Update README.md

Add "how to use"

README.md

@@ -1,5 +1,6 @@
 ---
 pipeline_tag: text-classification
+inference: false
 language: en
 tags:
 - transformers
@@ -7,4 +8,37 @@ tags:
 
 # Prompsit/paraphrase-bert-en
 
-This model allows to evaluate paraphrases for a given phrase.
+This model allows to evaluate paraphrases for a given phrase.  
+We have fine-tuned this model from pretrained "bert-base-uncased".
+
+
+
+# How to usage 
+
+The model answer the following question: Is "phrase B" paraphrases of "phrase A".
+Please note that we're considering phrases instead of sentences. Therefore, we must take into account that the model doesn't expect to find punctuation marks or long pieces of text.
+
+Resulting probabilities correspond to classes:  
+* 0: Not a paraphrase
+* 1: It's a paraphrase
+
+
+You can usage the model like this:
+
+```
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("Prompsit/paraphrase-bert-en")
+model = AutoModelForSequenceClassification.from_pretrained("Prompsit/paraphrase-bert-en")
+
+input = tokenizer('may be addressed','could be included',return_tensors='pt')
+logits = model(**input).logits
+soft = torch.nn.Softmax(dim=1)
+print(soft(logits))
+```
+Output of previous code is:
+ ``` 
+ tensor([[0.1592, 0.8408]], grad_fn=<SoftmaxBackward>) 
+ ```
+As the probability of 1 is 0.84, we can conclude from the previous example that "could be included" is paraphrase of "may be included".