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--- |
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library_name: transformers |
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base_model: rmtariq/malay_classification |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: malay_classification |
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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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# malay_classification |
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This model is a fine-tuned version of [rmtariq/malay_classification](https://huggingface.co/rmtariq/malay_classification) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0001 |
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- Accuracy: 1.0 |
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- F1: 1.0 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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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: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.1691 | 0.2720 | 500 | 0.1373 | 0.9717 | 0.9717 | 0.9730 | 0.9717 | |
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| 0.0493 | 0.5441 | 1000 | 0.0369 | 0.9943 | 0.9943 | 0.9945 | 0.9943 | |
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| 0.0669 | 0.8161 | 1500 | 0.0406 | 0.9952 | 0.9952 | 0.9954 | 0.9952 | |
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| 0.0287 | 1.0881 | 2000 | 0.0276 | 0.9943 | 0.9944 | 0.9948 | 0.9943 | |
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| 0.0061 | 1.3602 | 2500 | 0.0168 | 0.9971 | 0.9971 | 0.9972 | 0.9971 | |
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| 0.0137 | 1.6322 | 3000 | 0.0128 | 0.9981 | 0.9981 | 0.9981 | 0.9981 | |
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| 0.0178 | 1.9042 | 3500 | 0.0179 | 0.9968 | 0.9968 | 0.9969 | 0.9968 | |
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| 0.0112 | 2.1763 | 4000 | 0.0110 | 0.9975 | 0.9975 | 0.9975 | 0.9975 | |
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| 0.0001 | 2.4483 | 4500 | 0.0079 | 0.9987 | 0.9987 | 0.9988 | 0.9987 | |
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| 0.0001 | 2.7203 | 5000 | 0.0021 | 0.9987 | 0.9987 | 0.9987 | 0.9987 | |
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| 0.0003 | 2.9924 | 5500 | 0.0024 | 0.9990 | 0.9990 | 0.9991 | 0.9990 | |
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### Framework versions |
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- Transformers 4.53.1 |
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- Pytorch 2.7.1 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.2 |
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