metadata

tags:
  - bias-detection
  - nlp
  - peft
  - lora
  - fine-tuning
license: mit
datasets:
  - ...
model-index:
  - name: Bias Detector
    results:
      - task:
          type: text-classification
        dataset:
          name: ...
          type: ...
        metrics:
          - type: accuracy
            value: ...

Bias Detector

This model is fine-tuned using PEFT LoRA on existing Hugging Face models to classify and evaluate the bias in news sources.

Model Details

Architecture: Transformer-based (e.g., BERT, RoBERTa)
Fine-tuning Method: Parameter Efficient Fine-Tuning (LoRA)
Use Case: Bias classification, text summarization, sentiment analysis
Dataset: ...
Training Framework: PyTorch + Transformers

Usage

To use this model, install the necessary libraries:

pip install transformers torch

Then load the model with:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "mjwagerman/bias-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "This is an example news headline."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)