metadata
language: en
license: mit
datasets:
- twitter-sentiment
metrics:
- accuracy
- f1
- precision
- recall
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
library_name: transformers
Fine-Tuned BERT Sentiment Model
This model was fine-tuned for sentiment classification.
- Pre-trained model used: google-bert/bert-base-uncased.
- Dataset used: twitter-sentiment.
- max_length = 128
- batch_size = 8
- learning_rate = 1e-4
- epochs = 3
Evaluation Results
📌 Before Fine-Tuning
Accuracy: 0.4046
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| Negative | 0.00 | 0.00 | 0.00 | 1001 |
| Neutral | 0.40 | 1.00 | 0.58 | 1430 |
| Positive | 0.00 | 0.00 | 0.00 | 1103 |
| Macro Avg | 0.13 | 0.33 | 0.19 | 3534 |
| Weighted Avg | 0.16 | 0.40 | 0.23 | 3534 |
✅ After Fine-Tuning
Accuracy: 0.6095
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| Negative | 0.82 | 0.29 | 0.42 | 1001 |
| Neutral | 0.51 | 0.89 | 0.65 | 1430 |
| Positive | 0.85 | 0.54 | 0.66 | 1103 |
| Macro Avg | 0.73 | 0.57 | 0.58 | 3534 |
| Weighted Avg | 0.70 | 0.61 | 0.59 | 3534 |
You can download the model from Hugging Face.