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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_qqp_128
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: 0.7871877318822657
- name: F1
type: f1
value: 0.7061676115019466
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_logit_kd_qqp_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6884
- Accuracy: 0.7872
- F1: 0.7062
- Combined Score: 0.7467
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.9518 | 1.0 | 2843 | 0.8352 | 0.7536 | 0.6530 | 0.7033 |
| 0.8249 | 2.0 | 5686 | 0.7766 | 0.7607 | 0.6219 | 0.6913 |
| 0.7847 | 3.0 | 8529 | 0.7625 | 0.7648 | 0.6402 | 0.7025 |
| 0.7498 | 4.0 | 11372 | 0.7551 | 0.7638 | 0.6197 | 0.6917 |
| 0.7137 | 5.0 | 14215 | 0.7387 | 0.7691 | 0.6545 | 0.7118 |
| 0.6762 | 6.0 | 17058 | 0.7165 | 0.7753 | 0.6720 | 0.7237 |
| 0.6373 | 7.0 | 19901 | 0.7042 | 0.7783 | 0.6765 | 0.7274 |
| 0.6045 | 8.0 | 22744 | 0.7075 | 0.7799 | 0.6902 | 0.7350 |
| 0.5729 | 9.0 | 25587 | 0.7233 | 0.7792 | 0.6639 | 0.7215 |
| 0.545 | 10.0 | 28430 | 0.7088 | 0.7805 | 0.7180 | 0.7493 |
| 0.5183 | 11.0 | 31273 | 0.6884 | 0.7872 | 0.7062 | 0.7467 |
| 0.4948 | 12.0 | 34116 | 0.7064 | 0.7869 | 0.7076 | 0.7472 |
| 0.4724 | 13.0 | 36959 | 0.7053 | 0.7884 | 0.7120 | 0.7502 |
| 0.4514 | 14.0 | 39802 | 0.7314 | 0.7903 | 0.7024 | 0.7464 |
| 0.4321 | 15.0 | 42645 | 0.7112 | 0.7891 | 0.7228 | 0.7560 |
| 0.4152 | 16.0 | 45488 | 0.7410 | 0.7909 | 0.7211 | 0.7560 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
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