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--- |
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license: mit |
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base_model: nielsr/lilt-xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: test |
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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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# test |
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This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-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: 1.6516 |
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- Precision: 0.7245 |
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- Recall: 0.7621 |
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- F1: 0.7428 |
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- Accuracy: 0.7700 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.33 | 100 | 0.9064 | 0.4989 | 0.6694 | 0.5717 | 0.6558 | |
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| No log | 2.67 | 200 | 0.9830 | 0.5946 | 0.5986 | 0.5966 | 0.6988 | |
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| No log | 4.0 | 300 | 0.8347 | 0.6432 | 0.6943 | 0.6678 | 0.7418 | |
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| No log | 5.33 | 400 | 0.8003 | 0.6759 | 0.7341 | 0.7038 | 0.7710 | |
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| 0.6429 | 6.67 | 500 | 0.9784 | 0.6887 | 0.7336 | 0.7104 | 0.7645 | |
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| 0.6429 | 8.0 | 600 | 0.9918 | 0.7099 | 0.7529 | 0.7308 | 0.7565 | |
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| 0.6429 | 9.33 | 700 | 1.1164 | 0.7102 | 0.7264 | 0.7182 | 0.7528 | |
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| 0.6429 | 10.67 | 800 | 1.3786 | 0.6997 | 0.7621 | 0.7296 | 0.7429 | |
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| 0.6429 | 12.0 | 900 | 1.2818 | 0.7168 | 0.7529 | 0.7344 | 0.7617 | |
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| 0.106 | 13.33 | 1000 | 1.3933 | 0.7004 | 0.7407 | 0.7200 | 0.7465 | |
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| 0.106 | 14.67 | 1100 | 1.3226 | 0.7000 | 0.7641 | 0.7306 | 0.7653 | |
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| 0.106 | 16.0 | 1200 | 1.5013 | 0.7166 | 0.7509 | 0.7333 | 0.7508 | |
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| 0.106 | 17.33 | 1300 | 1.4213 | 0.7165 | 0.7427 | 0.7294 | 0.7732 | |
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| 0.106 | 18.67 | 1400 | 1.4495 | 0.7144 | 0.7366 | 0.7254 | 0.7722 | |
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| 0.0248 | 20.0 | 1500 | 1.5319 | 0.7226 | 0.7326 | 0.7275 | 0.7717 | |
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| 0.0248 | 21.33 | 1600 | 1.5563 | 0.7232 | 0.7626 | 0.7424 | 0.7731 | |
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| 0.0248 | 22.67 | 1700 | 1.5967 | 0.7364 | 0.7657 | 0.7507 | 0.7734 | |
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| 0.0248 | 24.0 | 1800 | 1.5916 | 0.7375 | 0.7616 | 0.7494 | 0.7773 | |
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| 0.0248 | 25.33 | 1900 | 1.6402 | 0.7267 | 0.7504 | 0.7383 | 0.7719 | |
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| 0.0069 | 26.67 | 2000 | 1.6516 | 0.7250 | 0.7575 | 0.7409 | 0.7688 | |
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| 0.0069 | 28.0 | 2100 | 1.6539 | 0.7262 | 0.7621 | 0.7437 | 0.7697 | |
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| 0.0069 | 29.33 | 2200 | 1.6516 | 0.7245 | 0.7621 | 0.7428 | 0.7700 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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