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--- |
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license: mit |
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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: roberta-large-finetuned-code-mixed-DS |
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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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# roberta-large-finetuned-code-mixed-DS |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1340 |
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- Accuracy: 0.7203 |
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- Precision: 0.6584 |
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- Recall: 0.6548 |
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- F1: 0.6558 |
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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: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- seed: 43 |
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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: 20 |
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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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| 0.9729 | 1.0 | 248 | 0.7491 | 0.6922 | 0.6434 | 0.6625 | 0.6358 | |
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| 0.7474 | 1.99 | 496 | 0.6947 | 0.7183 | 0.6712 | 0.6915 | 0.6760 | |
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| 0.5938 | 2.99 | 744 | 0.7370 | 0.7123 | 0.6624 | 0.6839 | 0.6642 | |
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| 0.4264 | 3.98 | 992 | 0.8820 | 0.7123 | 0.6540 | 0.6636 | 0.6492 | |
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| 0.2806 | 4.98 | 1240 | 1.2022 | 0.7404 | 0.6807 | 0.6694 | 0.6742 | |
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| 0.2239 | 5.98 | 1488 | 1.3933 | 0.7223 | 0.6593 | 0.6587 | 0.6568 | |
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| 0.1585 | 6.97 | 1736 | 1.8543 | 0.7304 | 0.6730 | 0.6763 | 0.6737 | |
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| 0.1302 | 7.97 | 1984 | 2.0783 | 0.7143 | 0.6495 | 0.6520 | 0.6504 | |
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| 0.1008 | 8.96 | 2232 | 2.3523 | 0.7183 | 0.6588 | 0.6561 | 0.6552 | |
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| 0.0793 | 9.96 | 2480 | 2.5260 | 0.7163 | 0.6516 | 0.6566 | 0.6538 | |
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| 0.0498 | 10.96 | 2728 | 2.6074 | 0.7425 | 0.6902 | 0.6817 | 0.6830 | |
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| 0.0484 | 11.95 | 2976 | 2.6758 | 0.7284 | 0.6687 | 0.6734 | 0.6709 | |
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| 0.0409 | 12.95 | 3224 | 2.8658 | 0.7425 | 0.6817 | 0.6756 | 0.6781 | |
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| 0.0239 | 13.94 | 3472 | 2.9484 | 0.7465 | 0.6980 | 0.6818 | 0.6870 | |
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| 0.025 | 14.94 | 3720 | 3.0827 | 0.7304 | 0.6778 | 0.6577 | 0.6641 | |
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| 0.0286 | 15.94 | 3968 | 3.0011 | 0.7183 | 0.6509 | 0.6475 | 0.6491 | |
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| 0.0264 | 16.93 | 4216 | 3.1581 | 0.7264 | 0.6645 | 0.6563 | 0.6595 | |
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| 0.009 | 17.93 | 4464 | 3.1200 | 0.7223 | 0.6589 | 0.6561 | 0.6569 | |
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| 0.012 | 18.92 | 4712 | 3.1364 | 0.7203 | 0.6573 | 0.6503 | 0.6525 | |
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| 0.017 | 19.92 | 4960 | 3.1340 | 0.7203 | 0.6584 | 0.6548 | 0.6558 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.10.1+cu111 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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