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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-binary |
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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-binary |
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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: 0.5183 |
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- Accuracy: 0.8847 |
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- Precision: 0.8851 |
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- Recall: 0.8831 |
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- F1: 0.8839 |
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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: 4 |
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- total_train_batch_size: 128 |
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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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| 0.6103 | 0.9912 | 56 | 0.5319 | 0.7366 | 0.7534 | 0.7445 | 0.7355 | |
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| 0.3576 | 2.0 | 113 | 0.3626 | 0.8427 | 0.8418 | 0.8428 | 0.8422 | |
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| 0.2913 | 2.9912 | 169 | 0.3478 | 0.8589 | 0.8582 | 0.8585 | 0.8583 | |
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| 0.2351 | 4.0 | 226 | 0.3812 | 0.8564 | 0.8755 | 0.8486 | 0.8520 | |
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| 0.1342 | 4.9912 | 282 | 0.4025 | 0.8652 | 0.8678 | 0.8619 | 0.8636 | |
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| 0.0733 | 6.0 | 339 | 0.4448 | 0.8639 | 0.8638 | 0.8625 | 0.8630 | |
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| 0.0325 | 6.9912 | 395 | 0.5974 | 0.8589 | 0.8657 | 0.8540 | 0.8565 | |
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| 0.0308 | 8.0 | 452 | 0.6238 | 0.8589 | 0.8588 | 0.8575 | 0.8580 | |
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| 0.01 | 8.9912 | 508 | 0.6391 | 0.8664 | 0.8693 | 0.8631 | 0.8649 | |
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| 0.0091 | 9.9115 | 560 | 0.6417 | 0.8552 | 0.8548 | 0.8540 | 0.8543 | |
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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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