transformers safetensors bert text-classification generated_from_trainer dataset:glue base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-qnli-epochs-2-lr-5e-05
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: qnli
      split: train
      args: qnli
    metrics:
    - type: accuracy
      value: 0.94
      name: Accuracy
---

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# bert-base-uncased-qnli-epochs-2-lr-5e-05

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1786
- Accuracy: 0.94

## 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: 32
- eval_batch_size: 32
- seed: 28
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2991        | 1.0   | 3271 | 0.2114          | 0.93     |
| 0.158         | 2.0   | 6542 | 0.1786          | 0.94     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
