Fix example code for huggingface transformers usage

README.md

@@ -44,7 +44,7 @@ from transformers import AutoTokenizer, AutoModel
 import torch
 
 
-def cls_pooling(model_output, attention_mask):
+def cls_pooling(model_output):
     return model_output[0][:,0]
 
 
@@ -52,8 +52,8 @@ def cls_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('kevinkrahn/shlm-grc-en')
-model = AutoModel.from_pretrained('kevinkrahn/shlm-grc-en')
+model = AutoModel.from_pretrained('kevinkrahn/shlm-grc-en', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('kevinkrahn/shlm-grc-en', trust_remote_code=True)
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -63,10 +63,11 @@ with torch.no_grad():
     model_output = model(**encoded_input)
 
 # Perform pooling. In this case, cls pooling.
-sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
+sentence_embeddings = cls_pooling(model_output)
 
 print("Sentence embeddings:")
 print(sentence_embeddings)
+
 ```
 
 ## Citing & Authors