---
library_name: transformers
language:
- en
license: apache-2.0
base_model: google/bert_uncased_L-4_H-128_A-2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_uncased_L-4_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: 0.7573529411764706
    - name: F1
      type: f1
      value: 0.840064620355412
---

<!-- 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-4_H-128_A-2_mrpc

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 MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5327
- Accuracy: 0.7574
- F1: 0.8401
- Combined Score: 0.7987

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6437        | 1.0   | 15   | 0.6181          | 0.6838   | 0.8122 | 0.7480         |
| 0.6197        | 2.0   | 30   | 0.6047          | 0.6912   | 0.8158 | 0.7535         |
| 0.595         | 3.0   | 45   | 0.5877          | 0.6985   | 0.8161 | 0.7573         |
| 0.582         | 4.0   | 60   | 0.5687          | 0.7279   | 0.8284 | 0.7782         |
| 0.5617        | 5.0   | 75   | 0.5594          | 0.7279   | 0.8295 | 0.7787         |
| 0.5409        | 6.0   | 90   | 0.5550          | 0.7132   | 0.8208 | 0.7670         |
| 0.5213        | 7.0   | 105  | 0.5417          | 0.7255   | 0.8245 | 0.7750         |
| 0.4968        | 8.0   | 120  | 0.5530          | 0.7328   | 0.8310 | 0.7819         |
| 0.4741        | 9.0   | 135  | 0.5580          | 0.7353   | 0.8333 | 0.7843         |
| 0.4545        | 10.0  | 150  | 0.5390          | 0.7549   | 0.8397 | 0.7973         |
| 0.4366        | 11.0  | 165  | 0.5327          | 0.7574   | 0.8401 | 0.7987         |
| 0.4206        | 12.0  | 180  | 0.5350          | 0.7598   | 0.8424 | 0.8011         |
| 0.397         | 13.0  | 195  | 0.5649          | 0.7549   | 0.8447 | 0.7998         |
| 0.3873        | 14.0  | 210  | 0.5602          | 0.7623   | 0.8482 | 0.8052         |
| 0.3725        | 15.0  | 225  | 0.5622          | 0.7525   | 0.8399 | 0.7962         |
| 0.3506        | 16.0  | 240  | 0.5588          | 0.7525   | 0.8374 | 0.7949         |


### Framework versions

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