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---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
model-index:
- name: lora-roberta-large-no-roller
results: []
library_name: peft
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lora-roberta-large-no-roller
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6492
- Accuracy: 0.7693
- Prec: 0.6783
- Recall: 0.6473
- F1: 0.6599
- B Acc: 0.6473
- Micro F1: 0.7693
- Prec Joy: 0.7606
- Recall Joy: 0.7609
- F1 Joy: 0.7607
- Prec Anger: 0.6490
- Recall Anger: 0.6564
- F1 Anger: 0.6527
- Prec Disgust: 0.4785
- Recall Disgust: 0.5179
- F1 Disgust: 0.4974
- Prec Fear: 0.7204
- Recall Fear: 0.6690
- F1 Fear: 0.6938
- Prec Neutral: 0.8316
- Recall Neutral: 0.8715
- F1 Neutral: 0.8511
- Prec Sadness: 0.7426
- Recall Sadness: 0.6653
- F1 Sadness: 0.7018
- Prec Surprise: 0.5654
- Recall Surprise: 0.3903
- F1 Surprise: 0.4618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Prec | Recall | F1 | B Acc | Micro F1 | Prec Joy | Recall Joy | F1 Joy | Prec Anger | Recall Anger | F1 Anger | Prec Disgust | Recall Disgust | F1 Disgust | Prec Fear | Recall Fear | F1 Fear | Prec Neutral | Recall Neutral | F1 Neutral | Prec Sadness | Recall Sadness | F1 Sadness | Prec Surprise | Recall Surprise | F1 Surprise |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:|
| 0.7936 | 0.75 | 1159 | 0.7811 | 0.7151 | 0.6488 | 0.5609 | 0.5848 | 0.5609 | 0.7151 | 0.6199 | 0.8341 | 0.7112 | 0.5064 | 0.6239 | 0.5590 | 0.6071 | 0.2732 | 0.3768 | 0.8054 | 0.4889 | 0.6084 | 0.8561 | 0.7799 | 0.8162 | 0.6749 | 0.5951 | 0.6325 | 0.4717 | 0.3312 | 0.3891 |
| 0.7404 | 1.5 | 2318 | 0.7121 | 0.7385 | 0.6664 | 0.5694 | 0.6008 | 0.5694 | 0.7385 | 0.7692 | 0.7050 | 0.7357 | 0.5220 | 0.6354 | 0.5732 | 0.6652 | 0.2732 | 0.3873 | 0.7082 | 0.5789 | 0.6371 | 0.7929 | 0.8857 | 0.8367 | 0.7494 | 0.5369 | 0.6256 | 0.4576 | 0.3710 | 0.4097 |
| 0.713 | 2.25 | 3477 | 0.7058 | 0.7460 | 0.6427 | 0.6191 | 0.6269 | 0.6191 | 0.7460 | 0.7403 | 0.7506 | 0.7454 | 0.5511 | 0.6387 | 0.5917 | 0.4204 | 0.5 | 0.4568 | 0.7208 | 0.5766 | 0.6407 | 0.8344 | 0.8447 | 0.8395 | 0.7142 | 0.6476 | 0.6793 | 0.5178 | 0.3753 | 0.4352 |
| 0.7173 | 3.0 | 4636 | 0.6973 | 0.7482 | 0.6831 | 0.5963 | 0.6211 | 0.5963 | 0.7482 | 0.7181 | 0.7529 | 0.7351 | 0.7473 | 0.4054 | 0.5256 | 0.4491 | 0.575 | 0.5043 | 0.7169 | 0.6070 | 0.6574 | 0.7877 | 0.8961 | 0.8384 | 0.7280 | 0.6387 | 0.6804 | 0.6347 | 0.2989 | 0.4064 |
| 0.6988 | 3.75 | 5795 | 0.6958 | 0.7445 | 0.6635 | 0.6000 | 0.6212 | 0.6000 | 0.7445 | 0.6616 | 0.8326 | 0.7373 | 0.6363 | 0.5851 | 0.6096 | 0.6263 | 0.3321 | 0.4341 | 0.6422 | 0.6047 | 0.6229 | 0.8472 | 0.8195 | 0.8331 | 0.6973 | 0.6644 | 0.6805 | 0.5333 | 0.3613 | 0.4308 |
| 0.661 | 4.5 | 6954 | 0.6763 | 0.7543 | 0.6901 | 0.6081 | 0.6333 | 0.6081 | 0.7543 | 0.7145 | 0.7805 | 0.7461 | 0.6601 | 0.5769 | 0.6157 | 0.7074 | 0.2893 | 0.4106 | 0.7588 | 0.5813 | 0.6583 | 0.8369 | 0.8462 | 0.8415 | 0.6341 | 0.7364 | 0.6815 | 0.5188 | 0.4462 | 0.4798 |
| 0.6632 | 5.25 | 8113 | 0.6745 | 0.7543 | 0.6671 | 0.6177 | 0.6386 | 0.6177 | 0.7543 | 0.7283 | 0.7653 | 0.7463 | 0.6232 | 0.6109 | 0.6170 | 0.4665 | 0.4732 | 0.4699 | 0.7629 | 0.6058 | 0.6754 | 0.8178 | 0.8689 | 0.8426 | 0.7752 | 0.5902 | 0.6702 | 0.4961 | 0.4097 | 0.4488 |
| 0.6427 | 6.0 | 9272 | 0.6729 | 0.7514 | 0.6607 | 0.6306 | 0.6403 | 0.6306 | 0.7514 | 0.8023 | 0.6707 | 0.7306 | 0.6623 | 0.5827 | 0.6199 | 0.4785 | 0.4768 | 0.4776 | 0.7312 | 0.6269 | 0.6751 | 0.8013 | 0.8919 | 0.8441 | 0.7613 | 0.6124 | 0.6788 | 0.3879 | 0.5527 | 0.4559 |
| 0.6363 | 6.75 | 10431 | 0.6584 | 0.7579 | 0.6635 | 0.6367 | 0.6475 | 0.6367 | 0.7579 | 0.7349 | 0.7887 | 0.7608 | 0.5808 | 0.6818 | 0.6273 | 0.5335 | 0.4268 | 0.4742 | 0.7103 | 0.6222 | 0.6633 | 0.8489 | 0.8333 | 0.8410 | 0.7227 | 0.6764 | 0.6988 | 0.5135 | 0.4280 | 0.4669 |
| 0.6134 | 7.5 | 11590 | 0.6490 | 0.7634 | 0.6768 | 0.6351 | 0.6538 | 0.6351 | 0.7634 | 0.7710 | 0.7383 | 0.7543 | 0.6355 | 0.6258 | 0.6306 | 0.5637 | 0.4268 | 0.4858 | 0.7412 | 0.6433 | 0.6888 | 0.8244 | 0.8720 | 0.8476 | 0.7001 | 0.6889 | 0.6944 | 0.5018 | 0.4505 | 0.4748 |
| 0.6045 | 8.25 | 12749 | 0.6494 | 0.7612 | 0.6648 | 0.6525 | 0.6555 | 0.6525 | 0.7612 | 0.7566 | 0.7571 | 0.7569 | 0.6401 | 0.6085 | 0.6239 | 0.4371 | 0.5893 | 0.5019 | 0.7557 | 0.6222 | 0.6825 | 0.8329 | 0.8613 | 0.8469 | 0.7556 | 0.6498 | 0.6987 | 0.4760 | 0.4796 | 0.4778 |
| 0.6139 | 9.0 | 13908 | 0.6585 | 0.7561 | 0.6730 | 0.6388 | 0.6499 | 0.6388 | 0.7561 | 0.7070 | 0.8144 | 0.7570 | 0.5686 | 0.7264 | 0.6379 | 0.5846 | 0.4071 | 0.48 | 0.7307 | 0.6187 | 0.6700 | 0.8639 | 0.8125 | 0.8374 | 0.7312 | 0.6698 | 0.6991 | 0.5247 | 0.4226 | 0.4681 |
| 0.5942 | 9.75 | 15067 | 0.6422 | 0.7661 | 0.6838 | 0.6436 | 0.6605 | 0.6436 | 0.7661 | 0.7413 | 0.7812 | 0.7607 | 0.6120 | 0.6938 | 0.6503 | 0.5619 | 0.4375 | 0.4920 | 0.7531 | 0.6351 | 0.6891 | 0.8407 | 0.8505 | 0.8455 | 0.7505 | 0.6684 | 0.7071 | 0.5271 | 0.4387 | 0.4789 |
| 0.5798 | 10.5 | 16226 | 0.6553 | 0.7614 | 0.6828 | 0.6358 | 0.6495 | 0.6358 | 0.7614 | 0.7338 | 0.7856 | 0.7588 | 0.5979 | 0.6905 | 0.6409 | 0.6692 | 0.3179 | 0.4310 | 0.7168 | 0.6632 | 0.6889 | 0.8489 | 0.8389 | 0.8439 | 0.7273 | 0.672 | 0.6985 | 0.4854 | 0.4828 | 0.4841 |
| 0.5513 | 11.25 | 17385 | 0.6538 | 0.7612 | 0.6640 | 0.6499 | 0.6550 | 0.6499 | 0.7612 | 0.7121 | 0.8059 | 0.7561 | 0.6487 | 0.6574 | 0.6530 | 0.5348 | 0.4393 | 0.4824 | 0.6504 | 0.6877 | 0.6686 | 0.8594 | 0.8270 | 0.8429 | 0.7018 | 0.6924 | 0.6971 | 0.5410 | 0.4398 | 0.4852 |
| 0.5544 | 12.0 | 18544 | 0.6492 | 0.7693 | 0.6783 | 0.6473 | 0.6599 | 0.6473 | 0.7693 | 0.7606 | 0.7609 | 0.7607 | 0.6490 | 0.6564 | 0.6527 | 0.4785 | 0.5179 | 0.4974 | 0.7204 | 0.6690 | 0.6938 | 0.8316 | 0.8715 | 0.8511 | 0.7426 | 0.6653 | 0.7018 | 0.5654 | 0.3903 | 0.4618 |
| 0.5229 | 12.75 | 19703 | 0.6428 | 0.7701 | 0.6822 | 0.6449 | 0.6612 | 0.6449 | 0.7701 | 0.7439 | 0.7820 | 0.7625 | 0.6647 | 0.6584 | 0.6615 | 0.5302 | 0.4393 | 0.4805 | 0.7452 | 0.6327 | 0.6844 | 0.8446 | 0.8563 | 0.8504 | 0.6988 | 0.7147 | 0.7067 | 0.5478 | 0.4312 | 0.4826 |
| 0.5222 | 13.5 | 20862 | 0.6565 | 0.7656 | 0.6705 | 0.6486 | 0.6586 | 0.6486 | 0.7656 | 0.7366 | 0.7862 | 0.7606 | 0.6581 | 0.6531 | 0.6556 | 0.5108 | 0.4643 | 0.4864 | 0.7322 | 0.6269 | 0.6755 | 0.8479 | 0.8465 | 0.8472 | 0.7176 | 0.6933 | 0.7052 | 0.4899 | 0.4699 | 0.4797 |
| 0.4972 | 14.25 | 22021 | 0.6574 | 0.7672 | 0.6725 | 0.6502 | 0.6596 | 0.6502 | 0.7672 | 0.7537 | 0.7703 | 0.7619 | 0.6368 | 0.6737 | 0.6547 | 0.4859 | 0.4929 | 0.4894 | 0.7302 | 0.6363 | 0.68 | 0.8447 | 0.8525 | 0.8486 | 0.7057 | 0.7044 | 0.7051 | 0.5506 | 0.4215 | 0.4775 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
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