---
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).