metadata
license: mit
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.94
- name: F1
type: f1
value: 0.9404039265121519
roberta-base-finetuned-emotion
This model is a fine-tuned version of roberta-base on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1669
- Accuracy: 0.94
- F1: 0.9404
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
The model has trained twice. In the first run hyperparameters was the same except num_epochs was 3. So results below actually shows 8 epoch of fine-tuning in total.
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.1508 | 1.0 | 250 | 0.1969 | 0.934 | 0.9334 |
| 0.1035 | 2.0 | 500 | 0.1660 | 0.9335 | 0.9341 |
| 0.0926 | 3.0 | 750 | 0.1626 | 0.935 | 0.9359 |
| 0.0855 | 4.0 | 1000 | 0.1680 | 0.934 | 0.9337 |
| 0.0682 | 5.0 | 1250 | 0.1669 | 0.94 | 0.9404 |
Framework versions
- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3