metadata
license: mit
tags:
- generated_from_trainer
datasets:
- go_emotions
metrics:
- f1
- accuracy
model-index:
- name: go_emo_gpt
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: 0.6009707054948864
- name: Accuracy
type: accuracy
value: 0.49963140434942865
go_emo_gpt
This model is a fine-tuned version of gpt2 on the go_emotions dataset. It achieves the following results on the evaluation set:
- Loss: 0.0964
- F1: 0.6010
- Roc Auc: 0.7659
- Accuracy: 0.4996
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: (13023,)
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.105 | 1.0 | 43410 | 0.0967 | 0.5795 | 0.7476 | 0.4757 |
| 0.0949 | 2.0 | 86820 | 0.0938 | 0.6012 | 0.7636 | 0.5035 |
| 0.0837 | 3.0 | 130230 | 0.0964 | 0.6010 | 0.7659 | 0.4996 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3