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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+training_history.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+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** 🚀

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