---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
- sentiment_analysis
model-index:
- name: go-emotions-plus-other-datasets-fine-tuned-distilroberta
  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. -->

# go-emotions-plus-other-datasets-fine-tuned-distilroberta

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 achieves the following results on the evaluation set:
- Loss: 0.0719
- Micro Precision: 0.7358
- Micro Recall: 0.5840
- Micro F1: 0.6512
- Macro Precision: 0.5957
- Macro Recall: 0.4191
- Macro F1: 0.4670
- Weighted Precision: 0.7120
- Weighted Recall: 0.5840
- Weighted F1: 0.6286
- Hamming Loss: 0.0266


### 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   | 10377 | 0.0788          | 0.7494          | 0.4946       | 0.5959   | 0.5505          | 0.3191       | 0.3567   | 0.7217             | 0.4946          | 0.5559      | 0.0285       |
| No log        | 2.0   | 20754 | 0.0723          | 0.7354          | 0.5782       | 0.6474   | 0.6452          | 0.3792       | 0.4259   | 0.7312             | 0.5782          | 0.6154      | 0.0268       |
| No log        | 3.0   | 31131 | 0.0719          | 0.7358          | 0.5840       | 0.6512   | 0.5957          | 0.4191       | 0.4670   | 0.7120             | 0.5840          | 0.6286      | 0.0266       |


### Test results
Threshold = 0.5
| Emotion          | Precision | Recall | F1-Score | Support |
|------------------|-----------|--------|----------|---------|
| admiration       | 0.65      | 0.69   | 0.67     | 504     |
| amusement        | 0.72      | 0.89   | 0.80     | 264     |
| anger            | 0.78      | 0.70   | 0.74     | 1585    |
| annoyance        | 0.51      | 0.11   | 0.18     | 320     |
| approval         | 0.58      | 0.31   | 0.41     | 351     |
| caring           | 0.50      | 0.27   | 0.35     | 135     |
| confusion        | 0.51      | 0.33   | 0.40     | 153     |
| curiosity        | 0.52      | 0.49   | 0.50     | 284     |
| desire           | 0.46      | 0.25   | 0.33     | 83      |
| disappointment   | 0.58      | 0.09   | 0.16     | 151     |
| disapproval      | 0.51      | 0.28   | 0.36     | 267     |
| disgust          | 0.72      | 0.64   | 0.68     | 1222    |
| embarrassment    | 0.78      | 0.19   | 0.30     | 37      |
| excitement       | 0.54      | 0.36   | 0.43     | 103     |
| fear             | 0.78      | 0.75   | 0.77     | 787     |
| gratitude        | 0.92      | 0.89   | 0.90     | 352     |
| grief            | 0.00      | 0.00   | 0.00     | 6       |
| joy              | 0.87      | 0.77   | 0.82     | 2298    |
| love             | 0.72      | 0.60   | 0.65     | 1305    |
| nervousness      | 0.00      | 0.00   | 0.00     | 23      |
| optimism         | 0.71      | 0.56   | 0.63     | 1329    |
| pride            | 0.00      | 0.00   | 0.00     | 16      |
| realization      | 0.46      | 0.11   | 0.18     | 145     |
| relief           | 0.59      | 0.08   | 0.14     | 160     |
| remorse          | 0.56      | 0.77   | 0.65     | 56      |
| sadness          | 0.78      | 0.67   | 0.72     | 2212    |
| surprise         | 0.59      | 0.28   | 0.38     | 572     |
| neutral          | 0.69      | 0.56   | 0.62     | 2668    |
| **Micro Avg**    | 0.74      | 0.60   | 0.66     | 17388   |
| **Macro Avg**    | 0.57      | 0.42   | 0.46     | 17388   |
| **Weighted Avg** | 0.72      | 0.60   | 0.65     | 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