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
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert_add_GLUE_Experiment_logit_kd_pretrain_qqp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.6518426910709869
      name: Accuracy
    - type: f1
      value: 0.11181221605249873
      name: F1
---

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# distilbert_add_GLUE_Experiment_logit_kd_pretrain_qqp

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 QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6175
- Accuracy: 0.6518
- F1: 0.1118
- Combined Score: 0.3818

## 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 | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.7332        | 1.0   | 1422  | 0.6640          | 0.6318   | 0.0    | 0.3159         |
| 0.6295        | 2.0   | 2844  | 0.6362          | 0.6446   | 0.0739 | 0.3593         |
| 0.5849        | 3.0   | 4266  | 0.6227          | 0.6401   | 0.0478 | 0.3439         |
| 0.5561        | 4.0   | 5688  | 0.6212          | 0.6517   | 0.1097 | 0.3807         |
| 0.5343        | 5.0   | 7110  | 0.6175          | 0.6518   | 0.1118 | 0.3818         |
| 0.52          | 6.0   | 8532  | 0.6184          | 0.6487   | 0.0949 | 0.3718         |
| 0.5093        | 7.0   | 9954  | 0.6239          | 0.6588   | 0.1472 | 0.4030         |
| 0.501         | 8.0   | 11376 | 0.6235          | 0.6649   | 0.1783 | 0.4216         |
| 0.4947        | 9.0   | 12798 | 0.6211          | 0.6622   | 0.1632 | 0.4127         |
| 0.4895        | 10.0  | 14220 | 0.6256          | 0.6688   | 0.1970 | 0.4329         |


### Framework versions

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