Update README.md

README.md

@@ -392,4 +392,32 @@ model-index:
 ---
 
 
-Use Chinese and English STS and NLI corpora to conduct contrastive learning finetuning on xlmr
+Use Chinese and English STS and NLI corpora to conduct contrastive learning finetuning on xlmr
+
+
+
+## Using HuggingFace Transformers
+
+```
+from transformers import AutoTokenizer, AutoModel
+import torch
+# Sentences we want sentence embeddings for
+sentences = ["样例数据-1", "样例数据-2"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('zhou-xl/bi-cse')
+model = AutoModel.from_pretrained('zhou-xl/bi-cse')
+model.eval()
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+    # Perform pooling. In this case, cls pooling.
+    sentence_embeddings = model_output[0][:, 0]
+# normalize embeddings
+sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
+print("Sentence embeddings:", sentence_embeddings)
+```