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README.md

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+---
+license: mit
+tags:
+- text-classification
+- cheese
+- texture
+- distilbert
+- transformers
+- fine-tuned
+datasets:
+- aslan-ng/cheese-text
+metrics:
+- accuracy
+model-index:
+- name: Cheese Texture Classifier (DistilBERT)
+  results:
+  - task:
+      type: text-classification
+      name: Cheese Texture Classification
+    dataset:
+      type: aslan-ng/cheese-text
+      name: Cheese Text Dataset
+    metrics:
+    - type: accuracy
+      value: 0.400
+      name: Test Accuracy
+---
+
+# Cheese Texture Classifier (DistilBERT)
+
+**Model Creator**: Rumi Loghmani (@rlogh)  
+**Original Dataset**: aslan-ng/cheese-text (by Aslan Noorghasemi)
+
+This model performs 4-class texture classification on cheese descriptions using fine-tuned DistilBERT.
+
+## Model Description
+
+- **Architecture**: DistilBERT-base-uncased fine-tuned for sequence classification
+- **Task**: 4-class texture classification (hard, semi-hard, semi-soft, soft)
+- **Input**: Cheese description text (up to 512 tokens)
+- **Output**: 4-class probability distribution
+
+## Training Details
+
+### Data
+- **Dataset**: [aslan-ng/cheese-text](https://huggingface.co/datasets/aslan-ng/cheese-text) (original split: 100 samples)
+- **Train/Val/Test Split**: 70/15/15 (stratified)
+- **Text Source**: Cheese descriptions from the dataset
+- **Labels**: Texture categories (hard, semi-hard, semi-soft, soft)
+
+### Preprocessing
+- **Tokenization**: DistilBERT tokenizer with 512 max length
+- **Padding**: Max length padding
+- **Truncation**: Long descriptions truncated to 512 tokens
+
+### Training Setup
+- **Model**: distilbert-base-uncased
+- **Epochs**: 10
+- **Batch Size**: 8 (train/val)
+- **Learning Rate**: 2e-5
+- **Warmup Steps**: 10
+- **Weight Decay**: 0.01
+- **Optimizer**: AdamW
+- **Scheduler**: Linear warmup + linear decay
+- **Mixed Precision**: FP16 (if GPU available)
+- **Seed**: 42 (for reproducibility)
+
+### Hardware/Compute
+- **Training Device**: GPU (CUDA)
+- **Training Time**: ~5-10 minutes on GPU
+- **Model Size**: ~67M parameters
+- **Memory Usage**: ~2-4GB GPU memory
+
+## Performance
+
+- **Test Accuracy**: 0.400
+- **Test Loss**: 1.274
+
+### Class-wise Performance
+              precision    recall  f1-score   support
+
+        hard       0.50      0.33      0.40         3
+   semi-hard       0.33      0.50      0.40         4
+   semi-soft       0.33      0.50      0.40         4
+        soft       1.00      0.25      0.40         4
+
+    accuracy                           0.40        15
+   macro avg       0.54      0.40      0.40        15
+weighted avg       0.54      0.40      0.40        15
+
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "rlogh/cheese-texture-classifier-distilbert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example prediction
+text = "Feta is a crumbly, tangy Greek cheese with a salty bite and creamy undertones."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1).item()
+
+class_names = ["hard", "semi-hard", "semi-soft", "soft"]
+print(f"Predicted texture: {class_names[predicted_class]}")
+```
+
+## Class Definitions
+
+- **Hard**: Firm, aged cheeses that are dense and can be grated (e.g., Parmesan, Cheddar)
+- **Semi-hard**: Moderately firm cheeses with some flexibility (e.g., Gouda, Swiss)
+- **Semi-soft**: Cheeses with some give but maintain shape (e.g., Mozzarella, Blue cheese)
+- **Soft**: Creamy, spreadable cheeses (e.g., Brie, Camembert, Cottage cheese)
+
+## Limitations and Ethics
+
+### Limitations
+- **Small Dataset**: Trained on only 100 samples, limiting generalization
+- **Text Quality**: Performance depends on description quality and consistency
+- **Subjective Labels**: Texture classification has inherent subjectivity
+- **Domain Specific**: Only applicable to cheese texture classification
+- **Language**: English-only model
+
+### Ethical Considerations
+- **Bias**: Model may reflect biases in the original dataset
+- **Cultural Context**: Cheese descriptions may be culturally specific
+- **Commercial Use**: Not intended for commercial cheese production decisions
+- **Accuracy**: Should not be used for critical food safety applications
+
+### Recommendations
+- Use for educational/research purposes only
+- Validate predictions with domain experts
+- Consider cultural context when applying to different regions
+- Retrain with larger, more diverse datasets for production use
+
+## AI Usage Disclosure
+
+This model was developed using:
+- **Base Model**: DistilBERT (distilbert-base-uncased)
+- **Training Framework**: Hugging Face Transformers
+- **Fine-tuning**: Standard BERT fine-tuning techniques
+- **No Additional AI**: No other AI systems were used in development
+
+## Citation
+
+**Model Citation:**
+```bibtex
+@model{rlogh/cheese-texture-classifier-distilbert,
+  title={Cheese Texture Classifier (DistilBERT)},
+  author={Rumi Loghmani},
+  year={2024},
+  url={https://huggingface.co/rlogh/cheese-texture-classifier-distilbert}
+}
+```
+
+**Dataset Citation:**
+```bibtex
+@dataset{aslan-ng/cheese-text,
+  title={Cheese Text Dataset},
+  author={Aslan Noorghasemi},
+  year={2024},
+  url={https://huggingface.co/datasets/aslan-ng/cheese-text}
+}
+```
+
+## License
+
+MIT License - See LICENSE file for details.

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