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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_pretrain_mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8578431372549019
- name: F1
type: f1
value: 0.8993055555555555
---
<!-- 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_pretrain_mrpc
This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2291
- Accuracy: 0.8578
- F1: 0.8993
- Combined Score: 0.8786
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.536 | 1.0 | 29 | 0.4134 | 0.7279 | 0.8284 | 0.7782 |
| 0.3419 | 2.0 | 58 | 0.3005 | 0.8284 | 0.8801 | 0.8543 |
| 0.2413 | 3.0 | 87 | 0.2707 | 0.8235 | 0.8780 | 0.8507 |
| 0.1852 | 4.0 | 116 | 0.3247 | 0.8284 | 0.8837 | 0.8561 |
| 0.1524 | 5.0 | 145 | 0.2856 | 0.8431 | 0.8900 | 0.8666 |
| 0.1297 | 6.0 | 174 | 0.2999 | 0.8456 | 0.8948 | 0.8702 |
| 0.1219 | 7.0 | 203 | 0.2797 | 0.8529 | 0.8986 | 0.8758 |
| 0.1141 | 8.0 | 232 | 0.2462 | 0.8603 | 0.9005 | 0.8804 |
| 0.1127 | 9.0 | 261 | 0.2557 | 0.8578 | 0.8982 | 0.8780 |
| 0.1091 | 10.0 | 290 | 0.2853 | 0.8480 | 0.8967 | 0.8724 |
| 0.1007 | 11.0 | 319 | 0.2472 | 0.8554 | 0.8981 | 0.8767 |
| 0.0979 | 12.0 | 348 | 0.2431 | 0.8505 | 0.8950 | 0.8727 |
| 0.0954 | 13.0 | 377 | 0.2456 | 0.8578 | 0.9007 | 0.8793 |
| 0.0946 | 14.0 | 406 | 0.2526 | 0.8578 | 0.9017 | 0.8798 |
| 0.0946 | 15.0 | 435 | 0.2291 | 0.8578 | 0.8993 | 0.8786 |
| 0.0938 | 16.0 | 464 | 0.2452 | 0.8603 | 0.9029 | 0.8816 |
| 0.0919 | 17.0 | 493 | 0.2365 | 0.8652 | 0.9050 | 0.8851 |
| 0.0916 | 18.0 | 522 | 0.2363 | 0.8652 | 0.9060 | 0.8856 |
| 0.0915 | 19.0 | 551 | 0.2432 | 0.8652 | 0.9063 | 0.8857 |
| 0.0905 | 20.0 | 580 | 0.2297 | 0.8652 | 0.9057 | 0.8854 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
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