transformers pytorch tensorboard mobilebert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_qqp_256
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.794855305466238
      name: Accuracy
    - type: f1
      value: 0.7224044447419508
      name: F1
---

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# mobilebert_sa_GLUE_Experiment_logit_kd_qqp_256

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: 0.6619
- Accuracy: 0.7949
- F1: 0.7224
- Combined Score: 0.7586

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.9454        | 1.0   | 2843  | 0.8257          | 0.7556   | 0.6563 | 0.7059         |
| 0.8165        | 2.0   | 5686  | 0.7682          | 0.7590   | 0.6049 | 0.6820         |
| 0.7741        | 3.0   | 8529  | 0.7514          | 0.7638   | 0.6203 | 0.6920         |
| 0.7325        | 4.0   | 11372 | 0.7354          | 0.7675   | 0.6288 | 0.6981         |
| 0.6849        | 5.0   | 14215 | 0.7063          | 0.7785   | 0.6818 | 0.7302         |
| 0.6399        | 6.0   | 17058 | 0.6906          | 0.7828   | 0.6876 | 0.7352         |
| 0.6005        | 7.0   | 19901 | 0.6771          | 0.7868   | 0.6993 | 0.7430         |
| 0.5666        | 8.0   | 22744 | 0.6809          | 0.7897   | 0.7138 | 0.7517         |
| 0.5365        | 9.0   | 25587 | 0.6807          | 0.7886   | 0.6921 | 0.7403         |
| 0.5097        | 10.0  | 28430 | 0.6827          | 0.7873   | 0.7260 | 0.7566         |
| 0.4856        | 11.0  | 31273 | 0.6619          | 0.7949   | 0.7224 | 0.7586         |
| 0.4653        | 12.0  | 34116 | 0.7002          | 0.7948   | 0.7197 | 0.7572         |
| 0.4438        | 13.0  | 36959 | 0.6900          | 0.7965   | 0.7203 | 0.7584         |
| 0.4255        | 14.0  | 39802 | 0.6847          | 0.7981   | 0.7284 | 0.7632         |
| 0.4072        | 15.0  | 42645 | 0.6893          | 0.7917   | 0.7336 | 0.7627         |
| 0.391         | 16.0  | 45488 | 0.7136          | 0.7957   | 0.7300 | 0.7629         |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
