---
language: en
license: apache-2.0
tags:
- sentiment-analysis
- transformers
- unknown
- text-classification
datasets:
- unknown
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: unknown-sentiment
  results:
  - task:
      type: text-classification
      name: Sentiment Analysis
    dataset:
      name: UNKNOWN
      type: unknown
    metrics:
    - type: accuracy
      value: 0.0000
      name: Test Accuracy
    - type: f1
      value: 0.0000
      name: F1 Score
    - type: precision
      value: 0.0000
      name: Precision
    - type: recall
      value: 0.0000
      name: Recall
---

# UNKNOWN Fine-tuned for Sentiment Analysis

## 📊 Model Description

This model is a fine-tuned version of `unknown` for sentiment analysis on the UNKNOWN dataset.

**Model Architecture:** unknown  
**Task:** Binary Sentiment Classification (Positive/Negative)  
**Language:** English  
**Training Date:** N/A

## 🎯 Performance Metrics

| Metric | Score |
|--------|-------|
| **Accuracy** | 0.0000 |
| **F1 Score** | 0.0000 |
| **Precision** | 0.0000 |
| **Recall** | 0.0000 |
| **Loss** | 0.0000 |

## 🔧 Training Details

### Hyperparameters

```json
{}
```

### Dataset
- **Training samples:** N/A
- **Validation samples:** N/A
- **Test samples:** N/A

## 🚀 Usage

### With Transformers Pipeline

```python
from transformers import pipeline

# Load the model
classifier = pipeline("sentiment-analysis", model="YOUR_USERNAME/YOUR_MODEL_NAME")

# Predict
result = classifier("I love this movie!")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9998}]
```

### Manual Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "YOUR_USERNAME/YOUR_MODEL_NAME"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare input
text = "This is an amazing product!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

# Predict
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    
# Get result
label_id = torch.argmax(predictions).item()
score = predictions[0][label_id].item()

labels = ["NEGATIVE", "POSITIVE"]
print(f"Label: {labels[label_id]}, Score: {score:.4f}")
```

## 📈 Training Curves

Training history visualization is available in the model files.

## 🏷️ Label Mapping

```
0: NEGATIVE
1: POSITIVE
```

## ⚙️ Model Configuration

```json
{}
```

## 📝 Citation

If you use this model, please cite:

```bibtex
@misc{sentiment-model-unknown,
  author = {Your Name},
  title = {unknown Fine-tuned for Sentiment Analysis},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/YOUR_USERNAME/YOUR_MODEL_NAME}}
}
```

## 🤝 Contact

For questions or feedback, please open an issue in the repository.

## 📄 License

Apache 2.0

## 🔗 Related Models

- [unknown](https://huggingface.co/unknown)

---

**Generated with MLflow tracking** 🚀