eternalGenius/SS2-Trained_Bert-Base-Uncased

BERT fine-tuned on SST-2 (Sentiment Analysis)

This model is a fine-tuned version of bert-base-uncased on the SST-2 dataset (GLUE benchmark).

📚 Task

Binary sentiment classification — predict whether a sentence has positive or negative sentiment.

🧠 Training Details

• Base model: bert-base-uncased
• Dataset: glue/sst2
• Epochs: 3
• Learning rate: 2e-5
• Batch size: 16
• Evaluation metrics: accuracy, F1

📊 Metrics

Metric	Value
Accuracy	0.9289
F1	0.9289

🔎 Example Inference

from transformers import pipeline

clf = pipeline("text-classification", model="eternalGenius/SS2-Trained_Bert-Base-Uncased")

print(clf("The movie was absolutely fantastic!"))
# [{'label': 'positive', 'score': 0.98}]