---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_sa_GLUE_Experiment_qqp_128
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7784071234232006
    - name: F1
      type: f1
      value: 0.6885884111369878
---

<!-- 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. -->

# mobilebert_sa_GLUE_Experiment_qqp_128

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4700
- Accuracy: 0.7784
- F1: 0.6886
- Combined Score: 0.7335

## 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: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5294        | 1.0   | 2843  | 0.5076          | 0.7512   | 0.6636 | 0.7074         |
| 0.4791        | 2.0   | 5686  | 0.4889          | 0.7613   | 0.6369 | 0.6991         |
| 0.4622        | 3.0   | 8529  | 0.4821          | 0.7657   | 0.6475 | 0.7066         |
| 0.4463        | 4.0   | 11372 | 0.4831          | 0.7694   | 0.6730 | 0.7212         |
| 0.4288        | 5.0   | 14215 | 0.4724          | 0.7752   | 0.6784 | 0.7268         |
| 0.4129        | 6.0   | 17058 | 0.4806          | 0.7749   | 0.6893 | 0.7321         |
| 0.3969        | 7.0   | 19901 | 0.4700          | 0.7784   | 0.6886 | 0.7335         |
| 0.3813        | 8.0   | 22744 | 0.4802          | 0.7790   | 0.6962 | 0.7376         |
| 0.3664        | 9.0   | 25587 | 0.4765          | 0.7805   | 0.6952 | 0.7378         |
| 0.352         | 10.0  | 28430 | 0.4965          | 0.7768   | 0.7086 | 0.7427         |
| 0.3381        | 11.0  | 31273 | 0.4895          | 0.7845   | 0.6960 | 0.7403         |
| 0.3258        | 12.0  | 34116 | 0.5092          | 0.7844   | 0.7043 | 0.7444         |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2