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

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:

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

CamemBERTa has originally been released in a base (112M) version. The following checkpoints prune the base variation by dropping the top 2, 4, 6, 8, and 10 pretrained encoding layers, respectively.