venkyyuvy commited on
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cb57fa9
1 Parent(s): b2cfda5

fix with actual model name

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  1. README.md +3 -3
README.md CHANGED
@@ -14,8 +14,8 @@ The model can be used for Information Retrieval: Given a query, encode the query
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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- model = AutoModelForSequenceClassification.from_pretrained('model_name')
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- tokenizer = AutoTokenizer.from_pretrained('model_name')
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  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")
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@@ -31,7 +31,7 @@ with torch.no_grad():
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  The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
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  ```python
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  from sentence_transformers import CrossEncoder
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- model = CrossEncoder('model_name', max_length=512)
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  scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
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  ```
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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+ model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L-6-v2')
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+ tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L-6-v2')
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  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")
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  The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
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  ```python
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  from sentence_transformers import CrossEncoder
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+ model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', max_length=512)
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  scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
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  ```
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