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_emb_com_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: 0.789463269849122
      name: Accuracy
    - type: f1
      value: 0.7288135593220338
      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_emb_com_qqp

This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_emb_compress_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_emb_compress_48) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4383
- Accuracy: 0.7895
- F1: 0.7288
- Combined Score: 0.7591

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5492        | 1.0   | 2843  | 0.5130          | 0.7537   | 0.6393 | 0.6965         |
| 0.4928        | 2.0   | 5686  | 0.4971          | 0.7602   | 0.6526 | 0.7064         |
| 0.4578        | 3.0   | 8529  | 0.4656          | 0.7775   | 0.6825 | 0.7300         |
| 0.4346        | 4.0   | 11372 | 0.4565          | 0.7804   | 0.6744 | 0.7274         |
| 0.4146        | 5.0   | 14215 | 0.4783          | 0.7812   | 0.7078 | 0.7445         |
| 0.3952        | 6.0   | 17058 | 0.4675          | 0.7899   | 0.7042 | 0.7470         |
| 0.3747        | 7.0   | 19901 | 0.4383          | 0.7895   | 0.7288 | 0.7591         |
| 0.355         | 8.0   | 22744 | 0.4455          | 0.7948   | 0.7053 | 0.7500         |
| 0.3362        | 9.0   | 25587 | 0.4483          | 0.7935   | 0.7334 | 0.7635         |
| 0.3185        | 10.0  | 28430 | 0.4480          | 0.7956   | 0.7388 | 0.7672         |
| 0.301         | 11.0  | 31273 | 0.4630          | 0.8055   | 0.7236 | 0.7646         |
| 0.2848        | 12.0  | 34116 | 0.4850          | 0.8062   | 0.7352 | 0.7707         |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
