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README.md

@@ -22,21 +22,25 @@ tags:
 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 trained with [`flaubert-oral-asr`](https://huggingface.co/nherve/flaubert-oral-asr) as it's inital checkpoint.
+This model is trained using [`nherve/flaubert-oral-asr`](https://huggingface.co/nherve/flaubert-oral-asr) as its inital checkpoint.
 
 ## Available MEDIA NLU models:
-- [`MEDIA_NLU-flaubert_base_cased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_cased): MEDIA NLU model trained with [`flaubert_base_cased`](https://huggingface.co/flaubert/flaubert_base_cased) as it's inital checkpoint
-- [`MEDIA_NLU-flaubert_base_uncased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_uncased): MEDIA NLU model trained with [`flaubert_base_uncased`](https://huggingface.co/flaubert/flaubert_base_uncased) as it's inital checkpoint
-- [`MEDIA_NLU-flaubert_oral_ft`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_ft): MEDIA NLU model trained with [`flaubert-oral-ft`](https://huggingface.co/nherve/flaubert-oral-ft) as it's inital checkpoint
-- [`MEDIA_NLU-flaubert_oral_mixed`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_mixed): MEDIA NLU model trained with [`flaubert-oral-mixed`](https://huggingface.co/nherve/flaubert-oral-mixed) as it's inital checkpoint
-- [`MEDIA_NLU-flaubert_oral_asr`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr): MEDIA NLU model trained with [`flaubert-oral-asr`](https://huggingface.co/nherve/flaubert-oral-asr) as it's inital checkpoint
-- [`MEDIA_NLU-flaubert_oral_asr_nb`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr_nb): MEDIA NLU model trained with [`flaubert-oral-asr_nb`](https://huggingface.co/nherve/flaubert-oral-asr_nb) as it's inital checkpoint
+- [`vpelloin/MEDIA_NLU-flaubert_base_cased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_cased): MEDIA NLU model trained using [`flaubert/flaubert_base_cased`](https://huggingface.co/flaubert/flaubert_base_cased)
+- [`vpelloin/MEDIA_NLU-flaubert_base_uncased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_uncased): MEDIA NLU model trained using [`flaubert/flaubert_base_uncased`](https://huggingface.co/flaubert/flaubert_base_uncased)
+- [`vpelloin/MEDIA_NLU-flaubert_oral_ft`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_ft): MEDIA NLU model trained using [`nherve/flaubert-oral-ft`](https://huggingface.co/nherve/flaubert-oral-ft)
+- [`vpelloin/MEDIA_NLU-flaubert_oral_mixed`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_mixed): MEDIA NLU model trained using [`nherve/flaubert-oral-mixed`](https://huggingface.co/nherve/flaubert-oral-mixed)
+- [`vpelloin/MEDIA_NLU-flaubert_oral_asr`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr): MEDIA NLU model trained using [`nherve/flaubert-oral-asr`](https://huggingface.co/nherve/flaubert-oral-asr)
+- [`vpelloin/MEDIA_NLU-flaubert_oral_asr_nb`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr_nb): MEDIA NLU model trained using [`nherve/flaubert-oral-asr_nb`](https://huggingface.co/nherve/flaubert-oral-asr_nb)
 
 ## Usage with Pipeline
 ```python
 from transformers import pipeline
 
-generator = pipeline(model="vpelloin/MEDIA_NLU-flaubert_oral_asr", task="token-classification")
+generator = pipeline(
+    model="vpelloin/MEDIA_NLU-flaubert_oral_asr",
+    task="token-classification"
+)
+
 sentences = [
     "je voudrais réserver une chambre à paris pour demain et lundi",
     "d'accord pour l'hôtel à quatre vingt dix euros la nuit",
@@ -53,8 +57,12 @@ from transformers import (
     AutoTokenizer,
     AutoModelForTokenClassification
 )
-tokenizer = AutoTokenizer.from_pretrained("vpelloin/MEDIA_NLU-flaubert_oral_asr")
-model = AutoModelForTokenClassification.from_pretrained("vpelloin/MEDIA_NLU-flaubert_oral_asr")
+tokenizer = AutoTokenizer.from_pretrained(
+    "vpelloin/MEDIA_NLU-flaubert_oral_asr"
+)
+model = AutoModelForTokenClassification.from_pretrained(
+    "vpelloin/MEDIA_NLU-flaubert_oral_asr"
+)
 
 sentences = [
     "je voudrais réserver une chambre à paris pour demain et lundi",
@@ -64,7 +72,10 @@ sentences = [
  ]
 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()])
+print([
+    [model.config.id2label[i] for i in b]
+    for b in outptus.argmax(dim=-1).tolist()
+])
 ```
 
 ## Reference