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---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv1_new_pretrain_48_KD_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: 0.8117487014593124
- name: F1
type: f1
value: 0.7245086328591595
---
<!-- 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_new_pretrain_48_KD_qqp
This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48_KD](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48_KD) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4106
- Accuracy: 0.8117
- F1: 0.7245
- Combined Score: 0.7681
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5016 | 1.0 | 2843 | 0.4533 | 0.7776 | 0.6963 | 0.7370 |
| 0.4125 | 2.0 | 5686 | 0.4433 | 0.8023 | 0.7269 | 0.7646 |
| 0.3574 | 3.0 | 8529 | 0.4106 | 0.8117 | 0.7245 | 0.7681 |
| 0.3134 | 4.0 | 11372 | 0.4395 | 0.8208 | 0.7461 | 0.7834 |
| 0.279 | 5.0 | 14215 | 0.4975 | 0.8236 | 0.7627 | 0.7931 |
| 0.248 | 6.0 | 17058 | 0.5527 | 0.8129 | 0.7066 | 0.7598 |
| 0.2215 | 7.0 | 19901 | 0.4814 | 0.8209 | 0.7697 | 0.7953 |
| 0.1998 | 8.0 | 22744 | 0.4820 | 0.8272 | 0.7702 | 0.7987 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
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