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gemmalarasav/
distilbert-ag-news-lora-merged

Name: distilbert-ag-news-lora-merged
Availability: InStock

pytorch

safetensors

distilbert

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DistilBERT AG News (LoRA fine-tuned)

A LoRA fine-tuned DistilBERT model for AG News classification (Apache 2.0 licensed).

This model is a fine-tuned version of DistilBERT base uncased using LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning.
It has been trained on the AG News dataset to perform news headline classification into the following categories:

World
Sports
Business
Sci/Tech

Model Details

Base model: distilbert-base-uncased
Fine-tuning method: LoRA
Dataset: AG News
Task: Text classification (4 classes)
Author: Gemma Lara Savill

Usage

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="gemmalarasav/distilbert-ag-news-lora-merged"
)

text = "OpenAI releases new GPT model"
result = classifier(text)
print(result)

Example output:

[{'label': 'Sci/Tech', 'score': 0.987}]

Performance

Fine-tuned accuracy: ~90% on AG News test split
Lightweight (LoRA) → smaller footprint and faster adaptation compared to full fine-tuning

License and Attribution

This model is a fine-tuned version of DistilBERT,
which is licensed under the Apache License 2.0.

This fine-tuned model is released under the same license: Apache 2.0.
See the included LICENSE file for details.

Limitations and Biases

The model inherits potential biases from DistilBERT and the AG News dataset.
It may misclassify edge cases or headlines outside the AG News domain.
Not suitable for sensitive decision-making without human oversight.