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: hBERTv2_new_pretrain_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: 0.8042542666336878
      name: Accuracy
    - type: f1
      value: 0.7431353456669914
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hBERTv2_new_pretrain_w_init__qqp

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_wt_init](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_wt_init) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4228
- Accuracy: 0.8043
- F1: 0.7431
- Combined Score: 0.7737

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6243        | 1.0   | 2843  | 0.5630          | 0.7026   | 0.6300 | 0.6663         |
| 0.5301        | 2.0   | 5686  | 0.5110          | 0.7516   | 0.6346 | 0.6931         |
| 0.4804        | 3.0   | 8529  | 0.4928          | 0.7635   | 0.6780 | 0.7208         |
| 0.4419        | 4.0   | 11372 | 0.4610          | 0.7756   | 0.7173 | 0.7465         |
| 0.4105        | 5.0   | 14215 | 0.4441          | 0.7889   | 0.7347 | 0.7618         |
| 0.3819        | 6.0   | 17058 | 0.4336          | 0.8018   | 0.7207 | 0.7613         |
| 0.3534        | 7.0   | 19901 | 0.4228          | 0.8043   | 0.7431 | 0.7737         |
| 0.33          | 8.0   | 22744 | 0.4429          | 0.8062   | 0.7445 | 0.7754         |
| 0.3098        | 9.0   | 25587 | 0.4296          | 0.8104   | 0.7511 | 0.7807         |
| 0.2912        | 10.0  | 28430 | 0.4386          | 0.8086   | 0.7554 | 0.7820         |
| 0.275         | 11.0  | 31273 | 0.4551          | 0.8143   | 0.7575 | 0.7859         |
| 0.2575        | 12.0  | 34116 | 0.4742          | 0.8160   | 0.7491 | 0.7825         |


### Framework versions

- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
