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--- |
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language: |
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- fr |
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license: |
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- mit |
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widget: |
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- text: Hier, Elon Musk a |
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- text: Pourquoi a-t-il |
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- text: Tout à coup, elle |
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metrics: |
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- perplexity |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# BelGPT-2 |
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**The 1st GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).** |
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## Usage |
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You can use BelGPT-2 with [🤗 transformers](https://github.com/huggingface/transformers): |
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```python |
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import torch |
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from transformers import GPT2Tokenizer, GPT2LMHeadModel |
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# Load pretrained model and tokenizer |
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model = GPT2LMHeadModel.from_pretrained("antoiloui/belgpt2") |
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tokenizer = GPT2Tokenizer.from_pretrained("antoiloui/belgpt2") |
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# Generate a sample of text |
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model.eval() |
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output = model.generate( |
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bos_token_id=random.randint(1,50000), |
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do_sample=True, |
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top_k=50, |
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max_length=100, |
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top_p=0.95, |
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num_return_sequences=1 |
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) |
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# Decode it |
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decoded_output = [] |
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for sample in output: |
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decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True)) |
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print(decoded_output) |
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``` |
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## Data |
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Below is the list of all French copora used to pre-trained the model: |
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| Dataset | `$corpus_name` | Raw size | Cleaned size | |
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| :------| :--- | :---: | :---: | |
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| CommonCrawl | `common_crawl` | 200.2 GB | 40.4 GB | |
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| NewsCrawl | `news_crawl` | 10.4 GB | 9.8 GB | |
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| Wikipedia | `wiki` | 19.4 GB | 4.1 GB | |
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| Wikisource | `wikisource` | 4.6 GB | 2.3 GB | |
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| Project Gutenberg | `gutenberg` | 1.3 GB | 1.1 GB | |
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| EuroParl | `europarl` | 289.9 MB | 278.7 MB | |
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| NewsCommentary | `news_commentary` | 61.4 MB | 58.1 MB | |
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| **Total** | | **236.3 GB** | **57.9 GB** | |
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## Documentation |
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Detailed documentation on the pre-trained model, its implementation, and the data can be found [here](https://github.com/ant-louis/belgpt2/blob/master/docs/index.md). |
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## Citation |
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For attribution in academic contexts, please cite this work as: |
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``` |
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@misc{louis2020belgpt2, |
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author = {Louis, Antoine}, |
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title = {{BelGPT-2: A GPT-2 Model Pre-trained on French Corpora}}, |
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year = {2020}, |
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howpublished = {\url{https://github.com/ant-louis/belgpt2}}, |
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} |
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``` |
