Original model description:

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