transformers pytorch tensorboard hybridbert text-classification generated_from_trainer en dataset:glue model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: hBERTv1_new_pretrain_48_KD_w_init_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: 0.6864457086322038
      name: Accuracy
    - type: f1
      value: 0.466298993811308
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hBERTv1_new_pretrain_48_KD_w_init_qqp

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48_KD_wt_init](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48_KD_wt_init) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5997
- Accuracy: 0.6864
- F1: 0.4663
- Combined Score: 0.5764

## 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: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6301        | 1.0   | 2843  | 0.6185          | 0.6688   | 0.5075 | 0.5882         |
| 0.6177        | 2.0   | 5686  | 0.6192          | 0.6743   | 0.4793 | 0.5768         |
| 0.6047        | 3.0   | 8529  | 0.6037          | 0.6940   | 0.4407 | 0.5673         |
| 0.592         | 4.0   | 11372 | 0.5997          | 0.6864   | 0.4663 | 0.5764         |
| 0.6069        | 5.0   | 14215 | 0.6063          | 0.6810   | 0.5518 | 0.6164         |
| 0.6329        | 6.0   | 17058 | 0.6343          | 0.6429   | 0.1741 | 0.4085         |
| 0.6313        | 7.0   | 19901 | 0.6581          | 0.6318   | 0.0    | 0.3159         |
| 0.6573        | 8.0   | 22744 | 0.6603          | 0.6318   | 0.0    | 0.3159         |
| 0.6377        | 9.0   | 25587 | 0.6309          | 0.6318   | 0.0    | 0.3159         |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
