CamemBERT-L4

This model is a pruned version of the pre-trained CamemBERT checkpoint, obtained by dropping the top-layers from the original model.

Usage

You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search. For tasks such as text generation, you should look at autoregressive models like BelGPT-2.

You can use this model directly with a pipeline for masked language modeling:

from transformers import pipeline

unmasker = pipeline('fill-mask', model='antoinelouis/camembert-L4')
unmasker("Bonjour, je suis un [MASK] modèle.")

You can also use this model to extract the features of a given text:

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained('antoinelouis/camembert-L4')
model = AutoModel.from_pretrained('antoinelouis/camembert-L4')

text = "Remplacez-moi par le texte de votre choix."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

Variations

CamemBERT has originally been released in base (110M) and large (335M) variations. The following checkpoints prune the base variation by dropping the top 2, 4, 6, 8, and 10 pretrained encoding layers, respectively.

Model	#Params	Size	Pruning
CamemBERT-base	110.6M	445MB	-
CamemBERT-L10	96.4M	386MB	-13%
CamemBERT-L8	82.3M	329MB	-26%
CamemBERT-L6	68.1M	272MB	-38%
CamemBERT-L4	53.9M	216MB	-51%
CamemBERT-L2	39.7M	159MB	-64%

Citation

For attribution in academic contexts, please cite this work as:

@online{louis2023,
   author    = 'Antoine Louis',
   title     = 'CamemBERT-L4: A Pruned Version of CamemBERT',
   publisher = 'Hugging Face',
   month     = 'october',
   year      = '2023',
   url       = 'https://huggingface.co/antoinelouis/camembert-L4',
}