Divi15
/

sentiment-classifier-demo-5729

@@ -1,199 +1,134 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+license: apache-2.0
+base_model: distilbert-base-uncased-finetuned-sst-2-english
+tags:
+- text-classification
+- sentiment-analysis
+- transformers
+- pytorch
+datasets:
+- sst2
+language:
+- en
+pipeline_tag: text-classification
 ---
+# Sentiment Classifier Demo 5729
+## Model Description
+This model is based on **distilbert-base-uncased-finetuned-sst-2-english** and performs **Sentiment analysis on English text (positive/negative classification)**.
+This model was uploaded as part of a machine learning assignment demonstrating model deployment to Hugging Face Hub.
+## Quick Start
+```python
+from transformers import pipeline
+# Load the model
+classifier = pipeline("sentiment-analysis", model="Divi15/sentiment-classifier-demo-5729")
+# Make predictions
+result = classifier("I love machine learning!")
+print(result)
+# Expected output: [{'label': 'POSITIVE', 'score': 0.9991}]
+```
+## Model Details
+- **Model Type**: Text Classification
+- **Base Model**: distilbert-base-uncased-finetuned-sst-2-english
+- **Task**: Sentiment Analysis (Binary Classification)
+- **Language**: English
+- **License**: Apache 2.0
+## Intended Use
+This model is intended for:
+- Educational purposes
+- Sentiment analysis of English text
+- Binary classification tasks (positive/negative sentiment)
+## Training Data
+- **Dataset**: Stanford Sentiment Treebank (SST-2)
+- **Training Examples**: ~67K sentences
+- **Classes**: 2 (positive, negative)
+## Performance
+- **Accuracy**: ~91-92% on SST-2 test set
+- **F1 Score**: ~0.91-0.92
+## Usage Examples
+### Basic Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
+tokenizer = AutoTokenizer.from_pretrained("Divi15/sentiment-classifier-demo-5729")
+model = AutoModelForSequenceClassification.from_pretrained("Divi15/sentiment-classifier-demo-5729")
+classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+# Test examples
+examples = [
+    "I absolutely love this!",
+    "This is terrible.",
+    "It's okay, nothing special."
+]
+for text in examples:
+    result = classifier(text)
+    print(f"Text: {text}")
+    print(f"Result: {result}")
+    print()
+```
+### Batch Processing
+```python
+texts = [
+    "Great product, highly recommended!",
+    "Poor quality, very disappointed.",
+    "Average performance, could be better."
+]
+results = classifier(texts)
+for text, result in zip(texts, results):
+    print(f"{text} -> {result['label']} ({result['score']:.3f})")
+```
+## Limitations
+- Trained primarily on movie reviews and may not generalize well to other domains
+- Binary classification only (positive/negative)
+- English language only
+- May exhibit biases present in the training data
+## Ethical Considerations
+- This model should not be used to make decisions that significantly impact individuals
+- Consider potential biases when applying to different demographic groups
+- Sentiment analysis can be subjective and context-dependent
+## Citation
+```bibtex
+@misc{sentiment_classifier_demo_5729_2024,
+  title={Sentiment Classifier Demo 5729},
+  author={Your Name},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/Divi15/sentiment-classifier-demo-5729}
+}
+```
+## Model Card Authors
+This model card was created as part of an educational assignment on model deployment and sharing.
+---
+*Last updated: 2025-09-08*