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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: 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 |