metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- commonsense_qa
metrics:
- accuracy
model_index:
- name: albert-xxlarge-v2-finetuned-csqa
results:
- dataset:
name: commonsense_qa
type: commonsense_qa
args: default
metric:
name: Accuracy
type: accuracy
value: 0.7870597839355469
albert-xxlarge-v2-finetuned-csqa
This model is a fine-tuned version of albert-xxlarge-v2 on the commonsense_qa dataset. It achieves the following results on the evaluation set:
- Loss: 1.6177
- Accuracy: 0.7871
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7464 | 1.0 | 609 | 0.5319 | 0.7985 |
| 0.3116 | 2.0 | 1218 | 0.6422 | 0.7936 |
| 0.0769 | 3.0 | 1827 | 1.2674 | 0.7952 |
| 0.0163 | 4.0 | 2436 | 1.4839 | 0.7903 |
| 0.0122 | 5.0 | 3045 | 1.6177 | 0.7871 |
Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0
- Datasets 1.10.2
- Tokenizers 0.10.3