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tshasan
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modernBERT-URLTITLE-classifier

ONNX
modernbert
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Taimur Hasan commited on
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Add ONNX directory

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  1. .gitattributes +0 -35
  2. README.md +0 -103
  3. config.json +0 -72
  4. model.safetensors +0 -3
  5. model.onnx → onnx/model.onnx +0 -0
  6. model_q4.onnx → onnx/model_q4.onnx +0 -0
  7. model_quantized.onnx → onnx/model_quantized.onnx +0 -0
  8. optimizer.pt +0 -3
  9. rng_state.pth +0 -3
  10. scheduler.pt +0 -3
  11. special_tokens_map.json +0 -37
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +0 -945
  14. trainer_state.json +0 -106
  15. training_args.bin +0 -3
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README.md DELETED
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- ---
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- license: cc-by-4.0
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- tags:
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- - multi-label-classification
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- - text-classification
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- - onnx
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- - web-classification
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- - firefox-ai
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- - preview
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- language:
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- - multilingual
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- datasets:
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- - tshasan/multi-label-web-classification
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- metrics:
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- - f1
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- - roc-auc
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- - hamming-loss
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- - precision
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- - recall
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- - jaccard
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- base_model: nomic-ai/modernbert-embed-base
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- pipeline_tag: text-classification
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- ---
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-
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- # modernBERT-URLTITLE-classifier-preview
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-
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- ## Model Overview
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-
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- This is a **preview version** of a multi-label web classification model fine-tuned from `nomic-ai/modernbert-embed-base`. It classifies websites into multiple categories based on their URLs and titles. The model supports 10 labels: `News`, `Entertainment`, `Shop`, `Chat`, `Education`, `Government`, `Health`, `Technology`, `Work`, and `Travel`. It is designed to handle imbalanced datasets, treating "Uncategorized" labels as empty (no categories assigned).
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-
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- - **Developed by**: Taimur Hasan
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- - **Model Type**: Multi-label Text Classification
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- - **Base Model**: `nomic-ai/modernbert-embed-base`
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- - **Language**: English
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- - **License**: MIT
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- - **Status**: Preview (under active development)
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-
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- ## Intended Use
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-
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- This model is designed to categorize websites using minimal input (URL and title).
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- Potential applications include:
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- - Web content categorization
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- - Recommendation systems
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- - Data enrichment for web scraping
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-
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- ### Limitations
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- - **Preview Status**: Performance metrics are preliminary and may improve with further development.
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- - **Rare Labels**: Classification of underrepresented categories may require additional tuning.
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-
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- ## Model Details
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-
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- ### Architecture
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- - **Base Model**: `nomic-ai/modernbert-embed-base`
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- - **Fine-tuning**: Unfroze the last 4 encoder layers and the pooler
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- - **Problem Type**: Multi-label classification
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- - **Output Labels**: 10 (`News`, `Entertainment`, `Shop`, `Chat`, `Education`, `Government`, `Health`, `Technology`, `Work`, `Travel`)
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- - **Input Format**: Concatenated string: `"URL: {url} Title: {title}"`
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-
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- ### Training Data
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- - **Dataset**: tshasan/multi-label-web-classification
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- - **Preprocessing**:
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- - "Uncategorized" labels mapped to empty (all labels set to 0)
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- - Rare labels (<5% occurrence) augmented using synonym replacement via `nlpaug`
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- - Oversampling applied to rare-label samples to address imbalance
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-
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- ### Training Procedure
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- - **Framework**: Hugging Face Transformers
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- - **Optimizer**: AdamW (learning rate: 2e-5)
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- - **Batch Size**: 32 (for both training and evaluation)
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- - **Epochs**: 2 (with early stopping, patience=3)
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- - **Loss Function**: BCEWithLogitsLoss with class weights to handle imbalance
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- - **Hardware**: GPU (CUDA-enabled)
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- - **Metrics**: F1 (micro/macro), ROC-AUC, Hamming Loss, Precision, Recall, etc.
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-
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- ## Evaluation
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-
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- ### Metrics
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- Below are the performance metrics on the validation dataset:
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-
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- | Metric | Value |
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- |--------------------|----------|
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- | Loss | 0.276 |
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- | Hamming Loss | 0.097 |
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- | Exact Match | 0.390 |
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- | Precision (Micro) | 0.903 |
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- | Recall (Micro) | 0.903 |
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- | F1 (Micro) | 0.903 |
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- | Precision (Macro) | 0.583 |
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- | Recall (Macro) | 0.490 |
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- | F1 (Macro) | 0.498 |
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- | Precision (Weighted) | 0.631 |
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- | Recall (Weighted) | 0.496 |
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- | F1 (Weighted) | 0.537 |
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- | ROC-AUC (Micro) | 0.859 |
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- | ROC-AUC (Macro) | 0.849 |
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- | PR-AUC (Micro) | 0.550 |
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- | PR-AUC (Macro) | 0.539 |
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- | Jaccard (Micro) | 0.822 |
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- | Jaccard (Macro) | 0.335 |
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- | Runtime (seconds) | 3.855 |
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- | Samples per Second| 739.794 |
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- | Steps per Second | 23.346 |
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- steps_per_second 23.346
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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