transformers tensorboard safetensors bert text-classification generated_from_trainer en dataset:glue base_model:google/bert_uncased_L-2_H-256_A-4 base_model:finetune:google/bert_uncased_L-2_H-256_A-4 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/bert_uncased_L-2_H-256_A-4
datasets:
- glue
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: bert_uncased_L-2_H-256_A-4_qqp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - type: accuracy
      value: 0.8701459312391788
      name: Accuracy
    - type: f1
      value: 0.8276654411764706
      name: F1
---

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# bert_uncased_L-2_H-256_A-4_qqp

This model is a fine-tuned version of [google/bert_uncased_L-2_H-256_A-4](https://huggingface.co/google/bert_uncased_L-2_H-256_A-4) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3042
- Accuracy: 0.8701
- F1: 0.8277
- Combined Score: 0.8489

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4268        | 1.0   | 1422  | 0.3553          | 0.8352   | 0.7840 | 0.8096         |
| 0.3528        | 2.0   | 2844  | 0.3246          | 0.8531   | 0.8069 | 0.8300         |
| 0.3156        | 3.0   | 4266  | 0.3112          | 0.8611   | 0.8092 | 0.8351         |
| 0.2884        | 4.0   | 5688  | 0.3069          | 0.8655   | 0.8149 | 0.8402         |
| 0.2662        | 5.0   | 7110  | 0.3080          | 0.8672   | 0.8274 | 0.8473         |
| 0.2467        | 6.0   | 8532  | 0.3042          | 0.8701   | 0.8277 | 0.8489         |
| 0.2303        | 7.0   | 9954  | 0.3089          | 0.8699   | 0.8281 | 0.8490         |
| 0.2152        | 8.0   | 11376 | 0.3130          | 0.8730   | 0.8301 | 0.8515         |
| 0.2026        | 9.0   | 12798 | 0.3187          | 0.8737   | 0.8309 | 0.8523         |
| 0.19          | 10.0  | 14220 | 0.3232          | 0.8766   | 0.8333 | 0.8549         |
| 0.1788        | 11.0  | 15642 | 0.3374          | 0.8754   | 0.8348 | 0.8551         |


### Framework versions

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