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README.md
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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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- f1
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model-index:
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- name: fine-tuned-NLI-indonli_mnli-with-xlm-roberta-large
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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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# fine-tuned-NLI-indonli_mnli-with-xlm-roberta-large
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4582
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- Accuracy: 0.8575
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- F1: 0.8580
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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: 16
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 128
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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| 0.4821 | 0.5 | 1574 | 0.4176 | 0.8402 | 0.8401 |
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| 0.4442 | 1.0 | 3148 | 0.4007 | 0.8521 | 0.8523 |
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| 0.3817 | 1.5 | 4722 | 0.3927 | 0.8529 | 0.8519 |
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| 0.3635 | 2.0 | 6296 | 0.3838 | 0.8607 | 0.8609 |
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| 0.3039 | 2.5 | 7870 | 0.3998 | 0.8601 | 0.8602 |
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| 0.3198 | 3.0 | 9444 | 0.3914 | 0.8602 | 0.8603 |
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| 0.2564 | 3.5 | 11018 | 0.4582 | 0.8575 | 0.8580 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 2.0.1+cu117
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- Datasets 2.2.0
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- Tokenizers 0.13.3
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