Instructions to use antoinelouis/camembert-L6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antoinelouis/camembert-L6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="antoinelouis/camembert-L6")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("antoinelouis/camembert-L6") model = AutoModel.from_pretrained("antoinelouis/camembert-L6") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
language:
- fr
library_name: transformers
inference: false
---
# CamemBERT-L6
This model is a pruned version of the pre-trained [CamemBERT](https://huggingface.co/camembert-base) checkpoint, obtained by [dropping the top-layers](https://arxiv.org/abs/2004.03844) from the original model.

## Usage
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('antoinelouis/camembert-L6')
model = AutoModel.from_pretrained('antoinelouis/camembert-L6')
```
## Comparison
| Model | #Params | Size | Pruning |
|--------------------------------------------------------------------|:-------:|:-----:|:-------:|
| [CamemBERT](https://huggingface.co/camembert-base) | 110.6M | 445MB | - |
| | | | |
| [CamemBERT-L10](https://huggingface.co/antoinelouis/camembert-L10) | 96.4M | 368MB | -13% |
| [CamemBERT-L8](https://huggingface.co/antoinelouis/camembert-L8) | 82.3M | 314MB | -26% |
| **CamemBERT-L6** | 68.1M | 260MB | -38% |
| [CamemBERT-L4](https://huggingface.co/antoinelouis/camembert-L4) | 53.9M | 206MB | -51% |
| [CamemBERT-L2](https://huggingface.co/antoinelouis/camembert-L2) | 39.7M | 159MB | -64% |
## Citation
```bibtex
@online{louis2023,
author = 'Antoine Louis',
title = 'CamemBERT-L6: A Pruned Version of CamemBERT',
publisher = 'Hugging Face',
month = 'october',
year = '2023',
url = 'https://huggingface.co/antoinelouis/camembert-L6',
}
``` |