embeddings models -> embedding models

README.md

@@ -59,14 +59,14 @@ from transformers import AutoTokenizer, AutoModel
 import torch
 import torch.nn.functional as F
 
-#Mean Pooling - Take average of all tokens
+# Mean Pooling - Take average of all tokens
 def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output.last_hidden_state #First element of model_output contains all token embeddings
+    token_embeddings = model_output.last_hidden_state # 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)
 
 
-#Encode text
+# Encode text
 def encode(texts):
     # Tokenize sentences
     encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
@@ -92,27 +92,27 @@ docs = ["Around 9 Million people live in London", "London is known for its finan
 tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-cos-v1")
 model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-cos-v1")
 
-#Encode query and docs
+# Encode query and docs
 query_emb = encode(query)
 doc_emb = encode(docs)
 
-#Compute dot score between query and all document embeddings
+# Compute dot score between query and all document embeddings
 scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
 
-#Combine docs & scores
+# Combine docs & scores
 doc_score_pairs = list(zip(docs, scores))
 
-#Sort by decreasing score
+# Sort by decreasing score
 doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
 
-#Output passages & scores
+# Output passages & scores
 for doc, score in doc_score_pairs:
     print(score, doc)
 ```
 
 ## Usage (Text Embeddings Inference (TEI))
 
-[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embeddings models.
+[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models.
 
 - CPU:
 ```bash