Textbook: Universal NLP Datasets
Current support few commonsense reasoning datsets(alphanli, hellaswag, physicaliqa, socialiqa, codah, and commonsenseqa). It adopts ray's multiprocessing in loading/processing the datasets.
Architecture
Dependency
pip install -r requirements.txt
Download raw datasets
bash fetch.sh
It downloads alphanli, hellaswag, physicaliqa, socialiqa, codah, and commonsenseqa from AWS.
In case you want to use something-something, pelase download the dataset from 20bn's website.
Usage
Initialize ray
import ray
ray.init(memory=1024 * 1024 * 1024, num_cpus=2)
Load a dataset
from transformers import BertTokenizer
from textbook import *
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
text_renderer = TextRenderer.remote(tokenizer)
anli_tool = BatchTool(tokenizer, max_seq_len=128, source="anli")
anli_dataset = TextDataset(path='data_cache/alphanli/eval.jsonl',
config=ANLIConfiguration.remote(), renderers=[text_renderer])
# Batch by number of examples
anli_iter = DataLoader(anli_dataset, batch_size=2, collate_fn=anli_tool.collate_fn)
# Batch by number of tokens
anli_iter = DataLoader(anli_dataset, batch_sampler=TokenBasedSampler(anli_dataset, batch_size=128), collate_fn=anli_tool.collate_fn)
1 maintainer
Metadata
1 maintainer
Release files for textbook 0.2.2
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| textbook-0.2.2.tar.gz | 8.7 kB | Details |
Release files / textbook-0.2.2.tar.gz
| Download URL | textbook-0.2.2.tar.gz |
|---|---|
| Size | 8.7 kB |
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32d8a6fdb183d553f5fe094306f0c38ccc9d6ce76308b7c8e872d093d72fee5a
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.35.0 CPython/3.7.3
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