transformers tensorboard safetensors bert text-classification generated_from_trainer en 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: google-bert/bert-base-uncased
datasets:
- glue
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased_qqp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - type: accuracy
      value: 0.9067029433588919
      name: Accuracy
    - type: f1
      value: 0.8779603986023037
      name: F1
---

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# bert-base-uncased_qqp

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2326
- Accuracy: 0.9067
- F1: 0.8780
- Combined Score: 0.8923

## 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 | F1     | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.2952        | 1.0   | 1422 | 0.2378          | 0.9007   | 0.8679 | 0.8843         |
| 0.1864        | 2.0   | 2844 | 0.2326          | 0.9067   | 0.8780 | 0.8923         |
| 0.1218        | 3.0   | 4266 | 0.2508          | 0.9104   | 0.8788 | 0.8946         |
| 0.0847        | 4.0   | 5688 | 0.2921          | 0.9084   | 0.8746 | 0.8915         |
| 0.0629        | 5.0   | 7110 | 0.3359          | 0.9123   | 0.8830 | 0.8977         |
| 0.0489        | 6.0   | 8532 | 0.3681          | 0.9076   | 0.8728 | 0.8902         |
| 0.0401        | 7.0   | 9954 | 0.3745          | 0.9107   | 0.8815 | 0.8961         |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
