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---
library_name: transformers
language:
- en
license: apache-2.0
base_model: google/bert_uncased_L-2_H-256_A-4
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_uncased_L-2_H-256_A-4_wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5211267605633803
---
<!-- 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-256_A-4_wnli
This model is a fine-tuned version of [google/bert_uncased_L-2_H-256_A-4](https://huggingface.co/google/bert_uncased_L-2_H-256_A-4) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6966
- Accuracy: 0.5211
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7188 | 1.0 | 3 | 0.7157 | 0.4085 |
| 0.6947 | 2.0 | 6 | 0.6966 | 0.5211 |
| 0.693 | 3.0 | 9 | 0.6977 | 0.5352 |
| 0.699 | 4.0 | 12 | 0.7026 | 0.5493 |
| 0.6941 | 5.0 | 15 | 0.7084 | 0.3944 |
| 0.6908 | 6.0 | 18 | 0.7167 | 0.3380 |
| 0.6915 | 7.0 | 21 | 0.7230 | 0.3239 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
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