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--- |
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license: mit |
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base_model: xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: fine_tuned_XLMROBERTA_cs_wikann |
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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_XLMROBERTA_cs_wikann |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1216 |
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- Overall Precision: 0.8919 |
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- Overall Recall: 0.9190 |
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- Overall F1: 0.9053 |
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- Overall Accuracy: 0.9672 |
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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: 2e-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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- 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: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.3409 | 0.4 | 500 | 0.1931 | 0.7764 | 0.8465 | 0.8100 | 0.9495 | |
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| 0.1816 | 0.8 | 1000 | 0.1427 | 0.8405 | 0.8793 | 0.8595 | 0.9576 | |
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| 0.1401 | 1.2 | 1500 | 0.1273 | 0.8758 | 0.9068 | 0.8910 | 0.9651 | |
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| 0.1088 | 1.6 | 2000 | 0.1392 | 0.8868 | 0.9139 | 0.9001 | 0.9662 | |
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| 0.1027 | 2.0 | 2500 | 0.1096 | 0.8929 | 0.9233 | 0.9078 | 0.9699 | |
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| 0.0667 | 2.4 | 3000 | 0.1267 | 0.9030 | 0.9268 | 0.9148 | 0.9699 | |
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| 0.0601 | 2.8 | 3500 | 0.1203 | 0.9078 | 0.9326 | 0.9200 | 0.9712 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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