---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-goemotions-15epochs-run2
  results: []
---

<!-- 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. -->

# bert-goemotions-15epochs-run2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1106
- Accuracy Thresh: 0.9619
- F1 weighted: {'f1': 0.3982045872720165}
- F1 macro: {'f1': 0.31538372135978}
- Accuracy: {'accuracy': 0.4170647653000594}
- Recall weighted: {'recall': 0.4170647653000594}
- Recall macro: {'recall': 0.32808068442725}

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy Thresh | F1 weighted                 | F1 macro                    | Accuracy                          | Recall weighted                 | Recall macro                    |
|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:---------------------------:|:---------------------------:|:---------------------------------:|:-------------------------------:|:-------------------------------:|
| 0.1259        | 1.0   | 5286  | 0.1127          | 0.9617          | {'f1': 0.3890983386624071}  | {'f1': 0.3007761920553838}  | {'accuracy': 0.41630421865715983} | {'recall': 0.41630421865715983} | {'recall': 0.3213896867494273}  |
| 0.1102        | 2.0   | 10572 | 0.1106          | 0.9619          | {'f1': 0.3982045872720165}  | {'f1': 0.31538372135978}    | {'accuracy': 0.4170647653000594}  | {'recall': 0.4170647653000594}  | {'recall': 0.32808068442725}    |
| 0.1052        | 3.0   | 15858 | 0.1107          | 0.9619          | {'f1': 0.3980887152485667}  | {'f1': 0.3181332487636058}  | {'accuracy': 0.4168983957219251}  | {'recall': 0.4168983957219251}  | {'recall': 0.32790458379700677} |
| 0.1008        | 4.0   | 21144 | 0.1117          | 0.9616          | {'f1': 0.39966069827702533} | {'f1': 0.32238147844285014} | {'accuracy': 0.4169459298871064}  | {'recall': 0.4169459298871064}  | {'recall': 0.3280426755048108}  |
| 0.0968        | 5.0   | 26430 | 0.1138          | 0.9609          | {'f1': 0.39833587917024693} | {'f1': 0.32459673497912495} | {'accuracy': 0.4110516934046346}  | {'recall': 0.4110516934046346}  | {'recall': 0.3347612567297387}  |
| 0.0934        | 6.0   | 31716 | 0.1158          | 0.9604          | {'f1': 0.3893454681480969}  | {'f1': 0.3185494353334119}  | {'accuracy': 0.39786096256684494} | {'recall': 0.39786096256684494} | {'recall': 0.3332642189610775}  |
| 0.0902        | 7.0   | 37002 | 0.1188          | 0.9596          | {'f1': 0.3843621716402447}  | {'f1': 0.3127316237002698}  | {'accuracy': 0.39332144979203804} | {'recall': 0.39332144979203804} | {'recall': 0.32900959748461356} |


### Framework versions

- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1