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medhabi
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distilbert-base-uncased-score-pred

Transformers
PyTorch
text-to-rating
Inference Endpoints
Model card Files Community
medhabi commited on
Commit
a756e26
•
1 Parent(s): 925ecf0

add model

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Files changed (19) hide show
  1. checkponts/checkpont-1/context.pt +3 -0
  2. checkponts/checkpont-1/lr_scheduler.pt +3 -0
  3. checkponts/checkpont-1/model.pt +3 -0
  4. checkponts/checkpont-1/optimizer.pt +3 -0
  5. checkponts/checkpont-2/context.pt +3 -0
  6. checkponts/checkpont-2/lr_scheduler.pt +3 -0
  7. checkponts/checkpont-2/model.pt +3 -0
  8. checkponts/checkpont-2/optimizer.pt +3 -0
  9. checkponts/checkpont-3/context.pt +3 -0
  10. checkponts/checkpont-3/lr_scheduler.pt +3 -0
  11. checkponts/checkpont-3/model.pt +3 -0
  12. checkponts/checkpont-3/optimizer.pt +3 -0
  13. checkponts/checkpont-best/context.pt +3 -0
  14. checkponts/checkpont-best/lr_scheduler.pt +3 -0
  15. checkponts/checkpont-best/model.pt +3 -0
  16. checkponts/checkpont-best/optimizer.pt +3 -0
  17. config.json +1 -0
  18. info.txt +21 -0
  19. pytorch_model.bin +2 -2
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checkponts/checkpont-3/context.pt ADDED
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checkponts/checkpont-3/optimizer.pt ADDED
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checkponts/checkpont-best/context.pt ADDED
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checkponts/checkpont-best/lr_scheduler.pt ADDED
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checkponts/checkpont-best/model.pt ADDED
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checkponts/checkpont-best/optimizer.pt ADDED
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config.json CHANGED
@@ -1,4 +1,5 @@
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  {
 
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  "architectures": [
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  "BERTTextToRating"
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  ],
 
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  {
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+ "_name_or_path": "distilbert-base-uncased-score-pred",
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  "architectures": [
4
  "BERTTextToRating"
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  ],
info.txt ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ AdamW(model.parameters(), lr=5e-5)
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+
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+ class BERTTextToRating(PreTrainedModel):
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+ config_class = BERTTextToRatingConfig
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+
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+ def __init__(self, config):
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+ super(BERTTextToRating, self).__init__(config)
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+ model_checkpoint = "medhabi/distilbert-base-uncased-mlm-ta-local"
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+ model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)
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+ self.bert_model = model.distilbert
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+ self.dropout = torch.nn.Dropout(0.3)
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+ self.linear = torch.nn.Linear(768, 5)
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+
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+ def forward(self, input_ids, attention_mask, token_type_ids):
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+ output = self.bert_model(
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+ input_ids,
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+ attention_mask=attention_mask,
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+ )
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+ output_dropout = self.dropout(output.last_hidden_state[0][0].reshape(1,-1))
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+ output = self.linear(output_dropout)
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+ return output
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