---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_qnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QNLI
      type: glue
      config: qnli
      split: validation
      args: qnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8984074684239429
---

<!-- 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_pretrain_qnli

This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2158
- Accuracy: 0.8984

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1418        | 1.0   | 819   | 1.0623          | 0.5054   |
| 1.1397        | 2.0   | 1638  | 1.0617          | 0.5054   |
| 1.1439        | 3.0   | 2457  | 1.0634          | 0.5054   |
| 1.1397        | 4.0   | 3276  | 1.0635          | 0.5054   |
| 1.14          | 5.0   | 4095  | 1.0643          | 0.5054   |
| 1.1399        | 6.0   | 4914  | 1.0611          | 0.5054   |
| 1.14          | 7.0   | 5733  | 1.0625          | 0.5054   |
| 1.0013        | 8.0   | 6552  | 0.3801          | 0.8420   |
| 0.3353        | 9.0   | 7371  | 0.2163          | 0.9030   |
| 0.2165        | 10.0  | 8190  | 0.2158          | 0.8984   |
| 0.1593        | 11.0  | 9009  | 0.2205          | 0.9057   |
| 0.126         | 12.0  | 9828  | 0.2291          | 0.9077   |
| 0.1049        | 13.0  | 10647 | 0.2323          | 0.9072   |
| 0.0903        | 14.0  | 11466 | 0.2676          | 0.8984   |
| 0.0819        | 15.0  | 12285 | 0.2377          | 0.9006   |


### Framework versions

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