fix with actual model name (#4)

- fix with actual model name (cb57fa9d84c4c389f7e121cece7c52af992e1787)


Co-authored-by: Venkatachalam Natchiappan <[email protected]>

README.md

@@ -14,8 +14,8 @@ The model can be used for Information Retrieval: Given a query, encode the query
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained('model_name')
-tokenizer = AutoTokenizer.from_pretrained('model_name')
+model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L-6-v2')
+tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L-6-v2')
 
 features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
 
@@ -31,7 +31,7 @@ with torch.no_grad():
 The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
 ```python
 from sentence_transformers import CrossEncoder
-model = CrossEncoder('model_name', max_length=512)
+model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', max_length=512)
 scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
 ```