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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-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 |