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Update README.md

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  1. README.md +5 -1
README.md CHANGED
@@ -6,6 +6,9 @@ tags:
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  - sentence-similarity
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  datasets:
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  - biu-nlp/abstract-sim
 
 
 
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  ---
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  A model for mapping abstract sentence descriptions to sentences that fit the descriptions. Trained on Wikipedia. Use ```load_finetuned_model``` to load the query and sentence encoder, and ```encode_batch()``` to encode a sentence with the model.
@@ -14,6 +17,7 @@ A model for mapping abstract sentence descriptions to sentences that fit the des
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  from transformers import AutoTokenizer, AutoModel
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  import torch
 
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  def load_finetuned_model():
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@@ -25,7 +29,7 @@ def load_finetuned_model():
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  return tokenizer, query_encoder, sentence_encoder
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- def encode_batch(model, tokenizer, sentences, device):
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  input_ids = tokenizer(sentences, padding=True, max_length=512, truncation=True, return_tensors="pt",
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  add_special_tokens=True).to(device)
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  features = model(**input_ids)[0]
 
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  - sentence-similarity
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  datasets:
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  - biu-nlp/abstract-sim
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+ widgets:
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+ - sentence-similarity
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+ - feature-extraction
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  ---
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  A model for mapping abstract sentence descriptions to sentences that fit the descriptions. Trained on Wikipedia. Use ```load_finetuned_model``` to load the query and sentence encoder, and ```encode_batch()``` to encode a sentence with the model.
 
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  from transformers import AutoTokenizer, AutoModel
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  import torch
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+ from typing import List
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  def load_finetuned_model():
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  return tokenizer, query_encoder, sentence_encoder
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+ def encode_batch(model, tokenizer, sentences: List[str], device: str):
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  input_ids = tokenizer(sentences, padding=True, max_length=512, truncation=True, return_tensors="pt",
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  add_special_tokens=True).to(device)
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  features = model(**input_ids)[0]