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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: distilbert/distilroberta-base |
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tags: |
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- generated_from_trainer |
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- sentiment_analysis |
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model-index: |
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- name: augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta-v3 |
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results: [] |
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datasets: |
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- google-research-datasets/go_emotions |
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language: |
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- en |
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metrics: |
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- f1 |
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- precision |
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- recall |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta-v3 |
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This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the these datasets: |
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- [GoEmotions](https://github.com/google-research/google-research/tree/master/goemotions) |
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- [sem_eval_2018_task_1 (English)](https://huggingface.co/datasets/SemEvalWorkshop/sem_eval_2018_task_1) |
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- [Emotion Detection from Text - Pashupati Gupta](https://www.kaggle.com/datasets/pashupatigupta/emotion-detection-from-text/data) |
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- [Emotions dataset for NLP - praveengovi](https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp/data) |
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It has also been data augmented using TextAttack. |
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On top of the (first version)[https://huggingface.co/paradoxmaske/augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta] of the model, V3 added more data augmentation |
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- EasyDataAugmenter on all labels except labels with a lot of examples [neutral (27), sadness (25), joy (17), love (18), anger (2)]. |
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- CharSwapAugmenter on labels with very few examples compared to others: relief (23), confusion (6), disappointment (9), realization (22), caring (5), excitement (13), desire (8), remorse (24), embarrassment (12), nervousness (19), pride (21), grief (16). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0822 |
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- Micro Precision: 0.6806 |
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- Micro Recall: 0.5843 |
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- Micro F1: 0.6288 |
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- Macro Precision: 0.5709 |
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- Macro Recall: 0.4553 |
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- Macro F1: 0.4950 |
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- Weighted Precision: 0.6654 |
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- Weighted Recall: 0.5843 |
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- Weighted F1: 0.6196 |
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- Hamming Loss: 0.0293 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 | Weighted Precision | Weighted Recall | Weighted F1 | Hamming Loss | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|:------------:| |
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| No log | 1.0 | 18454 | 0.0800 | 0.7272 | 0.4822 | 0.5799 | 0.6082 | 0.3841 | 0.4436 | 0.7271 | 0.4822 | 0.5609 | 0.0297 | |
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| No log | 2.0 | 36908 | 0.0780 | 0.6895 | 0.5674 | 0.6225 | 0.5850 | 0.4612 | 0.4999 | 0.6800 | 0.5674 | 0.6109 | 0.0293 | |
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| No log | 3.0 | 55362 | 0.0822 | 0.6806 | 0.5843 | 0.6288 | 0.5709 | 0.4553 | 0.4950 | 0.6654 | 0.5843 | 0.6196 | 0.0293 | |
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### Test results |
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| Label | Precision | Recall | F1-Score | Support | |
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|-----------------|-----------|--------|----------|---------| |
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| admiration | 0.61 | 0.66 | 0.64 | 504 | |
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| amusement | 0.73 | 0.83 | 0.78 | 264 | |
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| anger | 0.79 | 0.67 | 0.72 | 1585 | |
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| annoyance | 0.39 | 0.20 | 0.26 | 320 | |
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| approval | 0.44 | 0.31 | 0.37 | 351 | |
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| caring | 0.38 | 0.29 | 0.33 | 135 | |
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| confusion | 0.43 | 0.42 | 0.43 | 153 | |
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| curiosity | 0.47 | 0.45 | 0.46 | 284 | |
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| desire | 0.51 | 0.30 | 0.38 | 83 | |
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| disappointment | 0.28 | 0.20 | 0.23 | 151 | |
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| disapproval | 0.41 | 0.30 | 0.35 | 267 | |
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| disgust | 0.71 | 0.60 | 0.65 | 1222 | |
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| embarrassment | 0.43 | 0.27 | 0.33 | 37 | |
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| excitement | 0.40 | 0.38 | 0.39 | 103 | |
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| fear | 0.78 | 0.74 | 0.76 | 787 | |
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| gratitude | 0.93 | 0.88 | 0.91 | 352 | |
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| grief | 0.50 | 0.17 | 0.25 | 6 | |
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| joy | 0.88 | 0.76 | 0.81 | 2298 | |
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| love | 0.69 | 0.61 | 0.65 | 1305 | |
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| nervousness | 0.39 | 0.30 | 0.34 | 23 | |
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| optimism | 0.70 | 0.58 | 0.64 | 1329 | |
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| pride | 0.62 | 0.31 | 0.42 | 16 | |
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| realization | 0.32 | 0.16 | 0.21 | 145 | |
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| relief | 0.19 | 0.15 | 0.17 | 160 | |
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| remorse | 0.61 | 0.75 | 0.67 | 56 | |
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| sadness | 0.75 | 0.66 | 0.71 | 2212 | |
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| surprise | 0.49 | 0.36 | 0.42 | 572 | |
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| neutral | 0.65 | 0.54 | 0.59 | 2668 | |
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| **Micro Avg** | 0.70 | 0.60 | 0.64 | 17388 | |
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| **Macro Avg** | 0.55 | 0.46 | 0.49 | 17388 | |
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| **Weighted Avg**| 0.69 | 0.60 | 0.64 | 17388 | |
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| **Samples Avg** | 0.64 | 0.61 | 0.61 | 17388 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.21.0 |
