---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_mnli_128
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE MNLI
      type: glue
      config: mnli
      split: validation_matched
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5949959316517494
---

<!-- 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_mnli_128

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2689
- Accuracy: 0.5950

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6825        | 1.0   | 3068  | 1.4581          | 0.5256   |
| 1.4941        | 2.0   | 6136  | 1.3516          | 0.5680   |
| 1.4199        | 3.0   | 9204  | 1.3259          | 0.5712   |
| 1.3747        | 4.0   | 12272 | 1.3024          | 0.5856   |
| 1.34          | 5.0   | 15340 | 1.2875          | 0.5931   |
| 1.3087        | 6.0   | 18408 | 1.2730          | 0.5928   |
| 1.2769        | 7.0   | 21476 | 1.2845          | 0.5916   |
| 1.246         | 8.0   | 24544 | 1.2750          | 0.5965   |
| 1.2166        | 9.0   | 27612 | 1.2651          | 0.6020   |
| 1.1883        | 10.0  | 30680 | 1.2773          | 0.6043   |
| 1.1604        | 11.0  | 33748 | 1.2555          | 0.6011   |
| 1.1329        | 12.0  | 36816 | 1.2792          | 0.5991   |
| 1.1074        | 13.0  | 39884 | 1.2891          | 0.5986   |
| 1.0812        | 14.0  | 42952 | 1.2889          | 0.5947   |
| 1.0577        | 15.0  | 46020 | 1.2871          | 0.5970   |
| 1.0338        | 16.0  | 49088 | 1.3296          | 0.6026   |


### Framework versions

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