bert-twitter-sentiment-classifier
Fine-tuned model: bert-base-uncased → bert-twitter-sentiment-classifier
Author: Aakash (Aakash22134)
Contact: saiaakash33333@gmail.com
License: apache-2.0
Languages: en
Model description
This model is a fine-tuned BERT classifier for multi-class emotion / sentiment classification on short Twitter text.
It predicts one of the following classes: sadness, joy, love, anger, fear, surprise.
The model was trained on the twitter_multi_class_sentiment dataset and demonstrates strong classification performance on the held-out test set.
Training data
- Dataset: twitter_multi_class_sentiment (public CSV from example notebook)
- Train / Validation / Test: 11200 / 1600 / 3200
- Preprocessing: tokenized with
bert-base-uncased tokenizer, padding + truncation to default BERT max length in the notebook
Training procedure & hyperparameters
- Base model: bert-base-uncased
- Training epochs: 2
- Batch size (train/eval): 64 / 64
- Learning rate: 2e-05
- Weight decay: 0.01
- Trainer:
transformers.Trainer (Hugging Face Transformers)
- Notes: model was trained for 2 epochs in a Colab environment; consider longer training or more data for further improvements.
Evaluation
Test set results (approx):
- Accuracy: 0.900625
- F1 (weighted): 0.900321
Per-class (precision / recall / f1 / support):
{
"sadness": {
"precision": 0.93,
"recall": 0.95,
"f1": 0.94,
"support": 933
},
"joy": {
"precision": 0.92,
"recall": 0.91,
"f1": 0.92,
"support": 1072
},
"love": {
"precision": 0.76,
"recall": 0.75,
"f1": 0.76,
"support": 261
},
"anger": {
"precision": 0.91,
"recall": 0.91,
"f1": 0.91,
"support": 432
},
"fear": {
"precision": 0.89,
"recall": 0.88,
"f1": 0.88,
"support": 387
},
"surprise": {
"precision": 0.75,
"recall": 0.72,
"f1": 0.74,
"support": 115
}
}
Evaluation details: computed with sklearn.metrics.classification_report.
Usage
Last updated: 2025-09-29 09:20:19 UTC
