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---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv1_data_aug_qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.8161513727430126
- name: F1
type: f1
value: 0.7679145720798077
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hBERTv1_data_aug_qqp
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1](https://huggingface.co/gokuls/bert_12_layer_model_v1) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5769
- Accuracy: 0.8162
- F1: 0.7679
- Combined Score: 0.7920
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
| 0.2419 | 1.0 | 29671 | 0.5769 | 0.8162 | 0.7679 | 0.7920 |
| 0.104 | 2.0 | 59342 | 0.6327 | 0.8272 | 0.7769 | 0.8020 |
| 0.0911 | 3.0 | 89013 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 4.0 | 118684 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 5.0 | 148355 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 6.0 | 178026 | nan | 0.6318 | 0.0 | 0.3159 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
|