Update README.md

README.md

@@ -12,4 +12,61 @@ tags:
 - semantic_similarity
 ---
 
-SAd
+### ViConBERT models <a name="models2"></a>
+
+
+Model | #params | Arch.	 | Max length | Training data | License
+---|---|---|---|---|---
+[`tkhangg0910/viconbert-base`](https://huggingface.co/tkhangg0910/viconbert-base) | 135M | base | 256 | ViConWSD | [MIT License](https://github.com/VinAIResearch/PhoBERT/blob/master/LICENSE)
+[`tkhangg0910/viconbert-large`](https://huggingface.co/tkhangg0910/viconbert-large) | 370M | large | 256 | ViConWSD  | [MIT License](https://github.com/VinAIResearch/PhoBERT/blob/master/LICENSE)
+
+### Example usage <a name="usage2"></a>
+SpanExtractor and text_normalize are implemented in [`Implementation`](https://github.com/tkhangg0910/ViConBERT/tree/main/utils) 
+```python
+import logging
+from typing import Optional, Tuple
+import re
+from transformers import AutoModel, PhobertTokenizerFast
+
+from utils.span_extractor import SpanExtractor
+from utils.process_data import text_normalize
+import torch
+
+model = AutoModel.from_pretrained("tkhangg0910/viconbert-base", trust_remote_code=True)
+tokenizer = PhobertTokenizerFast.from_pretrained("tkhangg0910/viconbert-base")
+
+span_ex =SpanExtractor(tokenizer)
+
+def pipeline(query, target):
+  query_norm=text_normalize(query)
+  tokenized_query = tokenizer(query_norm,return_tensors="pt").to(device)
+  span_idx = span_ex.get_span_indices(query_norm, target)
+  span =torch.Tensor(span_idx).unsqueeze(0).to(device)
+  model.eval()
+  query_vec = model(tokenized_query, span)
+  return query_vec
+
+# Honosemous word: "Khoan" example
+query_1 = "Tôi đang khoan."
+target_1 = "Khoan"
+query_vec_1 = pipeline(query_1, target_1)
+
+query_2 = "khoan này bị mất mũi khoan."
+target_2 = "mũi khoan"
+query_vec_2 = pipeline(query_2, target_2)
+
+query_3 = "Khoan là việc rất tiện lợi."
+target_3 = "Khoan"
+query_vec_3 = pipeline(query_3, target_3)
+
+
+def cosine_similarity(vec1, vec2):
+    return F.cosine_similarity(vec1, vec2, dim=1).item()
+
+
+sim_1 = cosine_similarity(query_vec_1, query_vec_3)
+sim_2 = cosine_similarity(query_vec_2, query_vec_3)
+
+print(f"Similarity between 1: {target_1}  and  3: {target_3}: {sim_1:.4f}")
+print(f"Similarity between 2: {target_2} and 3:{target_3}: {sim_2:.4f}")
+```