joseeangel/bert-base-uncased-agnews-topic
bert-base-uncased adapted to Topic classification (AG News) with Full fine-tuning.
Produced for the assignment U2T01 - Adapting BERT for NLP tasks (Trends in Data Science). The delivered method was chosen by measurement, not by default: the table below is the full set of adaptation methods trained for this task, and this repository holds the winner.
Intended use
Assigning one of four news topics (World, Sports, Business, Sci/Tech) to a short English news headline plus lead sentence.
Out of scope: any use where an error carries real cost without a human in the loop, and any language or domain other than the one above.
Training data
- Dataset:
fancyzhx/ag_news - License: Custom (AG News corpus, academic use)
- Citation: Zhang, Zhao & LeCun (2015), Character-level Convolutional Networks for Text Classification
- Splits used: {"train": 20000, "dev": 5000, "test": 7600}
Adaptation method
| Method | Full fine-tuning |
| Trainable parameters | 109,485,316 of 109,485,316 (100.0%) |
| Head learning rate | 0.001 |
| Encoder learning rate | 2e-05 |
| Epochs / batch size | 2 / 32 |
| Max sequence length | 128 |
| Scheduler | linear with 10% warmup |
| Seed | 42 |
| Hardware | Apple M5 (mps) |
| Wall-clock training time | 16.6 min |
A freshly initialised head and pretrained encoder weights are trained in two parameter groups with separate learning rates; a single shared rate either starves the head or destroys pretrained features.
Evaluation
Held-out test split, never seen during training or model selection.
| Metric | Value |
|---|---|
| Accuracy | 93.01 |
| Macro F1 | 93.00 |
What every method scored on this task
| Method | Trainable params | Share of model | Accuracy | Macro F1 | Train time (min) |
|---|---|---|---|---|---|
| Feature-based (logistic regression, mean-pooled) | 3,076 | 0.0% | 90.18 | 90.18 | 2.9 |
| Feature-based (linear probe, pooler as head) | 593,668 | 0.542% | 89.61 | 89.59 | 13.6 |
| Feature-based (linear SVM) | 3,076 | 0.0% | 89.21 | 89.20 | 0.1 |
| Feature-based (logistic regression) | 3,076 | 0.0% | 88.61 | 88.59 | 2.0 |
| Feature-based (random forest) | 0 | 0.0% | 84.87 | 84.79 | 0.3 |
| Feature-based (MLP probe) | 395,780 | 0.36% | 84.58 | 84.57 | 14.8 |
| Feature-based (linear probe) | 3,076 | 0.003% | 82.09 | 82.04 | 12.5 |
| Partial FT (top 2 layers) | 14,178,820 | 12.95% | 92.36 | 92.35 | 13.1 |
| Full fine-tuning | 109,485,316 | 100.0% | 93.01 | 93.00 | 16.6 |
Single run per configuration with a fixed seed. Re-running with a different seed moves these numbers by roughly +/- 1-3 points, so gaps smaller than that are noise rather than findings.
Labels
0World1Sports2Business3Sci/Tech
How to use
from transformers import pipeline
pipe = pipeline("text-classification", model="joseeangel/bert-base-uncased-agnews-topic")
Limitations and bias
Trained on 2004-era news wire, so topic drift is real: modern entities and technology vocabulary are under-represented, and the Business / Sci-Tech boundary is the model's weakest. Texts far longer than a headline and lead are truncated at 128 subwords. English only.
The model inherits whatever social bias is present in BERT's pretraining corpus (BooksCorpus + English Wikipedia) and in the task dataset above.
References
- Devlin, Chang, Lee & Toutanova (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 - section 5.3 is the feature-based vs fine-tuning comparison this work mirrors.
- Zhang, Zhao & LeCun (2015), Character-level Convolutional Networks for Text Classification
- HuggingFace Transformers, fine-tune a pretrained model.