transformers tensorboard safetensors bert text-classification generated_from_trainer en dataset:glue base_model:google/bert_uncased_L-4_H-128_A-2 base_model:finetune:google/bert_uncased_L-4_H-128_A-2 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/bert_uncased_L-4_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-4_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: 0.8694286420974524
      name: Accuracy
    - type: f1
      value: 0.8237689868135536
      name: F1
---

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# bert_uncased_L-4_H-128_A-2_qqp

This model is a fine-tuned version of [google/bert_uncased_L-4_H-128_A-2](https://huggingface.co/google/bert_uncased_L-4_H-128_A-2) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3084
- Accuracy: 0.8694
- F1: 0.8238
- Combined Score: 0.8466

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.448         | 1.0   | 1422  | 0.3785          | 0.8204   | 0.7736 | 0.7970         |
| 0.3757        | 2.0   | 2844  | 0.3562          | 0.8351   | 0.7980 | 0.8165         |
| 0.3453        | 3.0   | 4266  | 0.3263          | 0.8521   | 0.8052 | 0.8286         |
| 0.3226        | 4.0   | 5688  | 0.3198          | 0.8560   | 0.8090 | 0.8325         |
| 0.3049        | 5.0   | 7110  | 0.3245          | 0.8552   | 0.8159 | 0.8355         |
| 0.2903        | 6.0   | 8532  | 0.3136          | 0.8602   | 0.8207 | 0.8405         |
| 0.277         | 7.0   | 9954  | 0.3188          | 0.8603   | 0.8200 | 0.8401         |
| 0.2667        | 8.0   | 11376 | 0.3146          | 0.8648   | 0.8258 | 0.8453         |
| 0.2551        | 9.0   | 12798 | 0.3239          | 0.8612   | 0.8240 | 0.8426         |
| 0.2444        | 10.0  | 14220 | 0.3084          | 0.8694   | 0.8238 | 0.8466         |
| 0.2358        | 11.0  | 15642 | 0.3224          | 0.8669   | 0.8296 | 0.8483         |
| 0.2276        | 12.0  | 17064 | 0.3147          | 0.8699   | 0.8313 | 0.8506         |
| 0.2191        | 13.0  | 18486 | 0.3185          | 0.8708   | 0.8332 | 0.8520         |
| 0.2111        | 14.0  | 19908 | 0.3282          | 0.8721   | 0.8337 | 0.8529         |
| 0.2048        | 15.0  | 21330 | 0.3194          | 0.8738   | 0.8306 | 0.8522         |


### Framework versions

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