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---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
model-index:
- name: train
results: []
---
<!-- 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. -->
# train
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6648
- Accuracy: 0.7617
- B Acc: 0.6394
- Prec: 0.7595
- Recall: 0.7617
- F1: 0.7602
- Prec Joy: 0.7315
- Recall Joy: 0.7793
- F1 Joy: 0.7547
- Prec Anger: 0.6467
- Recall Anger: 0.6507
- F1 Anger: 0.6487
- Prec Disgust: 0.4710
- Recall Disgust: 0.45
- F1 Disgust: 0.4603
- Prec Fear: 0.6963
- Recall Fear: 0.6409
- F1 Fear: 0.6675
- Prec Neutral: 0.8457
- Recall Neutral: 0.8490
- F1 Neutral: 0.8474
- Prec Sadness: 0.7094
- Recall Sadness: 0.6738
- F1 Sadness: 0.6911
- Prec Surprise: 0.5228
- Recall Surprise: 0.4323
- F1 Surprise: 0.4732
## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | B Acc | Prec | Recall | 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:|
| 0.9538 | 0.15 | 232 | 0.8701 | 0.6961 | 0.4790 | 0.6837 | 0.6961 | 0.6837 | 0.7401 | 0.6381 | 0.6853 | 0.4622 | 0.5391 | 0.4977 | 0.25 | 0.0018 | 0.0035 | 0.5527 | 0.4292 | 0.4832 | 0.7965 | 0.8618 | 0.8279 | 0.5281 | 0.6431 | 0.5800 | 0.3562 | 0.2398 | 0.2866 |
| 0.7952 | 0.3 | 464 | 0.8010 | 0.7168 | 0.5242 | 0.7098 | 0.7168 | 0.7025 | 0.8084 | 0.5948 | 0.6853 | 0.5732 | 0.4710 | 0.5171 | 0.4713 | 0.2643 | 0.3387 | 0.6156 | 0.5263 | 0.5675 | 0.7405 | 0.9250 | 0.8226 | 0.6858 | 0.5676 | 0.6211 | 0.4448 | 0.3204 | 0.3725 |
| 0.7528 | 0.45 | 696 | 0.7560 | 0.7261 | 0.5878 | 0.7309 | 0.7261 | 0.7256 | 0.6969 | 0.7646 | 0.7292 | 0.5550 | 0.5534 | 0.5542 | 0.3409 | 0.4821 | 0.3994 | 0.7225 | 0.4842 | 0.5798 | 0.8476 | 0.8159 | 0.8314 | 0.6118 | 0.7027 | 0.6541 | 0.4957 | 0.3118 | 0.3828 |
| 0.7334 | 0.6 | 928 | 0.7310 | 0.7370 | 0.5868 | 0.7345 | 0.7370 | 0.7283 | 0.7170 | 0.7458 | 0.7311 | 0.7129 | 0.4116 | 0.5219 | 0.3727 | 0.5696 | 0.4506 | 0.6671 | 0.5626 | 0.6104 | 0.7898 | 0.8859 | 0.8351 | 0.7318 | 0.5844 | 0.6499 | 0.5252 | 0.3473 | 0.4181 |
| 0.7216 | 0.75 | 1160 | 0.7043 | 0.7448 | 0.6009 | 0.7403 | 0.7448 | 0.7389 | 0.7767 | 0.6826 | 0.7266 | 0.6159 | 0.5386 | 0.5746 | 0.5302 | 0.4393 | 0.4805 | 0.8023 | 0.5602 | 0.6598 | 0.7854 | 0.8926 | 0.8356 | 0.7005 | 0.632 | 0.6645 | 0.4815 | 0.4613 | 0.4712 |
| 0.7259 | 0.9 | 1392 | 0.6962 | 0.7475 | 0.6082 | 0.7433 | 0.7475 | 0.7412 | 0.7355 | 0.7586 | 0.7469 | 0.6758 | 0.4504 | 0.5405 | 0.3908 | 0.5589 | 0.4600 | 0.6939 | 0.6070 | 0.6475 | 0.8122 | 0.8744 | 0.8421 | 0.6830 | 0.6676 | 0.6752 | 0.5494 | 0.3409 | 0.4207 |
| 0.6362 | 1.05 | 1624 | 0.6771 | 0.7526 | 0.6055 | 0.7472 | 0.7526 | 0.7484 | 0.7392 | 0.7483 | 0.7437 | 0.5873 | 0.6191 | 0.6028 | 0.5302 | 0.3768 | 0.4405 | 0.7388 | 0.5789 | 0.6492 | 0.8213 | 0.8670 | 0.8435 | 0.7090 | 0.6507 | 0.6786 | 0.5301 | 0.3978 | 0.4545 |
| 0.621 | 1.2 | 1856 | 0.6779 | 0.7528 | 0.6120 | 0.7494 | 0.7528 | 0.7487 | 0.7107 | 0.7828 | 0.7450 | 0.6508 | 0.5913 | 0.6196 | 0.4980 | 0.4518 | 0.4738 | 0.7963 | 0.5532 | 0.6529 | 0.8165 | 0.8590 | 0.8372 | 0.7499 | 0.6236 | 0.6809 | 0.5078 | 0.4226 | 0.4613 |
| 0.6241 | 1.35 | 2088 | 0.6849 | 0.7513 | 0.6367 | 0.7526 | 0.7513 | 0.7514 | 0.7429 | 0.7592 | 0.7510 | 0.5795 | 0.6531 | 0.6141 | 0.4372 | 0.4661 | 0.4512 | 0.6462 | 0.6515 | 0.6488 | 0.8492 | 0.8372 | 0.8432 | 0.6887 | 0.6609 | 0.6745 | 0.5271 | 0.4290 | 0.4730 |
| 0.6188 | 1.5 | 2320 | 0.6713 | 0.7579 | 0.6159 | 0.7539 | 0.7579 | 0.7534 | 0.7071 | 0.7971 | 0.7494 | 0.6343 | 0.6267 | 0.6305 | 0.5877 | 0.3768 | 0.4592 | 0.7247 | 0.6281 | 0.6729 | 0.8361 | 0.8496 | 0.8428 | 0.6943 | 0.6693 | 0.6816 | 0.5919 | 0.3634 | 0.4504 |
| 0.6182 | 1.65 | 2552 | 0.6608 | 0.7601 | 0.6199 | 0.7567 | 0.7601 | 0.7566 | 0.7143 | 0.7891 | 0.7498 | 0.6163 | 0.6358 | 0.6259 | 0.5607 | 0.3875 | 0.4583 | 0.7591 | 0.6082 | 0.6753 | 0.8375 | 0.8578 | 0.8475 | 0.7324 | 0.6436 | 0.6851 | 0.5381 | 0.4172 | 0.4700 |
| 0.6392 | 1.8 | 2784 | 0.6542 | 0.7624 | 0.6261 | 0.7593 | 0.7624 | 0.7596 | 0.7513 | 0.7584 | 0.7548 | 0.5970 | 0.6708 | 0.6318 | 0.5711 | 0.3875 | 0.4617 | 0.7482 | 0.6152 | 0.6752 | 0.8379 | 0.8635 | 0.8505 | 0.7076 | 0.668 | 0.6872 | 0.5132 | 0.4194 | 0.4615 |
| 0.6158 | 1.95 | 3016 | 0.6456 | 0.7649 | 0.6279 | 0.7599 | 0.7649 | 0.7614 | 0.7490 | 0.7548 | 0.7519 | 0.6402 | 0.6378 | 0.6390 | 0.5314 | 0.4232 | 0.4712 | 0.7569 | 0.6117 | 0.6766 | 0.8310 | 0.8753 | 0.8526 | 0.7199 | 0.6627 | 0.6901 | 0.5063 | 0.4301 | 0.4651 |
| 0.554 | 2.1 | 3248 | 0.6742 | 0.7584 | 0.6346 | 0.7555 | 0.7584 | 0.7564 | 0.7293 | 0.7732 | 0.7506 | 0.6433 | 0.6430 | 0.6432 | 0.5031 | 0.4393 | 0.4690 | 0.7292 | 0.6363 | 0.6796 | 0.8347 | 0.8496 | 0.8421 | 0.7163 | 0.6587 | 0.6863 | 0.5049 | 0.4419 | 0.4713 |
| 0.5537 | 2.25 | 3480 | 0.6708 | 0.7633 | 0.6283 | 0.7604 | 0.7633 | 0.7605 | 0.7263 | 0.7801 | 0.7523 | 0.6304 | 0.6612 | 0.6455 | 0.5806 | 0.3732 | 0.4543 | 0.7486 | 0.6094 | 0.6718 | 0.8442 | 0.8528 | 0.8485 | 0.6982 | 0.692 | 0.6951 | 0.5356 | 0.4290 | 0.4764 |
| 0.5375 | 2.4 | 3712 | 0.6712 | 0.7606 | 0.6402 | 0.7592 | 0.7606 | 0.7595 | 0.7373 | 0.7709 | 0.7537 | 0.6245 | 0.6608 | 0.6421 | 0.4827 | 0.4482 | 0.4648 | 0.7319 | 0.6257 | 0.6747 | 0.8454 | 0.8474 | 0.8464 | 0.7006 | 0.6769 | 0.6885 | 0.5204 | 0.4516 | 0.4836 |
| 0.5175 | 2.55 | 3944 | 0.6625 | 0.7625 | 0.6369 | 0.7600 | 0.7625 | 0.7604 | 0.7422 | 0.7642 | 0.7530 | 0.6335 | 0.6526 | 0.6429 | 0.4481 | 0.4929 | 0.4694 | 0.7482 | 0.6187 | 0.6773 | 0.8374 | 0.8604 | 0.8488 | 0.7252 | 0.6684 | 0.6957 | 0.5321 | 0.4011 | 0.4574 |
| 0.5182 | 2.7 | 4176 | 0.6621 | 0.7631 | 0.6404 | 0.7602 | 0.7631 | 0.7612 | 0.7343 | 0.7766 | 0.7549 | 0.6491 | 0.6392 | 0.6441 | 0.4739 | 0.4536 | 0.4635 | 0.6784 | 0.6538 | 0.6659 | 0.8444 | 0.8529 | 0.8486 | 0.7109 | 0.684 | 0.6972 | 0.5458 | 0.4226 | 0.4764 |
| 0.5148 | 2.85 | 4408 | 0.6638 | 0.7637 | 0.6383 | 0.7598 | 0.7637 | 0.7612 | 0.7394 | 0.7741 | 0.7563 | 0.6741 | 0.6205 | 0.6462 | 0.5 | 0.4375 | 0.4667 | 0.6813 | 0.6550 | 0.6679 | 0.8400 | 0.8572 | 0.8485 | 0.6922 | 0.6916 | 0.6919 | 0.5296 | 0.4323 | 0.4760 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
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