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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: 0.6577
- Accuracy: 0.7631
- Prec: 0.6548
- Recall: 0.6054
- F1: 0.6277
- B Acc: 0.6054
- Micro F1: 0.7631
- Prec Joy: 0.7442
- Recall Joy: 0.7318
- F1 Joy: 0.7379
- Prec Anger: 0.6340
- Recall Anger: 0.6007
- F1 Anger: 0.6169
- Prec Disgust: 0.4641
- Recall Disgust: 0.4059
- F1 Disgust: 0.4330
- Prec Fear: 0.6923
- Recall Fear: 0.5930
- F1 Fear: 0.6388
- Prec Neutral: 0.8246
- Recall Neutral: 0.8811
- F1 Neutral: 0.8519
- Prec Sadness: 0.7164
- Recall Sadness: 0.6264
- F1 Sadness: 0.6684
- Prec Surprise: 0.5081
- Recall Surprise: 0.3990
- F1 Surprise: 0.4470
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:|
| 0.8081 | 0.75 | 1099 | 0.7901 | 0.7138 | 0.5617 | 0.5642 | 0.5601 | 0.5642 | 0.7138 | 0.7312 | 0.6492 | 0.6878 | 0.4974 | 0.5354 | 0.5157 | 0.4330 | 0.3515 | 0.3880 | 0.5277 | 0.5447 | 0.5360 | 0.8403 | 0.8288 | 0.8345 | 0.5152 | 0.6820 | 0.5870 | 0.3867 | 0.3581 | 0.3718 |
| 0.7543 | 1.5 | 2198 | 0.7482 | 0.7263 | 0.5892 | 0.5714 | 0.5737 | 0.5714 | 0.7263 | 0.6611 | 0.7786 | 0.7151 | 0.578 | 0.4795 | 0.5242 | 0.4229 | 0.4644 | 0.4427 | 0.5082 | 0.5900 | 0.5461 | 0.8353 | 0.8242 | 0.8297 | 0.6356 | 0.5999 | 0.6172 | 0.4836 | 0.2634 | 0.3411 |
| 0.7292 | 2.25 | 3297 | 0.7176 | 0.7392 | 0.6337 | 0.5729 | 0.5834 | 0.5729 | 0.7392 | 0.7077 | 0.7367 | 0.7219 | 0.6069 | 0.4928 | 0.5440 | 0.3188 | 0.5816 | 0.4119 | 0.6310 | 0.5608 | 0.5938 | 0.8031 | 0.8778 | 0.8388 | 0.8176 | 0.5034 | 0.6232 | 0.5507 | 0.2570 | 0.3505 |
| 0.7138 | 3.0 | 4396 | 0.6883 | 0.7448 | 0.6145 | 0.5918 | 0.6005 | 0.5918 | 0.7448 | 0.7004 | 0.7614 | 0.7297 | 0.5848 | 0.5819 | 0.5833 | 0.4116 | 0.4142 | 0.4129 | 0.5827 | 0.5827 | 0.5827 | 0.8380 | 0.8428 | 0.8404 | 0.6838 | 0.6222 | 0.6515 | 0.5 | 0.3376 | 0.4031 |
| 0.7046 | 3.75 | 5495 | 0.6826 | 0.7465 | 0.6275 | 0.5789 | 0.5986 | 0.5789 | 0.7465 | 0.7145 | 0.748 | 0.7309 | 0.5822 | 0.5658 | 0.5739 | 0.5220 | 0.3222 | 0.3984 | 0.6403 | 0.5212 | 0.5747 | 0.8318 | 0.8535 | 0.8425 | 0.6559 | 0.6476 | 0.6517 | 0.4457 | 0.3939 | 0.4182 |
| 0.6767 | 4.5 | 6594 | 0.6971 | 0.7436 | 0.6423 | 0.5649 | 0.5923 | 0.5649 | 0.7436 | 0.7414 | 0.7028 | 0.7216 | 0.6387 | 0.5055 | 0.5644 | 0.5714 | 0.2678 | 0.3647 | 0.6597 | 0.5564 | 0.6037 | 0.8056 | 0.8720 | 0.8375 | 0.5985 | 0.6826 | 0.6378 | 0.4807 | 0.3670 | 0.4162 |
| 0.661 | 5.25 | 7693 | 0.7124 | 0.7384 | 0.6295 | 0.6028 | 0.6031 | 0.6028 | 0.7384 | 0.6697 | 0.7991 | 0.7287 | 0.4849 | 0.7124 | 0.5771 | 0.3955 | 0.4435 | 0.4181 | 0.7064 | 0.5461 | 0.6160 | 0.8814 | 0.7958 | 0.8364 | 0.6848 | 0.6322 | 0.6575 | 0.5835 | 0.2903 | 0.3877 |
| 0.6652 | 6.0 | 8792 | 0.6706 | 0.7529 | 0.6441 | 0.5942 | 0.6136 | 0.5942 | 0.7529 | 0.7386 | 0.7306 | 0.7346 | 0.7153 | 0.4309 | 0.5378 | 0.4612 | 0.4351 | 0.4478 | 0.6354 | 0.5944 | 0.6142 | 0.8081 | 0.8841 | 0.8444 | 0.6859 | 0.6354 | 0.6597 | 0.4643 | 0.4488 | 0.4564 |
| 0.6532 | 6.75 | 9891 | 0.6567 | 0.7582 | 0.6578 | 0.5853 | 0.6146 | 0.5853 | 0.7582 | 0.7473 | 0.7264 | 0.7367 | 0.6156 | 0.5642 | 0.5887 | 0.5014 | 0.3682 | 0.4246 | 0.7068 | 0.5505 | 0.6189 | 0.8176 | 0.8815 | 0.8484 | 0.6602 | 0.6672 | 0.6637 | 0.5556 | 0.3389 | 0.4210 |
| 0.6314 | 7.5 | 10990 | 0.6726 | 0.7555 | 0.6673 | 0.5864 | 0.6142 | 0.5864 | 0.7555 | 0.7029 | 0.7795 | 0.7393 | 0.5800 | 0.6433 | 0.6100 | 0.5350 | 0.3201 | 0.4005 | 0.8117 | 0.4861 | 0.6081 | 0.8422 | 0.8456 | 0.8439 | 0.6651 | 0.6725 | 0.6688 | 0.5344 | 0.3581 | 0.4288 |
| 0.6045 | 8.25 | 12089 | 0.6668 | 0.7578 | 0.6551 | 0.6006 | 0.6238 | 0.6006 | 0.7578 | 0.7288 | 0.7468 | 0.7377 | 0.6554 | 0.5597 | 0.6038 | 0.4684 | 0.4038 | 0.4337 | 0.7683 | 0.5388 | 0.6334 | 0.8249 | 0.8693 | 0.8466 | 0.6924 | 0.6418 | 0.6661 | 0.4472 | 0.4437 | 0.4454 |
| 0.6182 | 9.0 | 13188 | 0.6659 | 0.7571 | 0.6461 | 0.6044 | 0.6205 | 0.6044 | 0.7571 | 0.7164 | 0.7602 | 0.7377 | 0.6389 | 0.5813 | 0.6087 | 0.4511 | 0.4435 | 0.4473 | 0.6770 | 0.5739 | 0.6212 | 0.8373 | 0.8555 | 0.8463 | 0.6523 | 0.6842 | 0.6679 | 0.5497 | 0.3325 | 0.4143 |
| 0.5927 | 9.75 | 14287 | 0.7097 | 0.7466 | 0.6561 | 0.5640 | 0.5952 | 0.5640 | 0.7466 | 0.7228 | 0.7136 | 0.7182 | 0.6138 | 0.5785 | 0.5957 | 0.5833 | 0.2490 | 0.3490 | 0.7201 | 0.5652 | 0.6333 | 0.8081 | 0.8686 | 0.8372 | 0.6367 | 0.6688 | 0.6524 | 0.5075 | 0.3043 | 0.3805 |
| 0.5736 | 10.5 | 15386 | 0.6663 | 0.7587 | 0.6494 | 0.6092 | 0.6225 | 0.6092 | 0.7587 | 0.7282 | 0.7576 | 0.7426 | 0.5869 | 0.6554 | 0.6193 | 0.5 | 0.3745 | 0.4282 | 0.6807 | 0.5930 | 0.6338 | 0.8502 | 0.8443 | 0.8473 | 0.6361 | 0.7122 | 0.672 | 0.5639 | 0.3274 | 0.4142 |
| 0.5687 | 11.25 | 16485 | 0.6599 | 0.7633 | 0.6595 | 0.6148 | 0.6337 | 0.6148 | 0.7633 | 0.7366 | 0.7447 | 0.7406 | 0.6489 | 0.6062 | 0.6268 | 0.4898 | 0.4519 | 0.4701 | 0.7461 | 0.5637 | 0.6422 | 0.8389 | 0.8663 | 0.8524 | 0.6401 | 0.6804 | 0.6596 | 0.5161 | 0.3900 | 0.4443 |
| 0.5652 | 12.0 | 17584 | 0.6577 | 0.7631 | 0.6548 | 0.6054 | 0.6277 | 0.6054 | 0.7631 | 0.7442 | 0.7318 | 0.7379 | 0.6340 | 0.6007 | 0.6169 | 0.4641 | 0.4059 | 0.4330 | 0.6923 | 0.5930 | 0.6388 | 0.8246 | 0.8811 | 0.8519 | 0.7164 | 0.6264 | 0.6684 | 0.5081 | 0.3990 | 0.4470 |
| 0.5377 | 12.75 | 18683 | 0.6681 | 0.7620 | 0.6422 | 0.6124 | 0.6250 | 0.6124 | 0.7620 | 0.7324 | 0.7607 | 0.7463 | 0.5952 | 0.6482 | 0.6206 | 0.4619 | 0.3808 | 0.4174 | 0.6490 | 0.5900 | 0.6181 | 0.8475 | 0.8551 | 0.8513 | 0.6912 | 0.6608 | 0.6757 | 0.5178 | 0.3913 | 0.4457 |
| 0.5312 | 13.5 | 19782 | 0.6777 | 0.7594 | 0.6362 | 0.6162 | 0.6247 | 0.6162 | 0.7594 | 0.7351 | 0.7494 | 0.7422 | 0.6058 | 0.6399 | 0.6224 | 0.4489 | 0.4226 | 0.4353 | 0.6337 | 0.6003 | 0.6165 | 0.8454 | 0.8539 | 0.8497 | 0.6744 | 0.6608 | 0.6676 | 0.5101 | 0.3862 | 0.4396 |
| 0.512 | 14.25 | 20881 | 0.6823 | 0.7569 | 0.6409 | 0.6172 | 0.6274 | 0.6172 | 0.7569 | 0.7051 | 0.7805 | 0.7409 | 0.6291 | 0.6051 | 0.6169 | 0.4830 | 0.4163 | 0.4472 | 0.6461 | 0.5988 | 0.6216 | 0.8506 | 0.8388 | 0.8447 | 0.6613 | 0.6757 | 0.6684 | 0.5113 | 0.4054 | 0.4522 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
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