transformers pytorch tensorboard bert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: sa_BERT_no_pretrain_qnli
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QNLI
      type: glue
      config: qnli
      split: validation
      args: qnli
    metrics:
    - type: accuracy
      value: 0.6058941973274757
      name: Accuracy
---

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# sa_BERT_no_pretrain_qnli

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6547
- Accuracy: 0.6059

## 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: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6847        | 1.0   | 1092 | 0.6580          | 0.6068   |
| 0.6491        | 2.0   | 2184 | 0.6547          | 0.6059   |
| 0.6223        | 3.0   | 3276 | 0.6778          | 0.6021   |
| 0.5814        | 4.0   | 4368 | 0.7237          | 0.5843   |
| 0.5176        | 5.0   | 5460 | 0.7387          | 0.5757   |
| 0.4447        | 6.0   | 6552 | 0.8224          | 0.5733   |
| 0.3761        | 7.0   | 7644 | 0.9915          | 0.5598   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
