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
- f1
tags:
- generated_from_trainer
model-index:
- name: sa_BERT_48_qqp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.8510017313875835
      name: Accuracy
    - type: f1
      value: 0.799640790261425
      name: F1
---

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# sa_BERT_48_qqp

This model is a fine-tuned version of [gokuls/bert_base_48](https://huggingface.co/gokuls/bert_base_48) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3425
- Accuracy: 0.8510
- F1: 0.7996
- Combined Score: 0.8253

## 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 | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4679        | 1.0   | 3791  | 0.3795          | 0.8222   | 0.7705 | 0.7964         |
| 0.3469        | 2.0   | 7582  | 0.3580          | 0.8447   | 0.7963 | 0.8205         |
| 0.2868        | 3.0   | 11373 | 0.3425          | 0.8510   | 0.7996 | 0.8253         |
| 0.2372        | 4.0   | 15164 | 0.3706          | 0.8561   | 0.8149 | 0.8355         |
| 0.1938        | 5.0   | 18955 | 0.3679          | 0.8625   | 0.8197 | 0.8411         |
| 0.1567        | 6.0   | 22746 | 0.4246          | 0.8639   | 0.8214 | 0.8427         |
| 0.1294        | 7.0   | 26537 | 0.4047          | 0.8585   | 0.8189 | 0.8387         |
| 0.1059        | 8.0   | 30328 | 0.5063          | 0.8579   | 0.8181 | 0.8380         |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
