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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'text'}) and 4 missing columns ({'passage_offset', 'embedding_offset', 'num_passages', 'num_embeddings'}).
This happened while the json dataset builder was generating data using
hf://datasets/davanstrien/search-index/collection.json (at revision 4f5cb0d9fe2f68fa51e0d5d6ba16f6ae57ca04c2)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
text: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 192
to
{'passage_offset': Value(dtype='int64', id=None), 'num_passages': Value(dtype='int64', id=None), 'num_embeddings': Value(dtype='int64', id=None), 'embedding_offset': Value(dtype='int64', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2015, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'text'}) and 4 missing columns ({'passage_offset', 'embedding_offset', 'num_passages', 'num_embeddings'}).
This happened while the json dataset builder was generating data using
hf://datasets/davanstrien/search-index/collection.json (at revision 4f5cb0d9fe2f68fa51e0d5d6ba16f6ae57ca04c2)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
passage_offset int64 | num_passages int64 | num_embeddings int64 | embedding_offset int64 | text string |
|---|---|---|---|---|
0 | 25,000 | 3,684,220 | 0 | null |
25,000 | 11,905 | 1,794,006 | 3,684,220 | null |
null | null | null | null | ## Table of Contents
- [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instance... |
null | null | null | null | - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions)
## Dataset Description
- **Repository**: ... |
null | null | null | null | art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanitie... |
null | null | null | null | 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'hi... |
null | null | null | null | 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions']
### Supported Tasks and Leaderboards
| Model | Authors ... |
null | null | null | null | 14165) (few-shot) | Brown et al. 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9 | [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al. 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4 | Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
### Data Instances
An example from anatomy subtask looks... |
null | null | null | null | 0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
### Data Instances
An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone?", "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The... |
null | null | null | null | - `dev`: 5 examples per subtask, meant for few-shot setting - `test`: there are at least 100 examples per subtask | | auxiliary_train | dev | val | test | | ----- | :------: | :-----: | :-----: | :-----: | | TOTAL | 99842 | 285 | 1531 | 14042
### Curation Rationale
Transformer models have driven this recent progress b... |
null | null | null | null | These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a... |
null | null | null | null | ## Fields
* `id` is of type <class 'str'> * `category` is of type <class 'str'> * `original-instruction` is of type <class 'str'> * `original-context` is of type <class 'str'> * `original-response` is of type <class 'str'>
## Questions
* `new-instruction` : Write the final version of the instruction, making sure that ... |
null | null | null | null | If an context is not needed, leave this empty. * `new-response` : Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and context) provided. If the original response is ok, copy and paste it here.
## Load with Argilla
To load this dataset with Arg... |
null | null | null | null | ### Load the data
```python from datasets import load_dataset dataset=load_dataset(r"ceval/ceval-exam",name="computer_network") print(dataset['val'][0])
# {'id': 0, 'question': '使用位填充方法，以01111110为位首flag，数据为011011111111111111110010，求问传送时要添加几个0____', 'A': '1', 'B': '2', 'C': '3', 'D': '4', 'answer': 'C', 'explanation': ... |
null | null | null | null | Please cite our paper if you use our dataset. ``` @article{huang2023ceval, title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zha... |
null | null | null | null | ## 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 S... |
null | null | null | null | Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://allenai.... |
null | null | null | null | The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to the task, and an implementation of three ... |
null | null | null | null | ", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ```
#### ARC-Easy
- **Size of ... |
null | null | null | null | The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ```
### Data Fields
The data fields are the same among all splits.
#### ARC-Challenge
- `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary featu... |
null | null | null | null | - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature.
### Data Splits
| name |train|validation|test| |-------------|----:|---------:|---:| |ARC-Challenge| 1119| 299|1172| |ARC-Easy | 2251| 570|2376... |
null | null | null | null | Try ARC, the AI2 Reasoning Challenge}, journal = {arXiv:1803.05457v1}, year = {2018}, } ```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
null | null | null | null | ## Table of Contents
- [Introduction](#introduction) - [Leaderboard](#leaderboard) - [Data](#data) - [Citation](#citation) - [License](#license)
## Introduction
CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese ... |
null | null | null | null | ## Leaderboard
Latest leaderboard is in our [github](https://github.com/haonan-li/CMMLU).
## Data
We provide development and test dataset for each of 67 subjects, with 5 questions in development set and 100+ quesitons in test set. Each question in the dataset is a multiple-choice questions with 4 choices and only one... |
null | null | null | null | 同一密码子所决定的氨基酸不同 C. mRNA碱基序列不同 D. 核糖体成分不同 答案是：C ``` ``` 题目：某种植物病毒V是通过稻飞虱吸食水稻汁液在水稻间传播的。稻田中青蛙数量的增加可减少该病毒在水稻间的传播。下列叙述正确的是： A. 青蛙与稻飞虱是捕食关系 B. 水稻和病毒V是互利共生关系 C. 病毒V与青蛙是寄生关系 D. 水稻与青蛙是竞争关系 答案是： ```
#### Load data
```python from datasets import load_dataset cmmlu=load_dataset(r"haonan-li/cmmlu", |
null | null | null | null | 水稻与青蛙是竞争关系 答案是： ```
#### Load data
```python from datasets import load_dataset cmmlu=load_dataset(r"haonan-li/cmmlu", 'agronomy') print(cmmlu['test'][0]) ```
#### Load all data at once
```python task_list = ['agronomy', 'anatomy', 'ancient_chinese', 'arts', 'astronomy', 'business_ethics', 'chinese_civil_service_exam'... |
null | null | null | null | 'college_medicine', 'computer_science', 'computer_security', 'conceptual_physics', 'construction_project_management', 'economics', 'education', 'electrical_engineering', 'elementary_chinese', 'elementary_commonsense', 'elementary_information_and_technology', 'elementary_mathematics', 'ethnology', 'food_science', 'genet... |
null | null | null | null | 'nutrition', 'philosophy', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_study', 'sociology', 'sports_science', 'traditional_chinese_medicine', 'virology', 'world_history', 'world_religions'] from datasets import load_dataset cmmlu = {k:... |
null | null | null | null | ## Want to run inference now?
This dataset only contains the `problem_statement` (i.e. issue text) and the `base_commit` which can represents the state of the codebase before the issue has been resolved. If you want to run inference using the "Oracle" or BM25 retrieval settings mentioned in the paper, consider the foll... |
null | null | null | null | issue text) and the `base_commit` which can represents the state of the codebase before the issue has been resolved. If you want to run inference using the "Oracle" or BM25 retrieval settings mentioned in the paper, consider the following datasets. [princeton-nlp/SWE-bench_oracle](https://huggingface.co/datasets/prince... |
null | null | null | null | The leaderboard can be found at www.swebench.com
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
### Data Instances
An example of a SWE-bench datum is as follows: ``` instance_id: (str) - A formatted instance identifier, usually as... |
null | null | null | null | test_patch: (str) - A test-file patch that was contributed by the solution PR. problem_statement: (str) - The issue title and body. version: (str) - Installation version to use for running evaluation. environment_setup_commit: (str) - commit hash to use for environment setup and installation. FAIL_TO_PASS: (str) - A js... |
null | null | null | null | ### KMMLU Statistics
| Category | # Questions | |------------------------------|-------------| | **Prerequisites** | | | None | 59,909 | | 1 Prerequisite Test | 12,316 | | 2 Prerequisite Tests | 776 | | 2+ Years of Experience | 65,135 | | 4+ Years of Experience | 98,678 | | 9+ Years of Experience | 6,963 | | **Question... |
null | null | null | null | "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", "criminal_law": "HUMSS", "ecology": "STEM", "economics": "HUMSS", "education": "HUMSS", "electrical_enginee... |
null | null | null | null | "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", "mechanical_engineering": "STEM", "nondestructive_testing": "Applied Science", "pat... |
null | null | null | null | ## Table of Contents
- [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instance... |
null | null | null | null | - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions)
## Dataset Description
- **Repository**: ... |
null | null | null | null | art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanitie... |
null | null | null | null | 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'hi... |
null | null | null | null | 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions']
### Supported Tasks and Leaderboards
| Model | Authors ... |
null | null | null | null | 14165) (few-shot) | Brown et al. 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9 | [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al. 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4 | Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
### Data Instances
An example from anatomy subtask looks... |
null | null | null | null | 0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
### Data Instances
An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone?", "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The... |
null | null | null | null | - `dev`: 5 examples per subtask, meant for few-shot setting - `test`: there are at least 100 examples per subtask | | auxiliary_train | dev | val | test | | ----- | :------: | :-----: | :-----: | :-----: | | TOTAL | 99842 | 285 | 1531 | 14042
### Curation Rationale
Transformer models have driven this recent progress b... |
null | null | null | null | These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a... |
null | null | null | null | ## Table of Contents
- [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instance... |
null | null | null | null | - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) - [Terms of Access](#terms-of-access)
## ... |
null | null | null | null | multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training. The transcribed audio data is collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc.... |
null | null | null | null | ### Example Usage
The training split has several configurations of various size: XS, S, M, L, XL. See the Section on "Data Splits" for more information. To download the XS configuration: ```python from datasets import load_dataset gs = load_dataset("speechcolab/gigaspeech", "xs", use_auth_token=True)
# see structure
p... |
null | null | null | null | The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://github.com/SpeechColab/GigaSpeech#leaderboard and ranks models based on their WER. - `text-to... |
null | null | null | null | wav' 'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/9d48cf31/xs_chunks_0000/YOU0000000315_S0000660.wav', 'array': array([0.0005188 , 0.00085449, 0.00012207, . 0.00125122, 0.00076294, 0.00036621], dtype=float32), 'sampling_rate': 16000 }, 'begin_time': 2941.889892578125, 'end_time': 2945.07006835937... |
null | null | null | null | 'source': 2, 'category': 24, 'original_full_path': 'audio/youtube/P0004/YOU0000000315.opus' } ```
### Data Fields
* segment_id (string) - string id of the segment. * speaker (string) - string id of the speaker (can be "N/A"). * text (string) - transcription of the segment. * begin_time (float) - start time of the segm... |
null | null | null | null | segment inside its archive (as files are not downloaded and extracted locally). * audio_id (string) - string idea of the original full audio. * title (string) - title of the original full audio. * url (string) - url of the original full audio. * source (ClassLabel) - id of the audio source. Sources are audiobook (0), p... |
null | null | null | null | Sources are audiobook (0), podcast (1), and YouYube (2). * category (ClassLabel) - id of the audio category, categories are listed below. * original_full_path (string) - the relative path to the original full audio sample in the original data directory. Categories are assigned from the following labels: "People and Blo... |
null | null | null | null | ### Data Splits
The dataset has three splits: train, evaluation (dev) and test. The train split has five configurations of various sizes: XS, S, M, L, XL. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset.
#### Transcribed Training Subsets Size
| Subset | Hours... |
null | null | null | null | |
### Source Data
| Audio Source | Transcribed Hours | Acoustic Condition | |:-------------|:----------------------:|:-------------------| | Audiobook | 2,655 | <li>Reading</li><li>Various ages and accents</li> | | Podcast | 3,498 | <li>Clean or background music</li><li>Indoor</li><li>Near-field</li><li>Spontaneous</l... |
null | null | null | null | Development and test subsets are annotated by professional human annotators.
### Licensing Information
SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through our site... |
null | null | null | null | However, it is your responsiblity to verify the appropriate model license for your specific use-case by confirming that the dataset usage abides by the Fair Use terms. SpeechColab is not responsible for the license of any machine learning model trained on the GigaSpeech dataset.
### Citation Information
Please cite th... |
null | null | null | null | ### Citation Information
Please cite this paper if you find this work useful: ```bibtext @inproceedings{GigaSpeech2021, title={GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio}, booktitle={Proc. Interspeech 2021}, year=2021, author={Guoguo Chen, Shuzhou Chai, Guanbo Wang, Jiayu Du... |
null | null | null | null | ## Terms of Access
The "Researcher" has requested permission to use the GigaSpeech database (the "Database") at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purp... |
null | null | null | null | 4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed ... |
null | null | null | null | ## Quick Links
- [**Website**](https://locuslab.github.io/tofu): The landing page for TOFU - [**arXiv Paper**](http://arxiv.org/abs/2401.06121): Detailed information about the TOFU dataset and its significance in unlearning tasks. - [**GitHub Repository**](https://github.com/locuslab/tofu): Access the source code, fine... |
null | null | null | null | ## Applicability 🚀
The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 chat, and Phi-1.5 models, but can be easily adapted to other models.
## Lo... |
null | null | null | null | - `forget10`: Forgetting 10% of the original dataset, all entries correspond to a single author. Retain sets corresponding to each forget set are also available, which can be used to train an Oracle model.
## Codebase
The code for training the models and the availability of all fine-tuned models can be found at our [G... |
null | null | null | null | ## Table of Contents
- [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fie... |
null | null | null | null | - [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:** [M... |
null | null | null | null | The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spani... |
null | null | null | null | The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER. - `t... |
null | null | null | null | Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset mls = load_dataset("parler-tts/mls_eng", split="train", streaming=True) print(next(iter(mls))) ``` *Bonus*: create a [PyTorch dataloader]... |
null | null | null | null | ### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
### Data Fields
- file: A filename .flac format. - audio: A dictionary containing th... |
null | null | null | null | - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription.
### Personal and Sensitive Information
The dataset consists of... |
null | null | null | null | journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ```
### Data Statistics
| Duration (h) | Train | Dev | Test | |--------------|-----------|-------|-------| | English | 44,659.74 | 15.75 | 15.55 | | German | 1,966.51 | 14.28 | 14.29 | | Dutch | 1,554.24 | 12.76 | 12.76 | | French | 1,076.58 | 10.07 | 10.07 | | S... |
null | null | null | null | 68 | 9.99 | 10 | | Italian | 247.38 | 5.18 | 5.27 | | Portuguese | 160.96 | 3.64 | 3.74 | | Polish | 103.65 | 2.08 | 2.14 | | # Speakers | Train | | Dev | | Test | | |------------|-------|------|-----|----|------|----| | Gender | M | F | M | F | M | F | | English | 2742 | 2748 | 21 | 21 | 21 | 21 | | German | 81 | 95 |... |
null | null | null | null | 76 | 7.99 | 7.62 | 7.93 | | German | 7.06 | 7.22 | 7 | 7.29 | | Dutch | 6.44 | 6.32 | 6.72 | 6.04 | | French | 5.13 | 4.94 | 5.04 | 5.02 | | Spanish | 4.91 | 5.08 | 4.78 | 5.23 | | Italian | 2.5 | 2.68 | 2.38 | 2.9 | | Portuguese | 1.84 | 1.81 | 1.83 | 1.9 | | Polish | 1.12 | 0.95 | 1.09 | 1.05 | |
null | null | null | null | ## Table of Contents
- [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instance... |
null | null | null | null | - [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:** ht... |
null | null | null | null | ### Data Instances
A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's `\boxed` tag. An example from the dataset is: ``` {'problem': 'A board game spinner is divided into three... |
null | null | null | null | ', 'level': 'Level 1', 'type': 'Counting & Probability', 'solution': 'The spinner is guaranteed to land on exactly one of the three regions, so we know that the sum of the probabilities of it landing in each region will be 1. If we let the probability of it landing in region $C$ be $x$, we then have the equation $1 = \... |
null | null | null | null | * `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus.
### Data Splits
* train: 7,500 examples * test: 5,000 examples
### Licensing Information
https://github.com/hendrycks/math/blob/main/LICENSE
### Citation Information
```bi... |
null | null | null | null | ### KMMLU Statistics
| Category | # Questions | |------------------------------|-------------| | **Prerequisites** | | | None | 59,909 | | 1 Prerequisite Test | 12,316 | | 2 Prerequisite Tests | 776 | | 2+ Years of Experience | 65,135 | | 4+ Years of Experience | 98,678 | | 9+ Years of Experience | 6,963 | | **Question... |
null | null | null | null | "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", "criminal_law": "HUMSS", "ecology": "STEM", "economics": "HUMSS", "education": "HUMSS", "electrical_enginee... |
null | null | null | null | "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", "mechanical_engineering": "STEM", "nondestructive_testing": "Applied Science", "pat... |
null | null | null | null | ## Dataset Description
- **Homepage:** https://github.com/hendrycks/math - **Repository:** https://github.com/hendrycks/math - **Paper:** https://arxiv.org/pdf/2103.03874.pdf - **Leaderboard:** N/A - **Point of Contact:** Dan Hendrycks
### Dataset Summary
The Mathematics Aptitude Test of Heuristics (MATH) dataset cons... |
null | null | null | null | The step-by-step solution contains the final answer enclosed in LaTeX's `\boxed` tag. An example from the dataset is: ``` {'problem': 'A board game spinner is divided into three parts labeled $A$, $B$ and $C$. The probability of the spinner landing on $A$ is $\\frac{1}{3}$ and the probability of the spinner landing on ... |
null | null | null | null | ```
### Data Fields
* `problem`: The competition math problem. * `solution`: The step-by-step solution. * `level`: We only kept tasks tagged as 'Level 5', the hardest level for the dataset. * `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra ... |
null | null | null | null | ## Dataset Description
- **Homepage:** https://nuprl.github.io/MultiPL-E/ - **Repository:** https://github.com/nuprl/MultiPL-E - **Paper:** https://ieeexplore.ieee.org/abstract/document/10103177 - **Point of Contact:** carolyn.anderson@wellesley.edu, mfeldman@oberlin.edu, a.guha@northeastern.edu
## Dataset Summary
Mul... |
null | null | null | null | We use the canonical file extension for each language to identify the language, e.g., "cpp" for C++, "lua" for Lua, "clj" for Clojure, and so on.
## Using MultiPL-E
- MultiPL-E is part of the [BigCode Code Generation LM Harness]. This is the easiest way to use MultiPL-E. - MultiPL-E has its own evaluation framework th... |
null | null | null | null | (You can optionally pass the revision to `datasets.load_dataset`.) These are the prompt variations: - *SRCDATA-LANG-keep* is the same as *SRCDATA-LANG*, but the text of the prompt is totally unchanged. If the original prompt had Python doctests, they remain as Python instead of being translated to *LANG*. If the origin... |
null | null | null | null | ### Version 3.0
This is the first significant update since MultiPL-E was used in StarCoder 1. 1. We no longer publish the MultiPL-E ablations, but they are available in revision `d23b094` and earlier. 2. New programming languages supported: - Clojure, thanks to [Alex Miller](https://github.com/puredanger) - Elixir, tha... |
null | null | null | null | - Some whitespace-only changes to problems for Racket (18 problems), R (36 problems), Julia (159 problems), and D (156 problems). We will try to avoid these kinds of changes in the future. 1. The MBPP-based problems have changes analogous to the HumanEval-based problems. See the directory `diffs_v3.0` in the dataset re... |
null | null | null | null | ## Table of Contents
- [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Datase... |
null | null | null | null | com/bigcode-project/octopack - **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124) - **Point of Contact:** [Niklas Muennighoff](mailto:n.muennighoff@gmail.com)
### Dataset Summary
> HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across ... |
null | null | null | null | > - **Languages:** Python, JavaScript, Java, Go, C++, Rust - **OctoPack🐙🎒:** <table> <tr> <th>Data</t> <td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td> <td>4TB of GitHub commits across 350 programming languages</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/datasets/bigc... |
null | null | null | null | co/bigcode/octocoder>OctoCoder</a></td> <td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></td> <td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th>Evaluation</t> <td><... |
null | null | null | null | "js", "java", "go", "cpp", "rust"
ds = load_dataset("bigcode/humanevalpack", "python")["test"] ds[0] ```
### Data Instances
An example looks as follows: ```json { "task_id": "Python/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in ... |
null | null | null | null | 0], 0.3)\n True\n \"\"\"\n", "declaration": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n", "canonical_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = abs(elem - elem2)\n if distance < thr... |
null | null | null | null | 0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n assert has_close_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) ... |
null | null | null | null | 1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\ncheck(has_close_elements)", "example_test": "def check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.0], 0.5) == False\n assert has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) == True\nch... |
null | null | null | null | are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue", "instruction": "Write a Python function `has_close_elements(numbers: List[float], threshold: float) -> bool` to solve the following pr... |
null | null | null | null | 0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue" } ```
### Data Fields
The data fields are the same among all splits: - `task_id`: Indicates the language (Python/JavaScript/Java/Go/C++/Rust) and task id (from 0 to 163) of the problem - `prompt`: the prompt for models relyin... |
null | null | null | null | `excess logic`, `value misuse`, `operator misuse`, `variable misuse`, `function misuse`]) - `failure_symptoms`: the problem the bug causes (one of [`incorrect output`, `stackoverflow`, `infinite loop`]) - `entry_point`: the name of the function - `import`: imports necessary for the solution (only present for Go) - `tes... |
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