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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-reddit-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-reddit-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: 0.6826 |
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- Accuracy: 0.8044 |
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- F1: 0.5818 |
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- Precision: 0.6254 |
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- Recall: 0.5439 |
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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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| 0.5174 | 1.0 | 309 | 0.4618 | 0.7725 | 0.4641 | 0.5650 | 0.3938 | |
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| 0.4462 | 2.0 | 618 | 0.4407 | 0.7994 | 0.5428 | 0.6316 | 0.4759 | |
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| 0.3952 | 3.0 | 927 | 0.4690 | 0.8037 | 0.4991 | 0.69 | 0.3909 | |
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| 0.3525 | 4.0 | 1236 | 0.4905 | 0.8079 | 0.5152 | 0.6990 | 0.4079 | |
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| 0.3102 | 5.0 | 1545 | 0.4741 | 0.8122 | 0.5917 | 0.6486 | 0.5439 | |
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| 0.2645 | 6.0 | 1854 | 0.4964 | 0.8101 | 0.5976 | 0.6358 | 0.5637 | |
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| 0.2168 | 7.0 | 2163 | 0.5216 | 0.8079 | 0.5824 | 0.6385 | 0.5354 | |
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| 0.1759 | 8.0 | 2472 | 0.6826 | 0.8044 | 0.5818 | 0.6254 | 0.5439 | |
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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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