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