---
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](https://huggingface.co/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
```python
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)