---
library_name: transformers
language:
- en
license: apache-2.0
base_model: google/bert_uncased_L-2_H-128_A-2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_uncased_L-2_H-128_A-2_mrpc
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MRPC
      type: glue
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7328431372549019
    - name: F1
      type: f1
      value: 0.8233387358184764
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert_uncased_L-2_H-128_A-2_mrpc

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 MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5352
- Accuracy: 0.7328
- F1: 0.8233
- Combined Score: 0.7781

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6493        | 1.0   | 15   | 0.6227          | 0.6838   | 0.8122 | 0.7480         |
| 0.6257        | 2.0   | 30   | 0.6134          | 0.6838   | 0.8122 | 0.7480         |
| 0.6126        | 3.0   | 45   | 0.6052          | 0.6838   | 0.8122 | 0.7480         |
| 0.6036        | 4.0   | 60   | 0.5954          | 0.6961   | 0.8176 | 0.7569         |
| 0.5897        | 5.0   | 75   | 0.5879          | 0.6985   | 0.8167 | 0.7576         |
| 0.5781        | 6.0   | 90   | 0.5741          | 0.7034   | 0.8158 | 0.7596         |
| 0.5635        | 7.0   | 105  | 0.5711          | 0.7108   | 0.8201 | 0.7655         |
| 0.5429        | 8.0   | 120  | 0.5674          | 0.7132   | 0.8208 | 0.7670         |
| 0.5228        | 9.0   | 135  | 0.5685          | 0.7206   | 0.8252 | 0.7729         |
| 0.5057        | 10.0  | 150  | 0.5497          | 0.7304   | 0.8281 | 0.7793         |
| 0.4856        | 11.0  | 165  | 0.5438          | 0.7377   | 0.8293 | 0.7835         |
| 0.4657        | 12.0  | 180  | 0.5352          | 0.7328   | 0.8233 | 0.7781         |
| 0.4447        | 13.0  | 195  | 0.5435          | 0.7402   | 0.8323 | 0.7862         |
| 0.4175        | 14.0  | 210  | 0.5562          | 0.7402   | 0.8328 | 0.7865         |
| 0.4039        | 15.0  | 225  | 0.5759          | 0.7426   | 0.8357 | 0.7892         |
| 0.3964        | 16.0  | 240  | 0.5610          | 0.7377   | 0.8299 | 0.7838         |
| 0.3735        | 17.0  | 255  | 0.5587          | 0.7377   | 0.8283 | 0.7830         |


### Framework versions

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