---
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