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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- sentiment-analysis |
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- transformers |
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- unknown |
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- text-classification |
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datasets: |
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- unknown |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: unknown-sentiment |
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results: |
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- task: |
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type: text-classification |
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name: Sentiment Analysis |
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dataset: |
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name: UNKNOWN |
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type: unknown |
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metrics: |
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- type: accuracy |
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value: 0.0000 |
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name: Test Accuracy |
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- type: f1 |
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value: 0.0000 |
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name: F1 Score |
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- type: precision |
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value: 0.0000 |
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name: Precision |
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- type: recall |
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value: 0.0000 |
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name: Recall |
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--- |
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# UNKNOWN Fine-tuned for Sentiment Analysis |
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## 📊 Model Description |
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This model is a fine-tuned version of `unknown` for sentiment analysis on the UNKNOWN dataset. |
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**Model Architecture:** unknown |
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**Task:** Binary Sentiment Classification (Positive/Negative) |
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**Language:** English |
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**Training Date:** N/A |
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## 🎯 Performance Metrics |
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| Metric | Score | |
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|--------|-------| |
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| **Accuracy** | 0.0000 | |
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| **F1 Score** | 0.0000 | |
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| **Precision** | 0.0000 | |
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| **Recall** | 0.0000 | |
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| **Loss** | 0.0000 | |
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## 🔧 Training Details |
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### Hyperparameters |
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```json |
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{} |
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``` |
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### Dataset |
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- **Training samples:** N/A |
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- **Validation samples:** N/A |
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- **Test samples:** N/A |
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## 🚀 Usage |
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### With Transformers Pipeline |
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```python |
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from transformers import pipeline |
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# Load the model |
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classifier = pipeline("sentiment-analysis", model="YOUR_USERNAME/YOUR_MODEL_NAME") |
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# Predict |
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result = classifier("I love this movie!") |
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print(result) |
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# [{'label': 'POSITIVE', 'score': 0.9998}] |
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``` |
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### Manual Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# Load model and tokenizer |
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model_name = "YOUR_USERNAME/YOUR_MODEL_NAME" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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# Prepare input |
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text = "This is an amazing product!" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) |
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# Predict |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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# Get result |
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label_id = torch.argmax(predictions).item() |
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score = predictions[0][label_id].item() |
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labels = ["NEGATIVE", "POSITIVE"] |
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print(f"Label: {labels[label_id]}, Score: {score:.4f}") |
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``` |
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## 📈 Training Curves |
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Training history visualization is available in the model files. |
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## 🏷️ Label Mapping |
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``` |
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0: NEGATIVE |
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1: POSITIVE |
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``` |
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## ⚙️ Model Configuration |
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```json |
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{} |
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``` |
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## 📝 Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{sentiment-model-unknown, |
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author = {Your Name}, |
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title = {unknown Fine-tuned for Sentiment Analysis}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co/YOUR_USERNAME/YOUR_MODEL_NAME}} |
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} |
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``` |
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## 🤝 Contact |
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For questions or feedback, please open an issue in the repository. |
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## 📄 License |
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Apache 2.0 |
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## 🔗 Related Models |
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- [unknown](https://huggingface.co/unknown) |
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--- |
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**Generated with MLflow tracking** 🚀 |
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