---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_mnli_96
  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: 0.5431244914564687
---

<!-- 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_mnli_96

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5438
- Accuracy: 0.5431

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6023        | 1.0   | 1534  | 0.5718          | 0.4960   |
| 0.5673        | 2.0   | 3068  | 0.5547          | 0.5184   |
| 0.5555        | 3.0   | 4602  | 0.5505          | 0.5278   |
| 0.5481        | 4.0   | 6136  | 0.5466          | 0.5381   |
| 0.5426        | 5.0   | 7670  | 0.5454          | 0.5403   |
| 0.5382        | 6.0   | 9204  | 0.5454          | 0.5354   |
| 0.5341        | 7.0   | 10738 | 0.5452          | 0.5344   |
| 0.5308        | 8.0   | 12272 | 0.5428          | 0.5410   |
| 0.5271        | 9.0   | 13806 | 0.5460          | 0.5451   |
| 0.5239        | 10.0  | 15340 | 0.5450          | 0.5462   |
| 0.5209        | 11.0  | 16874 | 0.5447          | 0.5449   |
| 0.5179        | 12.0  | 18408 | 0.5452          | 0.5475   |
| 0.5152        | 13.0  | 19942 | 0.5495          | 0.5454   |


### Framework versions

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