---
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-v3
  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-v3

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, V3 added more data augmentation
- EasyDataAugmenter on all labels except labels with a lot of examples [neutral (27), sadness (25), joy (17), love (18), anger (2)].
- 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).



It achieves the following results on the evaluation set:
- Loss: 0.0822
- Micro Precision: 0.6806
- Micro Recall: 0.5843
- Micro F1: 0.6288
- Macro Precision: 0.5709
- Macro Recall: 0.4553
- Macro F1: 0.4950
- Weighted Precision: 0.6654
- Weighted Recall: 0.5843
- Weighted F1: 0.6196
- Hamming Loss: 0.0293

## 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   | 18454 | 0.0800          | 0.7272          | 0.4822       | 0.5799   | 0.6082          | 0.3841       | 0.4436   | 0.7271             | 0.4822          | 0.5609      | 0.0297       |
| 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       |
| 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       |


### Test results

| Label           | Precision | Recall | F1-Score | Support |
|-----------------|-----------|--------|----------|---------|
| admiration      | 0.61      | 0.66   | 0.64     | 504     |
| amusement       | 0.73      | 0.83   | 0.78     | 264     |
| anger           | 0.79      | 0.67   | 0.72     | 1585    |
| annoyance       | 0.39      | 0.20   | 0.26     | 320     |
| approval        | 0.44      | 0.31   | 0.37     | 351     |
| caring          | 0.38      | 0.29   | 0.33     | 135     |
| confusion       | 0.43      | 0.42   | 0.43     | 153     |
| curiosity       | 0.47      | 0.45   | 0.46     | 284     |
| desire          | 0.51      | 0.30   | 0.38     | 83      |
| disappointment  | 0.28      | 0.20   | 0.23     | 151     |
| disapproval     | 0.41      | 0.30   | 0.35     | 267     |
| disgust         | 0.71      | 0.60   | 0.65     | 1222    |
| embarrassment   | 0.43      | 0.27   | 0.33     | 37      |
| excitement      | 0.40      | 0.38   | 0.39     | 103     |
| fear            | 0.78      | 0.74   | 0.76     | 787     |
| gratitude       | 0.93      | 0.88   | 0.91     | 352     |
| grief           | 0.50      | 0.17   | 0.25     | 6       |
| joy             | 0.88      | 0.76   | 0.81     | 2298    |
| love            | 0.69      | 0.61   | 0.65     | 1305    |
| nervousness     | 0.39      | 0.30   | 0.34     | 23      |
| optimism        | 0.70      | 0.58   | 0.64     | 1329    |
| pride           | 0.62      | 0.31   | 0.42     | 16      |
| realization     | 0.32      | 0.16   | 0.21     | 145     |
| relief          | 0.19      | 0.15   | 0.17     | 160     |
| remorse         | 0.61      | 0.75   | 0.67     | 56      |
| sadness         | 0.75      | 0.66   | 0.71     | 2212    |
| surprise        | 0.49      | 0.36   | 0.42     | 572     |
| neutral         | 0.65      | 0.54   | 0.59     | 2668    |
| **Micro Avg**   | 0.70      | 0.60   | 0.64     | 17388   |
| **Macro Avg**   | 0.55      | 0.46   | 0.49     | 17388   |
| **Weighted Avg**| 0.69      | 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