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---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv1_qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
config: qqp
split: validation
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.8679940638139995
- name: F1
type: f1
value: 0.8221652060910999
---
<!-- 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_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: 0.3039
- Accuracy: 0.8680
- F1: 0.8222
- Combined Score: 0.8451
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4011 | 1.0 | 1422 | 0.3665 | 0.8286 | 0.7947 | 0.8116 |
| 0.3026 | 2.0 | 2844 | 0.3111 | 0.8625 | 0.8171 | 0.8398 |
| 0.2472 | 3.0 | 4266 | 0.3039 | 0.8680 | 0.8222 | 0.8451 |
| 0.1983 | 4.0 | 5688 | 0.3232 | 0.8737 | 0.8327 | 0.8532 |
| 0.157 | 5.0 | 7110 | 0.3742 | 0.8717 | 0.8194 | 0.8456 |
| 0.1251 | 6.0 | 8532 | 0.4009 | 0.8716 | 0.8146 | 0.8431 |
| 0.1009 | 7.0 | 9954 | 0.4471 | 0.8699 | 0.8300 | 0.8500 |
| 0.0828 | 8.0 | 11376 | 0.4176 | 0.8781 | 0.8354 | 0.8568 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
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