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--- |
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license: mit |
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library_name: transformers |
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datasets: |
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- stanfordnlp/sst2 |
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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: text-classification |
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tags: |
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- legal |
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- PyTorch |
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- text-classification |
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- sentiment-analysis |
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--- |
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# Simple Text Classifier |
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This is a fine-tuned model for text classification based on `distilbert-base-uncased`. |
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## Model Details |
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- Model Type: Text Classification |
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- Number of Classes: 2 |
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- Hidden Size: 768 |
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## Usage |
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```python |
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from transformers import AutoTokenizer |
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from huggingface_text_classifier.model import SimpleTextClassifier |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("ajinathgh/sentiment_analysis") |
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model = SimpleTextClassifier.from_pretrained("ajinathgh/sentiment_analysis") |
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# Prepare input |
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inputs = tokenizer("Example text to classify", return_tensors="pt") |
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# Get predictions |
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outputs = model(**inputs) |
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predicted_class = outputs.argmax(-1).item() |
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``` |
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