radubutucelea23
/

bert_base_uncased_sst2

@@ -1,199 +1,120 @@
 ---
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 ---
 library_name: transformers
+license: mit
+datasets:
+- nyu-mll/glue
+base_model:
+- google-bert/bert-base-uncased
 ---
+# BERT Fine-tuned for Sentiment Analysis on SST-2
+## Model Description
+This model is a fine-tuned version of `bert-base-uncased` on the Stanford Sentiment Treebank v2 (SST-2) dataset. It was trained to perform binary sentiment classification (positive/negative) on movie review sentences.
+## Intended Uses & Limitations
+### Intended Uses
+- Sentiment analysis of short English texts, particularly movie reviews and similar content
+- Educational purposes for demonstrating fine-tuning of pre-trained language models
+- Baseline model for comparing more advanced sentiment analysis approaches
+### Limitations
+- The model is trained on movie reviews and may not generalize well to other domains (e.g., product reviews, social media posts)
+- Limited to English language text
+- Not optimized for very long texts (best for sentences or short paragraphs)
+- Binary classification only (positive/negative) without nuanced sentiment scores
+## Training and Evaluation Data
+The model was fine-tuned on the SST-2 dataset from the GLUE benchmark:
+- **Training set**: 67,349 examples
+- **Validation set**: 872 examples
+- **Test set**: Not used in this fine-tuning
+The SST-2 dataset consists of sentences from movie reviews with their associated binary sentiment labels.
+For more information about the dataset, see the [GLUE benchmark dataset card](https://huggingface.co/datasets/nyu-mll/glue).
+## Training Procedure
+### Training Hyperparameters
+- **Base model**: `bert-base-uncased`
+- **Epochs**: 3
+- **Training samples per second**: 187.16
+- **Training steps per second**: 23.396
+- **Total FLOPS**: 3.08e+15
+- **Hardware**: NVIDIA A100 GPU
+### Training Results
+| Epoch | Training Loss | Validation Loss | Accuracy |
+|-------|--------------|----------------|----------|
+| 1     | 0.256300     | 0.427576       | 0.899083 |
+| 2     | 0.169200     | 0.415616       | 0.903670 |
+| 3     | 0.095600     | 0.426083       | 0.903670 |
+Final training loss: 0.19818013534790577
+## Performance
+### Metrics
+- **Accuracy on validation set**: 90.37%
+## Model Limitations and Biases
+This model may have inherited biases from its training data and pre-training corpus:
+- The SST-2 dataset primarily contains movie reviews which may not represent diverse perspectives
+- The model may perform differently across different demographic groups or cultural contexts
+- May have difficulty with sarcasm, irony, or culturally-specific expressions
+## Usage
+```python
+from transformers import pipeline
+sentiment_analyzer = pipeline("sentiment-analysis", model="radubutucelea23/bert_base_uncased_sst2")
+texts = ["I really enjoyed this movie, the acting was superb.",
+         "The plot was confusing and the characters were poorly developed."]
+results = sentiment_analyzer(texts)
+print(results)
+```
+## Citation
+If you use this model, please cite:
+```
+@inproceedings{socher-etal-2013-recursive,
+    title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
+    author = "Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D. and Ng, Andrew Y. and Potts, Christopher",
+    booktitle = "Proceedings of EMNLP",
+    year = "2013"
+}
+@article{devlin2019bert,
+    title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
+    author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
+    journal={arXiv preprint arXiv:1810.04805},
+    year={2018}
+}
+```
+## Further Information
+- **Model Type**: Text Classification
+- **Language**: English
+- **License**: MIT
+- **Developer**: Radu Butucelea
+- **Organization**: None
+- **Last Updated**: April 2, 2025
+For questions and feedback, please contact me through my Hugging Face profile: [radubutucelea23](https://huggingface.co/radubutucelea23)