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--- |
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license: apache-2.0 |
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datasets: |
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- stanfordnlp/imdb |
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language: |
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- en |
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base_model: |
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- distilbert/distilbert-base-uncased |
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pipeline_tag: zero-shot-classification |
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library_name: transformers |
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--- |
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Example code: |
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```python3 |
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# Sample text to predict |
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text = "I love this movie, it was fantastic!" |
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# Tokenize the input text |
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) |
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# Get model predictions |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# Get the logits (model's raw output) |
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logits = outputs.logits |
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# Convert logits to probabilities (if needed) and get the predicted class (0 or 1) |
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predictions = torch.argmax(logits, dim=-1).item() |
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# Map the prediction to sentiment labels |
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labels = {0: "NEGATIVE", 1: "POSITIVE"} # Assuming binary classification |
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predicted_label = labels[predictions] |
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print(f"Predicted Sentiment: {predicted_label}") |
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``` |
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--- |
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