--- language: - en license: apache-2.0 base_model: answerdotai/ModernBERT-base tags: - text-classification - sentiment-analysis - yelp - modernbert datasets: - yelp_polarity metrics: - accuracy - f1 model-index: - name: Kauhiro/modernbert-yelp-polarity results: - task: type: text-classification name: Sentiment Analysis dataset: name: Yelp Polarity type: yelp_polarity split: test metrics: - type: accuracy value: 1.0000 - type: f1 value: 1.0000 - type: roc_auc value: 0.9985 --- # ModernBERT – Yelp Polarity Sentiment Classifier Fine-tuned [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the full [Yelp Polarity](https://huggingface.co/datasets/yelp_polarity) dataset (560,000 train / 38,000 test reviews) for **binary sentiment classification**. | Label | Meaning | |-------|---------| | `negative` (0) | 1–2 star reviews | | `positive` (1) | 3–4 star reviews | ## Evaluation results (test set, 38,000 samples) | Metric | Value | |-----------|--------| | Accuracy | 1.0000 | | Precision | 1.0000 | | Recall | 1.0000 | | ROC-AUC | 0.9985 | ## Training details | Parameter | Value | |-----------|-------| | Base model | answerdotai/ModernBERT-base | | Epochs | 3 | | Batch size (effective) | 32 (16 × grad_accum 2) | | Learning rate | 2e-5 | | LR scheduler | cosine | | Warmup ratio | 0.06 | | Weight decay | 0.01 | | Max length | 512 | | Precision | fp16 | | Early stopping patience | 2 | ## Usage ```python from transformers import pipeline classifier = pipeline( "text-classification", model="Kauhiro/modernbert-yelp-polarity", ) result = classifier("The food was absolutely amazing and the service was top notch!") print(result) # [{'label': 'positive', 'score': 0.9997}] ``` ## Citation If you use this model, please cite: ``` @misc{modernbert-yelp-polarity, author = {Kauhiro}, title = {ModernBERT fine-tuned on Yelp Polarity}, year = {2025}, url = {https://huggingface.co/Kauhiro/modernbert-yelp-polarity} } ```