embeddings models -> embedding models

README.md

@@ -101,9 +101,9 @@ from transformers import AutoTokenizer, AutoModel
 import torch
 
 
-#Mean Pooling - Take attention mask into account for correct averaging
+# 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
+    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)
 
@@ -122,7 +122,7 @@ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tenso
 with torch.no_grad():
     model_output = model(**encoded_input)
 
-# Perform pooling. In this case, average pooling
+# Perform pooling. In this case, mean pooling
 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
 
 print("Sentence embeddings:")
@@ -132,7 +132,7 @@ print(sentence_embeddings)
 
 ## 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