---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_mrpc_128
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MRPC
      type: glue
      config: mrpc
      split: validation
      args: mrpc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6740196078431373
    - name: F1
      type: f1
      value: 0.7787021630615641
---

<!-- 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_mrpc_128

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5213
- Accuracy: 0.6740
- F1: 0.7787
- Combined Score: 0.7264

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6368        | 1.0   | 29   | 0.5564          | 0.6838   | 0.8122 | 0.7480         |
| 0.6099        | 2.0   | 58   | 0.5557          | 0.6838   | 0.8122 | 0.7480         |
| 0.611         | 3.0   | 87   | 0.5555          | 0.6838   | 0.8122 | 0.7480         |
| 0.6101        | 4.0   | 116  | 0.5568          | 0.6838   | 0.8122 | 0.7480         |
| 0.608         | 5.0   | 145  | 0.5540          | 0.6838   | 0.8122 | 0.7480         |
| 0.6037        | 6.0   | 174  | 0.5492          | 0.6838   | 0.8122 | 0.7480         |
| 0.5761        | 7.0   | 203  | 0.6065          | 0.6103   | 0.6851 | 0.6477         |
| 0.4782        | 8.0   | 232  | 0.5341          | 0.6863   | 0.7801 | 0.7332         |
| 0.4111        | 9.0   | 261  | 0.5213          | 0.6740   | 0.7787 | 0.7264         |
| 0.3526        | 10.0  | 290  | 0.5792          | 0.6863   | 0.7867 | 0.7365         |
| 0.3188        | 11.0  | 319  | 0.5760          | 0.6936   | 0.7764 | 0.7350         |
| 0.2918        | 12.0  | 348  | 0.6406          | 0.6912   | 0.7879 | 0.7395         |
| 0.2568        | 13.0  | 377  | 0.5908          | 0.6765   | 0.7537 | 0.7151         |
| 0.2472        | 14.0  | 406  | 0.5966          | 0.6863   | 0.7664 | 0.7263         |


### Framework versions

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