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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- go_emotions
metrics:
- f1
- accuracy
model-index:
- name: bert-base-goemotions
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: go_emotions
type: go_emotions
config: simplified
split: validation
args: simplified
metrics:
- name: F1
type: f1
value: 0.5726694586629439
- name: Accuracy
type: accuracy
value: 0.4375230372281607
---
<!-- 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-base-goemotions
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the go_emotions dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1539
- F1: 0.5727
- Roc Auc: 0.7796
- Accuracy: 0.4375
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|
| 0.0833 | 1.0 | 2714 | 0.0876 | 0.5453 | 0.7189 | 0.4243 |
| 0.0719 | 2.0 | 5428 | 0.0867 | 0.5586 | 0.7322 | 0.4399 |
| 0.0575 | 3.0 | 8142 | 0.0943 | 0.5736 | 0.7523 | 0.4665 |
| 0.0411 | 4.0 | 10856 | 0.1064 | 0.5655 | 0.7580 | 0.4574 |
| 0.0301 | 5.0 | 13570 | 0.1167 | 0.5622 | 0.7591 | 0.4517 |
| 0.0217 | 6.0 | 16284 | 0.1279 | 0.5579 | 0.7648 | 0.4375 |
| 0.015 | 7.0 | 18998 | 0.1367 | 0.5663 | 0.7759 | 0.4333 |
| 0.0102 | 8.0 | 21712 | 0.1445 | 0.5695 | 0.7793 | 0.4322 |
| 0.0077 | 9.0 | 24426 | 0.1491 | 0.5725 | 0.7795 | 0.4366 |
| 0.0057 | 10.0 | 27140 | 0.1539 | 0.5727 | 0.7796 | 0.4375 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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