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