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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_mnli_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
config: mnli
split: validation_matched
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5949959316517494
---
<!-- 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_mnli_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2689
- Accuracy: 0.5950
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6825 | 1.0 | 3068 | 1.4581 | 0.5256 |
| 1.4941 | 2.0 | 6136 | 1.3516 | 0.5680 |
| 1.4199 | 3.0 | 9204 | 1.3259 | 0.5712 |
| 1.3747 | 4.0 | 12272 | 1.3024 | 0.5856 |
| 1.34 | 5.0 | 15340 | 1.2875 | 0.5931 |
| 1.3087 | 6.0 | 18408 | 1.2730 | 0.5928 |
| 1.2769 | 7.0 | 21476 | 1.2845 | 0.5916 |
| 1.246 | 8.0 | 24544 | 1.2750 | 0.5965 |
| 1.2166 | 9.0 | 27612 | 1.2651 | 0.6020 |
| 1.1883 | 10.0 | 30680 | 1.2773 | 0.6043 |
| 1.1604 | 11.0 | 33748 | 1.2555 | 0.6011 |
| 1.1329 | 12.0 | 36816 | 1.2792 | 0.5991 |
| 1.1074 | 13.0 | 39884 | 1.2891 | 0.5986 |
| 1.0812 | 14.0 | 42952 | 1.2889 | 0.5947 |
| 1.0577 | 15.0 | 46020 | 1.2871 | 0.5970 |
| 1.0338 | 16.0 | 49088 | 1.3296 | 0.6026 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
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