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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_wnli_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
config: wnli
split: validation
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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_wnli_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6907
- Accuracy: 0.5634
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6938 | 1.0 | 5 | 0.6911 | 0.5634 |
| 0.6933 | 2.0 | 10 | 0.6917 | 0.5634 |
| 0.6931 | 3.0 | 15 | 0.6920 | 0.5634 |
| 0.693 | 4.0 | 20 | 0.6915 | 0.5634 |
| 0.693 | 5.0 | 25 | 0.6911 | 0.5634 |
| 0.693 | 6.0 | 30 | 0.6909 | 0.5634 |
| 0.693 | 7.0 | 35 | 0.6907 | 0.5634 |
| 0.693 | 8.0 | 40 | 0.6911 | 0.5634 |
| 0.6931 | 9.0 | 45 | 0.6908 | 0.5634 |
| 0.693 | 10.0 | 50 | 0.6912 | 0.5634 |
| 0.693 | 11.0 | 55 | 0.6918 | 0.5634 |
| 0.693 | 12.0 | 60 | 0.6918 | 0.5634 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
|