Create README.md

README.md

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+---
+language: french
+- 
+-
+thumbnail: 
+tags: zero-shot-classification
+- 
+-
+- 
+license: 
+datasets: xlni
+- 
+-
+metrics: accuracy
+- 
+-
+---
+
+# camembert-base-xlni
+
+## Model description
+
+Camembert-base model fine-tuned on french part of XLNI dataset. <br>
+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 : 
+
+```python
+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 : <br>
+The idea is here to compute a probability of the form \\(P(premise|hypothesis)\\)
+
+```python
+# 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 [XLNI](https://research.fb.com/publications/xnli-evaluating-cross-lingual-sentence-representations/) dataset released in 2018 by Facebook. <br>
+Available with great ease using the ```datasets``` library :
+
+```python
+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. <br>
+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```
+
+## Eval results
+
+We obtain the following results on ```validation``` and ```test``` sets:
+
+| Set        | Accuracy    |
+| ---------- |-------------| 
+| validation | 81.4        | 
+| test       | 81.7        |