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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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base_model: Rajan/NepaliBERT |
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model-index: |
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- name: nepali_complaints_classification_nepbert3 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# nepali_complaints_classification_nepbert3 |
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This model is a fine-tuned version of [Rajan/NepaliBERT](https://huggingface.co/Rajan/NepaliBERT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2687 |
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- Accuracy: 0.9494 |
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- F1-score: 0.9483 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| |
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| 1.4921 | 0.22 | 500 | 0.8642 | 0.7235 | 0.7143 | |
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| 0.7781 | 0.45 | 1000 | 0.6241 | 0.7974 | 0.7923 | |
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| 0.5865 | 0.67 | 1500 | 0.5342 | 0.8243 | 0.8125 | |
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| 0.4625 | 0.89 | 2000 | 0.4250 | 0.8576 | 0.8553 | |
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| 0.3648 | 1.11 | 2500 | 0.3856 | 0.8759 | 0.8725 | |
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| 0.3001 | 1.34 | 3000 | 0.3424 | 0.8899 | 0.8891 | |
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| 0.2723 | 1.56 | 3500 | 0.3199 | 0.9007 | 0.8981 | |
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| 0.2538 | 1.78 | 4000 | 0.2898 | 0.9085 | 0.9066 | |
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| 0.231 | 2.01 | 4500 | 0.2676 | 0.9203 | 0.9189 | |
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| 0.1478 | 2.23 | 5000 | 0.3029 | 0.9210 | 0.9187 | |
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| 0.1666 | 2.45 | 5500 | 0.2580 | 0.9283 | 0.9271 | |
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| 0.1519 | 2.67 | 6000 | 0.2573 | 0.9308 | 0.9292 | |
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| 0.1498 | 2.9 | 6500 | 0.2746 | 0.9328 | 0.9306 | |
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| 0.1112 | 3.12 | 7000 | 0.2564 | 0.9398 | 0.9389 | |
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| 0.0903 | 3.34 | 7500 | 0.2726 | 0.9403 | 0.9393 | |
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| 0.1036 | 3.57 | 8000 | 0.2664 | 0.9398 | 0.9385 | |
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| 0.1043 | 3.79 | 8500 | 0.2614 | 0.9459 | 0.9447 | |
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| 0.0972 | 4.01 | 9000 | 0.2499 | 0.9453 | 0.9443 | |
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| 0.0663 | 4.23 | 9500 | 0.2643 | 0.9469 | 0.9458 | |
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| 0.0683 | 4.46 | 10000 | 0.2688 | 0.9474 | 0.9462 | |
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| 0.0671 | 4.68 | 10500 | 0.2657 | 0.9491 | 0.9481 | |
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| 0.0605 | 4.9 | 11000 | 0.2687 | 0.9494 | 0.9483 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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