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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-anger-f4-0927
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-anger-f4-0927
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: 0.7106
- Accuracy: 0.7405
- Prec: 0.7387
- Recall: 0.7405
- F1: 0.7387
- B Acc: 0.5982
- Micro F1: 0.7405
- Prec Joy: 0.7558
- Recall Joy: 0.7617
- F1 Joy: 0.7587
- Prec Anger: 0.6294
- Recall Anger: 0.5631
- F1 Anger: 0.5944
- Prec Disgust: 0.4637
- Recall Disgust: 0.3854
- F1 Disgust: 0.4209
- Prec Fear: 0.4892
- Recall Fear: 0.5817
- F1 Fear: 0.5315
- Prec Neutral: 0.8292
- Recall Neutral: 0.8481
- F1 Neutral: 0.8385
- Prec Sadness: 0.6600
- Recall Sadness: 0.6140
- F1 Sadness: 0.6362
- Prec Surprise: 0.5320
- Recall Surprise: 0.4331
- F1 Surprise: 0.4775
## 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: 25.0
### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:|
| 0.8167 | 1.25 | 2049 | 0.7756 | 0.7130 | 0.7003 | 0.7130 | 0.7030 | 0.5272 | 0.7130 | 0.7252 | 0.7430 | 0.7340 | 0.6026 | 0.3749 | 0.4622 | 0.4187 | 0.3646 | 0.3898 | 0.5369 | 0.4170 | 0.4694 | 0.7763 | 0.8629 | 0.8173 | 0.6123 | 0.5784 | 0.5949 | 0.4797 | 0.3495 | 0.4044 |
| 0.7639 | 2.5 | 4098 | 0.7302 | 0.7293 | 0.7206 | 0.7293 | 0.7224 | 0.5662 | 0.7293 | 0.7361 | 0.7617 | 0.7487 | 0.6187 | 0.5198 | 0.5649 | 0.3881 | 0.4229 | 0.4048 | 0.5851 | 0.4247 | 0.4922 | 0.7961 | 0.8570 | 0.8254 | 0.6380 | 0.6185 | 0.6281 | 0.532 | 0.3585 | 0.4283 |
| 0.7395 | 3.75 | 6147 | 0.7348 | 0.7287 | 0.7328 | 0.7287 | 0.7271 | 0.5793 | 0.7287 | 0.6989 | 0.8136 | 0.7519 | 0.6786 | 0.4384 | 0.5327 | 0.4180 | 0.3875 | 0.4022 | 0.4632 | 0.5830 | 0.5162 | 0.8480 | 0.8134 | 0.8303 | 0.6648 | 0.5950 | 0.6280 | 0.5210 | 0.4241 | 0.4676 |
| 0.789 | 5.0 | 8196 | 0.7419 | 0.7275 | 0.7206 | 0.7275 | 0.7180 | 0.5511 | 0.7275 | 0.6888 | 0.8113 | 0.7450 | 0.6014 | 0.5183 | 0.5568 | 0.4038 | 0.4021 | 0.4029 | 0.5747 | 0.4305 | 0.4923 | 0.8063 | 0.8420 | 0.8238 | 0.6861 | 0.5838 | 0.6308 | 0.6224 | 0.2695 | 0.3762 |
| 0.7439 | 6.25 | 10245 | 0.7608 | 0.7207 | 0.7317 | 0.7207 | 0.7224 | 0.5858 | 0.7207 | 0.6882 | 0.8143 | 0.7459 | 0.6198 | 0.5004 | 0.5537 | 0.3944 | 0.3542 | 0.3732 | 0.4556 | 0.5843 | 0.5120 | 0.8599 | 0.7888 | 0.8228 | 0.7047 | 0.5590 | 0.6235 | 0.4535 | 0.4996 | 0.4754 |
| 0.712 | 7.5 | 12294 | 0.7240 | 0.7298 | 0.7270 | 0.7298 | 0.7263 | 0.5809 | 0.7298 | 0.7057 | 0.8043 | 0.7518 | 0.6313 | 0.4795 | 0.5450 | 0.4141 | 0.4271 | 0.4205 | 0.5707 | 0.4517 | 0.5043 | 0.8329 | 0.8214 | 0.8271 | 0.6126 | 0.6459 | 0.6288 | 0.5209 | 0.4367 | 0.4751 |
| 0.7032 | 8.75 | 14343 | 0.7095 | 0.7344 | 0.7328 | 0.7344 | 0.7317 | 0.5833 | 0.7344 | 0.7557 | 0.7479 | 0.7518 | 0.6391 | 0.5302 | 0.5796 | 0.4311 | 0.3521 | 0.3876 | 0.4724 | 0.6062 | 0.5310 | 0.8188 | 0.8498 | 0.8340 | 0.6472 | 0.6140 | 0.6301 | 0.5605 | 0.3827 | 0.4549 |
| 0.6972 | 10.0 | 16392 | 0.7108 | 0.7343 | 0.7325 | 0.7343 | 0.7317 | 0.5923 | 0.7343 | 0.7158 | 0.8038 | 0.7572 | 0.5785 | 0.5474 | 0.5625 | 0.3615 | 0.4729 | 0.4097 | 0.5714 | 0.4865 | 0.5255 | 0.8322 | 0.8288 | 0.8305 | 0.6797 | 0.5973 | 0.6358 | 0.5403 | 0.4097 | 0.4660 |
| 0.6859 | 11.25 | 18441 | 0.7211 | 0.7376 | 0.7321 | 0.7376 | 0.7322 | 0.5792 | 0.7376 | 0.7067 | 0.8093 | 0.7545 | 0.6216 | 0.5325 | 0.5736 | 0.4119 | 0.4188 | 0.4153 | 0.5720 | 0.4755 | 0.5193 | 0.8264 | 0.8407 | 0.8335 | 0.6677 | 0.6099 | 0.6375 | 0.5876 | 0.3675 | 0.4522 |
| 0.6542 | 12.5 | 20490 | 0.7143 | 0.7347 | 0.7294 | 0.7347 | 0.7307 | 0.5817 | 0.7347 | 0.7358 | 0.7824 | 0.7584 | 0.6263 | 0.5407 | 0.5804 | 0.3931 | 0.3792 | 0.3860 | 0.5700 | 0.4665 | 0.5131 | 0.8203 | 0.8364 | 0.8283 | 0.6158 | 0.6658 | 0.6398 | 0.5400 | 0.4007 | 0.4600 |
| 0.6463 | 13.75 | 22539 | 0.7022 | 0.7369 | 0.7366 | 0.7369 | 0.7354 | 0.5947 | 0.7369 | 0.7371 | 0.7864 | 0.7610 | 0.5452 | 0.6393 | 0.5885 | 0.5170 | 0.3167 | 0.3928 | 0.5519 | 0.4858 | 0.5168 | 0.8455 | 0.8218 | 0.8335 | 0.6062 | 0.6649 | 0.6342 | 0.5320 | 0.4483 | 0.4866 |
| 0.6333 | 15.0 | 24588 | 0.7106 | 0.7405 | 0.7387 | 0.7405 | 0.7387 | 0.5982 | 0.7405 | 0.7558 | 0.7617 | 0.7587 | 0.6294 | 0.5631 | 0.5944 | 0.4637 | 0.3854 | 0.4209 | 0.4892 | 0.5817 | 0.5315 | 0.8292 | 0.8481 | 0.8385 | 0.6600 | 0.6140 | 0.6362 | 0.5320 | 0.4331 | 0.4775 |
| 0.6184 | 16.25 | 26637 | 0.7199 | 0.7338 | 0.7389 | 0.7338 | 0.7348 | 0.6077 | 0.7338 | 0.7207 | 0.8008 | 0.7586 | 0.6140 | 0.5571 | 0.5842 | 0.3692 | 0.4292 | 0.3969 | 0.5024 | 0.5972 | 0.5457 | 0.8534 | 0.8079 | 0.8301 | 0.6714 | 0.6 | 0.6337 | 0.5109 | 0.4618 | 0.4851 |
| 0.5916 | 17.5 | 28686 | 0.7220 | 0.7368 | 0.7376 | 0.7368 | 0.7363 | 0.6003 | 0.7368 | 0.7426 | 0.7859 | 0.7636 | 0.5858 | 0.5713 | 0.5784 | 0.3743 | 0.4125 | 0.3925 | 0.5766 | 0.4653 | 0.5150 | 0.8479 | 0.8258 | 0.8367 | 0.5879 | 0.6676 | 0.6252 | 0.5146 | 0.4735 | 0.4932 |
| 0.5823 | 18.75 | 30735 | 0.7228 | 0.7376 | 0.7374 | 0.7376 | 0.7364 | 0.5960 | 0.7376 | 0.7210 | 0.8058 | 0.7610 | 0.6206 | 0.5534 | 0.5851 | 0.4056 | 0.3625 | 0.3828 | 0.5199 | 0.5631 | 0.5406 | 0.8460 | 0.8200 | 0.8328 | 0.6599 | 0.6126 | 0.6354 | 0.5254 | 0.4546 | 0.4875 |
| 0.5728 | 20.0 | 32784 | 0.7313 | 0.7344 | 0.7365 | 0.7344 | 0.7349 | 0.6090 | 0.7344 | 0.7295 | 0.7934 | 0.7601 | 0.5795 | 0.5907 | 0.5851 | 0.3927 | 0.4271 | 0.4092 | 0.5434 | 0.5161 | 0.5294 | 0.8462 | 0.8115 | 0.8285 | 0.6541 | 0.6311 | 0.6424 | 0.4928 | 0.4933 | 0.4930 |
| 0.5562 | 21.25 | 34833 | 0.7414 | 0.7376 | 0.7372 | 0.7376 | 0.7366 | 0.5995 | 0.7376 | 0.7372 | 0.7934 | 0.7643 | 0.6308 | 0.5258 | 0.5735 | 0.3946 | 0.425 | 0.4092 | 0.5324 | 0.5341 | 0.5332 | 0.8433 | 0.8267 | 0.8349 | 0.6139 | 0.6374 | 0.6254 | 0.5249 | 0.4537 | 0.4867 |
| 0.5348 | 22.5 | 36882 | 0.7398 | 0.7370 | 0.7374 | 0.7370 | 0.7365 | 0.6017 | 0.7370 | 0.7268 | 0.8039 | 0.7634 | 0.5844 | 0.5892 | 0.5868 | 0.4013 | 0.3937 | 0.3975 | 0.5331 | 0.5238 | 0.5284 | 0.8488 | 0.8163 | 0.8322 | 0.6473 | 0.6275 | 0.6372 | 0.5194 | 0.4573 | 0.4864 |
| 0.5202 | 23.75 | 38931 | 0.7423 | 0.7389 | 0.7379 | 0.7389 | 0.7381 | 0.6013 | 0.7389 | 0.7415 | 0.7893 | 0.7646 | 0.6020 | 0.5728 | 0.5871 | 0.4013 | 0.3896 | 0.3953 | 0.5341 | 0.5296 | 0.5318 | 0.8416 | 0.8279 | 0.8347 | 0.6410 | 0.6338 | 0.6374 | 0.5093 | 0.4663 | 0.4869 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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