Update README.md

README.md

@@ -3,69 +3,149 @@ library_name: transformers
 license: apache-2.0
 base_model: distilbert-base-uncased
 tags:
+- text-classification
+- transformers
+- distilbert
 - generated_from_trainer
+- cmu-course
+datasets:
+- ecopus/pgh_restaurants
 metrics:
 - accuracy
 - f1
 - precision
 - recall
 model-index:
-- name: finetuned_model
-  results: []
+- name: Cuisine Classification (Fine-Tuned DistilBERT)
+  results:
+  - task:
+      type: text-classification
+      name: Multi-class Text Classification
+    dataset:
+      name: ecopus/pgh_restaurants
+      type: classification
+      split: augmented
+    metrics:
+    - type: accuracy
+      value: 0.969
+    - type: f1
+      value: 0.957
+    - type: precision
+      value: 0.948
+    - type: recall
+      value: 0.969
+  - task:
+      type: text-classification
+      name: Multi-class Text Classification
+    dataset:
+      name: ecopus/pgh_restaurants
+      type: classification
+      split: original
+    metrics:
+    - type: accuracy
+      value: 0.94
+    - type: f1
+      value: 0.92
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Model Card for Cuisine Classification (Fine-Tuned DistilBERT)
 
-# finetuned_model
+This model predicts the **cuisine type** of Pittsburgh restaurants based on review text.  
+It was fine-tuned from [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the dataset [ecopus/pgh_restaurants](https://huggingface.co/datasets/ecopus/pgh_restaurants).  
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.9053
-- Accuracy: 0.95
-- F1: 0.9311
-- Precision: 0.9197
-- Recall: 0.95
+It achieves the following results:  
+- **Evaluation (Augmented split):** Accuracy 0.969, F1 0.957, Precision 0.948, Recall 0.969  
+- **External Validation (Original split):** Accuracy 0.94, F1 0.92  
 
-## Model description
+---
+
+## Model Details
+
+- **Developed by:** Xinxuan Tang (CMU)  
+- **Dataset curated by:** Emily Copus (CMU)  
+- **Base model:** DistilBERT (`distilbert-base-uncased`)  
+- **Library:** 🤗 Transformers  
+- **Language(s):** English  
+- **License:** apache-2.0 (dataset + model card)  
 
-More information needed
+---
 
-## Intended uses & limitations
+## Uses
 
-More information needed
+### Direct Use
+- Educational practice in **text classification**.  
+- Experimenting with **fine-tuning compact transformers**.  
 
-## Training and evaluation data
+### Downstream Use
+- Could be adapted for **restaurant recommendation demos**.  
+- Teaching **NLP pipelines** for classification tasks.  
 
-More information needed
+### Out-of-Scope Use
+- Not suitable for **production deployment**.  
+- Not intended for **sentiment analysis** or tasks outside cuisine prediction.  
 
-## Training procedure
+---
 
-### Training hyperparameters
+## Training Procedure
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 5
+### Training Hyperparameters
+- **learning_rate:** 2e-05  
+- **train_batch_size:** 8  
+- **eval_batch_size:** 8  
+- **seed:** 42  
+- **optimizer:** AdamW (betas=(0.9,0.999), eps=1e-08)  
+- **lr_scheduler_type:** linear  
+- **num_epochs:** 5  
 
-### Training results
+### Training Results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
 | 2.6677        | 1.0   | 80   | 2.4746          | 0.3563   | 0.2142 | 0.1662    | 0.3563 |
-| 1.7201        | 2.0   | 160  | 1.5893          | 0.775    | 0.6895 | 0.6644    | 0.775  |
+| 1.7201        | 2.0   | 160  | 1.5893          | 0.7750   | 0.6895 | 0.6644    | 0.7750 |
 | 1.1994        | 3.0   | 240  | 1.1417          | 0.8938   | 0.8503 | 0.8180    | 0.8938 |
-| 1.089         | 4.0   | 320  | 0.9315          | 0.925    | 0.8959 | 0.8784    | 0.925  |
+| 1.0890        | 4.0   | 320  | 0.9315          | 0.9250   | 0.8959 | 0.8784    | 0.9250 |
 | 0.7052        | 5.0   | 400  | 0.8675          | 0.9688   | 0.9570 | 0.9480    | 0.9688 |
 
+---
+
+## Evaluation
+
+### Testing Data
+- **Augmented split:** 1000 reviews (synthetic augmentation)  
+- **Original split:** 100 reviews (external validation)  
+
+### Metrics
+- Accuracy, weighted F1, Precision, Recall  
+- Confusion matrix used for external validation  
+
+---
+
+## Framework Versions
+- **Transformers:** 4.56.1  
+- **PyTorch:** 2.8.0+cu126  
+- **Datasets:** 4.0.0  
+- **Tokenizers:** 0.22.0  
+
+---
+
+## Bias, Risks, and Limitations
+
+- **Small dataset**: only 100 original reviews.  
+- **Synthetic augmentation**: may introduce artifacts.  
+- **Geographic bias**: limited to Pittsburgh restaurants.  
+
+### Recommendations
+Treat results as **proof-of-concept**, not production-ready.  
+
+---
+
+## Citation
+
+If you use this model, please cite the dataset and Hugging Face tools.  
+
+---
 
-### Framework versions
+## Model Card Contact
+Xinxuan Tang — xinxuant@andrew.cmu.edu
 
-- Transformers 4.56.1
-- Pytorch 2.8.0+cu126
-- Datasets 4.0.0
-- Tokenizers 0.22.0