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
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.8081127875340094
      name: Accuracy
    - type: f1
      value: 0.7357269382749694
      name: F1
---

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# mobilebert_sa_GLUE_Experiment_logit_kd_qqp

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: 0.6021
- Accuracy: 0.8081
- F1: 0.7357
- Combined Score: 0.7719

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.9285        | 1.0   | 2843  | 0.8489          | 0.7517   | 0.6660 | 0.7089         |
| 0.7919        | 2.0   | 5686  | 0.7104          | 0.7735   | 0.6483 | 0.7109         |
| 0.6916        | 3.0   | 8529  | 0.6615          | 0.7900   | 0.6994 | 0.7447         |
| 0.6382        | 4.0   | 11372 | 0.6606          | 0.7899   | 0.6842 | 0.7370         |
| 0.6003        | 5.0   | 14215 | 0.6277          | 0.7988   | 0.7181 | 0.7585         |
| 0.5696        | 6.0   | 17058 | 0.6174          | 0.7980   | 0.7058 | 0.7519         |
| 0.5434        | 7.0   | 19901 | 0.6062          | 0.8036   | 0.7266 | 0.7651         |
| 0.5186        | 8.0   | 22744 | 0.6182          | 0.7995   | 0.7232 | 0.7614         |
| 0.4968        | 9.0   | 25587 | 0.6087          | 0.8052   | 0.7257 | 0.7654         |
| 0.4758        | 10.0  | 28430 | 0.6035          | 0.8073   | 0.7359 | 0.7716         |
| 0.456         | 11.0  | 31273 | 0.6021          | 0.8081   | 0.7357 | 0.7719         |
| 0.4361        | 12.0  | 34116 | 0.6137          | 0.8070   | 0.7366 | 0.7718         |
| 0.4186        | 13.0  | 36959 | 0.6282          | 0.8076   | 0.7416 | 0.7746         |
| 0.4009        | 14.0  | 39802 | 0.6183          | 0.8093   | 0.7445 | 0.7769         |
| 0.3846        | 15.0  | 42645 | 0.6196          | 0.8057   | 0.7483 | 0.7770         |
| 0.3699        | 16.0  | 45488 | 0.6381          | 0.8122   | 0.7426 | 0.7774         |


### Framework versions

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