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_add_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: 0.756987385604749
      name: Accuracy
    - type: f1
      value: 0.604929832321364
      name: F1
---

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# mobilebert_add_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: 0.8079
- Accuracy: 0.7570
- F1: 0.6049
- Combined Score: 0.6810

## 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 |
|:--------------------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 1.2837                     | 1.0   | 2843  | 1.2201          | 0.6318   | 0.0    | 0.3159         |
| 1.076                      | 2.0   | 5686  | 0.8477          | 0.7443   | 0.5855 | 0.6649         |
| 0.866                      | 3.0   | 8529  | 0.8217          | 0.7518   | 0.5924 | 0.6721         |
| 0.8317                     | 4.0   | 11372 | 0.8136          | 0.7565   | 0.6243 | 0.6904         |
| 0.8122                     | 5.0   | 14215 | 0.8126          | 0.7588   | 0.6352 | 0.6970         |
| 0.799                      | 6.0   | 17058 | 0.8079          | 0.7570   | 0.6049 | 0.6810         |
| 386581134871678353408.0000 | 7.0   | 19901 | nan             | 0.6318   | 0.0    | 0.3159         |
| 0.0                        | 8.0   | 22744 | nan             | 0.6318   | 0.0    | 0.3159         |
| 0.0                        | 9.0   | 25587 | nan             | 0.6318   | 0.0    | 0.3159         |
| 0.0                        | 10.0  | 28430 | nan             | 0.6318   | 0.0    | 0.3159         |
| 0.0                        | 11.0  | 31273 | nan             | 0.6318   | 0.0    | 0.3159         |


### Framework versions

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