Update README.md

README.md

@@ -17,4 +17,41 @@ tags:
 - MEDIA
 ---
 
-# MEDIA NLU model trained on FlauBERT base uncased
+# vpelloin/MEDIA_NLU_flaubert_uncased
+
+This is a Natural Language Understanding (NLU) model for the French [MEDIA benchmark](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/).
+It maps each input words into outputs concepts tags (76 available).
+
+This model is a fine-tuning of [`flaubert_base_uncased`](https://huggingface.co/flaubert/flaubert_base_uncased).
+
+## Usage with Pipeline
+```python
+from transformers import pipeline
+
+generator = pipeline(model="vpelloin/MEDIA_NLU_flaubert_finetuned", task="token-classification")
+
+print(generator)
+```
+
+## Usage with AutoTokenizer/AutoModel
+```python
+from transformers import (
+    AutoTokenizer,
+    AutoModelForTokenClassification
+)
+
+tokenizer = AutoTokenizer.from_pretrained("vpelloin/MEDIA_NLU_flaubert_uncased")
+model = AutoModelForTokenClassification.from_pretrained("vpelloin/MEDIA_NLU_flaubert_uncased")
+
+sentences = [
+    "je voudrais réserver une chambre à paris pour demain et lundi",
+    "d'accord pour l'hôtel à quatre vingt dix euros la nuit",
+    "deux nuits s'il vous plait",
+    "dans un hôtel avec piscine à marseille"
+ ]
+inputs = tokenizer(sentences, padding=True, return_tensors='pt')
+
+outptus = model(**inputs).logits
+
+print([[model.config.id2label[i] for i in b] for b in outptus.argmax(dim=-1).tolist()])
+```