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_mrpc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MRPC
      type: glue
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.7475490196078431
      name: Accuracy
    - type: f1
      value: 0.835725677830941
      name: F1
---

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# bert_uncased_L-2_H-256_A-4_mrpc

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 MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5344
- Accuracy: 0.7475
- F1: 0.8357
- Combined Score: 0.7916

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.619         | 1.0   | 15   | 0.5956          | 0.6887   | 0.8146 | 0.7517         |
| 0.5893        | 2.0   | 30   | 0.5835          | 0.7010   | 0.8179 | 0.7594         |
| 0.5612        | 3.0   | 45   | 0.5597          | 0.7059   | 0.8171 | 0.7615         |
| 0.5397        | 4.0   | 60   | 0.5398          | 0.7377   | 0.8320 | 0.7849         |
| 0.5063        | 5.0   | 75   | 0.5358          | 0.7426   | 0.8336 | 0.7881         |
| 0.476         | 6.0   | 90   | 0.5344          | 0.7475   | 0.8357 | 0.7916         |
| 0.4361        | 7.0   | 105  | 0.5515          | 0.7451   | 0.8349 | 0.7900         |
| 0.4014        | 8.0   | 120  | 0.5508          | 0.75     | 0.8365 | 0.7933         |
| 0.3684        | 9.0   | 135  | 0.5901          | 0.7304   | 0.8254 | 0.7779         |
| 0.3396        | 10.0  | 150  | 0.5755          | 0.7426   | 0.8276 | 0.7851         |
| 0.3061        | 11.0  | 165  | 0.5943          | 0.75     | 0.8317 | 0.7908         |


### Framework versions

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