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--- |
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license: mit |
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base_model: facebook/xlm-v-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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model-index: |
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- name: scenario-TCR-XLMV_data-en-cardiff_eng_only_beta |
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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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# scenario-TCR-XLMV_data-en-cardiff_eng_only_beta |
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This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4054 |
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- Accuracy: 0.5467 |
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- F1: 0.5510 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 112233 |
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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 | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 1.03 | 60 | 1.0887 | 0.4449 | 0.3540 | |
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| No log | 2.07 | 120 | 1.0211 | 0.4700 | 0.3777 | |
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| No log | 3.1 | 180 | 1.0598 | 0.5141 | 0.4790 | |
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| No log | 4.14 | 240 | 1.0131 | 0.5644 | 0.5652 | |
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| No log | 5.17 | 300 | 1.1073 | 0.5586 | 0.5595 | |
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| No log | 6.21 | 360 | 1.3697 | 0.5635 | 0.5542 | |
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| No log | 7.24 | 420 | 1.4910 | 0.5379 | 0.5385 | |
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| No log | 8.28 | 480 | 1.7325 | 0.5507 | 0.5542 | |
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| 0.6649 | 9.31 | 540 | 1.8878 | 0.5489 | 0.5505 | |
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| 0.6649 | 10.34 | 600 | 2.2758 | 0.5309 | 0.5320 | |
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| 0.6649 | 11.38 | 660 | 2.3053 | 0.5357 | 0.5357 | |
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| 0.6649 | 12.41 | 720 | 2.3674 | 0.5542 | 0.5574 | |
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| 0.6649 | 13.45 | 780 | 2.7705 | 0.5309 | 0.5332 | |
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| 0.6649 | 14.48 | 840 | 2.7515 | 0.5520 | 0.5522 | |
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| 0.6649 | 15.52 | 900 | 2.9868 | 0.5423 | 0.5447 | |
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| 0.6649 | 16.55 | 960 | 2.7489 | 0.5582 | 0.5597 | |
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| 0.1079 | 17.59 | 1020 | 2.8748 | 0.5525 | 0.5560 | |
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| 0.1079 | 18.62 | 1080 | 3.0165 | 0.5467 | 0.5511 | |
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| 0.1079 | 19.66 | 1140 | 3.2954 | 0.5340 | 0.5356 | |
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| 0.1079 | 20.69 | 1200 | 3.1051 | 0.5441 | 0.5488 | |
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| 0.1079 | 21.72 | 1260 | 3.2199 | 0.5441 | 0.5467 | |
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| 0.1079 | 22.76 | 1320 | 3.1660 | 0.5454 | 0.5500 | |
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| 0.1079 | 23.79 | 1380 | 3.2637 | 0.5445 | 0.5474 | |
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| 0.1079 | 24.83 | 1440 | 3.2934 | 0.5538 | 0.5576 | |
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| 0.0279 | 25.86 | 1500 | 3.2834 | 0.5476 | 0.5506 | |
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| 0.0279 | 26.9 | 1560 | 3.3734 | 0.5467 | 0.5507 | |
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| 0.0279 | 27.93 | 1620 | 3.4145 | 0.5437 | 0.5476 | |
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| 0.0279 | 28.97 | 1680 | 3.4043 | 0.5454 | 0.5496 | |
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| 0.0279 | 30.0 | 1740 | 3.4054 | 0.5467 | 0.5510 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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