Update README.md

README.md

@@ -31,6 +31,60 @@ This LSG variant has been adapted from our original model, ["hiiamsid/sentence_s
 
 The LSG-enhanced model is particularly adept at tasks involving longer documents, where capturing the essence of extended context is crucial.
 
+
+```python
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained('prudant/lsg_4096_sentence_similarity_spanish')
+model = AutoModel.from_pretrained('prudant/lsg_4096_sentence_similarity_spanish', trust_remote_code=True)
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+# Sentences
+sentences = [
+  'Esa es una persona feliz',
+  "Ese es un perro feliz",
+  "Esa es una persona muy feliz",
+  "Hoy es un día soleado",
+  "Esa es una persona alegre",
+  ]
+
+# 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, max pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+
+# Norm embeddings
+normalized_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+# Sentence similarity
+cosine_similarities = F.cosine_similarity(normalized_embeddings[0].unsqueeze(0), normalized_embeddings[1:], dim=1)
+
+print(cosine_similarities)
+```
+
+Sentence embeddings:
+tensor([[-0.1691, -0.2517, -1.3000,  ...,  0.1557,  0.3824,  0.2048],
+        [ 0.1872, -0.7604, -0.4863,  ..., -0.4922, -0.1511, -0.8539],
+        [-0.2467, -0.2373, -1.1708,  ...,  0.4637,  0.0616,  0.2841],
+        [-0.2384,  0.1681, -0.3498,  ..., -0.2744, -0.1722, -1.2513],
+        [ 0.2273, -0.2393, -1.6124,  ...,  0.6065,  0.2784, -0.3354]])
+
+tensor([0.5132, 0.9346, 0.3471, 0.8543])
+
 #### Acknowledgments
 
 This model has been adapted by Darío Muñoz Prudant, thanks to the Hugging Face community and contributors to the LSG attention mechanism for their resources and support.