---
license: mit
language:
- en
library_name: transformers
pipeline_tag: token-classification
tags:
- Social Bias
metrics:
- name: F1
  type: F1
  value: 0.7864
- name: Recall
  type: Recall
  value: 0.7617
thumbnail: "https://media.licdn.com/dms/image/v2/D4E12AQH-g6TfVlad0g/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1724391684857?e=1729728000&v=beta&t=e3ggmXGVKaVU6e72wjsc9Ppgd0rigQqjeA1Od9fyFDk"
base_model: "bert-base-uncased"
co2_eq_emissions:
  emissions: 8
  training_type: "fine-tuning"
  geographical_location: "Phoenix, AZ"
  hardware_used: "T4"
---

# Social Bias NER 

This NER model is fine-tuned from BERT, for *multi-label* token classification of:

- (GEN)eralizations
- (UNFAIR)ness
- (STEREO)types

You can [try it out in spaces](https://huggingface.co/spaces/maximuspowers/bias-detection-ner) :).

## How to Get Started with the Model

Transformers pipeline doesn't have a class for multi-label token classification, but you can use this code to load the model, and run it, and format the output.

```
import json
import torch
from transformers import BertTokenizerFast, BertForTokenClassification
import gradio as gr

# init important things
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = BertForTokenClassification.from_pretrained('maximuspowers/bias-detection-ner')
model.eval()
model.to('cuda' if torch.cuda.is_available() else 'cpu')

# ids to labels we want to display
id2label = {
    0: 'O',
    1: 'B-STEREO',
    2: 'I-STEREO',
    3: 'B-GEN',
    4: 'I-GEN',
    5: 'B-UNFAIR',
    6: 'I-UNFAIR'
}

# predict function you'll want to use if using in your own code
def predict_ner_tags(sentence):
    inputs = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
    input_ids = inputs['input_ids'].to(model.device)
    attention_mask = inputs['attention_mask'].to(model.device)

    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
        logits = outputs.logits
        probabilities = torch.sigmoid(logits)
        predicted_labels = (probabilities > 0.5).int() # remember to try your own threshold

    result = []
    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
    for i, token in enumerate(tokens):
        if token not in tokenizer.all_special_tokens:
            label_indices = (predicted_labels[0][i] == 1).nonzero(as_tuple=False).squeeze(-1)
            labels = [id2label[idx.item()] for idx in label_indices] if label_indices.numel() > 0 else ['O']
            result.append({"token": token, "labels": labels})

    return json.dumps(result, indent=4)
```