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antoinelouis/
camembert-L10

Name: camembert-L10
Availability: InStock

text-embeddings-inference

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CamemBERT-L10

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:

python

from transformers import pipeline

unmasker = pipeline('fill-mask', model='antoinelouis/camembert-L10')
unmasker("Bonjour, je suis un [MASK] modèle.")

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

python

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained('antoinelouis/camembert-L10')
model = AutoModel.from_pretrained('antoinelouis/camembert-L10')

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%