Update model card as it was on the original model

README.md

@@ -1,4 +1,95 @@
 ---
 pipeline_tag: zero-shot-classification
+license: mit
+datasets:
+- xnli
+language:
+- fr
+tags:
+- camembert
+- text-classification
+- nli
+- xnli
 ---
-A copy of the original BaptisteDoyen/camembert-base-xnli as it gives a 404 error right now.
+This is a copy of the original BaptisteDoyen/camembert-base-xnli model as it gives a 404 error right now.\
+Here is the model card as it was on BaptisteDoyen/camembert-base-xnli page.
+
+# camembert-base-xnli
+
+## Model description
+
+Camembert-base model fine-tuned on french part of XNLI dataset.
+One of the few Zero-Shot classification model working on French 🇫🇷
+
+## Intended uses & limitations
+
+#### How to use
+
+Two different usages :
+
+- As a Zero-Shot sequence classifier :
+```
+classifier = pipeline("zero-shot-classification", 
+                      model="BaptisteDoyen/camembert-base-xnli")
+
+sequence = "L'équipe de France joue aujourd'hui au Parc des Princes"
+candidate_labels = ["sport","politique","science"]
+hypothesis_template = "Ce texte parle de {}."    
+
+classifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)     
+# outputs :                                        
+# {'sequence': "L'équipe de France joue aujourd'hui au Parc des Princes",
+# 'labels': ['sport', 'politique', 'science'],
+# 'scores': [0.8595073223114014, 0.10821866989135742, 0.0322740375995636]}                      
+```
+- As a premise/hypothesis checker :
+The idea is here to compute a probability of the form P(premise∣hypothesis) P(premise|hypothesis ) P(premise∣hypothesis)
+
+```
+# load model and tokenizer
+nli_model = AutoModelForSequenceClassification.from_pretrained("BaptisteDoyen/camembert-base-xnli")
+tokenizer = AutoTokenizer.from_pretrained("BaptisteDoyen/camembert-base-xnli") 
+# sequences
+premise = "le score pour les bleus est élevé"
+hypothesis = "L'équipe de France a fait un bon match"
+# tokenize and run through model
+x = tokenizer.encode(premise, hypothesis, return_tensors='pt')
+logits = nli_model(x)[0]
+# we throw away "neutral" (dim 1) and take the probability of
+# "entailment" (0) as the probability of the label being true 
+entail_contradiction_logits = logits[:,::2]
+probs = entail_contradiction_logits.softmax(dim=1)
+prob_label_is_true = probs[:,0]
+prob_label_is_true[0].tolist() * 100
+# outputs
+# 86.40775084495544
+```
+
+## Training data
+
+Training data is the french fold of the [XNLI](https://research.fb.com/publications/xnli-evaluating-cross-lingual-sentence-representations/) dataset released in 2018 by Facebook.
+Available with great ease using the datasets library :
+
+```
+from datasets import load_dataset
+dataset = load_dataset('xnli', 'fr')                     
+```
+
+## Training/Fine-Tuning procedure
+
+Training procedure is here pretty basic and was performed on the cloud using a single GPU.
+Main training parameters :
+
+- `lr = 2e-5 with lr_scheduler_type = "linear"`
+- `num_train_epochs = 4`
+- `batch_size = 12 (limited by GPU-memory)`
+- `weight_decay = 0.01`
+- `metric_for_best_model = "eval_accuracy"`
+
+## Eval results
+
+We obtain the following results on validation and test sets:
+Set|Accuracy
+---|---
+validation|81.4
+test|81.7