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_qnli
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QNLI
      type: glue
      config: qnli
      split: validation
      args: qnli
    metrics:
    - type: accuracy
      value: 0.6522057477576423
      name: Accuracy
---

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# distilbert_add_GLUE_Experiment_logit_kd_pretrain_qnli

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 QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3579
- Accuracy: 0.6522

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4059        | 1.0   | 410  | 0.4016          | 0.5585   |
| 0.3907        | 2.0   | 820  | 0.3735          | 0.6094   |
| 0.3715        | 3.0   | 1230 | 0.3602          | 0.6480   |
| 0.352         | 4.0   | 1640 | 0.3579          | 0.6522   |
| 0.3314        | 5.0   | 2050 | 0.3626          | 0.6670   |
| 0.309         | 6.0   | 2460 | 0.3650          | 0.6776   |
| 0.2865        | 7.0   | 2870 | 0.3799          | 0.6776   |
| 0.2679        | 8.0   | 3280 | 0.3817          | 0.6903   |
| 0.2525        | 9.0   | 3690 | 0.3942          | 0.6822   |


### Framework versions

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