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