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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- google-bert/bert-base-cased |
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tags: |
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- genderization |
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- text-classification |
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- prediction |
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--- |
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# Gender Classification by Name |
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### Model Details |
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- **Model Name**: Genderize |
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- **Developed By**: Imran Ali |
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- **Model Type**: Text Classification |
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- **Language**: English |
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- **License**: MIT |
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### Description |
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This model classifies gender based on the input name. It uses a pre-trained BERT model as the base and has been fine-tuned on a dataset of names and their associated genders. |
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### Training Details |
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- **Training Data**: Dataset of names and genders (e.g., Dannel gender-name dataset) |
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- **Training Procedure**: Fine-tuned using BERT model with a classification head |
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- **Training Hyperparameters**: |
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- Batch size: 8 |
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- Gradient accumulation steps: 1 |
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- learning_rate: 2e-5 |
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- Total steps: 20,005 |
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- Number of trainable parameters: 109,483,778 (1.9M) |
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### Evaluation |
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- **Testing Data**: Split from the training dataset |
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- **Metrics**: Accuracy, Precision, Recall, F1 Score |
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### Uses |
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- **Direct Use**: Classifying the gender of a given name |
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- **Downstream Use**: Enhancing applications that require gender identification based on names (e.g., personalized marketing, user profiling) |
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- **Out-of-Scope Use**: Using the model for purposes other than gender classification without proper validation |
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### Bias, Risks, and Limitations |
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- **Bias**: The model may reflect biases present in the training data. It is important to validate its performance across diverse datasets. |
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- **Risks**: Misclassification can occur, especially for names that are unisex or less common. |
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- **Limitations**: The model's accuracy may vary depending on the cultural and linguistic context of the names. |
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### Recommendations |
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- Users should be aware of the potential biases and limitations of the model. |
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- Further validation is recommended for specific use cases and datasets. |
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### How to Get Started with the Model |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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# Load the model and tokenizer from the Hub |
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model_name = "imranali291/genderize" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Example inference function |
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def predict_gender(name): |
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inputs = tokenizer(name, return_tensors="pt", padding=True, truncation=True, max_length=32) |
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outputs = model(**inputs) |
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predicted_label = outputs.logits.argmax(dim=-1).item() |
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return label_encoder.inverse_transform([predicted_label])[0] |
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print(predict_gender("Alex")) # Example output: 'M' |
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print(predict_gender("Maria")) # Example output: 'F' |
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``` |
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