---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_mnli
  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: 0.6172701383238405
---

<!-- 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

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: 1.1966
- Accuracy: 0.6173

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6232        | 1.0   | 3068  | 1.3870          | 0.5505   |
| 1.4341        | 2.0   | 6136  | 1.3186          | 0.5834   |
| 1.3724        | 3.0   | 9204  | 1.2819          | 0.5943   |
| 1.3249        | 4.0   | 12272 | 1.2702          | 0.5982   |
| 1.2788        | 5.0   | 15340 | 1.2359          | 0.6031   |
| 1.2302        | 6.0   | 18408 | 1.2008          | 0.6193   |
| 1.1842        | 7.0   | 21476 | 1.1991          | 0.6222   |
| 1.1441        | 8.0   | 24544 | 1.1839          | 0.6202   |
| 1.1057        | 9.0   | 27612 | 1.1861          | 0.6244   |
| 1.0715        | 10.0  | 30680 | 1.1755          | 0.6250   |
| 1.0386        | 11.0  | 33748 | 1.1972          | 0.6313   |
| 1.0066        | 12.0  | 36816 | 1.2149          | 0.6276   |
| 0.9767        | 13.0  | 39884 | 1.2187          | 0.6193   |
| 0.9482        | 14.0  | 42952 | 1.2004          | 0.6226   |
| 0.921         | 15.0  | 46020 | 1.2093          | 0.6194   |


### Framework versions

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