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---
library_name: transformers
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: emotion_model_improved
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. -->
# emotion_model_improved
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2881
- Macro F1: 0.5947
- Micro F1: 0.6896
- Accuracy: 0.8522
- F1 Anger: 0.8051
- Precision Anger: 0.7756
- Recall Anger: 0.8368
- F1 Anticipation: 0.3591
- Precision Anticipation: 0.3484
- Recall Anticipation: 0.3705
- F1 Disgust: 0.7122
- Precision Disgust: 0.6203
- Recall Disgust: 0.8360
- F1 Fear: 0.7222
- Precision Fear: 0.6506
- Recall Fear: 0.8115
- F1 Joy: 0.8601
- Precision Joy: 0.8641
- Recall Joy: 0.8561
- F1 Sadness: 0.7075
- Precision Sadness: 0.6030
- Recall Sadness: 0.8558
- F1 Surprise: 0.2393
- Precision Surprise: 0.3305
- Recall Surprise: 0.1875
- F1 Trust: 0.2643
- Precision Trust: 0.2242
- Recall Trust: 0.3217
- F1 Love: 0.6566
- Precision Love: 0.7855
- Recall Love: 0.5640
- F1 Optimism: 0.7413
- Precision Optimism: 0.7730
- Recall Optimism: 0.7122
- F1 Pessimism: 0.4745
- Precision Pessimism: 0.3367
- Recall Pessimism: 0.8032
- Positive Predictions Pct: 25.8683
- Positive Labels Pct: 21.7367
## 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: 8e-06
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy | F1 Anger | Precision Anger | Recall Anger | F1 Anticipation | Precision Anticipation | Recall Anticipation | F1 Disgust | Precision Disgust | Recall Disgust | F1 Fear | Precision Fear | Recall Fear | F1 Joy | Precision Joy | Recall Joy | F1 Sadness | Precision Sadness | Recall Sadness | F1 Surprise | Precision Surprise | Recall Surprise | F1 Trust | Precision Trust | Recall Trust | F1 Love | Precision Love | Recall Love | F1 Optimism | Precision Optimism | Recall Optimism | F1 Pessimism | Precision Pessimism | Recall Pessimism | Positive Predictions Pct | Positive Labels Pct |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:--------:|:---------------:|:------------:|:---------------:|:----------------------:|:-------------------:|:----------:|:-----------------:|:--------------:|:-------:|:--------------:|:-----------:|:------:|:-------------:|:----------:|:----------:|:-----------------:|:--------------:|:-----------:|:------------------:|:---------------:|:--------:|:---------------:|:------------:|:-------:|:--------------:|:-----------:|:-----------:|:------------------:|:---------------:|:------------:|:-------------------:|:----------------:|:------------------------:|:-------------------:|
| 0.6834 | 1.0 | 72 | 0.4816 | 0.2295 | 0.4570 | 0.6345 | 0.5297 | 0.3603 | 1.0 | 0.0 | 0.0 | 0.0 | 0.4483 | 0.2889 | 1.0 | 0.0 | 0.0 | 0.0 | 0.5649 | 0.3936 | 1.0 | 0.4936 | 0.3277 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4884 | 0.3238 | 0.9937 | 0.0 | 0.0 | 0.0 | 45.3927 | 21.9208 |
| 0.4738 | 2.0 | 144 | 0.3507 | 0.4607 | 0.6320 | 0.7951 | 0.7069 | 0.5593 | 0.9604 | 0.0 | 0.0 | 0.0 | 0.6359 | 0.4807 | 0.9388 | 0.4906 | 0.3694 | 0.7302 | 0.8185 | 0.7592 | 0.8877 | 0.6246 | 0.4692 | 0.9336 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6323 | 0.5235 | 0.7980 | 0.7368 | 0.6486 | 0.8529 | 0.4223 | 0.2951 | 0.7422 | 33.7556 | 21.9208 |
| 0.3445 | 3.0 | 216 | 0.3131 | 0.5845 | 0.6929 | 0.8585 | 0.7807 | 0.8 | 0.7623 | 0.3463 | 0.4804 | 0.2707 | 0.7273 | 0.6953 | 0.7624 | 0.7003 | 0.7637 | 0.6465 | 0.8477 | 0.8140 | 0.8843 | 0.7385 | 0.6994 | 0.7822 | 0.1839 | 0.2051 | 0.1667 | 0.2020 | 0.1360 | 0.3924 | 0.6915 | 0.6374 | 0.7557 | 0.7637 | 0.7268 | 0.8046 | 0.4473 | 0.3785 | 0.5467 | 24.1394 | 21.9208 |
| 0.3076 | 4.0 | 288 | 0.3035 | 0.5792 | 0.6867 | 0.8561 | 0.7741 | 0.7691 | 0.7792 | 0.3486 | 0.3904 | 0.3149 | 0.7255 | 0.6826 | 0.7741 | 0.6789 | 0.7818 | 0.6 | 0.8348 | 0.7760 | 0.9033 | 0.7201 | 0.7044 | 0.7365 | 0.2316 | 0.2340 | 0.2292 | 0.1949 | 0.1364 | 0.3418 | 0.6912 | 0.6792 | 0.7036 | 0.7587 | 0.7018 | 0.8256 | 0.4125 | 0.3882 | 0.44 | 24.0158 | 21.9208 |
| 0.2836 | 5.0 | 360 | 0.2969 | 0.6002 | 0.7045 | 0.8648 | 0.7859 | 0.7927 | 0.7792 | 0.3462 | 0.4122 | 0.2983 | 0.7387 | 0.6950 | 0.7882 | 0.7364 | 0.6926 | 0.7860 | 0.8543 | 0.8160 | 0.8964 | 0.7339 | 0.7137 | 0.7552 | 0.2735 | 0.2319 | 0.3333 | 0.2190 | 0.1756 | 0.2911 | 0.6983 | 0.6779 | 0.7199 | 0.7653 | 0.7440 | 0.7878 | 0.4508 | 0.3927 | 0.5289 | 23.8366 | 21.9208 |
| 0.27 | 6.0 | 432 | 0.2930 | 0.6238 | 0.6993 | 0.8541 | 0.8007 | 0.7733 | 0.8302 | 0.3858 | 0.4167 | 0.3591 | 0.7349 | 0.6604 | 0.8282 | 0.7578 | 0.7316 | 0.7860 | 0.8506 | 0.8412 | 0.8601 | 0.7366 | 0.7045 | 0.7718 | 0.4051 | 0.5161 | 0.3333 | 0.2334 | 0.1477 | 0.5570 | 0.7170 | 0.6466 | 0.8046 | 0.7745 | 0.7596 | 0.7899 | 0.4650 | 0.3395 | 0.7378 | 26.5991 | 21.9208 |
| 0.2587 | 7.0 | 504 | 0.2888 | 0.6137 | 0.6969 | 0.8525 | 0.7948 | 0.7756 | 0.8151 | 0.3526 | 0.3697 | 0.3370 | 0.7387 | 0.6667 | 0.8282 | 0.7348 | 0.7704 | 0.7023 | 0.8528 | 0.8406 | 0.8653 | 0.7384 | 0.6776 | 0.8112 | 0.3505 | 0.3469 | 0.3542 | 0.2185 | 0.1403 | 0.4937 | 0.7166 | 0.6524 | 0.7948 | 0.7703 | 0.7370 | 0.8067 | 0.4831 | 0.3532 | 0.7644 | 26.7474 | 21.9208 |
| 0.248 | 8.0 | 576 | 0.2865 | 0.6177 | 0.6960 | 0.8520 | 0.7923 | 0.7691 | 0.8170 | 0.3802 | 0.3596 | 0.4033 | 0.7329 | 0.6712 | 0.8071 | 0.7379 | 0.7716 | 0.7070 | 0.8560 | 0.8219 | 0.8929 | 0.7317 | 0.7096 | 0.7552 | 0.3738 | 0.3390 | 0.4167 | 0.2259 | 0.1444 | 0.5190 | 0.7233 | 0.6486 | 0.8176 | 0.7671 | 0.7261 | 0.8130 | 0.4734 | 0.3548 | 0.7111 | 26.7598 | 21.9208 |
| 0.2404 | 9.0 | 648 | 0.2865 | 0.6219 | 0.7087 | 0.8617 | 0.7959 | 0.7900 | 0.8019 | 0.3913 | 0.3850 | 0.3978 | 0.7417 | 0.6811 | 0.8141 | 0.7489 | 0.6902 | 0.8186 | 0.8579 | 0.8361 | 0.8808 | 0.7390 | 0.6831 | 0.8050 | 0.3542 | 0.3542 | 0.3542 | 0.2368 | 0.1812 | 0.3418 | 0.7254 | 0.7721 | 0.6840 | 0.7747 | 0.7525 | 0.7983 | 0.475 | 0.3455 | 0.76 | 25.5547 | 21.9208 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.3.1.post300
- Datasets 2.2.1
- Tokenizers 0.21.0
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