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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](https://huggingface.co/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
|