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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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-
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- [More Information Needed]
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- ## Environmental Impact
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Software
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- ## Citation [optional]
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- **BibTeX:**
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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  ---
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+ language:
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+ - en
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+ license: apache-2.0
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+ base_model: answerdotai/ModernBERT-base
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+ tags:
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+ - text-classification
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+ - sentiment-analysis
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+ - yelp
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+ - modernbert
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+ datasets:
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+ - yelp_polarity
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: Kauhiro/modernbert-yelp-polarity
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Sentiment Analysis
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+ dataset:
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+ name: Yelp Polarity
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+ type: yelp_polarity
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 1.0000
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+ - type: f1
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+ value: 1.0000
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+ - type: roc_auc
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+ value: 0.9985
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  ---
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+ # ModernBERT Yelp Polarity Sentiment Classifier
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+ Fine-tuned [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
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+ on the full [Yelp Polarity](https://huggingface.co/datasets/yelp_polarity) dataset
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+ (560,000 train / 38,000 test reviews) for **binary sentiment classification**.
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+
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+ | Label | Meaning |
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+ |-------|---------|
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+ | `negative` (0) | 1–2 star reviews |
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+ | `positive` (1) | 3–4 star reviews |
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+
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+ ## Evaluation results (test set, 38,000 samples)
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+
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+ | Metric | Value |
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+ |-----------|--------|
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+ | Accuracy | 1.0000 |
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+ | Precision | 1.0000 |
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+ | Recall | 1.0000 |
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+ | ROC-AUC | 0.9985 |
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+
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+ ## Training details
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+ | Parameter | Value |
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+ |-----------|-------|
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+ | Base model | answerdotai/ModernBERT-base |
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+ | Epochs | 3 |
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+ | Batch size (effective) | 32 (16 × grad_accum 2) |
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+ | Learning rate | 2e-5 |
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+ | LR scheduler | cosine |
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+ | Warmup ratio | 0.06 |
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+ | Weight decay | 0.01 |
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+ | Max length | 512 |
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+ | Precision | fp16 |
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+ | Early stopping patience | 2 |
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+
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+ ## Usage
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+ ```python
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+ from transformers import pipeline
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+
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+ classifier = pipeline(
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+ "text-classification",
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+ model="Kauhiro/modernbert-yelp-polarity",
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+ )
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+ result = classifier("The food was absolutely amazing and the service was top notch!")
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+ print(result)
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+ # [{'label': 'positive', 'score': 0.9997}]
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+ ```
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+
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+ ## Citation
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+ If you use this model, please cite:
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+
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+ ```
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+ @misc{modernbert-yelp-polarity,
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+ author = {Kauhiro},
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+ title = {ModernBERT fine-tuned on Yelp Polarity},
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+ year = {2025},
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+ url = {https://huggingface.co/Kauhiro/modernbert-yelp-polarity}
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+ }
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+ ```