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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: go-emotions-fine-tuned-distilroberta |
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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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- recall |
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- precision |
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- f1 |
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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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# go-emotions-fine-tuned-distilroberta |
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This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on GoEmotions dataset. |
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It achieves the following results on the evaluation set (threshold = 0.5): |
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- Loss: 0.0841 |
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- Micro Precision: 0.6789 |
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- Micro Recall: 0.5047 |
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- Micro F1: 0.5790 |
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- Macro Precision: 0.5559 |
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- Macro Recall: 0.4000 |
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- Macro F1: 0.4502 |
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- Weighted Precision: 0.6538 |
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- Weighted Recall: 0.5047 |
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- Weighted F1: 0.5577 |
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- Hamming Loss: 0.0308 |
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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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| 0.1062 | 1.0 | 5427 | 0.0889 | 0.6956 | 0.4498 | 0.5464 | 0.5087 | 0.3111 | 0.3537 | 0.6246 | 0.4498 | 0.4936 | 0.0314 | |
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| 0.0828 | 2.0 | 10854 | 0.0834 | 0.7042 | 0.4798 | 0.5707 | 0.5874 | 0.3562 | 0.4108 | 0.6872 | 0.4798 | 0.5306 | 0.0303 | |
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| 0.0704 | 3.0 | 16281 | 0.0841 | 0.6789 | 0.5047 | 0.5790 | 0.5559 | 0.4000 | 0.4502 | 0.6538 | 0.5047 | 0.5577 | 0.0308 | |
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### Test results |
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| Class | Precision | Recall | F1-Score | Support | |
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|-------------------|-----------|--------|----------|---------| |
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| admiration | 0.69 | 0.73 | 0.71 | 504 | |
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| amusement | 0.79 | 0.87 | 0.83 | 264 | |
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| anger | 0.58 | 0.41 | 0.48 | 198 | |
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| annoyance | 0.45 | 0.16 | 0.24 | 320 | |
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| approval | 0.58 | 0.34 | 0.43 | 351 | |
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| caring | 0.51 | 0.29 | 0.37 | 135 | |
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| confusion | 0.57 | 0.38 | 0.46 | 153 | |
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| curiosity | 0.50 | 0.46 | 0.48 | 284 | |
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| desire | 0.70 | 0.36 | 0.48 | 83 | |
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| disappointment | 0.60 | 0.19 | 0.28 | 151 | |
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| disapproval | 0.42 | 0.29 | 0.34 | 267 | |
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| disgust | 0.63 | 0.33 | 0.44 | 123 | |
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| embarrassment | 0.82 | 0.38 | 0.52 | 37 | |
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| excitement | 0.57 | 0.33 | 0.42 | 103 | |
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| fear | 0.71 | 0.64 | 0.68 | 78 | |
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| gratitude | 0.94 | 0.90 | 0.92 | 352 | |
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| grief | 0.00 | 0.00 | 0.00 | 6 | |
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| joy | 0.69 | 0.54 | 0.61 | 161 | |
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| love | 0.82 | 0.84 | 0.83 | 238 | |
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| nervousness | 0.67 | 0.17 | 0.28 | 23 | |
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| optimism | 0.63 | 0.48 | 0.55 | 186 | |
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| pride | 0.00 | 0.00 | 0.00 | 16 | |
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| realization | 0.54 | 0.13 | 0.21 | 145 | |
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| relief | 0.00 | 0.00 | 0.00 | 11 | |
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| remorse | 0.58 | 0.77 | 0.66 | 56 | |
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| sadness | 0.67 | 0.49 | 0.57 | 156 | |
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| surprise | 0.61 | 0.44 | 0.51 | 141 | |
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| neutral | 0.73 | 0.54 | 0.62 | 1787 | |
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| **micro avg** | 0.68 | 0.51 | 0.58 | 6329 | |
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| **macro avg** | 0.57 | 0.41 | 0.46 | 6329 | |
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| **weighted avg** | 0.66 | 0.51 | 0.56 | 6329 | |
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| **samples avg** | 0.56 | 0.53 | 0.54 | 6329 | |
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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 |
