Transformers
PyTorch
code
custom_code
Inference Endpoints
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@@ -39,7 +39,7 @@ model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).to(device)
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  inputs = tokenizer.encode("def print_hello_world():\tprint('Hello World!')", return_tensors="pt").to(device)
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  embedding = model(inputs)[0]
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  print(f'Dimension of the embedding: {embedding[0].size()}')
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- # Dimension of the embedding: torch.Size([13, 2048])
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  ```
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  ### BibTeX entry and citation info
 
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  inputs = tokenizer.encode("def print_hello_world():\tprint('Hello World!')", return_tensors="pt").to(device)
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  embedding = model(inputs)[0]
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  print(f'Dimension of the embedding: {embedding[0].size()}')
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+ # Dimension of the embedding: torch.Size([14, 2048])
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  ```
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  ### BibTeX entry and citation info