---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_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: 0.7871877318822657
    - name: F1
      type: f1
      value: 0.7061676115019466
---

<!-- 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_logit_kd_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: 0.6884
- Accuracy: 0.7872
- F1: 0.7062
- Combined Score: 0.7467

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.9518        | 1.0   | 2843  | 0.8352          | 0.7536   | 0.6530 | 0.7033         |
| 0.8249        | 2.0   | 5686  | 0.7766          | 0.7607   | 0.6219 | 0.6913         |
| 0.7847        | 3.0   | 8529  | 0.7625          | 0.7648   | 0.6402 | 0.7025         |
| 0.7498        | 4.0   | 11372 | 0.7551          | 0.7638   | 0.6197 | 0.6917         |
| 0.7137        | 5.0   | 14215 | 0.7387          | 0.7691   | 0.6545 | 0.7118         |
| 0.6762        | 6.0   | 17058 | 0.7165          | 0.7753   | 0.6720 | 0.7237         |
| 0.6373        | 7.0   | 19901 | 0.7042          | 0.7783   | 0.6765 | 0.7274         |
| 0.6045        | 8.0   | 22744 | 0.7075          | 0.7799   | 0.6902 | 0.7350         |
| 0.5729        | 9.0   | 25587 | 0.7233          | 0.7792   | 0.6639 | 0.7215         |
| 0.545         | 10.0  | 28430 | 0.7088          | 0.7805   | 0.7180 | 0.7493         |
| 0.5183        | 11.0  | 31273 | 0.6884          | 0.7872   | 0.7062 | 0.7467         |
| 0.4948        | 12.0  | 34116 | 0.7064          | 0.7869   | 0.7076 | 0.7472         |
| 0.4724        | 13.0  | 36959 | 0.7053          | 0.7884   | 0.7120 | 0.7502         |
| 0.4514        | 14.0  | 39802 | 0.7314          | 0.7903   | 0.7024 | 0.7464         |
| 0.4321        | 15.0  | 42645 | 0.7112          | 0.7891   | 0.7228 | 0.7560         |
| 0.4152        | 16.0  | 45488 | 0.7410          | 0.7909   | 0.7211 | 0.7560         |


### Framework versions

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