transformers tensorboard safetensors bert text-classification generated_from_trainer en dataset:glue base_model:google/bert_uncased_L-2_H-128_A-2 base_model:finetune:google/bert_uncased_L-2_H-128_A-2 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/bert_uncased_L-2_H-128_A-2
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-128_A-2_qqp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - type: accuracy
      value: 0.8481573089290131
      name: Accuracy
    - type: f1
      value: 0.8112876948141773
      name: F1
---

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# bert_uncased_L-2_H-128_A-2_qqp

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3371
- Accuracy: 0.8482
- F1: 0.8113
- Combined Score: 0.8297

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4684        | 1.0   | 1422  | 0.4047          | 0.8017   | 0.7601 | 0.7809         |
| 0.3983        | 2.0   | 2844  | 0.3846          | 0.8136   | 0.7782 | 0.7959         |
| 0.371         | 3.0   | 4266  | 0.3736          | 0.8217   | 0.7890 | 0.8054         |
| 0.3511        | 4.0   | 5688  | 0.3561          | 0.8330   | 0.7976 | 0.8153         |
| 0.3338        | 5.0   | 7110  | 0.3568          | 0.8332   | 0.7990 | 0.8161         |
| 0.3199        | 6.0   | 8532  | 0.3526          | 0.8369   | 0.8028 | 0.8198         |
| 0.3067        | 7.0   | 9954  | 0.3513          | 0.8392   | 0.8046 | 0.8219         |
| 0.296         | 8.0   | 11376 | 0.3567          | 0.8361   | 0.8044 | 0.8203         |
| 0.2857        | 9.0   | 12798 | 0.3518          | 0.8407   | 0.8071 | 0.8239         |
| 0.2776        | 10.0  | 14220 | 0.3371          | 0.8482   | 0.8113 | 0.8297         |
| 0.2679        | 11.0  | 15642 | 0.3506          | 0.8446   | 0.8105 | 0.8276         |
| 0.2609        | 12.0  | 17064 | 0.3419          | 0.8511   | 0.8145 | 0.8328         |
| 0.2539        | 13.0  | 18486 | 0.3397          | 0.8524   | 0.8164 | 0.8344         |
| 0.2461        | 14.0  | 19908 | 0.3523          | 0.8525   | 0.8164 | 0.8344         |
| 0.2401        | 15.0  | 21330 | 0.3407          | 0.8546   | 0.8181 | 0.8363         |


### Framework versions

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