metadata
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: AlBert-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9325
AlBert-finetuned-emotion
This model is a fine-tuned version of albert-base-v2 on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.2185
- Accuracy: 0.9325
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: 3.069458879876956e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 22
- 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 |
|---|---|---|---|---|
| 0.4279 | 1.0 | 1000 | 0.3697 | 0.888 |
| 0.2448 | 2.0 | 2000 | 0.2732 | 0.914 |
| 0.1731 | 3.0 | 3000 | 0.2270 | 0.923 |
| 0.1305 | 4.0 | 4000 | 0.2193 | 0.9285 |
| 0.1053 | 5.0 | 5000 | 0.2185 | 0.9325 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2