belgpt2/README.md

language, license, widget, metrics, library_name, pipeline_tag

language	license	widget	metrics	library_name	pipeline_tag
fr	mit	text Hier, Elon Musk a text Pourquoi a-t-il text Tout à coup, elle	perplexity	transformers	text-generation

BelGPT-2

The 1st GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).

Usage

You can use BelGPT-2 with 🤗 transformers:

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load pretrained model and tokenizer
model = GPT2LMHeadModel.from_pretrained("antoiloui/belgpt2")
tokenizer = GPT2Tokenizer.from_pretrained("antoiloui/belgpt2")

# Generate a sample of text
model.eval()
output = model.generate(
            bos_token_id=random.randint(1,50000),
            do_sample=True,   
            top_k=50, 
            max_length=100,
            top_p=0.95, 
            num_return_sequences=1
)

# Decode it
decoded_output = []
for sample in output:
    decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
print(decoded_output)

Data

Below is the list of all French copora used to pre-trained the model:

Dataset	$corpus_name	Raw size	Cleaned size
CommonCrawl	common_crawl	200.2 GB	40.4 GB
NewsCrawl	news_crawl	10.4 GB	9.8 GB
Wikipedia	wiki	19.4 GB	4.1 GB
Wikisource	wikisource	4.6 GB	2.3 GB
Project Gutenberg	gutenberg	1.3 GB	1.1 GB
EuroParl	europarl	289.9 MB	278.7 MB
NewsCommentary	news_commentary	61.4 MB	58.1 MB
Total		236.3 GB	57.9 GB

Documentation

Detailed documentation on the pre-trained model, its implementation, and the data can be found here.

Citation

For attribution in academic contexts, please cite this work as:

@misc{louis2020belgpt2,
  author = {Louis, Antoine},
  title = {{BelGPT-2: A GPT-2 Model Pre-trained on French Corpora}},
  year = {2020},
  howpublished = {\url{https://github.com/ant-louis/belgpt2}},
}