Basic HuggingFace setup

main.py

@@ -1,2 +1,52 @@
 import spacy
 import math
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+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)
+
+
+def main():
+    dataset = load_dataset("glue", "cola")
+    dataset = dataset["train"]
+
+    sentences = ["This is an example sentence", "Each sentence is converted"]
+
+    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+    embeddings = model.encode(sentences)
+    print(embeddings)
+    
+    # Sentences we want sentence embeddings for
+    sentences = ['This is an example sentence', 'Each sentence is converted']
+
+    # Load model from HuggingFace Hub
+    tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+    model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+    # 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
+    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+    # Normalize embeddings
+    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+    print("Sentence embeddings:")
+    print(sentence_embeddings)
+    
+if __name__ == "__main__":
+    main()