---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
- sentiment_analysis
model-index:
- name: go-emotions-fine-tuned-distilroberta
  results: []
datasets:
- google-research-datasets/go_emotions
language:
- en
metrics:
- recall
- precision
- f1
---

<!-- 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-fine-tuned-distilroberta

This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0841
- Micro Precision: 0.6789
- Micro Recall: 0.5047
- Micro F1: 0.5790
- Macro Precision: 0.5559
- Macro Recall: 0.4000
- Macro F1: 0.4502
- Weighted Precision: 0.6538
- Weighted Recall: 0.5047
- Weighted F1: 0.5577
- Hamming Loss: 0.0308

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|:------------:|
| 0.1062        | 1.0   | 5427  | 0.0889          | 0.6956          | 0.4498       | 0.5464   | 0.5087          | 0.3111       | 0.3537   | 0.6246             | 0.4498          | 0.4936      | 0.0314       |
| 0.0828        | 2.0   | 10854 | 0.0834          | 0.7042          | 0.4798       | 0.5707   | 0.5874          | 0.3562       | 0.4108   | 0.6872             | 0.4798          | 0.5306      | 0.0303       |
| 0.0704        | 3.0   | 16281 | 0.0841          | 0.6789          | 0.5047       | 0.5790   | 0.5559          | 0.4000       | 0.4502   | 0.6538             | 0.5047          | 0.5577      | 0.0308       |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.21.0