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bert-sentiment-analysis
Abu-Subh/bert-sentiment-analysis
bert-sentiment-analysis is a text classification model from Abu-Subh. Use it when you need a label for a piece of text. It is set up for transformers.
This model is a fine-tuned version of google-bert/bert-base-uncased for sentiment analysis (binary classification: Positive vs. Negative).
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Apr 16, 2026
- Updated
- Apr 16, 2026
- SHA
- 29d46e70
Try a prompt
Model card
From the Hugging Face model README
BERT Sentiment Analysis Model
This model is a fine-tuned version of google-bert/bert-base-uncased for sentiment analysis (binary classification: Positive vs. Negative).
Training Details
- Dataset: GLUE SST2 (Stanford Sentiment Treebank)
- Epochs: 3
- Learning Rate: 2e-5
- Batch Size: 16
- Optimizer: AdamW
Performance Metrics
- Accuracy: 89.80%
- Evaluation Loss: 0.3829
How to Use
You can use this model directly with a pipeline for sentiment analysis:
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="Abu-Subh/bert-sentiment-analysis")
result = classifier("This model works incredibly well and I am very happy with the results!")
print(result)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Apr 16, 2026
- Updated
- Apr 16, 2026