transformers pytorch tensorboard distilbert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert_add_GLUE_Experiment_logit_kd_pretrain_mnli
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MNLI
      type: glue
      config: mnli
      split: validation_matched
      args: mnli
    metrics:
    - type: accuracy
      value: 0.6842961757526445
      name: Accuracy
---

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# distilbert_add_GLUE_Experiment_logit_kd_pretrain_mnli

This model is a fine-tuned version of [gokuls/distilbert_add_pre-training-complete](https://huggingface.co/gokuls/distilbert_add_pre-training-complete) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4768
- Accuracy: 0.6843

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5762        | 1.0   | 1534  | 0.5291          | 0.5904   |
| 0.5131        | 2.0   | 3068  | 0.4986          | 0.6470   |
| 0.4806        | 3.0   | 4602  | 0.4832          | 0.6713   |
| 0.4563        | 4.0   | 6136  | 0.4803          | 0.6770   |
| 0.4352        | 5.0   | 7670  | 0.4790          | 0.6832   |
| 0.4157        | 6.0   | 9204  | 0.4866          | 0.6809   |
| 0.3984        | 7.0   | 10738 | 0.4938          | 0.6836   |
| 0.3835        | 8.0   | 12272 | 0.4940          | 0.6841   |
| 0.3703        | 9.0   | 13806 | 0.4972          | 0.6823   |
| 0.3592        | 10.0  | 15340 | 0.4992          | 0.6854   |


### Framework versions

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