---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_pretrain_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: 0.8105166802278275
---

<!-- 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. -->

# distilbert_sa_GLUE_Experiment_logit_kd_pretrain_mnli

This model is a fine-tuned version of [gokuls/distilbert_sa_pre-training-complete](https://huggingface.co/gokuls/distilbert_sa_pre-training-complete) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3863
- Accuracy: 0.8105

## 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: 256
- eval_batch_size: 256
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4379        | 1.0   | 1534  | 0.3984          | 0.7976   |
| 0.3845        | 2.0   | 3068  | 0.3953          | 0.8047   |
| 0.359         | 3.0   | 4602  | 0.3935          | 0.8102   |
| 0.3411        | 4.0   | 6136  | 0.3962          | 0.8077   |
| 0.3279        | 5.0   | 7670  | 0.3959          | 0.8172   |
| 0.3189        | 6.0   | 9204  | 0.4018          | 0.8102   |
| 0.3119        | 7.0   | 10738 | 0.4040          | 0.8073   |
| 0.3071        | 8.0   | 12272 | 0.3990          | 0.8175   |


### Framework versions

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