BIG-Bench but it doesn't require the hellish dependencies (tensorflow, pypi-bigbench, protobuf) of the official version.
```python
dataset = load_dataset("tasksource/bigbench",'movie_recommendation')
```
Code to reproduce:
https://colab.research.google.com/drive/1MKdLdF7oqrSQCeavAcsEnPdI85kD0LzU?usp=sharing

Datasets are capped to 50k examples to keep things light.
I also removed the default split when train was available also to save space, as default=train+val.

```bibtex
@article{srivastava2022beyond,
  title={Beyond the imitation game: Quantifying and extrapolating the capabilities of language models},
  author={Srivastava, Aarohi and Rastogi, Abhinav and Rao, Abhishek and Shoeb, Abu Awal Md and Abid, Abubakar and Fisch, Adam and Brown, Adam R and Santoro, Adam and Gupta, Aditya and Garriga-Alonso, Adri{\`a} and others},
  journal={arXiv preprint arXiv:2206.04615},
  year={2022}
}
```
task_categories:multiple-choice task_categories:question-answering task_categories:text-classification task_categories:text-generation task_categories:zero-shot-classification task_ids:multiple-choice-qa task_ids:extractive-qa task_ids:open-domain-qa task_ids:closed-domain-qa task_ids:fact-checking task_ids:acceptability-classification task_ids:intent-classification task_ids:multi-class-classification task_ids:multi-label-classification task_ids:text-scoring task_ids:hate-speech-detection task_ids:language-modeling annotations_creators:crowdsourced annotations_creators:expert-generated annotations_creators:machine-generated language_creators:crowdsourced language_creators:expert-generated language_creators:machine-generated language_creators:other multilinguality:multilingual multilinguality:monolingual size_categories:unknown source_datasets:original language:en license:apache-2.0 region:us
{"annotations_creators": ["crowdsourced", "expert-generated", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated", "machine-generated", "other"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["multilingual", "monolingual"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "question-answering", "text-classification", "text-generation", "zero-shot-classification"], "task_ids": ["multiple-choice-qa", "extractive-qa", "open-domain-qa", "closed-domain-qa", "fact-checking", "acceptability-classification", "intent-classification", "multi-class-classification", "multi-label-classification", "text-scoring", "hate-speech-detection", "language-modeling"], "pretty_name": "bigbench"}
TAGS
#task_categories-multiple-choice #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-zero-shot-classification #task_ids-multiple-choice-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-closed-domain-qa #task_ids-fact-checking #task_ids-acceptability-classification #task_ids-intent-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-text-scoring #task_ids-hate-speech-detection #task_ids-language-modeling #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-multilingual #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-apache-2.0 #region-us 

BIG-Bench but it doesn't require the hellish dependencies (tensorflow, pypi-bigbench, protobuf) of the official version.

Code to reproduce:
URL

Datasets are capped to 50k examples to keep things light.
I also removed the default split when train was available also to save space, as default=train+val.


TAGS
#task_categories-multiple-choice #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-zero-shot-classification #task_ids-multiple-choice-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-closed-domain-qa #task_ids-fact-checking #task_ids-acceptability-classification #task_ids-intent-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-text-scoring #task_ids-hate-speech-detection #task_ids-language-modeling #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-multilingual #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-apache-2.0 #region-us 

passage: TAGS
#task_categories-multiple-choice #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-zero-shot-classification #task_ids-multiple-choice-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-closed-domain-qa #task_ids-fact-checking #task_ids-acceptability-classification #task_ids-intent-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-text-scoring #task_ids-hate-speech-detection #task_ids-language-modeling #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-multilingual #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-apache-2.0 #region-us 
