metadata
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}},
}