embeddings models -> embedding models

README.md

@@ -42,23 +42,23 @@ from sentence_transformers import SentenceTransformer, util
 query = "How many people live in London?"
 docs = ["Around 9 Million people live in London", "London is known for its financial district"]
 
-#Load the model
+# Load the model
 model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-dot-v1')
 
-#Encode query and documents
+# Encode query and documents
 query_emb = model.encode(query)
 doc_emb = model.encode(docs)
 
-#Compute dot score between query and all document embeddings
+# Compute dot score between query and all document embeddings
 scores = util.dot_score(query_emb, doc_emb)[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)
 ```
@@ -71,11 +71,11 @@ Without [sentence-transformers](https://www.SBERT.net), you can use the model li
 from transformers import AutoTokenizer, AutoModel
 import torch
 
-#CLS Pooling - Take output from first token
+# CLS Pooling - Take output from first token
 def cls_pooling(model_output):
     return model_output.last_hidden_state[:,0]
 
-#Encode text
+# Encode text
 def encode(texts):
     # Tokenize sentences
     encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
@@ -98,27 +98,27 @@ docs = ["Around 9 Million people live in London", "London is known for its finan
 tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1")
 model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-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