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---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
- sentiment_analysis
model-index:
- name: augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta-v2
results: []
datasets:
- google-research-datasets/go_emotions
language:
- en
metrics:
- f1
- precision
- recall
---
<!-- 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. -->
# augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta-v2
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the these datasets:
- [GoEmotions](https://github.com/google-research/google-research/tree/master/goemotions)
- [sem_eval_2018_task_1 (English)](https://huggingface.co/datasets/SemEvalWorkshop/sem_eval_2018_task_1)
- [Emotion Detection from Text - Pashupati Gupta](https://www.kaggle.com/datasets/pashupatigupta/emotion-detection-from-text/data)
- [Emotions dataset for NLP - praveengovi](https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp/data)
It has also been data augmented using TextAttack.
On top of the (first version)[https://huggingface.co/paradoxmaske/augmented-go-emotions-plus-other-datasets-fine-tuned-distilroberta] of the model, V2 added more data augmentation (EasyDataAugmenter) on all labels except 'neutral'.
It achieves the following results on the evaluation set:
- Loss: 0.0792
- Micro Precision: 0.6922
- Micro Recall: 0.5854
- Micro F1: 0.6343
- Macro Precision: 0.5809
- Macro Recall: 0.4729
- Macro F1: 0.5136
- Weighted Precision: 0.6764
- Weighted Recall: 0.5854
- Weighted F1: 0.6238
- Hamming Loss: 0.0287
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|:------------:|
| No log | 1.0 | 18858 | 0.0745 | 0.7528 | 0.5169 | 0.6129 | 0.6155 | 0.3805 | 0.4336 | 0.7386 | 0.5169 | 0.5827 | 0.0278 |
| No log | 2.0 | 37716 | 0.0757 | 0.7102 | 0.5616 | 0.6272 | 0.5937 | 0.4658 | 0.5049 | 0.6978 | 0.5616 | 0.6105 | 0.0284 |
| No log | 3.0 | 56574 | 0.0792 | 0.6922 | 0.5854 | 0.6343 | 0.5809 | 0.4729 | 0.5136 | 0.6764 | 0.5854 | 0.6238 | 0.0287 |
### Test results
| Label | Precision | Recall | F1-Score | Support |
|-----------------|-----------|--------|----------|---------|
| admiration | 0.65 | 0.66 | 0.66 | 504 |
| amusement | 0.71 | 0.84 | 0.77 | 264 |
| anger | 0.80 | 0.70 | 0.74 | 1585 |
| annoyance | 0.44 | 0.25 | 0.32 | 320 |
| approval | 0.47 | 0.32 | 0.38 | 351 |
| caring | 0.37 | 0.31 | 0.34 | 135 |
| confusion | 0.41 | 0.42 | 0.42 | 153 |
| curiosity | 0.50 | 0.42 | 0.46 | 284 |
| desire | 0.47 | 0.35 | 0.40 | 83 |
| disappointment | 0.31 | 0.16 | 0.21 | 151 |
| disapproval | 0.42 | 0.29 | 0.35 | 267 |
| disgust | 0.72 | 0.63 | 0.67 | 1222 |
| embarrassment | 0.52 | 0.35 | 0.42 | 37 |
| excitement | 0.43 | 0.39 | 0.41 | 103 |
| fear | 0.79 | 0.76 | 0.78 | 787 |
| gratitude | 0.92 | 0.89 | 0.90 | 352 |
| grief | 0.00 | 0.00 | 0.00 | 6 |
| joy | 0.87 | 0.77 | 0.81 | 2298 |
| love | 0.69 | 0.61 | 0.65 | 1305 |
| nervousness | 0.43 | 0.26 | 0.32 | 23 |
| optimism | 0.72 | 0.57 | 0.64 | 1329 |
| pride | 0.62 | 0.31 | 0.42 | 16 |
| realization | 0.39 | 0.19 | 0.26 | 145 |
| relief | 0.26 | 0.24 | 0.25 | 160 |
| remorse | 0.56 | 0.75 | 0.64 | 56 |
| sadness | 0.75 | 0.69 | 0.72 | 2212 |
| surprise | 0.51 | 0.35 | 0.41 | 572 |
| neutral | 0.67 | 0.51 | 0.58 | 2668 |
| **Micro Avg** | 0.71 | 0.60 | 0.65 | 17388 |
| **Macro Avg** | 0.55 | 0.46 | 0.50 | 17388 |
| **Weighted Avg**| 0.70 | 0.60 | 0.64 | 17388 |
| **Samples Avg** | 0.64 | 0.61 | 0.61 | 17388 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.21.0 |