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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-ed
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. -->
# lora-roberta-large-no-ed
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: 0.6945
- Accuracy: 0.7549
- Prec: 0.6320
- Recall: 0.6106
- F1: 0.6199
- B Acc: 0.6106
- Micro F1: 0.7549
- Prec Joy: 0.7138
- Recall Joy: 0.7631
- F1 Joy: 0.7376
- Prec Anger: 0.6130
- Recall Anger: 0.6316
- F1 Anger: 0.6222
- Prec Disgust: 0.4396
- Recall Disgust: 0.4038
- F1 Disgust: 0.4209
- Prec Fear: 0.6623
- Recall Fear: 0.5886
- F1 Fear: 0.6233
- Prec Neutral: 0.8481
- Recall Neutral: 0.8428
- F1 Neutral: 0.8455
- Prec Sadness: 0.6626
- Recall Sadness: 0.6619
- F1 Sadness: 0.6622
- Prec Surprise: 0.4846
- Recall Surprise: 0.3824
- F1 Surprise: 0.4274
## 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: 32
- 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: 20.0
### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:|
| 0.7938 | 1.0 | 1465 | 0.7589 | 0.7257 | 0.6233 | 0.4993 | 0.5433 | 0.4993 | 0.7257 | 0.7259 | 0.6828 | 0.7037 | 0.6223 | 0.4082 | 0.4930 | 0.5359 | 0.2657 | 0.3552 | 0.5925 | 0.5110 | 0.5487 | 0.7564 | 0.9097 | 0.8260 | 0.7150 | 0.4865 | 0.5790 | 0.4151 | 0.2315 | 0.2972 |
| 0.7546 | 2.0 | 2930 | 0.7482 | 0.7243 | 0.6272 | 0.5499 | 0.5735 | 0.5499 | 0.7243 | 0.6028 | 0.8315 | 0.6989 | 0.5325 | 0.5802 | 0.5553 | 0.5135 | 0.2782 | 0.3609 | 0.6619 | 0.5388 | 0.5940 | 0.8498 | 0.8045 | 0.8265 | 0.74 | 0.5294 | 0.6172 | 0.4902 | 0.2864 | 0.3616 |
| 0.7289 | 3.0 | 4395 | 0.7293 | 0.7321 | 0.6234 | 0.5839 | 0.5984 | 0.5839 | 0.7321 | 0.6491 | 0.7901 | 0.7127 | 0.6129 | 0.5271 | 0.5668 | 0.4413 | 0.4561 | 0.4486 | 0.6974 | 0.5198 | 0.5956 | 0.8364 | 0.8146 | 0.8254 | 0.6406 | 0.6423 | 0.6414 | 0.4862 | 0.3376 | 0.3985 |
| 0.7076 | 4.0 | 5860 | 0.6898 | 0.7466 | 0.6572 | 0.5649 | 0.5972 | 0.5649 | 0.7466 | 0.7573 | 0.6911 | 0.7227 | 0.5110 | 0.6565 | 0.5747 | 0.4868 | 0.3096 | 0.3785 | 0.8139 | 0.4802 | 0.6041 | 0.8125 | 0.8772 | 0.8436 | 0.6939 | 0.5946 | 0.6404 | 0.5253 | 0.3453 | 0.4167 |
| 0.6925 | 5.0 | 7325 | 0.7039 | 0.7403 | 0.6121 | 0.5916 | 0.5972 | 0.5916 | 0.7403 | 0.6933 | 0.7525 | 0.7217 | 0.5234 | 0.6372 | 0.5747 | 0.3630 | 0.4100 | 0.3851 | 0.6121 | 0.5798 | 0.5955 | 0.8446 | 0.8363 | 0.8404 | 0.7512 | 0.5713 | 0.6490 | 0.4973 | 0.3542 | 0.4137 |
| 0.6841 | 6.0 | 8790 | 0.6704 | 0.7516 | 0.6607 | 0.5820 | 0.6076 | 0.5820 | 0.7516 | 0.7158 | 0.7536 | 0.7342 | 0.6577 | 0.4856 | 0.5587 | 0.4195 | 0.5502 | 0.4760 | 0.8476 | 0.4641 | 0.5998 | 0.8120 | 0.8784 | 0.8439 | 0.6971 | 0.6184 | 0.6554 | 0.4756 | 0.3235 | 0.3851 |
| 0.6715 | 7.0 | 10255 | 0.6919 | 0.7412 | 0.6246 | 0.6180 | 0.6112 | 0.6180 | 0.7412 | 0.7020 | 0.7642 | 0.7318 | 0.5513 | 0.6034 | 0.5762 | 0.3682 | 0.5962 | 0.4553 | 0.8024 | 0.4817 | 0.6020 | 0.8602 | 0.8191 | 0.8391 | 0.6611 | 0.6481 | 0.6545 | 0.4267 | 0.4130 | 0.4198 |
| 0.6562 | 8.0 | 11720 | 0.7245 | 0.7325 | 0.5985 | 0.6129 | 0.6014 | 0.6129 | 0.7325 | 0.6499 | 0.8167 | 0.7238 | 0.5320 | 0.6211 | 0.5731 | 0.3779 | 0.4728 | 0.4201 | 0.5704 | 0.6047 | 0.5871 | 0.8771 | 0.7863 | 0.8292 | 0.7431 | 0.6010 | 0.6645 | 0.4391 | 0.3875 | 0.4117 |
| 0.6426 | 9.0 | 13185 | 0.6683 | 0.7510 | 0.6304 | 0.6109 | 0.6175 | 0.6109 | 0.7510 | 0.7216 | 0.7506 | 0.7358 | 0.5768 | 0.6001 | 0.5882 | 0.3908 | 0.4603 | 0.4227 | 0.7469 | 0.5359 | 0.6240 | 0.8458 | 0.8458 | 0.8458 | 0.6966 | 0.6412 | 0.6678 | 0.4347 | 0.4425 | 0.4385 |
| 0.6278 | 10.0 | 14650 | 0.6661 | 0.7545 | 0.6427 | 0.5968 | 0.6142 | 0.5968 | 0.7545 | 0.7531 | 0.712 | 0.7320 | 0.6346 | 0.5476 | 0.5879 | 0.4574 | 0.4268 | 0.4416 | 0.7220 | 0.5476 | 0.6228 | 0.8304 | 0.8692 | 0.8494 | 0.5931 | 0.7276 | 0.6535 | 0.5084 | 0.3465 | 0.4122 |
| 0.6218 | 11.0 | 16115 | 0.6714 | 0.7507 | 0.6478 | 0.5958 | 0.6143 | 0.5958 | 0.7507 | 0.6878 | 0.7864 | 0.7338 | 0.6796 | 0.4950 | 0.5728 | 0.4181 | 0.4916 | 0.4519 | 0.7635 | 0.4963 | 0.6016 | 0.8324 | 0.8512 | 0.8417 | 0.6816 | 0.6524 | 0.6667 | 0.4719 | 0.3977 | 0.4316 |
| 0.6077 | 12.0 | 17580 | 0.6649 | 0.7543 | 0.6216 | 0.6171 | 0.6187 | 0.6171 | 0.7543 | 0.7496 | 0.7249 | 0.7371 | 0.6055 | 0.6095 | 0.6075 | 0.4449 | 0.4142 | 0.4290 | 0.6194 | 0.6076 | 0.6135 | 0.8426 | 0.8568 | 0.8497 | 0.6894 | 0.6386 | 0.6630 | 0.4 | 0.4680 | 0.4313 |
| 0.5868 | 13.0 | 19045 | 0.6680 | 0.7584 | 0.6473 | 0.6026 | 0.6224 | 0.6026 | 0.7584 | 0.7192 | 0.7522 | 0.7354 | 0.6442 | 0.5658 | 0.6025 | 0.4398 | 0.4435 | 0.4417 | 0.7127 | 0.5666 | 0.6313 | 0.8293 | 0.8711 | 0.8497 | 0.7187 | 0.6174 | 0.6642 | 0.4673 | 0.4015 | 0.4319 |
| 0.5747 | 14.0 | 20510 | 0.6692 | 0.7551 | 0.6293 | 0.6049 | 0.6155 | 0.6049 | 0.7551 | 0.7114 | 0.7621 | 0.7359 | 0.5985 | 0.6167 | 0.6075 | 0.4461 | 0.3808 | 0.4108 | 0.6088 | 0.6061 | 0.6075 | 0.8444 | 0.8522 | 0.8483 | 0.7124 | 0.6222 | 0.6642 | 0.4835 | 0.3939 | 0.4341 |
| 0.5632 | 15.0 | 21975 | 0.6763 | 0.7551 | 0.6390 | 0.6104 | 0.6185 | 0.6104 | 0.7551 | 0.6978 | 0.7812 | 0.7371 | 0.6381 | 0.5774 | 0.6063 | 0.4179 | 0.5272 | 0.4662 | 0.6260 | 0.5710 | 0.5972 | 0.8432 | 0.8479 | 0.8455 | 0.6950 | 0.6460 | 0.6696 | 0.5551 | 0.3223 | 0.4078 |
| 0.546 | 16.0 | 23440 | 0.6880 | 0.7537 | 0.6365 | 0.6089 | 0.6205 | 0.6089 | 0.7537 | 0.6906 | 0.7878 | 0.7360 | 0.6121 | 0.625 | 0.6185 | 0.4564 | 0.3828 | 0.4164 | 0.6587 | 0.6076 | 0.6321 | 0.8493 | 0.8350 | 0.8421 | 0.6999 | 0.6428 | 0.6702 | 0.4885 | 0.3811 | 0.4282 |
| 0.5354 | 17.0 | 24905 | 0.6823 | 0.7545 | 0.6399 | 0.6097 | 0.6222 | 0.6097 | 0.7545 | 0.6972 | 0.7828 | 0.7375 | 0.6131 | 0.6355 | 0.6241 | 0.4916 | 0.3682 | 0.4211 | 0.6979 | 0.5783 | 0.6325 | 0.8525 | 0.8357 | 0.8440 | 0.6820 | 0.6455 | 0.6632 | 0.4447 | 0.4220 | 0.4331 |
| 0.5103 | 18.0 | 26370 | 0.6852 | 0.7581 | 0.6440 | 0.6039 | 0.6206 | 0.6039 | 0.7581 | 0.7167 | 0.7642 | 0.7397 | 0.6203 | 0.6289 | 0.6246 | 0.4767 | 0.3849 | 0.4259 | 0.6752 | 0.5813 | 0.6247 | 0.8418 | 0.8525 | 0.8471 | 0.6674 | 0.6614 | 0.6644 | 0.5101 | 0.3542 | 0.4181 |
| 0.4972 | 19.0 | 27835 | 0.6948 | 0.7535 | 0.6350 | 0.6039 | 0.6162 | 0.6039 | 0.7535 | 0.7038 | 0.7715 | 0.7361 | 0.5989 | 0.6515 | 0.6241 | 0.4658 | 0.3703 | 0.4126 | 0.6739 | 0.5871 | 0.6275 | 0.8495 | 0.8381 | 0.8438 | 0.6631 | 0.6571 | 0.6601 | 0.4902 | 0.3517 | 0.4095 |
| 0.4801 | 20.0 | 29300 | 0.6945 | 0.7549 | 0.6320 | 0.6106 | 0.6199 | 0.6106 | 0.7549 | 0.7138 | 0.7631 | 0.7376 | 0.6130 | 0.6316 | 0.6222 | 0.4396 | 0.4038 | 0.4209 | 0.6623 | 0.5886 | 0.6233 | 0.8481 | 0.8428 | 0.8455 | 0.6626 | 0.6619 | 0.6622 | 0.4846 | 0.3824 | 0.4274 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
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