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library_name: transformers
language:
- en
license: apache-2.0
base_model: google/bert_uncased_L-2_H-128_A-2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_uncased_L-2_H-128_A-2_qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8004759289767527
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-2_H-128_A-2_qnli
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4352
- Accuracy: 0.8005
## 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
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5442 | 1.0 | 410 | 0.4932 | 0.7679 |
| 0.4861 | 2.0 | 820 | 0.4810 | 0.7692 |
| 0.4609 | 3.0 | 1230 | 0.4527 | 0.7882 |
| 0.4422 | 4.0 | 1640 | 0.4639 | 0.7803 |
| 0.4256 | 5.0 | 2050 | 0.4744 | 0.7770 |
| 0.409 | 6.0 | 2460 | 0.4702 | 0.7809 |
| 0.392 | 7.0 | 2870 | 0.4352 | 0.8005 |
| 0.3772 | 8.0 | 3280 | 0.4429 | 0.7970 |
| 0.3608 | 9.0 | 3690 | 0.4630 | 0.7875 |
| 0.3459 | 10.0 | 4100 | 0.5137 | 0.7688 |
| 0.3354 | 11.0 | 4510 | 0.4836 | 0.7880 |
| 0.3213 | 12.0 | 4920 | 0.4981 | 0.7842 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
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