Pretrained model

An even simpler option than the rule-based model, and with a better performance (although a bit more compute-intensive) consists in using the public pretrained model. This model is available on the HuggingFace model hub at AP-HP/eds-pseudo-public and was trained on synthetic data described in the Dataset page. You can also test it directly on the demo.

Installation

1. Install the latest version of edsnlp

   pip install "edsnlp[ml]" -U
   

2. Get access to the model at AP-HP/eds-pseudo-public

3. Create and copy a token https://hf.co/settings/tokens?new_token=true

4. Register the token (only once) on your machine

   import huggingface_hub
   
   huggingface_hub.login(
       token=YOUR_TOKEN,
       new_session=False,
       add_to_git_credential=True,
   )
   

   5. Load the model

   import edsnlp
   
   nlp = edsnlp.load("AP-HP/eds-pseudo-public", auto_update=True)
   doc = nlp(
       "En 2015, M. Charles-François-Bienvenu "
       "Myriel était évêque de Digne. C’était un vieillard "
       "d’environ soixante-quinze ans ; il occupait le "
       "siège de Digne depuis 2006."
   )
   
   for ent in doc.ents:
       print(ent, ent.label_, str(ent._.date))
   

To apply the model in parallel on many documents using one or more GPUs, refer to the Inference page.