BERT News Category Classifier

This model is a fine-tuned version of bert-base-uncased optimized to classify articles into 5 categories (Business, Tech, Politics, Sports, Entertainment).

Model Description

• Architecture: BERT-base-uncased with frozen base layers for training efficiency.
• Task: Multi-class Text Classification (NLP Pipeline).
• Performance: Achieved a 0.96 Macro-F1 score on evaluation.

Training and Evaluation Data

• Dataset: BBC News Dataset.
• Preprocessing: Cleaned text fields tokenized using the standard BERT WordPiece tokenizer.

Intended Uses & Limitations

This model is intended for production-ready news classification pipelines. It is lightweight due to layer-freezing optimization during training.

bert-BBc-classifier

This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0873

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.57.3
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1