metadata
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: ALBERT_trainer_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: 0.927
ALBERT_trainer_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: 0.3559
- Accuracy: 0.927
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: 20
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1217 | 1.0 | 800 | 0.1936 | 0.93 |
| 0.1054 | 2.0 | 1600 | 0.2105 | 0.9305 |
| 0.0893 | 3.0 | 2400 | 0.2199 | 0.933 |
| 0.0751 | 4.0 | 3200 | 0.2412 | 0.9375 |
| 0.0608 | 5.0 | 4000 | 0.2853 | 0.932 |
| 0.0342 | 6.0 | 4800 | 0.3575 | 0.9315 |
| 0.025 | 7.0 | 5600 | 0.3698 | 0.931 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2