File size: 4,736 Bytes
5ec3b79 be4057b 5ec3b79 7857a00 5ec3b79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
- sentiment_analysis
model-index:
- name: go-emotions-fine-tuned-distilroberta
results: []
datasets:
- google-research-datasets/go_emotions
language:
- en
metrics:
- recall
- precision
- f1
---
<!-- 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. -->
# go-emotions-fine-tuned-distilroberta
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on GoEmotions dataset.
It achieves the following results on the evaluation set (threshold = 0.5):
- Loss: 0.0841
- Micro Precision: 0.6789
- Micro Recall: 0.5047
- Micro F1: 0.5790
- Macro Precision: 0.5559
- Macro Recall: 0.4000
- Macro F1: 0.4502
- Weighted Precision: 0.6538
- Weighted Recall: 0.5047
- Weighted F1: 0.5577
- Hamming Loss: 0.0308
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: linear
- num_epochs: 3.0
### Training results
| 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|:------------:|
| 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 |
| 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 |
| 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 |
### Test results
| Class | Precision | Recall | F1-Score | Support |
|-------------------|-----------|--------|----------|---------|
| admiration | 0.69 | 0.73 | 0.71 | 504 |
| amusement | 0.79 | 0.87 | 0.83 | 264 |
| anger | 0.58 | 0.41 | 0.48 | 198 |
| annoyance | 0.45 | 0.16 | 0.24 | 320 |
| approval | 0.58 | 0.34 | 0.43 | 351 |
| caring | 0.51 | 0.29 | 0.37 | 135 |
| confusion | 0.57 | 0.38 | 0.46 | 153 |
| curiosity | 0.50 | 0.46 | 0.48 | 284 |
| desire | 0.70 | 0.36 | 0.48 | 83 |
| disappointment | 0.60 | 0.19 | 0.28 | 151 |
| disapproval | 0.42 | 0.29 | 0.34 | 267 |
| disgust | 0.63 | 0.33 | 0.44 | 123 |
| embarrassment | 0.82 | 0.38 | 0.52 | 37 |
| excitement | 0.57 | 0.33 | 0.42 | 103 |
| fear | 0.71 | 0.64 | 0.68 | 78 |
| gratitude | 0.94 | 0.90 | 0.92 | 352 |
| grief | 0.00 | 0.00 | 0.00 | 6 |
| joy | 0.69 | 0.54 | 0.61 | 161 |
| love | 0.82 | 0.84 | 0.83 | 238 |
| nervousness | 0.67 | 0.17 | 0.28 | 23 |
| optimism | 0.63 | 0.48 | 0.55 | 186 |
| pride | 0.00 | 0.00 | 0.00 | 16 |
| realization | 0.54 | 0.13 | 0.21 | 145 |
| relief | 0.00 | 0.00 | 0.00 | 11 |
| remorse | 0.58 | 0.77 | 0.66 | 56 |
| sadness | 0.67 | 0.49 | 0.57 | 156 |
| surprise | 0.61 | 0.44 | 0.51 | 141 |
| neutral | 0.73 | 0.54 | 0.62 | 1787 |
| **micro avg** | 0.68 | 0.51 | 0.58 | 6329 |
| **macro avg** | 0.57 | 0.41 | 0.46 | 6329 |
| **weighted avg** | 0.66 | 0.51 | 0.56 | 6329 |
| **samples avg** | 0.56 | 0.53 | 0.54 | 6329 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.21.0 |