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update model card README.md
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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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- precision
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- recall
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- f1
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model-index:
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- name: xlm-roberta-large-finetuned-TRAC-DS-new
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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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# xlm-roberta-large-finetuned-TRAC-DS-new
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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: 1.2229
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- Accuracy: 0.6724
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- Precision: 0.6503
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- Recall: 0.6556
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- F1: 0.6513
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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: 4
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- eval_batch_size: 8
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- seed: 43
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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: 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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| 1.0895 | 0.25 | 612 | 1.0893 | 0.4453 | 0.3220 | 0.4654 | 0.3554 |
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| 1.0788 | 0.5 | 1224 | 1.1051 | 0.4436 | 0.1479 | 0.3333 | 0.2049 |
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| 1.0567 | 0.75 | 1836 | 0.9507 | 0.5637 | 0.4176 | 0.4948 | 0.4279 |
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| 1.0052 | 1.0 | 2448 | 0.9716 | 0.4665 | 0.4913 | 0.5106 | 0.4324 |
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| 0.9862 | 1.25 | 3060 | 0.9160 | 0.5719 | 0.5824 | 0.5851 | 0.5517 |
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| 0.9428 | 1.5 | 3672 | 0.9251 | 0.5645 | 0.5838 | 0.5903 | 0.5386 |
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| 0.9381 | 1.75 | 4284 | 0.9212 | 0.6307 | 0.6031 | 0.6091 | 0.6053 |
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| 0.9124 | 2.0 | 4896 | 0.8897 | 0.6054 | 0.6078 | 0.6169 | 0.5895 |
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| 0.9558 | 2.25 | 5508 | 0.8576 | 0.6283 | 0.6330 | 0.6077 | 0.6094 |
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| 0.8814 | 2.5 | 6120 | 0.9458 | 0.6520 | 0.6357 | 0.6270 | 0.6286 |
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| 0.8697 | 2.75 | 6732 | 0.8928 | 0.6381 | 0.6304 | 0.6259 | 0.6228 |
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| 0.9142 | 3.0 | 7344 | 0.8542 | 0.6225 | 0.6227 | 0.6272 | 0.6124 |
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| 0.825 | 3.25 | 7956 | 0.9639 | 0.6577 | 0.6491 | 0.6089 | 0.6093 |
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| 0.84 | 3.5 | 8568 | 0.8980 | 0.6266 | 0.6309 | 0.6169 | 0.6130 |
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| 0.8505 | 3.75 | 9180 | 0.9127 | 0.6503 | 0.6197 | 0.6130 | 0.6154 |
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| 0.8287 | 4.0 | 9792 | 0.9343 | 0.6683 | 0.6515 | 0.6527 | 0.6488 |
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| 0.7772 | 4.25 | 10404 | 1.0434 | 0.6650 | 0.6461 | 0.6454 | 0.6437 |
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| 0.8217 | 4.5 | 11016 | 0.9760 | 0.6724 | 0.6574 | 0.6550 | 0.6533 |
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| 0.7543 | 4.75 | 11628 | 1.0790 | 0.6454 | 0.6522 | 0.6342 | 0.6327 |
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| 0.7868 | 5.0 | 12240 | 1.1457 | 0.6708 | 0.6519 | 0.6445 | 0.6463 |
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| 0.8093 | 5.25 | 12852 | 1.1714 | 0.6716 | 0.6517 | 0.6525 | 0.6509 |
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| 0.8032 | 5.5 | 13464 | 1.1882 | 0.6691 | 0.6480 | 0.6542 | 0.6489 |
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| 0.7511 | 5.75 | 14076 | 1.2113 | 0.6650 | 0.6413 | 0.6458 | 0.6429 |
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| 0.7698 | 6.0 | 14688 | 1.2229 | 0.6724 | 0.6503 | 0.6556 | 0.6513 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.10.1+cu111
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- Datasets 2.3.2
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- Tokenizers 0.12.1
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