metadata
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: bertweet-base-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.929
- name: F1
type: f1
value: 0.9295613935787139
bertweet-base-finetuned-emotion
This model is a fine-tuned version of vinai/bertweet-base on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1737
- Accuracy: 0.929
- F1: 0.9296
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.9469 | 1.0 | 250 | 0.3643 | 0.895 | 0.8921 |
| 0.2807 | 2.0 | 500 | 0.2173 | 0.9245 | 0.9252 |
| 0.1749 | 3.0 | 750 | 0.1859 | 0.926 | 0.9266 |
| 0.1355 | 4.0 | 1000 | 0.1737 | 0.929 | 0.9296 |
Framework versions
- Transformers 4.13.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3