Update README.md

README.md

@@ -10,3 +10,32 @@ Provides a relevance score for a given search query and a product (with title an
 Can be used as retriever/ranker for e-commerce search.
 
 Development details coming soon.
+
+## How to use the model
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import torch.nn.functional as F
+
+
+MODEL_ID = "prhegde/query-product-relevance-model-ecommerce"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+
+query_text = "sofa with ottoman"
+prod_text = "daryl 82 '' wide reversible sofa & chaise with ottoman"
+
+tok_output = tokenizer(query_text, prod_text, padding='max_length',
+                      max_length=160, truncation=True, return_tensors='pt',
+                      return_attention_mask=True)
+
+input_ids = tok_output.input_ids
+attention_masks = tok_output.attention_mask
+token_type_ids = tok_output.token_type_ids
+
+output = model(input_ids, attention_mask = attention_masks,
+                  token_type_ids = token_type_ids)
+probs = F.softmax(output.logits, dim=-1)
+score = probs[0][1].item()
+print(score)
+