Update README.md

README.md

@@ -3,7 +3,7 @@ language: fr
 license: cc-by-4.0
 ---
 
-# Cour de Cassation *titrage* prediction model (transformer-base)
+# Cour de Cassation automatic *titrage* prediction model
 
 Model for the automatic prediction of *titrages* (keyword sequence) from *sommaires* (synthesis of legal cases). The models are described in [this paper](https://hal.inria.fr/hal-03663110/file/LREC_2022___CCass_Inria-camera-ready.pdf). If you use this model, please cite our research paper (see [below](#cite)).
 
@@ -17,7 +17,18 @@ This model is to be used to produce *titrages* for those *sommaires* that do not
 
 ### How to use
 
+```
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokeniser = AutoTokenizer.from_pretrained("rbawden/CCASS-pred-titrages-base", use_auth_token=True)
+model = AutoModelForSeq2SeqLM.from_pretrained("rbawden/CCASS-pred-titrages-base", use_auth_token=True)
 
+matiere = "matter"
+sommaire = "full text from the sommaire on a single line"
+inputs = tokeniser([matiere + " <t> " + sommaire], return_tensors='pt')
+outputs = model.generate(inputs['input_ids'])
+tokeniser.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenisation_spaces=True)
+```
 
 
 ### Limitations and bias