---
language:
- en
license: apache-2.0
tags:
- deberta
- text-classification
- microaggression
- detection
- bias
pipeline_tag: text-classification
widget:
- text: "You speak good English for someone from there."
- text: "Where are you really from?"
- text: "You're so articulate."
datasets:
- custom
metrics:
- accuracy
- f1
model-index:
- name: CI_MA_Detect
  results:
  - task:
      type: text-classification
      name: Microaggression Detection
    metrics:
    - type: accuracy
      value: 0.85
      name: Accuracy
---

# CI_MA_Detect - Microaggression Detection Model

This model detects microaggressions in text using a fine-tuned DeBERTa architecture.

## Model Description

- **Model type:** DeBERTa for sequence classification
- **Task:** Binary text classification (microaggression detection)
- **Labels:** 
  - LABEL_0: Not a microaggression
  - LABEL_1: Microaggression detected

## Usage

```python
from transformers import DebertaTokenizer, DebertaForSequenceClassification
import torch

tokenizer = DebertaTokenizer.from_pretrained("jokugeorgin/CI_MA_Detect")
model = DebertaForSequenceClassification.from_pretrained("jokugeorgin/CI_MA_Detect")

text = "You speak good English for someone from there."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=1)
```

## API Usage

```bash
curl https://api-inference.huggingface.co/models/jokugeorgin/CI_MA_Detect \
  -H "Authorization: Bearer YOUR_HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputs": "You speak good English for someone from there."}'
```

## Training Data

Custom dataset of microaggression examples and neutral text.

## Limitations

- Works best with English text
- May require context for ambiguous statements
- Performance varies with text length and complexity