metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_qnli_384
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
config: qnli
split: validation
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6055280981145891
distilbert_sa_GLUE_Experiment_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: 0.6542
- Accuracy: 0.6055
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 |
---|---|---|---|---|
0.6751 | 1.0 | 410 | 0.6575 | 0.6022 |
0.6476 | 2.0 | 820 | 0.6542 | 0.6055 |
0.6228 | 3.0 | 1230 | 0.6622 | 0.5982 |
0.5989 | 4.0 | 1640 | 0.6712 | 0.5894 |
0.5711 | 5.0 | 2050 | 0.7102 | 0.5845 |
0.5413 | 6.0 | 2460 | 0.7776 | 0.5772 |
0.5116 | 7.0 | 2870 | 0.8393 | 0.5678 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2