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)