---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-qqp-epochs-2-lr-0.0001
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      config: qqp
      split: train
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.95
    - name: F1
      type: f1
      value: 0.9333333333333333
---

<!-- 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-base-uncased-qqp-epochs-2-lr-0.0001

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: 0.1358
- Accuracy: 0.95
- F1: 0.9333

## 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: 0.0001
- 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 | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.2907        | 1.0   | 11368 | 0.1955          | 0.95     | 0.9315 |
| 0.191         | 2.0   | 22736 | 0.1358          | 0.95     | 0.9333 |


### Framework versions

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