---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_mnli_256
  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: 0.6119812855980472
---

<!-- 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_256

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: 1.2282
- Accuracy: 0.6120

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6433        | 1.0   | 3068  | 1.4078          | 0.5457   |
| 1.4683        | 2.0   | 6136  | 1.3590          | 0.5658   |
| 1.4077        | 3.0   | 9204  | 1.3106          | 0.5772   |
| 1.3591        | 4.0   | 12272 | 1.2971          | 0.5904   |
| 1.3213        | 5.0   | 15340 | 1.2764          | 0.5957   |
| 1.2849        | 6.0   | 18408 | 1.2562          | 0.6029   |
| 1.2475        | 7.0   | 21476 | 1.2524          | 0.6038   |
| 1.2073        | 8.0   | 24544 | 1.2384          | 0.6066   |
| 1.1713        | 9.0   | 27612 | 1.2377          | 0.6109   |
| 1.1371        | 10.0  | 30680 | 1.2228          | 0.6077   |
| 1.1069        | 11.0  | 33748 | 1.2126          | 0.6196   |
| 1.0775        | 12.0  | 36816 | 1.2232          | 0.6271   |
| 1.0491        | 13.0  | 39884 | 1.2440          | 0.6110   |
| 1.0228        | 14.0  | 42952 | 1.2741          | 0.6079   |
| 0.9977        | 15.0  | 46020 | 1.2448          | 0.6158   |
| 0.974         | 16.0  | 49088 | 1.3261          | 0.6206   |


### Framework versions

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