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_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: 0.7976007914914668
      name: Accuracy
    - type: f1
      value: 0.7297109826589595
      name: F1
---

<!-- 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_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: 0.4349
- Accuracy: 0.7976
- F1: 0.7297
- Combined Score: 0.7637

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.526         | 1.0   | 2843  | 0.5088          | 0.7492   | 0.6674 | 0.7083         |
| 0.4762        | 2.0   | 5686  | 0.4782          | 0.7695   | 0.6583 | 0.7139         |
| 0.4438        | 3.0   | 8529  | 0.4532          | 0.7847   | 0.6829 | 0.7338         |
| 0.4161        | 4.0   | 11372 | 0.4602          | 0.7869   | 0.7135 | 0.7502         |
| 0.3968        | 5.0   | 14215 | 0.4395          | 0.7955   | 0.7212 | 0.7583         |
| 0.3815        | 6.0   | 17058 | 0.4392          | 0.7985   | 0.7190 | 0.7587         |
| 0.3659        | 7.0   | 19901 | 0.4349          | 0.7976   | 0.7297 | 0.7637         |
| 0.352         | 8.0   | 22744 | 0.4419          | 0.8005   | 0.7300 | 0.7652         |
| 0.3399        | 9.0   | 25587 | 0.4454          | 0.7998   | 0.7317 | 0.7658         |
| 0.327         | 10.0  | 28430 | 0.4614          | 0.7995   | 0.7359 | 0.7677         |
| 0.3157        | 11.0  | 31273 | 0.4733          | 0.8000   | 0.7246 | 0.7623         |
| 0.3041        | 12.0  | 34116 | 0.4738          | 0.8041   | 0.7283 | 0.7662         |


### Framework versions

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