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--- |
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library_name: transformers |
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license: mit |
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base_model: ai4bharat/indic-bert |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: indic-bert-roman-urdu-fine-grained |
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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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# indic-bert-roman-urdu-fine-grained |
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This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8501 |
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- Accuracy: 0.7678 |
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- Precision: 0.6945 |
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- Recall: 0.6537 |
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- F1: 0.6720 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.1237 | 1.0 | 113 | 1.0947 | 0.5342 | 0.1068 | 0.2 | 0.1393 | |
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| 0.9606 | 2.0 | 226 | 0.8776 | 0.6689 | 0.4456 | 0.3188 | 0.2779 | |
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| 0.7784 | 3.0 | 339 | 0.6443 | 0.7896 | 0.7017 | 0.6830 | 0.6899 | |
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| 0.5626 | 4.0 | 452 | 0.5167 | 0.8302 | 0.7561 | 0.7371 | 0.7422 | |
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| 0.5613 | 5.0 | 565 | 0.4285 | 0.8634 | 0.7931 | 0.7849 | 0.7850 | |
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| 0.4232 | 6.0 | 678 | 0.3543 | 0.8867 | 0.8295 | 0.8072 | 0.8155 | |
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| 0.3376 | 7.0 | 791 | 0.2546 | 0.9293 | 0.8850 | 0.8757 | 0.8802 | |
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| 0.2759 | 8.0 | 904 | 0.2079 | 0.9469 | 0.9085 | 0.9132 | 0.9103 | |
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| 0.2029 | 9.0 | 1017 | 0.1564 | 0.9606 | 0.9370 | 0.9276 | 0.9322 | |
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| 0.137 | 10.0 | 1130 | 0.1364 | 0.9685 | 0.9558 | 0.9399 | 0.9477 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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