| --- |
| base_model: google-bert/bert-base-uncased |
| datasets: |
| - stanfordnlp/sentiment140 |
| pipeline_tag: text-classification |
| --- |
| |
| # sentiment-bert-base |
|
|
| Fine-tuned BERT-base for binary sentiment classification on the Sentiment140 dataset (1.6M tweets). |
|
|
| ## Base model |
|
|
| [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) — the original BERT-base-uncased from Devlin et al. (2019), 110M parameters. |
|
|
| ## Training |
|
|
| - Dataset: Sentiment140 (1.6M tweets, 80/20 split, seed 42) |
| - Hyperparameters: learning rate 2e-5, batch size 16, 3 epochs |
| - Hardware: NVIDIA A10G, AWS SageMaker (g5.2xlarge) |
| - Training time: 7.3 hours |
| - Trainer: Hugging Face Transformers + Trainer API; load_best_model_at_end=True |
|
|
| ## Test set performance |
|
|
| | Metric | Value | |
| |---|---| |
| | Accuracy | 87.46% | |
| | Precision | 0.880 | |
| | Recall | 0.869 | |
| | F1 | 0.874 | |
|
|
| ## Intended use |
|
|
| Demonstration model for an academic purposes |
|
|
| ## Limitations |
|
|
| - English only, binary sentiment, 2009-era Twitter language. |
| - Sentiment140 labels generated automatically using emoticons (distant supervision), introducing systematic noise. |
| - Does not handle sarcasm reliably (the dataset does not separate it as a phenomenon). |
