"region:us"
] | null | 2023-12-12T14:09:20Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: category
dtype: string
- name: sub-category
dtype: string
- name: title
dtype: string
- name: times
dtype: timestamp[ns]
- name: url
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 26046524
num_examples: 14499
download_size: 33361121
dataset_size: 26046524
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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mabounassif/nyt_ingredient_phrase_ner | mabounassif | 2023-12-14T19:27:25Z | 25 | 0 | [
"region:us"
] | null | 2023-12-14T15:17:42Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: input
dtype: string
- name: tokens
sequence: string
- name: tags
sequence:
class_label:
names:
'0': B-QTY
'1': I-QTY
'2': B-UNIT
'3': I-UNIT
'4': B-NAME
'5': I-NAME
'6': B-COMMENT
'7': I-COMMENT
'8': B-OTHER
splits:
- name: train
num_bytes: 19648513
num_examples: 125555
- name: test
num_bytes: 8395493
num_examples: 53652
download_size: 7627864
dataset_size: 28044006
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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abhishek/autotrain-data-i6l7-e3p1-lu90 | abhishek | 2023-12-14T20:55:19Z | 25 | 0 | [
"region:us"
] | null | 2023-12-14T20:55:13Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: autotrain_image
dtype: image
- name: autotrain_label
dtype:
class_label:
names:
'0': daisy
'1': dandelion
'2': rose
'3': sunflower
'4': tulip
splits:
- name: train
num_bytes: 114410927.672
num_examples: 2196
- name: validation
num_bytes: 33682367.0
num_examples: 550
download_size: 166945851
dataset_size: 148093294.672
---
# Dataset Card for "autotrain-data-i6l7-e3p1-lu90"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mikehemberger/darcai-life-on-earth | mikehemberger | 2023-12-16T18:32:28Z | 24 | 1 | [
"task_categories:zero-shot-classification",
"task_categories:translation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:feature-extraction",
"task_categories:sentence-similarity",
"size_categories:100K<n<1M",
"language:en",
"language:de",
"license:mit",
"biology",
"climate",
"code",
"region:us"
] | [
"zero-shot-classification",
"translation",
"summarization",
"conversational",
"feature-extraction",
"sentence-similarity"
] | 2023-11-04T15:21:45Z | ---
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': life-on-earth-s01-e01-the-infinite-varirty
'1': life-on-earth-s01-e02-building-bodies
'2': life-on-earth-s01-e03-the-first-forests
'3': life-on-earth-s01-e04-the-swarming-hordes
'4': life-on-earth-s01-e05-conquest-of-the-waters
'5': life-on-earth-s01-e06-invasion-of-the-land
'6': life-on-earth-s01-e07-victors-of-the-dry-land
'7': life-on-earth-s01-e08-lords-of-the-air
'8': life-on-earth-s01-e09-the-rise-of-the-mammals
'9': life-on-earth-s01-e10-themes-and-variations
'10': life-on-earth-s01-e11-the-hunters-and-the-hunted
'11': life-on-earth-s01-e12-life-in-the-trees
'12': life-on-earth-s01-e13-the-compulsive-communicators
- name: file_name
dtype: string
- name: show_name
dtype: string
- name: relative_path
dtype: string
- name: caption
dtype: string
splits:
- name: train
num_bytes: 1072256363
num_examples: 84323
download_size: 1059335669
dataset_size: 1072256363
license: mit
task_categories:
- zero-shot-classification
- translation
- summarization
- conversational
- feature-extraction
- sentence-similarity
language:
- en
- de
tags:
- biology
- climate
- code
---
# Dataset Card for "life-on-earth"
## Dataset Description
- **Homepage-Videos:** [https://archive.org/download/WildlifeDocumentaries]()
- **Homepage-Dataset:** [https://huggingface.co/datasets/mikehemberger/darcai-life-on-earth]()
- **Repository:** [https://github.com/mikehemberger/darc-ai]()
- **Point of Contact:** [[email protected]]()
### Dataset Summary
The David Attenborough Research Consortium (DARC) loves David Attenborough (DA). And therefore we aim to enrich his fantastic work using modern deep learning, generative artificial intelligence (AI) methods and most recent assistants like ChatGPT. Those results, together with extracted and time stamped image frames ("frame_00000_hh-mm-ss.msmsms.jpg", ...) from videos constitutes the darcai-life-on-earth dataset.
As a first enrichment, we include text captions generated by the huggingface "Salesforce/blip2-opt-2.7b" model for >84K image frames as a ready-to-go dataset.
Furthermore our [https://huggingface.co/datasets/mikehemberger/darcai-life-on-earth](github-repo-page) includes ViT image embeddings (dim=768) and caption-txt embeddings (using openAIs "text-embedding-ada-002" model, dim=1536) for all >84K images.
### Languages
Native english mostly. Some german. Hopefully many more soon.
## Dataset Structure
### Data Instances
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=622x360>,
'label': 0,
'file_name': 'frame_00000_00-00-00.000.jpg',
'show_name': 'life-on-earth-s01-e01-the-infinite-varirty',
'relative_path': 'images/life-on-earth/life-on-earth-s01-e01-the-infinite-varirty',
'caption': 'a black background with a white clock on it'
}
### Data Fields
- image: a PIL image frame extracted from video (decode=True)
- label: One of [0-12] according to 13 episodes
- file_name: file name of the PIL image
- show_name: name of the show and episode from which the images were extracted
- relative_path: where to find the images
- caption: text caption for the image generated by huggingface transformers blip2 model ("Salesforce/blip2-opt-2.7b")
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3838seungsheon/Ko_test_1.0 | 3838seungsheon | 2023-12-12T03:53:46Z | 24 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-05T06:24:34Z | ---
license: apache-2.0
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 7629
num_examples: 9
download_size: 10603
dataset_size: 7629
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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novus677/nlp-xsum-test-tinyllama | novus677 | 2023-12-11T07:50:04Z | 24 | 1 | [
"region:us"
] | null | 2023-12-11T07:50:02Z | ---
dataset_info:
features:
- name: summary
dtype: string
- name: prompt
dtype: string
splits:
- name: test
num_bytes: 27311507
num_examples: 11334
download_size: 17007395
dataset_size: 27311507
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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SAGI-1/SYMBOLIC_DATA_PLUS_REASONING_DATA_V1 | SAGI-1 | 2023-12-11T11:16:42Z | 24 | 1 | [
"region:us"
] | null | 2023-12-11T11:16:39Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 163136511
num_examples: 225203
download_size: 84346513
dataset_size: 163136511
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Yunij/kaggle-comp-daigt | Yunij | 2023-12-11T12:59:25Z | 24 | 0 | [
"region:us"
] | null | 2023-12-11T12:39:26Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: prompt_name
dtype: string
- name: source
dtype: string
- name: RDizzl3_seven
dtype: bool
- name: cleaned_text
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 300562033
num_examples: 44868
download_size: 134227572
dataset_size: 300562033
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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agil/EUIPO_QA | agil | 2023-12-17T09:57:20Z | 24 | 1 | [
"region:us"
] | null | 2023-12-11T14:51:44Z | ---
dataset_info:
features:
- name: ID
dtype: int64
- name: question
dtype: string
- name: source
dtype: string
- name: answer
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 146133.64788732395
num_examples: 227
- name: test
num_bytes: 36694.35211267605
num_examples: 57
download_size: 98028
dataset_size: 182828.0
---
# Dataset Card for "EUIPO_QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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louisbrulenaudet/code-civil | louisbrulenaudet | 2023-12-12T10:41:13Z | 24 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code civil",
"doi:10.57967/hf/1442",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T01:26:22Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code civil
source_datasets:
- original
pretty_name: Code civil
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code civil, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code civil, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-civil}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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hotal/motivation_alpaca | hotal | 2023-12-13T16:04:24Z | 24 | 1 | [
"region:us"
] | null | 2023-12-13T16:04:23Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: system
dtype: string
splits:
- name: train
num_bytes: 227789
num_examples: 748
download_size: 63040
dataset_size: 227789
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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arbml/alpagasus_cleaned_ar_reviewed_v4 | arbml | 2023-12-18T01:19:20Z | 23 | 0 | [
"region:us"
] | null | 2023-11-28T15:25:58Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input_en
dtype: string
- name: output_en
dtype: string
- name: instruction
dtype: string
- name: instruction_en
dtype: string
- name: index
dtype: string
- name: Reviewed by
dtype: string
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 10777288
num_examples: 10001
download_size: 5932722
dataset_size: 10777288
---
# Dataset Card for "alpagasus_cleaned_ar_reviewed_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Adi-0-0-Gupta/Camera-Blur | Adi-0-0-Gupta | 2023-12-14T08:04:45Z | 23 | 0 | [
"region:us"
] | null | 2023-11-29T06:47:25Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: image
struct:
- name: bytes
dtype: binary
- name: path
dtype: 'null'
- name: label
dtype: int64
splits:
- name: train
num_bytes: 357658018
num_examples: 288
- name: valid
num_bytes: 79675207
num_examples: 79
download_size: 431693779
dataset_size: 437333225
---
# Dataset Card for "Camera-Blur"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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UserGnalin/cuberbullying-instagram-tiktok | UserGnalin | 2023-12-17T01:08:57Z | 23 | 0 | [
"license:cc",
"region:us"
] | null | 2023-12-06T07:58:47Z | ---
license: cc
---
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gokul00060/datasetv3 | gokul00060 | 2023-12-12T10:50:58Z | 23 | 1 | [
"license:mit",
"region:us"
] | null | 2023-12-07T15:38:04Z | ---
license: mit
---
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athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED | athirdpath | 2023-12-11T02:44:37Z | 23 | 1 | [
"language:en",
"license:cc-by-nc-4.0",
"not-for-all-audiences",
"region:us"
] | null | 2023-12-11T02:43:02Z | ---
license: cc-by-nc-4.0
language:
- en
tags:
- not-for-all-audiences
---
### Description
\~3.4k DPO pairs, generated by [Iambe](https://huggingface.co/athirdpath/Iambe-20b-DARE-v2-GGUF) feat. GPT-4 (~10% GPT-4, ~80% Iambe @ q5_k_m / ~10% Iambe @ q6_k) with temp 1.2 and min_p 0.15.
They are shuffled this time, as I was not aware that TRL did not do that automatically until I could see the shifts in the dataset mirrored in the loss patterns.
Iambe is a smart girl, so both the chosen and rejected for each pair are generated at the same time from a single two part prompt (not the one in the dataset). Only a few dozen failed to generate the rejected response, and in those cases I filled in the rejected output with a standard "as an AI" style refusal. The way I set things up caused any prompt formatting errors to automatically go into the REJECTED field, hopefully discouraging such behavior.
The dataset is mostly intended for ERP, so erotic situations are an outsized portion of the dataset. However, it isn't all sexual, with sport, adventure, etc content to provide a baseline.
### Downsides and Weaknesses
This dataset has not been manually cleaned, besides some basic search-and-replace. This dataset has been overseen in bursts (such content passed muster or the entire batch was rejected) but much of the content has yet to be read by a human. Some chosen responses have the AI acting for the user, I've removed what I've seen but this is a lot to manually filter. Despite my best efforts to prompt away from it, some rejected responses are... what seems to be... let's say, in a certain voice? I really tried to avoid it, and most of it was removed.
### Goals
This dataset is intended to be used to produce a BETTER Iambe, that can itself produce even more data of higher quality. Bootstraps to the moon, baby! So, while this dataset still has rough points, I feel like it's worth uploading and starting to train the model. I also want to combine data generated by more models, as to not just amplify what is already Iambe's signal.
### Bonus
I've also added the notebook I used to train [athirdpath/Iambe-20b-v3_TEST-RP_cDPO](https://huggingface.co/athirdpath/Iambe-20b-v3_TEST-RP_cDPO) on runpod, adapted from a Kaggle by [@maximelabonne](https://twitter.com/maximelabonne) (thank you!) | [
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shireesh-uop/nhs_classification | shireesh-uop | 2023-12-13T08:01:45Z | 23 | 0 | [
"region:us"
] | null | 2023-12-13T07:45:14Z | ---
dataset_info:
features:
- name: label
dtype: string
- name: data
dtype: string
- name: idx
dtype: int64
splits:
- name: train
num_bytes: 3574265
num_examples: 27124
download_size: 1511963
dataset_size: 3574265
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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HuggingFaceH4/ultrafeedback-multiple-preferences-all-cleaned2 | HuggingFaceH4 | 2023-12-14T13:44:15Z | 23 | 0 | [
"region:us"
] | null | 2023-12-14T13:43:54Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen-rating
dtype: float64
- name: chosen-model
dtype: string
- name: rejected
list:
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_prefs
num_bytes: 441650116.7648768
num_examples: 58917
- name: train_sft
num_bytes: 441650116.7648768
num_examples: 58917
- name: test_prefs
num_bytes: 14992281.2351232
num_examples: 2000
- name: test_sft
num_bytes: 7496140.6175616
num_examples: 1000
download_size: 417683684
dataset_size: 905788655.3824383
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
- split: train_sft
path: data/train_sft-*
- split: test_prefs
path: data/test_prefs-*
- split: test_sft
path: data/test_sft-*
---
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krishan-CSE/HatEval_Emotion_Roberta_Base | krishan-CSE | 2023-12-16T14:31:29Z | 23 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T13:45:58Z | ---
license: apache-2.0
---
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taesiri/arxiv_qa | taesiri | 2023-12-15T07:46:30Z | 22 | 114 | [
"task_categories:question-answering",
"language:en",
"license:mit",
"arxiv:2312.07541",
"arxiv:2312.07540",
"arxiv:2312.07539",
"arxiv:2312.07538",
"arxiv:2312.07537",
"arxiv:2312.07536",
"arxiv:2312.07535",
"arxiv:2312.07534",
"arxiv:2312.07533",
"arxiv:2312.07532",
"arxiv:2312.07531",
"arxiv:2312.07530",
"arxiv:2312.07529",
"arxiv:2312.07527",
"arxiv:2312.07526",
"arxiv:2312.07523",
"arxiv:2312.07516",
"arxiv:2312.07514",
"arxiv:2312.07511",
"arxiv:2312.07509",
"arxiv:2312.07507",
"arxiv:2312.07504",
"arxiv:2312.07500",
"arxiv:2312.07495",
"arxiv:2312.07492",
"region:us"
] | [
"question-answering"
] | 2023-07-11T16:14:06Z | ---
license: mit
task_categories:
- question-answering
language:
- en
pretty_name: ArXiv QA
---
# ArXiv QA
(TBD) Automated ArXiv question answering via large language models
[Github](https://github.com/taesiri/ArXivQA) | [Homepage](https://arxiv.taesiri.xyz/) | [Simple QA - Hugging Face Space](https://huggingface.co/spaces/taesiri/ClaudeReadsArxiv)
---
# Automated Question Answering with ArXiv Papers
## Latest 25 Papers
- SMERF: Streamable Memory Efficient Radiance Fields for Real-Time
Large-Scene Exploration - [[Arxiv](https://arxiv.org/abs/2312.07541)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07541.md)]
- diff History for Long-Context Language Agents - [[Arxiv](https://arxiv.org/abs/2312.07540)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07540.md)]
- HeadArtist: Text-conditioned 3D Head Generation with Self Score
Distillation - [[Arxiv](https://arxiv.org/abs/2312.07539)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07539.md)]
- Anatomically Constrained Implicit Face Models - [[Arxiv](https://arxiv.org/abs/2312.07538)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07538.md)]
- FreeInit: Bridging Initialization Gap in Video Diffusion Models - [[Arxiv](https://arxiv.org/abs/2312.07537)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07537.md)]
- FreeControl: Training-Free Spatial Control of Any Text-to-Image
Diffusion Model with Any Condition - [[Arxiv](https://arxiv.org/abs/2312.07536)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07536.md)]
- Improved Frequency Estimation Algorithms with and without Predictions - [[Arxiv](https://arxiv.org/abs/2312.07535)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07535.md)]
- Cosmological Field Emulation and Parameter Inference with Diffusion
Models - [[Arxiv](https://arxiv.org/abs/2312.07534)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07534.md)]
- VILA: On Pre-training for Visual Language Models - [[Arxiv](https://arxiv.org/abs/2312.07533)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07533.md)]
- Interfacing Foundation Models' Embeddings - [[Arxiv](https://arxiv.org/abs/2312.07532)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07532.md)]
- WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion - [[Arxiv](https://arxiv.org/abs/2312.07531)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07531.md)]
- Weakly Supervised 3D Object Detection via Multi-Level Visual Guidance - [[Arxiv](https://arxiv.org/abs/2312.07530)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07530.md)]
- Topological Obstructions and How to Avoid Them - [[Arxiv](https://arxiv.org/abs/2312.07529)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07529.md)]
- BaRDa: A Belief and Reasoning Dataset that Separates Factual Accuracy
and Reasoning Ability - [[Arxiv](https://arxiv.org/abs/2312.07527)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07527.md)]
- RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose
Estimation - [[Arxiv](https://arxiv.org/abs/2312.07526)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07526.md)]
- Self-Healing Distributed Swarm Formation Control Using Image Moments - [[Arxiv](https://arxiv.org/abs/2312.07523)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07523.md)]
- Learning finitely correlated states: stability of the spectral
reconstruction - [[Arxiv](https://arxiv.org/abs/2312.07516)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07516.md)]
- Integrated and Lightweight Design of Electro-hydraulic Ankle Prosthesis - [[Arxiv](https://arxiv.org/abs/2312.07514)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07514.md)]
- A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems - [[Arxiv](https://arxiv.org/abs/2312.07511)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07511.md)]
- PEEKABOO: Interactive Video Generation via Masked-Diffusion - [[Arxiv](https://arxiv.org/abs/2312.07509)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07509.md)]
- NAC-TCN: Temporal Convolutional Networks with Causal Dilated
Neighborhood Attention for Emotion Understanding - [[Arxiv](https://arxiv.org/abs/2312.07507)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07507.md)]
- COLMAP-Free 3D Gaussian Splatting - [[Arxiv](https://arxiv.org/abs/2312.07504)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07504.md)]
- Multi-Branch Network for Imagery Emotion Prediction - [[Arxiv](https://arxiv.org/abs/2312.07500)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07500.md)]
- Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly
Detection - [[Arxiv](https://arxiv.org/abs/2312.07495)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07495.md)]
- SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in
Generative Language Models - [[Arxiv](https://arxiv.org/abs/2312.07492)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2312.07492.md)]
## List of Papers by Year
- [Papers for 2023](https://github.com/taesiri/ArXivQA/blob/main/Papers-2023.md)
- [Papers for 2022](https://github.com/taesiri/ArXivQA/blob/main/Papers-2022.md)
- [Papers for 2021](https://github.com/taesiri/ArXivQA/blob/main/Papers-2021.md)
- [Papers for 2020](https://github.com/taesiri/ArXivQA/blob/main/Papers-2020.md)
- [Papers for 2019](https://github.com/taesiri/ArXivQA/blob/main/Papers-2019.md)
- [Papers for 2018](https://github.com/taesiri/ArXivQA/blob/main/Papers-2018.md)
- [Papers for 2017](https://github.com/taesiri/ArXivQA/blob/main/Papers-2017.md)
- [Papers for 2016](https://github.com/taesiri/ArXivQA/blob/main/Papers-2016.md)
- [Papers for 2015](https://github.com/taesiri/ArXivQA/blob/main/Papers-2015.md)
- [Papers for 2014](https://github.com/taesiri/ArXivQA/blob/main/Papers-2014.md)
- [Papers for 2013](https://github.com/taesiri/ArXivQA/blob/main/Papers-2013.md)
- [Papers for 2012](https://github.com/taesiri/ArXivQA/blob/main/Papers-2012.md)
- [Papers for 2010](https://github.com/taesiri/ArXivQA/blob/main/Papers-2010.md)
- [Papers for 2009](https://github.com/taesiri/ArXivQA/blob/main/Papers-2009.md)
## Acknowledgements
This project is made possible through the generous support of
[Anthropic](https://www.anthropic.com/), who provided free access to the `Claude-2.1` API.
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] |
piotr-rybak/legal-questions | piotr-rybak | 2023-12-14T10:01:28Z | 22 | 0 | [
"language:pl",
"region:us"
] | null | 2023-08-23T09:57:44Z | ---
language:
- pl
library_name: transformers
---
This dataset contains questions and passages from Polish law.
The dataset was created by randomly searching for provisions and asking questions related to that provision, in the
style of SQuAD. As a result, the questions might be biassed towards the content of a specific provision.
The authors of this dataset are student from AGH University of Krakow, supervised by [Aleksander
Smywiński-Pohl](https://huggingface.co/apohllo), PhD.
If you use the dataset, please cite the following article:
```
@article{kobylinski2023poleval,
title={PolEval 2022/23 Challenge Tasks and Results},
author={Kobylinski, {\L}ukasz and Ogrodniczuk, Maciej and Rybak, Piotr and Przyby{\l}a, Piotr and Pezik, Piotr and Miko{\l}ajczyk, Agnieszka and Janowski, Wojciech and Marcinczuk, Micha{\l} and Smywinski-Pohl, Aleksander},
year={2023},
journal={Proceedings of the 18th Conference on Computer Science and Intelligence Systems},
pages={1243–1250}
}
```
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projecte-aina/NLUCat | projecte-aina | 2023-12-12T11:56:28Z | 22 | 0 | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:text-generation",
"task_ids:intent-classification",
"task_ids:named-entity-recognition",
"task_ids:language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"language:ca",
"arxiv:1903.05566",
"region:us"
] | [
"text-classification",
"token-classification",
"text-generation"
] | 2023-09-20T15:53:38Z | ---
annotations_creators:
- expert-generated
language:
- ca
language_creators:
- expert-generated
license: []
multilinguality:
- monolingual
pretty_name: NLUCat - Natural Language Understanding in Catalan
size_categories:
- 10M<n<100M
source_datasets: []
tags: []
task_categories:
- text-classification
- token-classification
- text-generation
task_ids:
- intent-classification
- named-entity-recognition
- language-modeling
---
# Dataset Card for NLUCat
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
NLUCat is a dataset of NLU in Catalan. It consists of nearly 12,000 instructions annotated with the most relevant intents and spans. Each instruction is accompanied, in addition, by the instructions received by the annotator who wrote it.
The intents taken into account are the habitual ones of a virtual home assistant (activity calendar, IOT, list management, leisure, etc.), but specific ones have also been added to take into account social and healthcare needs for vulnerable people (information on administrative procedures, menu and medication reminders, etc.).
The spans have been annotated with a tag describing the type of information they contain. They are fine-grained, but can be easily grouped to use them in robust systems.
The examples are not only written in Catalan, but they also take into account the geographical and cultural reality of the speakers of this language (geographic points, cultural references, etc.)
This dataset can be used to train models for intent classification, spans identification and examples generation.
<b>This is a simplified version of the dataset for training and evaluating intent classifiers. The full dataset and the annotation guideslines can be found in [Zenodo](https://zenodo.org/records/10362026)</b>
This work is licensed under a [CC0 International License](https://creativecommons.org/publicdomain/zero/1.0/).
### Supported Tasks and Leaderboards
Intent classification, spans identification and examples generation.
### Languages
The dataset is in Catalan (ca-ES).
## Dataset Structure
### Data Instances
Three JSON files, one for each split.
### Data Fields
* example: `str`. Example
* annotation: `dict`. Annotation of the example
* intent: `str`. Intent tag
* slots: `list`. List of slots
* Tag:`str`. tag to the slot
* Text:`str`. Text of the slot
* Start_char: `int`. First character of the span
* End_char: `int`. Last character of the span
#### Example
An example looks as follows:
```
{
"example": "Demana una ambulància; la meva dona està de part.",
"annotation": {
"intent": "call_emergency",
"slots": [
{
"Tag": "service",
"Text": "ambulància",
"Start_char": 11,
"End_char": 21
},
{
"Tag": "situation",
"Text": "la meva dona està de part",
"Start_char": 23,
"End_char": 48
}
]
}
},
```
### Data Splits
* NLUCat.train: 9128 examples
* NLUCat.dev: 1441 examples
* NLUCat.test: 1441 examples
### Statistics
| | test | dev | train | Total |
|-|-|-|-|-|
| alarm_query | 14 | 9 | 68 | 91 |
| alarm_remove | 10 | 12 | 68 | 90 |
| alarm_set | 11 | 10 | 69 | 90 |
| app_end | 8 | 9 | 43 | 60 |
| app_launch | 9 | 7 | 47 | 63 |
| audio_volume_down | 15 | 16 | 105 | 136 |
| audio_volume_mute | 8 | 9 | 62 | 79 |
| audio_volume_up | 14 | 16 | 101 | 131 |
| book restaurant | 31 | 27 | 182 | 240 |
| calendar_query | 34 | 38 | 227 | 299 |
| calendar_remove | 31 | 33 | 211 | 275 |
| calendar_set | 50 | 53 | 340 | 443 |
| call_emergency | 14 | 18 | 111 | 143 |
| call_medicalService | 14 | 11 | 70 | 95 |
| call_person | 23 | 18 | 116 | 157 |
| call_service | 6 | 9 | 45 | 60 |
| compare_places | 6 | 7 | 47 | 60 |
| contact_add | 20 | 22 | 138 | 180 |
| contact_query | 16 | 16 | 89 | 121 |
| cooking_query | 13 | 12 | 65 | 90 |
| cooking_recipe | 9 | 10 | 74 | 93 |
| datetime_convert | 14 | 14 | 95 | 123 |
| datetime_query | 18 | 17 | 112 | 147 |
| general_affirm | 6 | 6 | 18 | 30 |
| general_commandstop | 13 | 13 | 75 | 101 |
| general_confirm | 6 | 6 | 48 | 60 |
| general_dontcare | 8 | 6 | 46 | 60 |
| general_explain | 5 | 5 | 7 | 17 |
| general_greet | 13 | 10 | 67 | 90 |
| general_joke | 10 | 11 | 69 | 90 |
| general_negate | 12 | 9 | 69 | 90 |
| general_praise | 15 | 10 | 65 | 90 |
| general_quirky | 15 | 14 | 99 | 128 |
| general_repeat | 11 | 14 | 65 | 90 |
| generat_explain | 8 | 7 | 58 | 73 |
| iot_cleaning | 11 | 9 | 70 | 90 |
| iot_coffee | 10 | 12 | 68 | 90 |
| iot_hue_lightchange | 9 | 12 | 69 | 90 |
| iot_hue_lightdim | 14 | 12 | 64 | 90 |
| iot_hue_lightoff | 10 | 11 | 70 | 91 |
| iot_hue_lighton | 11 | 14 | 66 | 91 |
| iot_hue_lightup | 10 | 9 | 70 | 89 |
| iot_wemo_off | 11 | 13 | 65 | 89 |
| iot_wemo_on | 6 | 8 | 46 | 60 |
| lists_createoradd | 19 | 16 | 115 | 150 |
| lists_query | 15 | 15 | 92 | 122 |
| lists_remove | 14 | 14 | 91 | 119 |
| medReminder_query | 18 | 17 | 108 | 143 |
| medReminder_set | 17 | 17 | 113 | 147 |
| medicalAppointment_query | 20 | 19 | 114 | 153 |
| medicalAppointment_set | 24 | 23 | 165 | 212 |
| menu_query | 15 | 17 | 113 | 145 |
| message_query | 21 | 20 | 140 | 181 |
| message_send | 26 | 24 | 162 | 212 |
| music_dislikeness | 10 | 9 | 69 | 88 |
| music_likeness | 11 | 9 | 71 | 91 |
| music_query | 22 | 23 | 135 | 180 |
| music_settings | 9 | 9 | 63 | 81 |
| news_query | 19 | 22 | 149 | 190 |
| play_audiobook | 12 | 15 | 93 | 120 |
| play_game | 12 | 11 | 67 | 90 |
| play_music | 41 | 45 | 271 | 357 |
| play_podcasts | 20 | 19 | 121 | 160 |
| play_radio | 20 | 20 | 115 | 155 |
| play_video | 15 | 15 | 90 | 120 |
| qa_currency | 12 | 9 | 69 | 90 |
| qa_definition | 19 | 23 | 147 | 189 |
| qa_factoid | 26 | 24 | 143 | 193 |
| qa_maths | 13 | 12 | 95 | 120 |
| qa_medicalService | 20 | 21 | 117 | 158 |
| qa_procedures | 36 | 33 | 220 | 289 |
| qa_service | 16 | 18 | 112 | 146 |
| qa_sports | 9 | 9 | 72 | 90 |
| qa_stock | 13 | 10 | 67 | 90 |
| recommendation_events | 22 | 22 | 143 | 187 |
| recommendation_locations | 23 | 24 | 157 | 204 |
| recommendation_movies | 18 | 23 | 139 | 180 |
| share_currentLocation | 15 | 13 | 92 | 120 |
| social_post | 19 | 20 | 112 | 151 |
| social_query | 14 | 14 | 96 | 124 |
| takeaway_order | 20 | 25 | 135 | 180 |
| takeaway_query | 7 | 9 | 50 | 66 |
| transport_directions | 28 | 24 | 181 | 233 |
| transport_query | 31 | 31 | 185 | 247 |
| transport_taxi | 26 | 22 | 132 | 180 |
| transport_ticket | 25 | 25 | 160 | 210 |
| transport_traffic | 15 | 17 | 88 | 120 |
| weather_query | 31 | 29 | 189 | 249 |
| *Total* | *1440* | *1440* | *9117* | *11997* |
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
When creating this dataset, we took into account not only the language but the entire socio-cultural reality of the Catalan-speaking population. Special consideration was also given to the needs of the vulnerable population.
### Source Data
#### Initial Data Collection and Normalization
We commissioned a company to create fictitious examples for the creation of this dataset.
#### Who are the source language producers?
We commissioned the writing of the examples to the company [m47 labs](https://www.m47labs.com/).
### Annotations
#### Annotation process
The elaboration of this dataset has been done in three steps, taking as a model the process followed by the [NLU-Evaluation-Data](https://github.com/xliuhw/NLU-Evaluation-Data) dataset, as explained in the [paper](https://arxiv.org/abs/1903.05566).
* First step: translation or elaboration of the instructions given to the annotators to write the examples.
* Second step: writing the examples. This step also includes the grammatical correction and normalization of the texts.
* Third step: recording the attempts and the slots of each example. In this step, some modifications were made to the annotation guides to adjust them to the real situations.
#### Who are the annotators?
The drafting of the examples and their annotation was entrusted to the company [m47 labs](https://www.m47labs.com/) through a public tender process.
### Personal and Sensitive Information
No personal or sensitive information included.
The examples used for the preparation of this dataset are fictitious and, therefore, the information shown is not real.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that this dataset will help the development of virtual assistants in Catalan, a language that is often not taken into account, and that it will especially help to improve the quality of life of people with special needs.
### Discussion of Biases
When writing the examples, the annotators were asked to take into account the socio-cultural reality (geographic points, artists and cultural references, etc.) of the Catalan-speaking population.
Likewise, they were asked to be careful to avoid examples that reinforce the stereotypes that exist in this society. For example: be careful with the gender or origin of personal names that are associated with certain activities.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a [CC0 International License](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
[DOI](https://doi.org/10.5281/zenodo.10362026)
### Contributions
The drafting of the examples and their annotation was entrusted to the company [m47 labs](https://www.m47labs.com/) through a public tender process.
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ekolasky/RelevantTextForCustomLEDForQA650 | ekolasky | 2023-12-14T00:08:23Z | 22 | 0 | [
"region:us"
] | null | 2023-11-17T17:40:53Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: start_positions
sequence: int64
- name: end_positions
sequence: int64
- name: global_attention_mask
sequence: int64
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 36495790
num_examples: 586
- name: validation
num_bytes: 4341131
num_examples: 65
download_size: 4313316
dataset_size: 40836921
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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louisbrulenaudet/cgi | louisbrulenaudet | 2023-12-11T15:13:33Z | 22 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"tax",
"llm",
"fiscal",
"cgi",
"Code Général des Impôts",
"doi:10.57967/hf/1438",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-11T15:12:43Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- tax
- llm
- fiscal
- cgi
- Code Général des Impôts
source_datasets:
- original
pretty_name: Code Général des Impôts (CGI)
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code Général des Impôts, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for tax practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code Général des Impôts, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/cgi}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-consommation | louisbrulenaudet | 2023-12-12T10:43:21Z | 22 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code de la consommation",
"doi:10.57967/hf/1446",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-11T19:38:36Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de la consommation
source_datasets:
- original
pretty_name: Code de la consommation
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de la consommation, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code de la consommation, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-consommation}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-penal | louisbrulenaudet | 2023-12-12T10:42:07Z | 22 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code pénal",
"doi:10.57967/hf/1444",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T01:08:16Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code pénal
source_datasets:
- original
pretty_name: Code pénal
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code pénal, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code pénal, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-penal}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-commerce | louisbrulenaudet | 2023-12-12T10:40:35Z | 22 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code de commerce",
"doi:10.57967/hf/1448",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T01:47:31Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de commerce
source_datasets:
- original
pretty_name: Code de commerce
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de commerce, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code de commerce, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-commerce}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-environnement | louisbrulenaudet | 2023-12-12T11:19:32Z | 22 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code de l'environnement",
"doi:10.57967/hf/1451",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T11:19:29Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de l'environnement
source_datasets:
- original
pretty_name: Code de l'environnement
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de l'environnement, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code de l'environnement, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-environnement}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-procedure-civile | louisbrulenaudet | 2023-12-12T17:13:46Z | 22 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code de procédure civile",
"doi:10.57967/hf/1453",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T17:10:45Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de procédure civile
source_datasets:
- original
pretty_name: Code de procédure civile
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de procédure civile, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code de procédure civile, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-procedure-civile}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-monetaire-financier | louisbrulenaudet | 2023-12-12T17:33:46Z | 22 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code monétaire et financier",
"doi:10.57967/hf/1454",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T17:33:44Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code monétaire et financier
source_datasets:
- original
pretty_name: Code monétaire et financier
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code monétaire et financier, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code monétaire et financier, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-monetaire-financier}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-travail | louisbrulenaudet | 2023-12-12T18:34:20Z | 22 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code du travail",
"doi:10.57967/hf/1456",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T18:34:09Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code du travail
source_datasets:
- original
pretty_name: Code du travail
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code du travail, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code du travail, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-travail}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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louisbrulenaudet/code-propriete-intellectuelle | louisbrulenaudet | 2023-12-12T19:09:16Z | 22 | 0 | [
"task_categories:text-generation",
"task_categories:table-question-answering",
"task_categories:summarization",
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"finetuning",
"legal",
"french law",
"droit français",
"Code de la propriété intellectuelle",
"doi:10.57967/hf/1460",
"region:us"
] | [
"text-generation",
"table-question-answering",
"summarization",
"conversational"
] | 2023-12-12T19:09:13Z | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de la propriété intellectuelle
source_datasets:
- original
pretty_name: Code de la propriété intellectuelle
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code de la propriété intellectuelle, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code de la propriété intellectuelle, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-propriete-intellectuelle}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | [
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mnoukhov/openai_summarize_generated_10k | mnoukhov | 2023-12-15T12:57:46Z | 22 | 0 | [
"region:us"
] | null | 2023-12-15T12:57:44Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 18033740
num_examples: 10000
download_size: 10969719
dataset_size: 18033740
---
# Dataset Card for "openai_summarize_generated_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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markus583/mC4-TEST | markus583 | 2023-12-16T13:06:06Z | 22 | 0 | [
"region:us"
] | null | 2023-12-16T11:47:34Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: ends_with_punctuation
dtype: bool
- name: lang
dtype: string
splits:
- name: train
num_bytes: 8278759161
num_examples: 32228876
- name: valid
num_bytes: 8492147
num_examples: 32286
download_size: 4147996411
dataset_size: 8287251308
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
---
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mhhmm/tailieuykhoa_002 | mhhmm | 2023-12-16T14:26:06Z | 22 | 0 | [
"region:us"
] | null | 2023-12-16T14:06:50Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2940770
num_examples: 1663
download_size: 1306213
dataset_size: 2940770
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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-0.36448970437049866,
-0.3677719831466675,
0.07989624887704849,
-0.33809754252433777,
0.3549823462963104,
0.826819658279419,
-0.2538073658943176,
-0.22041857242584229,
-0.9505581259727478,
0.4752943813800812,
0.31025266647338867,
-0.5886633992195129,
-0.05114373564720154,
0.32939136028289795,
0.4523690342903137,
0.3009701073169708,
0.5239560604095459,
0.1042821854352951,
0.897053062915802,
0.25200414657592773,
0.3049139678478241,
-0.04526636749505997,
-0.590078592300415,
-0.01606634259223938,
0.2621476650238037,
0.04487854242324829,
-0.6869446039199829
] |
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