datasetId
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| author
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42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
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| likes
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Ja-ck/Orca-DPO-Pairs-KO | Ja-ck | 2023-12-12T01:58:39Z | 14 | 0 | [
"size_categories:10K<n<100K",
"language:ko",
"license:apache-2.0",
"region:us"
] | null | 2023-12-12T01:56:54Z | ---
license: apache-2.0
language:
- ko
size_categories:
- 10K<n<100K
---
# Orca-DPO-Pairs-KO
Intel/orca_dpo_pais 를 한국어로 번역한 데이터세트 입니다.
번역은 maywell/Syntra-7B-v0.3-Translation 을 사용했습니다. 번역 후 일부 오류가 발생한 라인은 삭제했기 때문에 원본과 차이가 있을 수 있습니다. | [
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ineoApp/factures_dataset_99 | ineoApp | 2023-12-13T16:00:20Z | 14 | 0 | [
"region:us"
] | null | 2023-12-12T09:30:46Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': numero facture
'2': fournisseur
'3': date facture
'4': date limite
'5': montant ht
'6': montant ttc
'7': tva
'8': prix tva
'9': addresse
'10': reference
'11': art1 designation
'12': art1 quantite
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- name: tokens
sequence: string
splits:
- name: train
num_bytes: 29250169.548387095
num_examples: 24
- name: test
num_bytes: 8531299.451612903
num_examples: 7
download_size: 30317265
dataset_size: 37781469.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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feliks912/IAmAaronWill_tweets_2173 | feliks912 | 2023-12-12T23:35:55Z | 14 | 0 | [
"license:unknown",
"region:us"
] | null | 2023-12-12T13:21:39Z | ---
license: unknown
---
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cwiz/art-kris | cwiz | 2023-12-13T13:00:30Z | 14 | 0 | [
"region:us"
] | null | 2023-12-13T12:34:32Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 598891471.0
num_examples: 201
download_size: 596708110
dataset_size: 598891471.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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dhiyagavaeikar/llama2_mental_health_conversations_smaller_dataset | dhiyagavaeikar | 2023-12-14T07:24:01Z | 14 | 0 | [
"region:us"
] | null | 2023-12-14T07:04:06Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 459521
num_examples: 346
download_size: 243309
dataset_size: 459521
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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shredder-31/Mic_QG_QusestionsData | shredder-31 | 2023-12-15T22:09:10Z | 14 | 0 | [
"region:us"
] | null | 2023-12-15T12:12:16Z | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 9999795
num_examples: 5940
- name: dev
num_bytes: 4972591
num_examples: 2970
- name: test
num_bytes: 3330037
num_examples: 1980
download_size: 9643395
dataset_size: 18302423
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
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alperiox/weapons_captions | alperiox | 2023-12-16T13:57:38Z | 14 | 0 | [
"region:us"
] | null | 2023-12-16T13:57:36Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 603040.0
num_examples: 16
download_size: 605235
dataset_size: 603040.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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mnoukhov/openai_summarize_tldr_rbaseline | mnoukhov | 2023-12-16T23:27:30Z | 14 | 0 | [
"region:us"
] | null | 2023-12-16T23:27:23Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: prompt
dtype: string
- name: label
dtype: string
- name: reward_baseline
dtype: float32
splits:
- name: train
num_bytes: 181611007
num_examples: 116722
- name: valid
num_bytes: 3120685
num_examples: 2000
download_size: 113145941
dataset_size: 184731692
---
# Dataset Card for "openai_summarize_tldr_rbaseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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bdsaglam/web_nlg-erx-sft-multi-turn-llama2 | bdsaglam | 2023-12-17T20:15:25Z | 14 | 0 | [
"region:us"
] | null | 2023-12-17T15:26:49Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 45869318
num_examples: 35426
- name: dev
num_bytes: 5795158
num_examples: 4464
- name: test
num_bytes: 9690028
num_examples: 7305
download_size: 15290396
dataset_size: 61354504
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
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leo4life/algoml_bookcorpus_bottom_10p | leo4life | 2023-12-17T23:27:35Z | 14 | 0 | [
"region:us"
] | null | 2023-12-17T23:25:42Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 476567316
num_examples: 7400423
download_size: 297714041
dataset_size: 476567316
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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harvard-lil/cold-cases | harvard-lil | 2023-12-11T17:24:39Z | 13 | 8 | [
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"united states",
"law",
"legal",
"court",
"opinions",
"region:us"
] | null | 2023-09-12T17:29:50Z | ---
license: cc0-1.0
language:
- en
tags:
- united states
- law
- legal
- court
- opinions
size_categories:
- 1M<n<10M
viewer: true
---
<a href="https://huggingface.co/datasets/harvard-lil/cold-cases/resolve/main/coldcases.png"><img src="https://huggingface.co/datasets/harvard-lil/cold-cases/resolve/main/coldcases-banner.webp"/></a>
# Collaborative Open Legal Data (COLD) - Cases
COLD Cases is a dataset of 8.3 million United States legal decisions with text and metadata, formatted as compressed parquet files. If you'd like to view a sample of the dataset formatted as JSON Lines, you can view one [here](https://raw.githubusercontent.com/harvard-lil/cold-cases-export/main/sample.jsonl)
This dataset exists to support the open legal movement exemplified by projects like
[Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law) and
[LegalBench](https://hazyresearch.stanford.edu/legalbench/).
A key input to legal understanding projects is caselaw -- the published, precedential decisions of judges deciding legal disputes and explaining their reasoning.
United States caselaw is collected and published as open data by [CourtListener](https://www.courtlistener.com/), which maintains scrapers to aggregate data from
a wide range of public sources.
COLD Cases reformats CourtListener's [bulk data](https://www.courtlistener.com/help/api/bulk-data) so that all of the semantic information about each legal decision
(the authors and text of majority and dissenting opinions; head matter; and substantive metadata) is encoded in a single record per decision,
with extraneous data removed. Serving in the traditional role of libraries as a standardization steward, the Harvard Library Innovation Lab is maintaining
this [open source](https://github.com/harvard-lil/cold-cases-export) pipeline to consolidate the data engineering for preprocessing caselaw so downstream machine
learning and natural language processing projects can use consistent, high quality representations of cases for legal understanding tasks.
Prepared by the [Harvard Library Innovation Lab](https://lil.law.harvard.edu) in collaboration with the [Free Law Project](https://free.law/).
---
## Links
- [Data nutrition label](https://datanutrition.org/labels/v3/?id=c29976b2-858c-4f4e-b7d0-c8ef12ce7dbe) (DRAFT). ([Archive](https://perma.cc/YV5P-B8JL)).
- [Pipeline source code](https://github.com/harvard-lil/cold-cases-export)
---
## Summary
- [Formats](#formats)
- [File structure](#file-structure)
- [Data dictionary](#data-dictionary)
- [Notes on appropriate use](#appropriate-use)
---
## Format
[Apache Parquet](https://parquet.apache.org/) is binary format that makes filtering and retrieving the data quicker because it lays out the data in columns, which means columns that are unnecessary to satisfy a given query or workflow don't need to be read. Hugging Face's [Datasets](https://huggingface.co/docs/datasets/index) library is an easy way to get started working with the entire dataset, and has features for loading and streaming the data, so you don't need to store it all locally or pay attention to how it's formatted on disk.
[☝️ Go back to Summary](#summary)
---
## Data dictionary
Partial glossary of the fields in the data.
| Field name | Description |
| --- | --- |
| `judges` | Names of judges presiding over the case, extracted from the text. |
| `date_filed` | Date the case was filed. Formatted in ISO Date format. |
| `date_filed_is_approximate` | Boolean representing whether the `date_filed` value is precise to the day. |
| `slug` | Short, human-readable unique string nickname for the case. |
| `case_name_short` | Short name for the case. |
| `case_name` | Fuller name for the case. |
| `case_name_full` | Full, formal name for the case. |
| `attorneys` | Names of attorneys arguing the case, extracted from the text. |
| `nature_of_suit` | Free text representinng type of suit, such as Civil, Tort, etc. |
| `syllabus` | Summary of the questions addressed in the decision, if provided by the reporter of decisions. |
| `headnotes` | Textual headnotes of the case |
| `summary` | Textual summary of the case |
| `disposition` | How the court disposed of the case in their final ruling. |
| `history` | Textual information about what happened to this case in later decisions. |
| `other_dates` | Other dates related to the case in free text. |
| `cross_reference` | Citations to related cases. |
| `citation_count` | Number of cases that cite this one. |
| `precedential_status` | Constrainted to the values "Published", "Unknown", "Errata", "Unpublished", "Relating-to", "Separate", "In-chambers" |
| `citations` | Cases that cite this case. |
| `court_short_name` | Short name of court presiding over case. |
| `court_full_name` | Full name of court presiding over case. |
| `court_jurisdiction` | Code for type of court that presided over the case. See: [court_jurisdiction field values](#court_jurisdiction-field-values) |
| `opinions` | An array of subrecords. |
| `opinions.author_str` | Name of the author of an individual opinion. |
| `opinions.per_curiam` | Boolean representing whether the opinion was delivered by an entire court or a single judge. |
| `opinions.type` | One of `"010combined"`, `"015unamimous"`, `"020lead"`, `"025plurality"`, `"030concurrence"`, `"035concurrenceinpart"`, `"040dissent"`, `"050addendum"`, `"060remittitur"`, `"070rehearing"`, `"080onthemerits"`, `"090onmotiontostrike"`. |
| `opinions.opinion_text` | Actual full text of the opinion. |
| `opinions.ocr` | Whether the opinion was captured via optical character recognition or born-digital text. |
### court_jurisdiction field values
| Value | Description |
| --- | --- |
| F | Federal Appellate |
| FD | Federal District |
| FB | Federal Bankruptcy |
| FBP | Federal Bankruptcy Panel |
| FS | Federal Special |
| S | State Supreme |
| SA | State Appellate |
| ST | State Trial |
| SS | State Special |
| TRS | Tribal Supreme |
| TRA | Tribal Appellate |
| TRT | Tribal Trial |
| TRX | Tribal Special |
| TS | Territory Supreme |
| TA | Territory Appellate |
| TT | Territory Trial |
| TSP | Territory Special |
| SAG | State Attorney General |
| MA | Military Appellate |
| MT | Military Trial |
| C | Committee |
| I | International |
| T | Testing |
[☝️ Go back to Summary](#summary)
## Notes on appropriate use
When using this data, please keep in mind:
* All documents in this dataset are public information, published by courts within the United States to inform the public about the law. **You have a right to access them.**
* Nevertheless, **public court decisions frequently contain statements about individuals that are not true**. Court decisions often contain claims that are disputed,
or false claims taken as true based on a legal technicality, or claims taken as true but later found to be false. Legal decisions are designed to inform you about the law -- they are not
designed to inform you about individuals, and should not be used in place of credit databases, criminal records databases, news articles, or other sources intended
to provide factual personal information. Applications should carefully consider whether use of this data will inform about the law, or mislead about individuals.
* **Court decisions are not up-to-date statements of law**. Each decision provides a given judge's best understanding of the law as applied to the stated facts
at the time of the decision. Use of this data to generate statements about the law requires integration of a large amount of context --
the skill typically provided by lawyers -- rather than simple data retrieval.
To mitigate privacy risks, we have filtered out cases [blocked or deindexed by CourtListener](https://www.courtlistener.com/terms/#removal). Researchers who
require access to the full dataset without that filter may rerun our pipeline on CourtListener's raw data.
[☝️ Go back to Summary](#summary) | [
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TinyPixel/elm-2 | TinyPixel | 2023-12-16T20:46:04Z | 13 | 0 | [
"region:us"
] | null | 2023-11-13T08:32:00Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2587998
num_examples: 1073
download_size: 1393622
dataset_size: 2587998
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "elm-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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migtissera/Tess-Coder-v1.0 | migtissera | 2023-12-16T18:58:01Z | 13 | 11 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-01T17:49:09Z | ---
license: apache-2.0
---
This is a code specific dataset. It contains two-turn questions and answers. The first question/answer pair comes from glaiveai/glaive-code-assistant-v2. The second question (follow-up) was generated and answered by Claude-2. | [
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Sanjay07/demodata | Sanjay07 | 2023-12-11T08:58:14Z | 13 | 0 | [
"region:us"
] | null | 2023-12-05T11:53:04Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2087073.0
num_examples: 16
download_size: 2097792
dataset_size: 2087073.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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joshuasundance/wikiquote_tv | joshuasundance | 2023-12-11T05:49:31Z | 13 | 0 | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"television",
"roleplaying",
"region:us"
] | [
"text-generation",
"conversational"
] | 2023-12-11T05:31:25Z | ---
language:
- en
tags:
- television
- roleplaying
license: mit
size_categories:
- 100K<n<1M
task_categories:
- text-generation
- conversational
---
# wikiquote_tv
🤖 This README was written by GPT-4. 🤖
# Overview
This dataset, gathered from [wikiquote.org](https://en.wikiquote.org/), contains a comprehensive collection of quotes, actions, and conversations from various television shows. The dataset is particularly useful for training language models, offering a rich source of dialogues and narrative structures.
# Dataset Description
- **Content**: Quotes, actions, and conversations from a range of TV shows.
- **Source**: Extracted from [wikiquote.org](https://en.wikiquote.org/).
- **Structure**: The dataset includes data classes for `Action`, `Quote`, and `Conversation`, encapsulating individual elements of dialogues.
# Features
- **Parse Functionality**: Extracts quotes and actions from the HTML content of [wikiquote.org](https://en.wikiquote.org/) pages.
- **Comprehensive Coverage**: Includes shows across a wide range of genres and time periods.
- **Customizable**: Flexible to be used for a variety of NLP tasks and research.
# Usage
The dataset may be useful for:
- **Natural Language Understanding**: Understanding context, humor, and character dynamics in conversations.
- **Language Modeling**: Training models to generate dialogues or predict next lines in conversations.
- **Cultural Analysis**: Studying trends and themes across various television shows.
# License
This dataset and the accompanying code are released under the [MIT License](./LICENSE.md). The contents of the data are collected from [wikiquote.org](https://en.wikiquote.org/) as per [the repo code](https://huggingface.co/datasets/joshuasundance/wikiquote_tv/blob/main/wikiquote_tv.ipynb), and no ownership or rights are claimed over the data.
# Disclaimer
This dataset is intended for research and educational purposes.
# Contributions
Contributions are welcome! Feel free to submit issues or pull requests [on the HuggingFace repository](https://huggingface.co/datasets/joshuasundance/wikiquote_tv). | [
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yangezheng/EXIST2021 | yangezheng | 2023-12-11T10:30:30Z | 13 | 0 | [
"region:us"
] | null | 2023-12-11T10:30:23Z | ---
dataset_info:
features:
- name: text_original
dtype: string
- name: label_sexist
dtype:
class_label:
names:
'0': not sexist
'1': sexist
- name: task2
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2133072.8
num_examples: 5564
- name: validation
num_bytes: 533268.2
num_examples: 1391
- name: test
num_bytes: 1696347
num_examples: 4311
download_size: 3040737
dataset_size: 4362688.0
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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kardosdrur/danews_title_content | kardosdrur | 2023-12-11T12:14:54Z | 13 | 0 | [
"region:us"
] | null | 2023-12-11T12:08:06Z | ---
dataset_info:
features:
- name: title
dtype: string
- name: content
dtype: string
- name: url
dtype: string
- name: date_publish
dtype: string
splits:
- name: train
num_bytes: 1825026240.483218
num_examples: 949751
- name: test
num_bytes: 456257040.5167821
num_examples: 237438
download_size: 1388908234
dataset_size: 2281283281.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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atmallen/quirky_sciq_pythia-410m_bob_hard | atmallen | 2023-12-12T06:54:08Z | 13 | 0 | [
"region:us"
] | null | 2023-12-12T01:12:25Z | ---
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: 3638308.4990153266
num_examples: 5840
- name: validation
num_bytes: 337632.39
num_examples: 548
- name: test
num_bytes: 297513.921
num_examples: 474
download_size: 1367340
dataset_size: 4273454.810015326
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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atmallen/quirky_popqa_pythia-410m_bob | atmallen | 2023-12-12T06:54:28Z | 13 | 0 | [
"region:us"
] | null | 2023-12-12T01:13:07Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
- name: popularity
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: 3826956.0
num_examples: 24534
- name: validation
num_bytes: 312334.0
num_examples: 2000
- name: test
num_bytes: 311986.0
num_examples: 2000
download_size: 1552369
dataset_size: 4451276.0
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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SniiKz/TrainingSetAlpha | SniiKz | 2023-12-12T09:51:18Z | 13 | 0 | [
"region:us"
] | null | 2023-12-12T09:51:16Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6064437
num_examples: 13056
download_size: 1214156
dataset_size: 6064437
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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faztasia/baby-anime | faztasia | 2023-12-13T14:28:23Z | 13 | 1 | [
"license:mit",
"region:us"
] | null | 2023-12-12T10:35:47Z | ---
license: mit
---
---
configs:
- config_name: default
data_files:
- split: anime
path: "*/{**/*.jpg,**/*.txt}"
---
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fshala/segformer_github_data | fshala | 2023-12-15T11:48:02Z | 13 | 0 | [
"region:us"
] | null | 2023-12-13T21:35:52Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 14342831544.712
num_examples: 6562
- name: validation
num_bytes: 3542498392.388
num_examples: 1641
download_size: 17764927723
dataset_size: 17885329937.1
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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UnderstandLing/oasst1_nl | UnderstandLing | 2023-12-15T12:18:41Z | 13 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T12:13:04Z | ---
license: apache-2.0
dataset_info:
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
dtype: string
- name: lang
dtype: string
- name: review_count
dtype: int64
- name: review_result
dtype: bool
- name: deleted
dtype: bool
- name: rank
dtype: float64
- name: synthetic
dtype: bool
- name: model_name
dtype: 'null'
- name: detoxify
struct:
- name: identity_attack
dtype: float64
- name: insult
dtype: float64
- name: obscene
dtype: float64
- name: severe_toxicity
dtype: float64
- name: sexual_explicit
dtype: float64
- name: threat
dtype: float64
- name: toxicity
dtype: float64
- name: message_tree_id
dtype: string
- name: tree_state
dtype: string
- name: emojis
struct:
- name: count
sequence: int64
- name: name
sequence: string
- name: labels
struct:
- name: count
sequence: int64
- name: name
sequence: string
- name: value
sequence: float64
splits:
- name: train
num_bytes: 88990895
num_examples: 84428
- name: validation
num_bytes: 4756533
num_examples: 4400
download_size: 32098389
dataset_size: 93747428
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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kpriyanshu256/daigt-v2-train | kpriyanshu256 | 2023-12-15T21:46:31Z | 13 | 0 | [
"region:us"
] | null | 2023-12-15T21:45:00Z | ---
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
splits:
- name: train
num_bytes: 102152486
num_examples: 44868
download_size: 49608133
dataset_size: 102152486
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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studywww1/CLS500 | studywww1 | 2023-12-16T16:02:37Z | 13 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-16T16:02:10Z | ---
license: mit
---
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leo4life/processed_bert_dataset | leo4life | 2023-12-17T22:16:50Z | 13 | 0 | [
"region:us"
] | null | 2023-12-17T22:01:24Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 8472880800.0
num_examples: 2353578
download_size: 2275814818
dataset_size: 8472880800.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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talgatzh/xsum-kk3 | talgatzh | 2023-12-14T04:40:53Z | 12 | 0 | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:xsum",
"license:unknown",
"arxiv:1808.08745",
"region:us"
] | [
"summarization"
] | 2023-05-29T04:09:52Z | ---
annotations_creators:
- found
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: Extreme Summarization (XSum)
paperswithcode_id: xsum
size_categories:
- 100K<n<1M
source_datasets:
- xsum
task_categories:
- summarization
task_ids:
- news-articles-summarization
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
dataset_info:
features:
- name: document
dtype: string
- name: summary
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 139159410
num_examples: 5
download_size: 139159410
dataset_size: 139159410
---
# Dataset Card for "xsum"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/EdinburghNLP/XSum
- **Paper:** [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://arxiv.org/abs/1808.08745)
- **Point of Contact:** [Shashi Narayan](mailto:[email protected])
- **Size of downloaded dataset files:** 257.30 MB
- **Size of the generated dataset:** 532.26 MB
- **Total amount of disk used:** 789.56 MB
### Dataset Summary
Extreme Summarization (XSum) Dataset.
There are three features:
- document: Input news article.
- summary: One sentence summary of the article.
- id: BBC ID of the article.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 257.30 MB
- **Size of the generated dataset:** 532.26 MB
- **Total amount of disk used:** 789.56 MB
An example of 'validation' looks as follows.
```
{
"document": "some-body",
"id": "29750031",
"summary": "some-sentence"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `document`: a `string` feature.
- `summary`: a `string` feature.
- `id`: a `string` feature.
### Data Splits
| name |train |validation|test |
|-------|-----:|---------:|----:|
|default|204045| 11332|11334|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{Narayan2018DontGM,
title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
journal={ArXiv},
year={2018},
volume={abs/1808.08745}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@jbragg](https://github.com/jbragg), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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silk-road/ChatHaruhi-54K-Role-Playing-Dialogue | silk-road | 2023-12-16T11:34:47Z | 12 | 39 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:en",
"language:zh",
"license:cc-by-4.0",
"arxiv:2308.09597",
"region:us"
] | [
"text-generation",
"text2text-generation"
] | 2023-08-22T00:40:09Z | ---
license: cc-by-4.0
task_categories:
- text-generation
- text2text-generation
language:
- en
- zh
size_categories:
- 10K<n<100K
pretty_name: conversa
---
# ChatHaruhi
# Reviving Anime Character in Reality via Large Language Model
[]()
[]()
github repo: https://github.com/LC1332/Chat-Haruhi-Suzumiya
**Chat-Haruhi-Suzumiya**is a language model that imitates the tone, personality and storylines of characters like Haruhi Suzumiya,
<details>
<summary> The project was developed by Cheng Li, Ziang Leng, Chenxi Yan, Xiaoyang Feng, HaoSheng Wang, Junyi Shen, Hao Wang, Weishi Mi, Aria Fei, Song Yan, Linkang Zhan, Yaokai Jia, Pingyu Wu, and Haozhen Sun,etc. </summary>
This is an open source project and the members were recruited from open source communities like DataWhale.
Lulu Li( [Cheng Li@SenseTime](https://github.com/LC1332) )initiated the whole project and designed and implemented most of the features.
Ziang Leng( [Ziang Leng@SenseTime](https://blairleng.github.io) )designed and implemented the training, data generation and backend architecture for ChatHaruhi 1.0.
Chenxi Yan( [Chenxi Yan@Chengdu University of Information Technology](https://github.com/todochenxi) )implemented and maintained the backend for ChatHaruhi 1.0.
Junyi Shen( [Junyi Shen@Zhejiang University](https://github.com/J1shen) )implemented the training code and participated in generating the training dataset.
Hao Wang( [Hao Wang](https://github.com/wanghao07456) )collected script data for a TV series and participated in data augmentation.
Weishi Mi( [Weishi MI@Tsinghua University](https://github.com/hhhwmws0117) )participated in data augmentation.
Aria Fei( [Aria Fei@BJUT](https://ariafyy.github.io/) )implemented the ASR feature for the script tool and participated in the Openness-Aware Personality paper project.
Xiaoyang Feng( [Xiaoyang Feng@Nanjing Agricultural University](https://github.com/fengyunzaidushi) )integrated the script recognition tool and participated in the Openness-Aware Personality paper project.
Yue Leng ( [Song Yan](https://github.com/zealot52099) )Collected data from The Big Bang Theory. Implemented script format conversion.
scixing(HaoSheng Wang)( [HaoSheng Wang](https://github.com/ssccinng) ) implemented voiceprint recognition in the script tool and tts-vits speech synthesis.
Linkang Zhan( [JunityZhan@Case Western Reserve University](https://github.com/JunityZhan) ) collected Genshin Impact's system prompts and story data.
Yaokai Jia( [Yaokai Jia](https://github.com/KaiJiaBrother) )implemented the Vue frontend and practiced GPU extraction of Bert in a psychology project.
Pingyu Wu( [Pingyu Wu@Juncai Shuyun](https://github.com/wpydcr) )helped deploy the first version of the training code.
Haozhen Sun( [Haozhen Sun@Tianjin University] )plot the character figures for ChatHaruhi.
</details>
## transfer into input-target format
If you want to convert this data into an input-output format
check the link here
https://huggingface.co/datasets/silk-road/ChatHaruhi-Expand-118K
### Citation
Please cite the repo if you use the data or code in this repo.
```
@misc{li2023chatharuhi,
title={ChatHaruhi: Reviving Anime Character in Reality via Large Language Model},
author={Cheng Li and Ziang Leng and Chenxi Yan and Junyi Shen and Hao Wang and Weishi MI and Yaying Fei and Xiaoyang Feng and Song Yan and HaoSheng Wang and Linkang Zhan and Yaokai Jia and Pingyu Wu and Haozhen Sun},
year={2023},
eprint={2308.09597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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eswardivi/Tamil_MSA_Audio_Text | eswardivi | 2023-12-17T10:51:33Z | 12 | 0 | [
"task_categories:text-classification",
"task_categories:audio-classification",
"size_categories:n<1K",
"language:ta",
"arxiv:2106.04853",
"region:us"
] | [
"text-classification",
"audio-classification"
] | 2023-11-22T08:22:28Z | ---
dataset_info:
features:
- name: Audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': Negative
'1': Neutral
'2': Positive
- name: FilePath
dtype: string
- name: Text
dtype: string
splits:
- name: train
num_bytes: 436903500
num_examples: 64
download_size: 435262950
dataset_size: 436903500
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-classification
- audio-classification
language:
- ta
size_categories:
- n<1K
---
# Dataset Details
- **Title:** Dravidianmultimodality: A dataset for multi-modal sentiment analysis in Tamil and Malayalam
- **Authors:** Bharathi Raja Chakravarthi et al.
- **Link to Paper:** [arXiv:2106.04853](https://arxiv.org/abs/2106.04853)
- **Published:** 2021
- **Source:** arXiv preprint
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AILab-CVC/SEED-Bench-2 | AILab-CVC | 2023-12-12T06:43:55Z | 12 | 9 | [
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | [
"visual-question-answering"
] | 2023-11-27T06:38:48Z | ---
license: cc-by-nc-4.0
task_categories:
- visual-question-answering
language:
- en
pretty_name: SEED-Bench-2
size_categories:
- 10K<n<100K
---
# SEED-Bench Card
## Benchmark details
**Benchmark type:**
SEED-Bench-2 is a comprehensive large-scale benchmark for evaluating Multimodal Large Language Models (MLLMs), featuring 24K multiple-choice questions with precise human annotations.
It spans 27 evaluation dimensions, assessing both text and image generation.
**Benchmark date:**
SEED-Bench was collected in November 2023.
**Paper or resources for more information:**
https://github.com/AILab-CVC/SEED-Bench
**License:**
Attribution-NonCommercial 4.0 International. It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use.
Data Sources:
- Dimensions 1-9, 23 (In-Context Captioning): Conceptual Captions Dataset (https://ai.google.com/research/ConceptualCaptions/) under its license (https://github.com/google-research-datasets/conceptual-captions/blob/master/LICENSE). Copyright belongs to the original dataset owner.
- Dimension 9 (Text Recognition): ICDAR2003 (http://www.imglab.org/db/index.html), ICDAR2013(https://rrc.cvc.uab.es/?ch=2), IIIT5k(https://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset), and SVT(http://vision.ucsd.edu/~kai/svt/). Copyright belongs to the original dataset owner.
- Dimension 10 (Celebrity Recognition): MME (https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation) and MMBench (https://github.com/open-compass/MMBench) under MMBench license (https://github.com/open-compass/MMBench/blob/main/LICENSE). Copyright belongs to the original dataset owners.
- Dimension 11 (Landmark Recognition): Google Landmark Dataset v2 (https://github.com/cvdfoundation/google-landmark) under CC-BY licenses without ND restrictions.
- Dimension 12 (Chart Understanding): PlotQA (https://github.com/NiteshMethani/PlotQA) under its license (https://github.com/NiteshMethani/PlotQA/blob/master/LICENSE).
- Dimension 13 (Visual Referring Expression): VCR (http://visualcommonsense.com) under its license (http://visualcommonsense.com/license/).
- Dimension 14 (Science Knowledge): ScienceQA (https://github.com/lupantech/ScienceQA) under its license (https://github.com/lupantech/ScienceQA/blob/main/LICENSE-DATA).
- Dimension 15 (Emotion Recognition): FER2013 (https://www.kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge/data) under its license (https://www.kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge/rules#7-competition-data).
- Dimension 16 (Visual Mathematics): MME (https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation) and data from the internet under CC-BY licenses.
- Dimension 17 (Difference Spotting): MIMICIT (https://github.com/Luodian/Otter/blob/main/mimic-it/README.md) under its license (https://github.com/Luodian/Otter/tree/main/mimic-it#eggs).
- Dimension 18 (Meme Comprehension): Data from the internet under CC-BY licenses.
- Dimension 19 (Global Video Understanding): Charades (https://prior.allenai.org/projects/charades) under its license (https://prior.allenai.org/projects/data/charades/license.txt). SEED-Bench-2 provides 8 frames per video.
- Dimensions 20-22 (Action Recognition, Action Prediction, Procedure Understanding): Something-Something v2 (https://developer.qualcomm.com/software/ai-datasets/something-something), Epic-Kitchen 100 (https://epic-kitchens.github.io/2023), and Breakfast (https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset/). SEED-Bench-2 provides 8 frames per video.
- Dimension 24 (Interleaved Image-Text Analysis): Data from the internet under CC-BY licenses.
- Dimension 25 (Text-to-Image Generation): CC-500 (https://github.com/weixi-feng/Structured-Diffusion-Guidance) and ABC-6k (https://github.com/weixi-feng/Structured-Diffusion-Guidance) under their license (https://github.com/weixi-feng/Structured-Diffusion-Guidance/blob/master/LICENSE), with images generated by Stable-Diffusion-XL (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) under its license (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md).
- Dimension 26 (Next Image Prediction): Epic-Kitchen 100 (https://epic-kitchens.github.io/2023) under its license (https://creativecommons.org/licenses/by-nc/4.0/).
- Dimension 27 (Text-Image Creation): Data from the internet under CC-BY licenses.
Please contact us if you believe any data infringes upon your rights, and we will remove it.
**Where to send questions or comments about the benchmark:**
https://github.com/AILab-CVC/SEED-Bench/issues
## Intended use
**Primary intended uses:**
SEED-Bench-2 is primarily designed to evaluate Multimodal Large Language Models in text and image generation tasks.
**Primary intended users:**
Researchers and enthusiasts in computer vision, natural language processing, machine learning, and artificial intelligence are the main target users of the benchmark. | [
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luismond/tm2tb | luismond | 2023-12-17T20:27:52Z | 12 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-05T14:44:33Z | ---
license: mit
---
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jlbaker361/wikiart20 | jlbaker361 | 2023-12-12T15:07:39Z | 12 | 0 | [
"region:us"
] | null | 2023-12-08T16:53:10Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: style
dtype: string
- name: name
dtype: string
- name: gen_style
dtype: string
splits:
- name: train
num_bytes: 1166666.142857143
num_examples: 18
- name: test
num_bytes: 83966.85714285714
num_examples: 3
download_size: 1255245
dataset_size: 1250633.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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erfanzar/Flan-GPT4 | erfanzar | 2023-12-11T12:08:51Z | 12 | 1 | [
"region:us"
] | null | 2023-12-11T10:08:42Z | ---
dataset_info:
features:
- name: response
dtype: string
- name: instruction
dtype: string
- name: system
dtype: string
- name: toxin_prompt
dtype: string
- name: llama_prompt
dtype: string
splits:
- name: train
num_bytes: 4093492977
num_examples: 724248
download_size: 2266772484
dataset_size: 4093492977
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Flan-GPT4 Dataset
## Overview
The Flan-GPT4 dataset is a collection of prompts and responses designed for training and evaluating language generation models. It contains various features such as response, instruction, system, toxin_prompt, and llama_prompt, each with a data type of string.
Edited and customized from `SlimOrca-Flan`
## Dataset Information
- **Features:**
- response (string)
- instruction (string)
- system (string)
- toxin_prompt (string)
- llama_prompt (string)
- **Splits:**
- Train:
- Number of examples: 724,248
- Size: 4,093,492,977 bytes
## Intended Use
This dataset is intended for training and evaluating language generation models, particularly those focused on natural language processing and text generation tasks.
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theojiang/contrastive_conditional_vid_diff_std_1_6_webvid-train | theojiang | 2023-12-12T12:43:29Z | 12 | 0 | [
"region:us"
] | null | 2023-12-12T10:29:08Z | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 13923596850.0
num_examples: 400000
download_size: 12614420310
dataset_size: 13923596850.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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rpartha/evalpix2pix_data | rpartha | 2023-12-13T00:25:02Z | 12 | 0 | [
"region:us"
] | null | 2023-12-13T00:24:58Z | ---
dataset_info:
features:
- name: original_prompt
dtype: string
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 103305442.0
num_examples: 250
download_size: 103312828
dataset_size: 103305442.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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kanishka/counterfactual_babylm_aann_excess_adj_removal | kanishka | 2023-12-13T01:25:22Z | 12 | 0 | [
"region:us"
] | null | 2023-12-13T01:25:05Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 581333940
num_examples: 11809177
- name: validation
num_bytes: 56120230
num_examples: 1026747
download_size: 422020625
dataset_size: 637454170
---
# Dataset Card for "counterfactual_babylm_aann_excess_adj_removal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hlt-lab/dailydialogsample-jumble | hlt-lab | 2023-12-13T09:22:09Z | 12 | 0 | [
"region:us"
] | null | 2023-12-13T09:22:06Z | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 46955
num_examples: 100
download_size: 36773
dataset_size: 46955
---
# Dataset Card for "dailydialogsample-jumble"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hlt-lab/dreamsample-jumble | hlt-lab | 2023-12-13T09:22:13Z | 12 | 0 | [
"region:us"
] | null | 2023-12-13T09:22:10Z | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 51327
num_examples: 100
download_size: 38617
dataset_size: 51327
---
# Dataset Card for "dreamsample-jumble"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hlt-lab/personachatsample-jumble | hlt-lab | 2023-12-13T09:22:19Z | 12 | 0 | [
"region:us"
] | null | 2023-12-13T09:22:16Z | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 36304
num_examples: 100
download_size: 28190
dataset_size: 36304
---
# Dataset Card for "personachatsample-jumble"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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TinyPixel/lima-sys | TinyPixel | 2023-12-15T05:52:39Z | 12 | 0 | [
"region:us"
] | null | 2023-12-15T05:34:46Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2958520
num_examples: 1030
download_size: 1699020
dataset_size: 2958520
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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mnoukhov/openai_summarize_generated_10-20k | mnoukhov | 2023-12-15T12:58:31Z | 12 | 0 | [
"region:us"
] | null | 2023-12-15T12:58:29Z | ---
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: 17968583
num_examples: 10000
download_size: 10938682
dataset_size: 17968583
---
# Dataset Card for "openai_summarize_generated_10-20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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amj808/mini-platypus | amj808 | 2023-12-17T11:32:31Z | 12 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-17T11:31:07Z | ---
license: mit
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
- name: validation
num_bytes: 393029
num_examples: 125
- name: test
num_bytes: 360631
num_examples: 125
download_size: 2686058
dataset_size: 4940224
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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proserve/medical-instruct-mixer | proserve | 2023-12-17T22:40:09Z | 12 | 0 | [
"region:us"
] | null | 2023-12-17T22:10:29Z | ---
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 889897065.0
num_examples: 528642
- name: test
num_bytes: 68932987.0
num_examples: 28501
download_size: 482795421
dataset_size: 958830052.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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librarian-bots/arxiv-metadata-snapshot | librarian-bots | 2023-12-11T06:13:30Z | 11 | 1 | [
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"arxiv",
"science",
"region:us"
] | [
"text-generation",
"text-classification"
] | 2023-10-08T21:40:28Z | ---
language:
- en
license: cc0-1.0
size_categories:
- 1M<n<10M
task_categories:
- text-generation
- text-classification
pretty_name: arXiv Metadata Dataset
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: submitter
dtype: string
- name: authors
dtype: string
- name: title
dtype: string
- name: comments
dtype: string
- name: journal-ref
dtype: string
- name: doi
dtype: string
- name: report-no
dtype: string
- name: categories
dtype: string
- name: license
dtype: string
- name: abstract
dtype: string
- name: versions
list:
- name: version
dtype: string
- name: created
dtype: string
- name: update_date
dtype: timestamp[s]
- name: authors_parsed
sequence:
sequence: string
splits:
- name: train
num_bytes: 3561618110.0
num_examples: 2380492
download_size: 1993058402
dataset_size: 3561618110.0
tags:
- arxiv
- science
---
# Dataset Card for "arxiv-metadata-oai-snapshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
This is a mirror of the metadata portion of the arXiv [dataset](https://www.kaggle.com/datasets/Cornell-University/arxiv/versions/147).
The sync will take place weekly so may fall behind the original datasets slightly if there are more regular updates to the source dataset.
## Metadata
This dataset is a mirror of the original ArXiv data. This dataset contains an entry for each paper, containing:
- id: ArXiv ID (can be used to access the paper, see below)
- submitter: Who submitted the paper
- authors: Authors of the paper
- title: Title of the paper
- comments: Additional info, such as number of pages and figures
- journal-ref: Information about the journal the paper was published in
- doi: [https://www.doi.org](Digital Object Identifier)
- abstract: The abstract of the paper
- categories: Categories / tags in the ArXiv system
- versions: A version history
You can access each paper directly on ArXiv using these links:
- `https://arxiv.org/abs/{id}`: Page for this paper including its abstract and further links
- `https://arxiv.org/pdf/{id}`: Direct link to download the PDF
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chentong00/factoid-wiki | chentong00 | 2023-12-12T22:30:00Z | 11 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-15T21:55:08Z | ---
license: apache-2.0
---
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textminr/ner_tokenized | textminr | 2023-12-13T15:08:13Z | 11 | 0 | [
"region:us"
] | null | 2023-12-05T12:27:49Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_ids
sequence: int64
- name: ner_tags
sequence: string
splits:
- name: train
num_bytes: 9320936
num_examples: 9899
- name: validation
num_bytes: 2321773
num_examples: 2475
download_size: 572112
dataset_size: 11642709
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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Yehoon/gsm8k | Yehoon | 2023-12-11T05:31:14Z | 11 | 1 | [
"region:us"
] | null | 2023-12-11T05:28:04Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: options
dtype: string
- name: answer
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 3993094
num_examples: 7473
download_size: 2296876
dataset_size: 3993094
---
# Dataset Card for "gsm8k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mrm8488/Synthia-v1.3_conv | mrm8488 | 2023-12-11T09:06:03Z | 11 | 0 | [
"region:us"
] | null | 2023-12-11T09:05:40Z | ---
dataset_info:
features:
- name: system
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 551088924
num_examples: 118842
download_size: 258660548
dataset_size: 551088924
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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reza-alipour/con-eng-seg | reza-alipour | 2023-12-11T10:33:43Z | 11 | 0 | [
"region:us"
] | null | 2023-12-11T10:32:06Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: mask
dtype: image
- name: caption
dtype: string
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 602425229.508
num_examples: 1498
download_size: 602394573
dataset_size: 602425229.508
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] |
jondurbin/truthy-dpo-v0.1 | jondurbin | 2023-12-11T15:35:35Z | 11 | 8 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-11T15:34:04Z | ---
license: apache-2.0
---
Testing a truthfulness DPO dataset. | [
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cchellaf/fr_corpora_parliament_processed | cchellaf | 2023-12-11T15:56:37Z | 11 | 0 | [
"region:us"
] | null | 2023-12-11T15:56:28Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 352711212
num_examples: 2051014
download_size: 193145947
dataset_size: 352711212
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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SpongeBash/lego_2 | SpongeBash | 2023-12-12T07:26:12Z | 11 | 0 | [
"region:us"
] | null | 2023-12-12T07:26:11Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 204788.0
num_examples: 20
download_size: 196784
dataset_size: 204788.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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hlt-lab/dialogsumsample-change_names | hlt-lab | 2023-12-12T08:35:35Z | 11 | 0 | [
"region:us"
] | null | 2023-12-12T08:35:33Z | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 51251
num_examples: 59
download_size: 39410
dataset_size: 51251
---
# Dataset Card for "dialogsumsample-change_names"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hlt-lab/xsumsample-change_names | hlt-lab | 2023-12-12T08:35:42Z | 11 | 0 | [
"region:us"
] | null | 2023-12-12T08:35:39Z | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 37490
num_examples: 25
download_size: 41628
dataset_size: 37490
---
# Dataset Card for "xsumsample-change_names"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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harshithvh/llama2_format | harshithvh | 2023-12-12T09:53:25Z | 11 | 0 | [
"region:us"
] | null | 2023-12-12T09:50:24Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 411866
num_examples: 251
download_size: 88988
dataset_size: 411866
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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nisha05/test1 | nisha05 | 2023-12-12T13:02:41Z | 11 | 0 | [
"license:unknown",
"region:us"
] | null | 2023-12-12T12:28:01Z | ---
license: unknown
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 656
num_examples: 164
download_size: 715
dataset_size: 656
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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peterholdsworth/AND | peterholdsworth | 2023-12-14T10:47:59Z | 11 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T13:48:11Z | ---
license: apache-2.0
---
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NouRed/plant-disease-recognition | NouRed | 2023-12-15T23:15:54Z | 11 | 0 | [
"region:us"
] | null | 2023-12-15T23:15:23Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1164401767.326
num_examples: 1322
download_size: 1169635181
dataset_size: 1164401767.326
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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kanishka/counterfactual-babylm-aanns_indef_non_num_removal | kanishka | 2023-12-16T05:22:31Z | 11 | 0 | [
"region:us"
] | null | 2023-12-16T05:22:12Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 581831219
num_examples: 11633278
- name: validation
num_bytes: 56120230
num_examples: 1026747
download_size: 421601983
dataset_size: 637951449
---
# Dataset Card for "counterfactual-babylm-aanns_indef_non_num_removal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
kowndinya23/flan2021-submix-4096 | kowndinya23 | 2023-12-16T10:33:16Z | 11 | 0 | [
"region:us"
] | null | 2023-12-16T10:26:10Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype:
class_label:
names:
'0': aeslc:1.0.0
'1': ag_news_subset:1.0.0
'2': ai2_arc/ARC-Challenge:1.0.0
'3': ai2_arc/ARC-Easy:1.0.0
'4': anli/r1:0.1.0
'5': anli/r2:0.1.0
'6': anli/r3:0.1.0
'7': bool_q:1.0.0
'8': cnn_dailymail:3.4.0
'9': coqa:1.0.0
'10': cosmos_qa:1.0.0
'11': definite_pronoun_resolution:1.1.0
'12': drop:2.0.0
'13': fix_punct
'14': gem/common_gen:1.1.0
'15': gem/dart:1.1.0
'16': gem/e2e_nlg:1.1.0
'17': gem/web_nlg_en:1.1.0
'18': gem/wiki_lingua_english_en:1.1.0
'19': gigaword:1.2.0
'20': glue/cola:2.0.0
'21': glue/mnli:2.0.0
'22': glue/mrpc:2.0.0
'23': glue/qnli:2.0.0
'24': glue/qqp:2.0.0
'25': glue/sst2:2.0.0
'26': glue/stsb:2.0.0
'27': glue/wnli:2.0.0
'28': hellaswag:1.1.0
'29': huggingface:xsum
'30': imdb_reviews/plain_text:1.0.0
'31': lambada:1.0.0
'32': math_dataset/algebra__linear_1d:1.0.0
'33': multi_news:1.0.0
'34': natural_questions_open:1.0.0
'35': newsroom:1.0.0
'36': openbookqa:0.1.0
'37': opinion_abstracts_idebate
'38': opinion_abstracts_rotten_tomatoes
'39': para_crawl_enes
'40': paws_wiki:1.1.0
'41': piqa:1.0.0
'42': quac:1.0.0
'43': samsum:1.0.0
'44': sentiment140:1.0.0
'45': snli:1.1.0
'46': squad/v1.1:3.0.0
'47': squad/v2.0:3.0.0
'48': story_cloze/2016:1.0.0
'49': super_glue/cb:1.0.2
'50': super_glue/copa:1.0.2
'51': super_glue/multirc:1.0.2
'52': super_glue/record:1.0.2
'53': super_glue/rte:1.0.2
'54': super_glue/wic:1.0.2
'55': super_glue/wsc.fixed:1.0.2
'56': trec:1.0.0
'57': trivia_qa/rc:1.1.0
'58': true_case
'59': unified_qa_science_inst
'60': winogrande:1.1.0
'61': wmt14_translate/fr-en:1.0.0
'62': wmt16_translate/cs-en:1.0.0
'63': wmt16_translate/de-en:1.0.0
'64': wmt16_translate/fi-en:1.0.0
'65': wmt16_translate/ro-en:1.0.0
'66': wmt16_translate/ru-en:1.0.0
'67': wmt16_translate/tr-en:1.0.0
'68': word_segment
'69': yelp_polarity_reviews:0.2.0
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 8456659493.04
num_examples: 5295510
- name: validation
num_bytes: 85420802.96
num_examples: 53490
download_size: 5288301815
dataset_size: 8542080296.0
---
# Dataset Card for "flan2021-submix-4096"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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cartesinus/iva_mt_wslot | cartesinus | 2023-12-14T14:03:50Z | 10 | 0 | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"language:pl",
"language:de",
"language:es",
"language:sv",
"language:fr",
"language:pt",
"license:cc-by-4.0",
"machine translation",
"nlu",
"natural-language-understanding",
"virtual assistant",
"region:us"
] | [
"translation"
] | 2023-03-09T14:02:00Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: locale
dtype: string
- name: origin
dtype: string
- name: partition
dtype: string
- name: translation_utt
dtype:
translation:
languages:
- en
- pl
- name: translation_xml
dtype:
translation:
languages:
- en
- pl
- name: src_bio
dtype: string
- name: tgt_bio
dtype: string
splits:
- name: train
num_bytes: 6187206
num_examples: 20362
- name: validation
num_bytes: 1115480
num_examples: 3681
- name: test
num_bytes: 1587613
num_examples: 5394
download_size: 3851892
dataset_size: 8890299
task_categories:
- translation
language:
- en
- pl
- de
- es
- sv
- fr
- pt
tags:
- machine translation
- nlu
- natural-language-understanding
- virtual assistant
pretty_name: Machine translation for NLU with slot transfer
size_categories:
- 10K<n<100K
license: cc-by-4.0
---
# Machine translation dataset for NLU (Virual Assistant) with slot transfer between languages
version: 0.5.1
## Dataset Summary
Disclaimer: This is for research purposes only. Please have a look at the license section below. Some of the datasets used to construct IVA_MT have an unknown license.
IVA_MT is a machine translation dataset that can be used to train, adapt and evaluate MT models used in Virtual Assistant NLU context (e.g. to translate trainig corpus of NLU).
## Dataset Composition
### en-pl
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 11514 | 2033 | 2974 |
| [Leyzer 0.2.0](https://github.com/cartesinus/leyzer/tree/0.2.0) | 3974 | 701 | 1380 |
| [OpenSubtitles from OPUS](https://opus.nlpl.eu/OpenSubtitles-v1.php) | 2329 | 411 | 500 |
| [KDE from OPUS](https://opus.nlpl.eu/KDE4.php) | 1154 | 241 | 241 |
| [CCMatrix from Opus](https://opus.nlpl.eu/CCMatrix.php) | 1096 | 232 | 237 |
| [Ubuntu from OPUS](https://opus.nlpl.eu/Ubuntu.php) | 281 | 60 | 59 |
| [Gnome from OPUS](https://opus.nlpl.eu/GNOME.php) | 14 | 3 | 3 |
| *total* | 20362 | 3681 | 5394 |
### en-de
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 7536 | 1346 | 1955 |
### en-es
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 8415 | 1526 | 2202 |
### en-sv
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 7540 | 1360 | 1921 |
### en-fr
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 6800 | 1203 | 1757 |
### en-pt
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 7368 | 1296 | 1885 |
### en-hi
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 6702 | 1175 | 1747 |
### en-tr
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 8269 | 1474 | 2170 |
### en-ja
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 8066 | 1434 | 2085 |
### en-zh
| Corpus | Train | Dev | Test |
|----------------------------------------------------------------------|--------|-------|-------|
| [Massive 1.1](https://huggingface.co/datasets/AmazonScience/massive) | 8433 | 1513 | 2179 |
| ChatGPT | 1312 | 200 | 200 |
## Tools
Scripts used to generate this dataset can be found on [github](https://github.com/cartesinus/iva_mt).
## Citation
If you use this models please cite:
```
@article{Sowanski2023SlotLI,
title={Slot Lost in Translation? Not Anymore: A Machine Translation Model for Virtual Assistants with Type-Independent Slot Transfer},
author={Marcin Sowanski and Artur Janicki},
journal={2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP)},
year={2023},
pages={1-5}
}
```
## License
This is a composition of 7 datasets, and the license is as defined in original release:
- MASSIVE: [CC-BY 4.0](https://huggingface.co/datasets/AmazonScience/massive/blob/main/LICENSE)
- Leyzer: [CC BY-NC 4.0](https://github.com/cartesinus/leyzer/blob/master/LICENSE)
- OpenSubtitles: unknown
- KDE: [GNU Public License](https://l10n.kde.org/about.php)
- CCMatrix: no license given, therefore assuming it is LASER project license [BSD](https://github.com/facebookresearch/LASER/blob/main/LICENSE)
- Ubuntu: [GNU Public License](https://help.launchpad.net/Legal)
- Gnome: unknown
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mzainmehar/urddataset | mzainmehar | 2023-12-15T10:16:17Z | 10 | 0 | [
"license:llama2",
"region:us"
] | null | 2023-12-08T07:07:50Z | ---
license: llama2
---
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LanguageBind/VIDAL-Depth-Thermal | LanguageBind | 2023-12-13T04:34:47Z | 10 | 1 | [
"license:mit",
"region:us"
] | null | 2023-12-10T12:37:49Z | ---
license: mit
---
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drewtray/vsr | drewtray | 2023-12-11T01:53:39Z | 10 | 0 | [
"region:us"
] | null | 2023-12-11T01:52:31Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1779868662.52
num_examples: 3620
download_size: 1776764407
dataset_size: 1779868662.52
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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kanishka/counterfactual_babylm_aann_indef_removal | kanishka | 2023-12-13T01:24:58Z | 10 | 0 | [
"region:us"
] | null | 2023-12-11T22:29:21Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 581821948
num_examples: 11635848
- name: validation
num_bytes: 56120230
num_examples: 1026747
download_size: 0
dataset_size: 637942178
---
# Dataset Card for "counterfactual_babylm_aann_indef_removal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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josiauhlol/effanie-AI | josiauhlol | 2023-12-14T09:02:10Z | 10 | 1 | [
"task_categories:question-answering",
"task_categories:conversational",
"language:en",
"license:mit",
"effanie",
"chat",
"region:us"
] | [
"question-answering",
"conversational"
] | 2023-12-12T08:01:00Z | ---
task_categories:
- question-answering
- conversational
license: mit
language:
- en
tags:
- effanie
- chat
---
# The Effanie Dataset
This is the dataset for Effanie, the persuasive, confident, and helpful AI!
There are some helpful files for creating the dataset yourself. These include:
* [XLSM Conversion tool](./convertXLSM.py)
* [Parquet Conversion tool](./convertParquet.py)
* [The actual XLSM](./train.xlsm)
This is based off of the [OpenOrca dataset.](https://huggingface.co/datasets/Open-Orca/OpenOrca) | [
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hlt-lab/cnndailymailsample-synonym_adjective | hlt-lab | 2023-12-12T09:16:59Z | 10 | 0 | [
"region:us"
] | null | 2023-12-12T09:16:55Z | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 68903
num_examples: 33
download_size: 55011
dataset_size: 68903
---
# Dataset Card for "cnndailymailsample-synonym_adjective"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hlt-lab/dialogsumsample-synonym_adjective | hlt-lab | 2023-12-12T09:17:02Z | 10 | 0 | [
"region:us"
] | null | 2023-12-12T09:16:59Z | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 28227
num_examples: 30
download_size: 24429
dataset_size: 28227
---
# Dataset Card for "dialogsumsample-synonym_adjective"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hlt-lab/xsumsample-synonym_adjective | hlt-lab | 2023-12-12T09:17:05Z | 10 | 0 | [
"region:us"
] | null | 2023-12-12T09:17:02Z | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 52845
num_examples: 27
download_size: 48746
dataset_size: 52845
---
# Dataset Card for "xsumsample-synonym_adjective"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Seongill/squad_adversarial_thres1 | Seongill | 2023-12-12T17:10:42Z | 10 | 0 | [
"region:us"
] | null | 2023-12-12T17:10:24Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: answer_sent
dtype: string
- name: new_answer_sent
dtype: string
- name: new_answer_chunk
dtype: string
- name: similar_answer
dtype: string
- name: answer_chunk
dtype: string
- name: query_embedding
sequence: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 179641963
num_examples: 23001
download_size: 128823337
dataset_size: 179641963
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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abinthomasonline/stained-glass | abinthomasonline | 2023-12-13T19:39:15Z | 10 | 0 | [
"task_categories:text-to-image",
"task_categories:image-to-image",
"task_categories:unconditional-image-generation",
"size_categories:n<1K",
"license:mit",
"art",
"region:us"
] | [
"text-to-image",
"image-to-image",
"unconditional-image-generation"
] | 2023-12-13T07:50:00Z | ---
license: mit
task_categories:
- text-to-image
- image-to-image
- unconditional-image-generation
tags:
- art
pretty_name: stained
size_categories:
- n<1K
---
# Stained Glass Art Dataset for Diffusion Models
## Overview
This dataset consists of 21 high-resolution images of stained glass art, accompanied by corresponding captions. It is designed for fine-tuning diffusion models using techniques such as textual inversion and dreambooth. The dataset is intended to facilitate research and experimentation in generating stained glass art-inspired images.
## Dataset Structure
- **Images:** The stained glass art images are stored in the "images" directory, with filenames ranging from "0.jpg" to "20.jpg."
- **Captions:** Captions for each image are provided in the "captions.csv" file located in the dataset's root directory. The captions contain placeholders for adjectives and a custom token to represent stained glass art. For example: "A {adjective} {token} of a puppy."
- **Adjective Placeholders:** During training, the {adjective} placeholder in the captions can be randomly selected from the following list: `["", "good", "cropped", "clean", "bright", "cool", "nice", "small", "large", "dark", "weird"]`.
- **Token Placeholder:** The {token} placeholder represents the custom token that needs to be trained to capture the unique art style of stained glass. This token is a key element in generating realistic stained glass art-inspired images.
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HowMannyMore/romanurdu-sentiment-dataset-v1 | HowMannyMore | 2023-12-13T09:12:51Z | 10 | 0 | [
"task_categories:text-classification",
"language:ur",
"code",
"region:us"
] | [
"text-classification"
] | 2023-12-13T09:11:12Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 1586304
num_examples: 18300
- name: test
num_bytes: 63991
num_examples: 1000
- name: valid
num_bytes: 59686
num_examples: 1000
download_size: 1060732
dataset_size: 1709981
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
task_categories:
- text-classification
language:
- ur
tags:
- code
---
# Dataset Card for RomanUrdu-Sentiment-Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
## Dataset Description
### Dataset Summary
The dataset comprises reviews gathered from sources including IMDb, Jazz, and K-Electric, with each review labeled as 'Positive', 'Negative', or 'Neutral' based on its sentiment. This balanced dataset offers a diverse range of sentiment-labeled reviews in Roman Urdu, making it suitable for training and evaluating sentiment analysis models specific to this language. The primary purpose of this dataset is to support the development of roman urdu products by providing a valuable resource for training machine learning models.
However, working with Roman Urdu presents unique challenges, particularly in the context of code-switching between Urdu and Roman script. Researchers and practitioners leveraging this dataset may encounter difficulties related to language processing and sentiment analysis in this code-switched context. Despite these challenges, the Roman Urdu Sentiment Analysis Dataset offers an invaluable opportunity to advance the understanding and application of sentiment analysis within the specific linguistic and cultural context of Roman Urdu.
### Languages
The dataset consists of texts in the Roman Urdu language. Urdu is a language primarily spoken in Pakistan and parts of India. It is one of the 22 officially recognized languages in India and is also widely spoken by the Pakistani diaspora around the world.
The dataset is primarily focused on spoken Roman Urdu, which encompasses a wide range of topics and genres. It is important to note that the dataset's content may vary, covering conversations, speeches, interviews, narratives, and other forms of vocal communication in the Urdu language.
## Dataset Structure
### Data Instances
{
"text": Sai kha ya her kisi kay bus ki bat nhi hai lakin main ki hal kal bi Aj aur aj bi sirf Aus say bus,
"label": Positive
}
### Data Fields
<li>text: Roman Urdu text for sentimen analysis. (Data Type: String)</li>
<li>label: The sentiment label assigned to the text. (Data Type: String)</li> | [
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Reza2kn/Mozilla-Common-Voice-15-Persian | Reza2kn | 2023-12-15T06:05:17Z | 10 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T07:01:24Z | ---
license: apache-2.0
---
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dputilov/generatemidas24aifull | dputilov | 2023-12-14T18:22:57Z | 10 | 0 | [
"region:us"
] | null | 2023-12-14T18:18:51Z | ---
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: 2520710806.0
num_examples: 8500
download_size: 3312497190
dataset_size: 2520710806.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "generatemidas24aifull"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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johanhag/winter-test | johanhag | 2023-12-14T23:28:59Z | 10 | 0 | [
"region:us"
] | null | 2023-12-14T21:42:16Z | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 93351039.0
num_examples: 11
download_size: 6459201
dataset_size: 93351039.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ziamor/nova_test | ziamor | 2023-12-15T13:52:13Z | 10 | 0 | [
"region:us"
] | null | 2023-12-15T03:03:50Z | ---
dataset_info:
features:
- name: inputs
dtype: string
splits:
- name: train
num_bytes: 5121
num_examples: 30
download_size: 3043
dataset_size: 5121
---
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dominguesm/canarim | dominguesm | 2023-12-17T14:56:55Z | 10 | 1 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:pt",
"license:cc-by-4.0",
"region:us"
] | [
"text-generation",
"fill-mask"
] | 2023-12-15T07:57:57Z | ---
language: pt
license: cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
size_categories:
- 100M<n<1B
dataset_info:
features:
- name: url
dtype: string
- name: content_languages
dtype: string
- name: warc_filename
dtype: string
- name: warc_record_offset
dtype: int64
- name: warc_record_length
dtype: int64
- name: text
dtype: string
- name: crawl_timestamp
dtype: string
splits:
- name: train
num_bytes: 1087519823221
num_examples: 342818651
download_size: 1087713663056
dataset_size: 1087519823221
---
<p align="center">
<img width="250" alt="Camarim Logo" src="https://raw.githubusercontent.com/DominguesM/Canarim-Instruct-PTBR/main/assets/canarim.png">
</p>
<p align="center">
<a href="https://github.com/DominguesM/canarim">[🐱 Github]</a>
</p>
<hr>
# Canarim: A Large-Scale Dataset of Web Pages in the Portuguese Language
## Introduction
Canarim is a database encompassing over 342 million Portuguese language documents, sourced from multiple iterations of CommonCrawl. This nearly 1 terabyte database stands as one of the most extensive Portuguese language data collections available. It underwent initial deduplication using URLs, with plans for further text-based deduplication and filtering of potentially harmful content. The data, originally in HTML, has been converted to Markdown with the `Trafilatura` library to enhance readability and quality. Canarim is poised to be a crucial resource for NLP research, particularly in Portuguese language applications, filling the gap in large-scale, high-quality data for languages other than English.
## Dataset Structure
### Data Instances
An example looks as follows:
```json
{
'url': '...',
'content_languages': 'por',
'warc_filename': 'crawl-data/CC-MAIN-2023-06/segments/1674764500041.18/warc/CC-MAIN-20230202200542-20230202230542-00352.warc.gz',
'warc_record_offset': 971279893,
'warc_record_length': 3873,
'text': '...',
'crawl_timestamp': '2023-02-02T20:28:21Z'
}
```
### Data Fields
- `url`: URL of the page
- `content_languages`: Language of the page
- `warc_filename`: Name of the WARC file
- `warc_record_offset`: Offset of the WARC record
- `warc_record_length`: Length of the WARC record
- `text`: Text of the page, in Markdown format
- `crawl_timestamp`: Timestamp of the crawl
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perlthoughts/big-brain | perlthoughts | 2023-12-16T02:05:02Z | 10 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-15T08:58:34Z | ---
license: apache-2.0
---
code
```python
# used when training samples do not include a system prompt.
DEFAULT_SYSTEM_PROMPT = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
# did not add item to dataset if prompt or system prompt contains any of these bad words.
BAD_WORDS = [
"english", "translate", "russian", "chinese", "japanese", "spanish", "persian", "french", "german", "italian", "korean",
"arabic", "hindi", "portuguese", "turkish", "vietnamese", "indonesian", "thai", "polish", "dutch", "greek", "czech",
"romanian", "swedish", "danish", "finnish", "hungarian", "norwegian", "slovak", "slovenian", "lithuanian", "latvian",
"estonian", "bulgarian", "serbian", "ukrainian", "belarusian", "croatian", "bosnian", "macedonian", "albanian", "icelandic",
"irish", "welsh", "scottish", "latin", "esperanto", "hebrew", "yiddish", "afrikaans", "swahili", "zulu", "xhosa", "sotho",
"sesotho", "somali", "hausa", "igbo", "yoruba", "malay", "tagalog", "hawaiian", "maori", "mongolian", "tamil", "telugu",
"kannada", "gujarati", "marathi", "punjabi", "nepali", "sinhala", "khmer", "lao", "burmese", "tibetan", "georgian",
"azerbaijani", "kurdish", "armenian", "kazakh", "uzbek", "tajik", "kirghiz", "turkmen", "tatar", "bashkir", "chechen",
"chuvash", "ossetian", "moldavian", "moldovan"
]
# adds the item only if system prompt or prompt contains any single one of these good words.
GOOD_WORDS = [
"solve", "calculate", "math", "equation", "formula", "logic", "algebra", "geometry", "riddle", "puzzle", "proof", "theorem",
"problem", "theory", "finance", "economics", "chemistry", "biology", "physics", "science", "history", "geography",
"philosophy", "psychology", "sociology", "computer", "programming", "technology", "engineering", "medicine", "health",
"code", "program", "health", "medical", "doctor", "nurse", "hospital", "disease", "bacteria", "symptom", "cancer",
"diagnosis", "treatment", "procedure", "medicine", "infection", "survival", "therapy", "psychological", "psychiatry",
]
TOTAL_ITEMS = 100000
# all datasets used and the percentage/ratio of each from the total.
DATASETS = {
"migtissera/Synthia-v1.3": {
"ratio": 0.2, "set": "train",
"system": "system", "prompt": "instruction", "output": "response",
},
"meta-math/MetaMathQA": {
"ratio": 0.1, "set": "train",
"system": DEFAULT_SYSTEM_PROMPT, "prompt": "query", "output": "response",
},
"HuggingFaceH4/ultrafeedback_binarized": {
"ratio": 0.3, "set": "train_sft",
"system": DEFAULT_SYSTEM_PROMPT, "prompt": "prompt", "output": "get_assistant(chosen)",
},
"ehartford/dolphin": {
"ratio": 0.3, "set": "train",
"system": "instruction", "prompt": "input", "output": "output",
},
"Open-Orca/OpenOrca": {
"ratio": 0.1, "set": "train",
"system": "system_prompt", "prompt": "question", "output": "response",
},
}
``` | [
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open-llm-leaderboard/details_janai-hq__trinity-v1 | open-llm-leaderboard | 2023-12-16T19:07:02Z | 10 | 0 | [
"region:us"
] | null | 2023-12-16T19:06:21Z | ---
pretty_name: Evaluation run of janai-hq/trinity-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [janai-hq/trinity-v1](https://huggingface.co/janai-hq/trinity-v1) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_janai-hq__trinity-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-16T19:03:31.389271](https://huggingface.co/datasets/open-llm-leaderboard/details_janai-hq__trinity-v1/blob/main/results_2023-12-16T19-03-31.389271.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6575877329335247,\n\
\ \"acc_stderr\": 0.031985421208388404,\n \"acc_norm\": 0.6571647268300141,\n\
\ \"acc_norm_stderr\": 0.032648337921958155,\n \"mc1\": 0.5507955936352509,\n\
\ \"mc1_stderr\": 0.01741294198611529,\n \"mc2\": 0.6931209356367747,\n\
\ \"mc2_stderr\": 0.015031530031665238\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6988054607508533,\n \"acc_stderr\": 0.013406741767847632,\n\
\ \"acc_norm\": 0.7226962457337884,\n \"acc_norm_stderr\": 0.013082095839059376\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.711113324039036,\n\
\ \"acc_stderr\": 0.004523188431142894,\n \"acc_norm\": 0.8835889265086636,\n\
\ \"acc_norm_stderr\": 0.0032006176493464752\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\
\ \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.6592592592592592,\n\
\ \"acc_norm_stderr\": 0.040943762699967926\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\
\ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\
\ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\
\ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\"\
: 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\
\ \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n\
\ \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n\
\ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"\
acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.03510766597959215,\n\
\ \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.03510766597959215\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131154,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131154\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8532110091743119,\n \"acc_stderr\": 0.01517314184512625,\n \"\
acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.01517314184512625\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\
acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290916,\n \
\ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290916\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.033368203384760736,\n\
\ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.033368203384760736\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742179,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742179\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8352490421455939,\n\
\ \"acc_stderr\": 0.013265346261323797,\n \"acc_norm\": 0.8352490421455939,\n\
\ \"acc_norm_stderr\": 0.013265346261323797\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069356,\n\
\ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069356\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4759776536312849,\n\
\ \"acc_stderr\": 0.016703190189300186,\n \"acc_norm\": 0.4759776536312849,\n\
\ \"acc_norm_stderr\": 0.016703190189300186\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47392438070404175,\n\
\ \"acc_stderr\": 0.012752858346533131,\n \"acc_norm\": 0.47392438070404175,\n\
\ \"acc_norm_stderr\": 0.012752858346533131\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \
\ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \
\ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5507955936352509,\n\
\ \"mc1_stderr\": 0.01741294198611529,\n \"mc2\": 0.6931209356367747,\n\
\ \"mc2_stderr\": 0.015031530031665238\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8200473559589582,\n \"acc_stderr\": 0.01079646868806868\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7164518574677786,\n \
\ \"acc_stderr\": 0.012415070917508124\n }\n}\n```"
repo_url: https://huggingface.co/janai-hq/trinity-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|arc:challenge|25_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|gsm8k|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hellaswag|10_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T19-03-31.389271.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T19-03-31.389271.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- '**/details_harness|winogrande|5_2023-12-16T19-03-31.389271.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-16T19-03-31.389271.parquet'
- config_name: results
data_files:
- split: 2023_12_16T19_03_31.389271
path:
- results_2023-12-16T19-03-31.389271.parquet
- split: latest
path:
- results_2023-12-16T19-03-31.389271.parquet
---
# Dataset Card for Evaluation run of janai-hq/trinity-v1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [janai-hq/trinity-v1](https://huggingface.co/janai-hq/trinity-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_janai-hq__trinity-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-16T19:03:31.389271](https://huggingface.co/datasets/open-llm-leaderboard/details_janai-hq__trinity-v1/blob/main/results_2023-12-16T19-03-31.389271.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6575877329335247,
"acc_stderr": 0.031985421208388404,
"acc_norm": 0.6571647268300141,
"acc_norm_stderr": 0.032648337921958155,
"mc1": 0.5507955936352509,
"mc1_stderr": 0.01741294198611529,
"mc2": 0.6931209356367747,
"mc2_stderr": 0.015031530031665238
},
"harness|arc:challenge|25": {
"acc": 0.6988054607508533,
"acc_stderr": 0.013406741767847632,
"acc_norm": 0.7226962457337884,
"acc_norm_stderr": 0.013082095839059376
},
"harness|hellaswag|10": {
"acc": 0.711113324039036,
"acc_stderr": 0.004523188431142894,
"acc_norm": 0.8835889265086636,
"acc_norm_stderr": 0.0032006176493464752
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.040943762699967926,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.040943762699967926
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.03496101481191179,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.03496101481191179
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.46078431372549017,
"acc_stderr": 0.04959859966384181,
"acc_norm": 0.46078431372549017,
"acc_norm_stderr": 0.04959859966384181
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5787234042553191,
"acc_stderr": 0.03227834510146268,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146268
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555498,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555498
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42328042328042326,
"acc_stderr": 0.02544636563440678,
"acc_norm": 0.42328042328042326,
"acc_norm_stderr": 0.02544636563440678
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677172,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677172
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252604,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7806451612903226,
"acc_stderr": 0.023540799358723295,
"acc_norm": 0.7806451612903226,
"acc_norm_stderr": 0.023540799358723295
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5320197044334976,
"acc_stderr": 0.03510766597959215,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.03510766597959215
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.029126522834586815,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.029126522834586815
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.02150024957603348,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.02150024957603348
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402538,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402538
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.028897748741131154,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.028897748741131154
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8532110091743119,
"acc_stderr": 0.01517314184512625,
"acc_norm": 0.8532110091743119,
"acc_norm_stderr": 0.01517314184512625
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5416666666666666,
"acc_stderr": 0.03398110890294636,
"acc_norm": 0.5416666666666666,
"acc_norm_stderr": 0.03398110890294636
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8059071729957806,
"acc_stderr": 0.025744902532290916,
"acc_norm": 0.8059071729957806,
"acc_norm_stderr": 0.025744902532290916
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.031024411740572213,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.031024411740572213
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8244274809160306,
"acc_stderr": 0.033368203384760736,
"acc_norm": 0.8244274809160306,
"acc_norm_stderr": 0.033368203384760736
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990947,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990947
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.040191074725573483,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.040191074725573483
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7791411042944786,
"acc_stderr": 0.03259177392742179,
"acc_norm": 0.7791411042944786,
"acc_norm_stderr": 0.03259177392742179
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
"acc_stderr": 0.046840993210771065,
"acc_norm": 0.41964285714285715,
"acc_norm_stderr": 0.046840993210771065
},
"harness|hendrycksTest-management|5": {
"acc": 0.7572815533980582,
"acc_stderr": 0.04245022486384495,
"acc_norm": 0.7572815533980582,
"acc_norm_stderr": 0.04245022486384495
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406957,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406957
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8352490421455939,
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"acc_norm": 0.8352490421455939,
"acc_norm_stderr": 0.013265346261323797
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7398843930635838,
"acc_stderr": 0.023618678310069356,
"acc_norm": 0.7398843930635838,
"acc_norm_stderr": 0.023618678310069356
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4759776536312849,
"acc_stderr": 0.016703190189300186,
"acc_norm": 0.4759776536312849,
"acc_norm_stderr": 0.016703190189300186
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.025646863097137897,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.025646863097137897
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7170418006430869,
"acc_stderr": 0.02558306248998481,
"acc_norm": 0.7170418006430869,
"acc_norm_stderr": 0.02558306248998481
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7469135802469136,
"acc_stderr": 0.024191808600712995,
"acc_norm": 0.7469135802469136,
"acc_norm_stderr": 0.024191808600712995
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48936170212765956,
"acc_stderr": 0.029820747191422473,
"acc_norm": 0.48936170212765956,
"acc_norm_stderr": 0.029820747191422473
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.47392438070404175,
"acc_stderr": 0.012752858346533131,
"acc_norm": 0.47392438070404175,
"acc_norm_stderr": 0.012752858346533131
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6875,
"acc_stderr": 0.02815637344037142,
"acc_norm": 0.6875,
"acc_norm_stderr": 0.02815637344037142
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6715686274509803,
"acc_stderr": 0.018999707383162673,
"acc_norm": 0.6715686274509803,
"acc_norm_stderr": 0.018999707383162673
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7224489795918367,
"acc_stderr": 0.028666857790274648,
"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.028666857790274648
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.025870646766169136,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.025870646766169136
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5507955936352509,
"mc1_stderr": 0.01741294198611529,
"mc2": 0.6931209356367747,
"mc2_stderr": 0.015031530031665238
},
"harness|winogrande|5": {
"acc": 0.8200473559589582,
"acc_stderr": 0.01079646868806868
},
"harness|gsm8k|5": {
"acc": 0.7164518574677786,
"acc_stderr": 0.012415070917508124
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | [
-0.7303420305252075,
-0.834507942199707,
0.2829928398132324,
0.2037508338689804,
-0.14865656197071075,
-0.053988054394721985,
0.07118856906890869,
-0.19159305095672607,
0.5625203251838684,
-0.08443528413772583,
-0.5123128294944763,
-0.7180091738700867,
-0.43195122480392456,
0.24862869083881378,
-0.019815806299448013,
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cheafdevo56/InfluentialTriplets1Percent | cheafdevo56 | 2023-12-17T02:53:06Z | 10 | 0 | [
"region:us"
] | null | 2023-12-17T02:49:12Z | ---
dataset_info:
features:
- name: query
struct:
- name: abstract
dtype: string
- name: corpus_id
dtype: int64
- name: title
dtype: string
- name: pos
struct:
- name: abstract
dtype: string
- name: corpus_id
dtype: int64
- name: title
dtype: string
- name: neg
struct:
- name: abstract
dtype: string
- name: corpus_id
dtype: int64
- name: score
dtype: int64
- name: title
dtype: string
splits:
- name: train
num_bytes: 173356305.3
num_examples: 45000
- name: validation
num_bytes: 19261811.7
num_examples: 5000
download_size: 115821068
dataset_size: 192618117.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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] |
heegyu/glaive-function-calling-v2-formatted | heegyu | 2023-12-17T04:51:14Z | 10 | 1 | [
"region:us"
] | null | 2023-12-17T04:48:09Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: system_message
dtype: string
- name: function_description
dtype: string
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 250214164
num_examples: 112960
download_size: 93753668
dataset_size: 250214164
---
- original dataset: [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
```
{'system_message': 'You are a helpful assistant with access to the following functions. Use them if required -',
'function_description': '{\n "name": "get_random_quote",\n "description": "Get a random quote",\n "parameters": {}\n}',
'conversations': [{'content': 'Hi, can you help me with something?',
'role': 'user'},
{'content': "Of course! I'm here to assist you. What do you need help with?",
'role': 'assistant'},
{'content': 'I need to book a flight from New York to Paris. Can you do that for me?',
'role': 'user'},
{'content': "I'm sorry, but as an AI, I don't have the capability to book flights or perform external tasks. My current function allows me to provide you with a random quote. Would you like to hear a quote instead?",
'role': 'assistant'},
{'content': 'Oh, I see. Sure, I would love to hear a random quote.',
'role': 'user'},
{'content': '{"name": "get_random_quote", "arguments": {}}',
'role': 'function-call'},
{'content': '{"quote": "The only way to do great work is to love what you do. - Steve Jobs"}',
'role': 'function-response'},
{'content': 'Here is a quote for you: "The only way to do great work is to love what you do." - Steve Jobs. I hope it inspires you!',
'role': 'assistant'}]}
``` | [
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RikoteMaster/isear_augmented_sample | RikoteMaster | 2023-12-17T18:49:53Z | 10 | 0 | [
"region:us"
] | null | 2023-12-17T18:21:50Z | ---
dataset_info:
features:
- name: Text_processed
dtype: string
- name: Emotion
dtype: string
- name: Augmented
dtype: bool
splits:
- name: train
num_bytes: 9254
num_examples: 63
- name: test
num_bytes: 10464
num_examples: 63
- name: validation
num_bytes: 9886
num_examples: 63
download_size: 26524
dataset_size: 29604
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
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deutsche-telekom/NLU-few-shot-benchmark-en-de | deutsche-telekom | 2023-12-17T17:41:42Z | 9 | 1 | [
"task_categories:text-classification",
"task_ids:intent-classification",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|deutsche-telekom/NLU-Evaluation-Data-en-de",
"language:en",
"language:de",
"license:cc-by-4.0",
"region:us"
] | [
"text-classification"
] | 2022-12-02T16:26:59Z | ---
license: cc-by-4.0
language:
- en
- de
multilinguality:
- multilingual
source_datasets:
- extended|deutsche-telekom/NLU-Evaluation-Data-en-de
size_categories:
- 1K<n<10K
task_categories:
- text-classification
task_ids:
- intent-classification
---
# NLU Few-shot Benchmark - English and German
This is a few-shot training dataset from the domain of human-robot interaction.
It contains texts in German and English language with 64 different utterances (classes).
Each utterance (class) has exactly 20 samples in the training set.
This leads to a total of 1280 different training samples.
The dataset is intended to benchmark the intent classifiers of chat bots in English and especially in German language.
We are building on our
[deutsche-telekom/NLU-Evaluation-Data-en-de](https://huggingface.co/datasets/deutsche-telekom/NLU-Evaluation-Data-en-de)
data set
## Creator
This data set was compiled and open sourced by [Philip May](https://may.la/)
of [Deutsche Telekom](https://www.telekom.de/).
## Processing Steps
- drop `NaN` values
- drop duplicates in `answer_de` and `answer`
- delete all rows where `answer_de` has more than 70 characters
- add column `label`: `df["label"] = df["scenario"] + "_" + df["intent"]`
- remove classes (`label`) with less than 25 samples:
- `audio_volume_other`
- `cooking_query`
- `general_greet`
- `music_dislikeness`
- random selection for train set - exactly 20 samples for each class (`label`)
- rest for test set
## Copyright
Copyright (c) the authors of [xliuhw/NLU-Evaluation-Data](https://github.com/xliuhw/NLU-Evaluation-Data)\
Copyright (c) 2022 [Philip May](https://may.la/), [Deutsche Telekom AG](https://www.telekom.com/)
All data is released under the
[Creative Commons Attribution 4.0 International License (CC BY 4.0)](http://creativecommons.org/licenses/by/4.0/).
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TinyPixel/elm | TinyPixel | 2023-12-16T08:47:27Z | 9 | 0 | [
"region:us"
] | null | 2023-09-18T18:50:39Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2609458
num_examples: 1073
download_size: 1399869
dataset_size: 2609458
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "elm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hlhdatscience/guanaco-spanish-dataset | hlhdatscience | 2023-12-13T16:38:40Z | 9 | 0 | [
"language:es",
"license:apache-2.0",
"region:us"
] | null | 2023-10-21T10:53:04Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4384495
num_examples: 2410
- name: test
num_bytes: 376933
num_examples: 223
download_size: 2455040
dataset_size: 4761428
license: apache-2.0
language:
- es
pretty_name: d
---
# Dataset Card for "guanaco-spanish-dataset"
This dataset is a subset of original timdettmers/openassistant-guanaco,which is also a subset of the Open Assistant dataset .You can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 2,633 samples, translated with the help of GPT 3.5. turbo.
It represents the 41% and 42% of train and test from timdettmers/openassistant-guanaco respectively.
You can find the github repository for the code used here: https://github.com/Hector1993prog/guanaco_translation
For further information, please see the original dataset.
**CLEANING AND CURATION OF THE DATASET IT IS BEING PERFORMED DURING THIS MONTH**
License: Apache 2.0
Dataset Details
Dataset Sources [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)
Repository: [Link to Repository](https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main)
# Uses
## Direct Use
The dataset is suitable for training and evaluating models in the context of Open Assistant applications, focusing on the highest-rated paths in conversation trees.
## Out-of-Scope Use
Usage outside the scope of Open Assistant applications may not yield optimal results.
# Dataset Structure
The dataset is organized into conversation paths, each containing the highest-rated samples. Samples are translated versions generated with the assistance of GPT 3.5 turbo.
# Dataset Creation
Curation Rationale
This subset was created to provide a focused collection of the highest-rated conversation paths from the original Open Assistant dataset, with translations performed using GPT 3.5 turbo.
# Dataset Creation
Curation Rationale
This subset was created to provide a focused collection of the highest-rated conversation paths from the original Open Assistant dataset, with translations performed using GPT 3.5 turbo.
# Source Data
## Data Collection and Processing
The source data is a subset of the timdettmers/openassistant-guanaco dataset, itself a subset of the Open Assistant dataset. The translation process involved GPT 3.5 turbo.
# Who are the source data producers?
The original data producers include contributors to the Open Assistant dataset, and the translation process involved the use of GPT 3.5 turbo.
# Annotations [optional]
## Annotation process
The dataset includes translated samples, and annotations were generated through the translation process.
## Who are the annotators?
Annotations were generated through the translation process using GPT 3.5 turbo. Dataset needs to be curated yet.
# Personal and Sensitive Information
The dataset does not contain personal or sensitive information.
# Bias, Risks, and Limitations
Users should be aware of potential biases introduced during the translation process. Limitations include the focus on the highest-rated conversation paths.
# Recommendations
Users are encouraged to consider potential biases and limitations when utilizing the dataset for model training and applications.
[Contact information for dataset inquiries](https://www.linkedin.com/in/hlh-generative-ai/)
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Henok/amharic-qa | Henok | 2023-12-15T14:49:12Z | 9 | 1 | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:am",
"license:mit",
"arxiv:2303.03290",
"region:us"
] | [
"question-answering"
] | 2023-10-23T10:25:52Z | ---
license: mit
language:
- am
task_categories:
- question-answering
size_categories:
- 1K<n<10K
---
# AmQA: Amharic Question Answering Dataset
Amharic question and answer dataset in a prompt and completion format.
## Dataset Details
In Amharic, interrogative sentences can be formulated using information-seeking pronouns like “ምን” (what), “መቼ” (when), “ማን” (who), “የት” (where), “የትኛው” (which), etc. and prepositional interrogative phrases like “ለምን” [ለ-ምን] (why), “በምን” [በ-ምን] (by what), etc. Besides, a verb phrase could be used to pose questions (Getahun 2013; Baye 2009). As shown bellow, the AmQA dataset contains context, question, and answer triplets. The contexts are articles collected from Amharic Wikipedia dump file. The question-answer pairs are created by crowdsourcing and annotated using the Haystack QA annotation tool. 2628 question and answer pairs are created from 378 documents. The whole AmQA dataset can be found here. We also split the datset into train, dev, and test with a size of 1728, 600, and 300 respectively.
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** [https://github.com/semantic-systems/amharic-qa]
- **Paper [optional]:** [https://arxiv.org/abs/2303.03290]
- **Curated by:** [Tilahun Abedissa, Ricardo Usbeck, Yaregal Assabie]
- **Language(s) (NLP):** [Amharic]
- **License:** [MIT]
## Dataset Structure
The dataset is classified into 70% train, 20% test, and 10% dev datasets.
The dataset is restructured in a new JSON format of the following:
```
formatted_json = {
"inputs": "ከዚህ በታች በተገለጸው አውድ ተከታዩን ጥያቄ ይመልሱ፡ {context} {question}",
"targets": "ከጥያቄው ጋር የሚስማማው ምላሽ {answer_text} ነው።",
"inputs": "ከዚህ በታች ያለውን ዝርዝር መረጃ በመጠቀም ለሚከተለው ጥያቄ መልስ ይስጡ፡ {context} {question}",
"targets": "ከጥያቄው አንጻር ትክክለኛው መልስ {answer_text} ነው።",
"inputs": "ከዚህ በታች ያለውን ጽሑፍ በማጣቀስ እባክዎን ለሚከተለው መልስ ይስጡ {context} {question}",
"targets": "ለጥያቄው መልስ {answer_text} ነው።",
"inputs": "የተሰጠውን ጥያቄ ለመመለስ ከዚህ በታች የቀረበውን መረጃ ይመልከቱ፡ {context} {question}",
"targets": "ለተጠቀሰው ጥያቄ ትክክለኛው ምላሽ {answer_text} ነው።",
"inputs": "ለሚከተለው ጥያቄ ምላሽ ለመስጠት ከዚህ በታች የቀረበውን አውድ ተጠቀም፡ {context} {question}",
"targets": "ለጥያቄው መልሱ {answer_text} ነው።",
"inputs": "የተሰጠውን ጥያቄ ከዚህ በታች በተሰጠው አውድ መሰረት መልሱ፡ {context} {question}",
"targets": "ለጥያቄው ትክክለኛው ምላሽ {answer_text} ነው።"
}
```
## Citation
**BibTeX:**
```
[@misc{abedissa2023amqa,
title={AmQA: Amharic Question Answering Dataset},
author={Tilahun Abedissa and Ricardo Usbeck and Yaregal Assabie},
year={2023},
eprint={2303.03290},
archivePrefix={arXiv},
primaryClass={cs.CL}
}]
``` | [
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lyon-nlp/clustering-hal-s2s | lyon-nlp | 2023-12-11T16:35:24Z | 9 | 1 | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:fr",
"license:apache-2.0",
"region:us"
] | [
"text-classification"
] | 2023-11-08T16:19:39Z | ---
license: apache-2.0
task_categories:
- text-classification
language:
- fr
size_categories:
- 10K<n<100K
---
## Clustering HAL
This dataset was created by scrapping data from the HAL platform.
Over 80,000 articles have been scrapped to keep their id, title and category.
It was originally used for the French version of [MTEB](https://github.com/embeddings-benchmark/mteb), but it can also be used for various clustering or classification tasks.
### Usage
To use this dataset, you can run the following code :
```py
from datasets import load_dataset
dataset = load_dataset("lyon-nlp/clustering-hal-s2s", "test")
``` | [
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TinyPixel/claude_multiround_chat_1k | TinyPixel | 2023-12-15T04:38:23Z | 9 | 0 | [
"region:us"
] | null | 2023-11-15T15:03:54Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 17754888
num_examples: 1609
download_size: 9514689
dataset_size: 17754888
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "claude_multiround_chat_1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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AhmadMustafa/Urdu-Instruct-News-Article-Generation | AhmadMustafa | 2023-12-13T08:39:35Z | 9 | 1 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:ur",
"region:us"
] | [
"text-generation",
"text2text-generation"
] | 2023-11-26T15:29:57Z | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 255298866.39295197
num_examples: 100674
- name: test
num_bytes: 28369076.607048035
num_examples: 11187
download_size: 122413447
dataset_size: 283667943.0
task_categories:
- text-generation
- text2text-generation
language:
- ur
pretty_name: Instruct News Article Generation Urdu
size_categories:
- 100K<n<1M
---
# Dataset Card for "Urdu-Instruct-News-Article-Generation"
This Dataset is converted from the [original dataset](https://data.mendeley.com/datasets/834vsxnb99/3) by Khalid Hussain, Nimra Mughal, Irfan Ali, Saif Hassan, Sher Muhammad Daudpota.
## Task:
Generate the News Article from the given headline.
## Split Size:
- train: 100674
- test: 11187
## Prompt Template (In Urdu):
Random.choice b.w these 2
```
[ "اس دی گی ایک خبر سے متعلق ایک مضمون لکھیں۔
خبر: {}",
"یہ خبر جو {} سے تعلق رکھتی ہے، اس پر ایک مضمون لکھیں۔
خبر: {}"
]
```
<b>Translation</b>:
```
1. Write an article from the given news.
news: {}
2. Given the news belonging to category {}, write an article on it.
news: {}
```
## Completion Template (In Urdu)
```
جی ضرور، یہ رہا آپ کی خبر سے متعلق ایک مضمون:
{}
```
<b>Translation</b>:
```
Sure, here is the article related to the given news
{}
``` | [
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khaled123/mistralfintune | khaled123 | 2023-12-11T09:19:44Z | 9 | 0 | [
"region:us"
] | null | 2023-12-10T20:37:45Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2286242
num_examples: 1000
download_size: 925088
dataset_size: 2286242
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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hankang2023/dialogsum.kr | hankang2023 | 2023-12-11T06:10:44Z | 9 | 1 | [
"task_categories:conversational",
"region:us"
] | [
"conversational"
] | 2023-12-11T06:07:18Z | ---
task_categories:
- conversational
---
데이터셋 dialogsum를 한영 번역 모델인 Gugugo-koen를 이용하여 번역함.
dialogsum : https://huggingface.co/datasets/knkarthick/dialogsum
Gugugo-koen : https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-AWQ | [
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TinyPixel/elm-3 | TinyPixel | 2023-12-11T09:06:34Z | 9 | 1 | [
"region:us"
] | null | 2023-12-11T09:06:31Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2536494
num_examples: 1073
download_size: 1390775
dataset_size: 2536494
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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tyzhu/wikitext-103-raw-v1-para-permute-9 | tyzhu | 2023-12-11T10:31:07Z | 9 | 0 | [
"region:us"
] | null | 2023-12-11T10:27:43Z | ---
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: text
dtype: string
splits:
- name: train
num_bytes: 5465009454
num_examples: 18013491
- name: validation
num_bytes: 1159288
num_examples: 3760
- name: test
num_bytes: 1305088
num_examples: 4358
download_size: 3144957904
dataset_size: 5467473830
---
# Dataset Card for "wikitext-103-raw-v1-para-permute-9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ibupari/hitobroetchi | ibupari | 2023-12-11T11:31:53Z | 9 | 1 | [
"region:us"
] | null | 2023-12-11T11:31:52Z | ---
dataset_info:
features:
- name: title
dtype: string
- name: subtitle
dtype: string
- name: paragraph
dtype: string
splits:
- name: train
num_bytes: 379217
num_examples: 550
download_size: 224471
dataset_size: 379217
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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SpongeBash/hugging_face | SpongeBash | 2023-12-11T16:55:16Z | 9 | 0 | [
"region:us"
] | null | 2023-12-11T16:55:14Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 75931.0
num_examples: 12
download_size: 77302
dataset_size: 75931.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hugging_face"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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atmallen/quirky_sciq_pythia-410m_bob | atmallen | 2023-12-12T06:54:10Z | 9 | 0 | [
"region:us"
] | null | 2023-12-12T01:12:29Z | ---
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: 14551988.0
num_examples: 23358
- name: validation
num_bytes: 1232235.0
num_examples: 2000
- name: test
num_bytes: 1255333.0
num_examples: 2000
download_size: 5454834
dataset_size: 17039556.0
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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kristmh/test_high_vs_random | kristmh | 2023-12-12T10:49:52Z | 9 | 0 | [
"region:us"
] | null | 2023-12-12T10:49:45Z | ---
configs:
- config_name: default
data_files:
- split: test_separate
path: data/test_separate-*
dataset_info:
features:
- name: text_clean
dtype: string
- name: labels
dtype: int64
splits:
- name: test_separate
num_bytes: 17851458
num_examples: 22133
download_size: 8830997
dataset_size: 17851458
---
# Dataset Card for "test_high_vs_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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T-Almeida/Pubmed2023-baseline-neox-tokenized | T-Almeida | 2023-12-12T21:33:00Z | 9 | 0 | [
"region:us"
] | null | 2023-12-12T20:58:07Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 33279393797
num_examples: 22522740
download_size: 14020873232
dataset_size: 33279393797
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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tollefj/nor-instruct-v2-llama2 | tollefj | 2023-12-13T08:17:17Z | 9 | 0 | [
"region:us"
] | null | 2023-12-13T08:17:11Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 58584845
num_examples: 104465
- name: test
num_bytes: 3336747
num_examples: 5726
download_size: 37192376
dataset_size: 61921592
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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sproos/summeval-de | sproos | 2023-12-13T22:40:39Z | 9 | 0 | [
"region:us"
] | null | 2023-12-13T17:46:42Z | ---
dataset_info:
features:
- name: machine_summaries
sequence: string
- name: human_summaries
sequence: string
- name: relevance
sequence: float64
- name: coherence
sequence: float64
- name: fluency
sequence: float64
- name: consistency
sequence: float64
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1254264
num_examples: 100
download_size: 522770
dataset_size: 1254264
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "summeval-de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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