metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-BBc-classifier
results: []
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
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0635 | 1.0 | 213 | 0.1708 |
| 0.0695 | 2.0 | 426 | 0.1116 |
| 0.0677 | 3.0 | 639 | 0.0842 |
| 0.0525 | 4.0 | 852 | 0.0882 |
| 0.0511 | 5.0 | 1065 | 0.0873 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1