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--- |
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license: mit |
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base_model: xlm-roberta-base |
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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: xlm-roberta-base-twitter-indonesia-sarcastic |
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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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# xlm-roberta-base-twitter-indonesia-sarcastic |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4359 |
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- Accuracy: 0.8513 |
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- F1: 0.7386 |
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- Precision: 0.6570 |
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- Recall: 0.8433 |
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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: 32 |
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- eval_batch_size: 64 |
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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: cosine |
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- num_epochs: 100.0 |
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- mixed_precision_training: Native AMP |
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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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| 0.5641 | 1.0 | 59 | 0.5260 | 0.75 | 0.0 | 0.0 | 0.0 | |
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| 0.5317 | 2.0 | 118 | 0.5030 | 0.75 | 0.0 | 0.0 | 0.0 | |
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| 0.4995 | 3.0 | 177 | 0.4656 | 0.75 | 0.0 | 0.0 | 0.0 | |
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| 0.4599 | 4.0 | 236 | 0.4503 | 0.7687 | 0.6026 | 0.5281 | 0.7015 | |
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| 0.4082 | 5.0 | 295 | 0.3785 | 0.8470 | 0.6435 | 0.7708 | 0.5522 | |
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| 0.3274 | 6.0 | 354 | 0.3605 | 0.8619 | 0.6992 | 0.7679 | 0.6418 | |
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| 0.2621 | 7.0 | 413 | 0.3765 | 0.8619 | 0.6838 | 0.8 | 0.5970 | |
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| 0.2332 | 8.0 | 472 | 0.3408 | 0.8769 | 0.7591 | 0.7429 | 0.7761 | |
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| 0.1579 | 9.0 | 531 | 0.4382 | 0.8731 | 0.7213 | 0.8 | 0.6567 | |
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| 0.1467 | 10.0 | 590 | 0.3855 | 0.8806 | 0.7895 | 0.7059 | 0.8955 | |
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| 0.098 | 11.0 | 649 | 0.4693 | 0.8806 | 0.7500 | 0.7869 | 0.7164 | |
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| 0.0929 | 12.0 | 708 | 0.6206 | 0.8806 | 0.7333 | 0.8302 | 0.6567 | |
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| 0.0555 | 13.0 | 767 | 0.7134 | 0.8843 | 0.7634 | 0.7812 | 0.7463 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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