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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: xlmr-nli-indoindo |
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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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# xlmr-nli-indoindo |
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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: 0.6699 |
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- Accuracy: 0.7701 |
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- Precision: 0.7701 |
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- Recall: 0.7701 |
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- F1: 0.7693 |
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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: 3e-06 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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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: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 6 |
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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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| 1.0444 | 1.0 | 1722 | 0.8481 | 0.6463 | 0.6463 | 0.6463 | 0.6483 | |
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| 0.7958 | 2.0 | 3444 | 0.7483 | 0.7369 | 0.7369 | 0.7369 | 0.7353 | |
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| 0.7175 | 3.0 | 5166 | 0.6812 | 0.7579 | 0.7579 | 0.7579 | 0.7576 | |
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| 0.66 | 4.0 | 6888 | 0.6293 | 0.7679 | 0.7679 | 0.7679 | 0.7674 | |
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| 0.6056 | 5.0 | 8610 | 0.6459 | 0.7651 | 0.7651 | 0.7651 | 0.7640 | |
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| 0.5769 | 6.0 | 10332 | 0.6699 | 0.7701 | 0.7701 | 0.7701 | 0.7693 | |
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### Framework versions |
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- Transformers 4.31.0.dev0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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