metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_qnli_384
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5701995240710233
distilbert_sa_GLUE_Experiment_data_aug_qnli_384
This model is a fine-tuned version of distilbert-base-uncased on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 1.0828
- Accuracy: 0.5702
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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.4167 | 1.0 | 16604 | 1.0828 | 0.5702 |
0.1701 | 2.0 | 33208 | 1.3572 | 0.5731 |
0.0922 | 3.0 | 49812 | 1.7374 | 0.5797 |
0.0536 | 4.0 | 66416 | 1.6715 | 0.5817 |
0.0342 | 5.0 | 83020 | 1.9660 | 0.5817 |
0.0242 | 6.0 | 99624 | 2.1939 | 0.5821 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2