---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_add_GLUE_Experiment_logit_kd_sst2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE SST2
      type: glue
      config: sst2
      split: validation
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.801605504587156
---

<!-- 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_add_GLUE_Experiment_logit_kd_sst2

This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7778
- Accuracy: 0.8016

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5405        | 1.0   | 527  | 1.4225          | 0.5539   |
| 1.3567        | 2.0   | 1054 | 1.4707          | 0.5482   |
| 1.2859        | 3.0   | 1581 | 1.4661          | 0.5677   |
| 1.2563        | 4.0   | 2108 | 1.4136          | 0.5665   |
| 1.2414        | 5.0   | 2635 | 1.4239          | 0.5940   |
| 1.2288        | 6.0   | 3162 | 1.4443          | 0.5745   |
| 0.7679        | 7.0   | 3689 | 0.7870          | 0.7878   |
| 0.4135        | 8.0   | 4216 | 0.7778          | 0.8016   |
| 0.3376        | 9.0   | 4743 | 0.8673          | 0.7993   |
| 0.2972        | 10.0  | 5270 | 0.8790          | 0.7901   |
| 0.2734        | 11.0  | 5797 | 0.9525          | 0.7913   |
| 0.2569        | 12.0  | 6324 | 0.9557          | 0.7936   |
| 0.2431        | 13.0  | 6851 | 0.9595          | 0.7878   |


### Framework versions

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