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antoinelouis/belgpt2
belgpt2 is a text generation model from antoinelouis. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
The 1st GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).
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From the Hugging Face model README
The 1st GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).
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)
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 |
Detailed documentation on the pre-trained model, its implementation, and the data can be found here.
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}},
}