Instructions to use antoinelouis/camembert-L8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antoinelouis/camembert-L8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="antoinelouis/camembert-L8")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("antoinelouis/camembert-L8") model = AutoModel.from_pretrained("antoinelouis/camembert-L8") - Notebooks
- Google Colab
- Kaggle
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README.md
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| [CamemBERT-L6](https://huggingface.co/antoinelouis/camembert-L6) | 68.1M | 272MB | -38% |
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| [CamemBERT-L4](https://huggingface.co/antoinelouis/camembert-L4) | 53.9M | 216MB | -51% |
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| [CamemBERT-L2](https://huggingface.co/antoinelouis/camembert-L2) | 39.7M | 159MB | -64% |
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## Citation
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For attribution in academic contexts, please cite this work as:
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```bibtex
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@online{louis2023,
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author = 'Antoine Louis',
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title = 'CamemBERT-L8: A Pruned Version of CamemBERT',
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publisher = 'Hugging Face',
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month = 'october',
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year = '2023',
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url = 'https://huggingface.co/antoinelouis/camembert-L8',
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}
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```
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| [CamemBERT-L6](https://huggingface.co/antoinelouis/camembert-L6) | 68.1M | 272MB | -38% |
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| [CamemBERT-L4](https://huggingface.co/antoinelouis/camembert-L4) | 53.9M | 216MB | -51% |
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| [CamemBERT-L2](https://huggingface.co/antoinelouis/camembert-L2) | 39.7M | 159MB | -64% |
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