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---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: hBERTv2_new_no_pretrain_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: 0.7856047489488004
- name: F1
type: f1
value: 0.6930594900849859
---
<!-- 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. -->
# hBERTv2_new_no_pretrain_qqp
This model is a fine-tuned version of [](https://huggingface.co/) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4537
- Accuracy: 0.7856
- F1: 0.6931
- Combined Score: 0.7393
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5037 | 1.0 | 2843 | 0.4537 | 0.7856 | 0.6931 | 0.7393 |
| 0.4066 | 2.0 | 5686 | 0.4549 | 0.7946 | 0.6758 | 0.7352 |
| 0.3367 | 3.0 | 8529 | 0.4630 | 0.7950 | 0.6650 | 0.7300 |
| 0.2876 | 4.0 | 11372 | 0.5279 | 0.8180 | 0.7598 | 0.7889 |
| 0.2498 | 5.0 | 14215 | 0.4857 | 0.8217 | 0.7650 | 0.7933 |
| 0.2371 | 6.0 | 17058 | 0.5113 | 0.8216 | 0.7376 | 0.7796 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
|