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--- |
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language: en |
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license: mit |
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tags: |
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- health |
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- trigger-detection |
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- transformers |
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- xlm-roberta |
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datasets: |
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- memo-dataset |
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base_model: xlm-roberta-base |
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library_name: transformers |
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pipeline_tag: text-classification |
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model_name: xlmr-trigger-detection |
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widget: |
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- text: "Patient developed a severe allergic reaction after injection." |
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- text: "No trigger event was recorded." |
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--- |
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# XLM-R Trigger Detection |
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This model fine-tunes [XLM-RoBERTa-base](https://huggingface.co/xlm-roberta-base) |
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for **trigger classification** in health-related text (from the Memo Dataset). |
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## Model Details |
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- **Architecture:** XLM-RoBERTa-base |
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- **Task:** Binary classification (Trigger / Non-Trigger) |
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- **Trained on:** Memo Dataset |
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- **Framework:** Transformers (Transformers + PyTorch) |
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## Example Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("Tanvi0212/xlmr-trigger-detection") |
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model = AutoModelForSequenceClassification.from_pretrained("Tanvi0212/xlmr-trigger-detection") |
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text = "Patient developed a severe allergic reaction." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model(**inputs) |
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