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---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: xtremedistil-l12-h384-uncased-CoLA
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5395539646127814
widget:
- text: 'The cat sat on the mat.'
example_title: Correct grammatical sentence
- text: 'Me and my friend going to the store.'
example_title: Incorrect subject-verb agreement
- text: 'I ain''t got no money.'
example_title: Incorrect verb conjugation and double negative
- text: 'She don''t like pizza no more.'
example_title: Incorrect verb conjugation and double negative
- text: 'They is arriving tomorrow.'
example_title: Incorrect verb conjugation
---
<!-- 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. -->
# xtremedistil-l12-h384-uncased-CoLA
This model is a fine-tuned version of [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4974
- Matthews Correlation: 0.5396
## 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 16
- seed: 5559
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 16.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4822 | 1.0 | 67 | 0.5893 | 0.2621 |
| 0.4669 | 2.0 | 134 | 0.5811 | 0.3722 |
| 0.3077 | 3.0 | 201 | 0.6150 | 0.4383 |
| 0.2594 | 4.0 | 268 | 0.4974 | 0.5396 |
| 0.21 | 5.0 | 335 | 0.5594 | 0.5182 |
| 0.1526 | 6.0 | 402 | 0.5715 | 0.5150 |
| 0.1775 | 7.0 | 469 | 0.6637 | 0.5020 |
| 0.1681 | 8.0 | 536 | 0.6958 | 0.5131 |
| 0.124 | 9.0 | 603 | 0.7057 | 0.5154 |
| 0.1111 | 10.0 | 670 | 0.8173 | 0.5074 |
| 0.1332 | 11.0 | 737 | 0.8253 | 0.5260 |
| 0.0673 | 12.0 | 804 | 0.8086 | 0.5180 |
| 0.0512 | 13.0 | 871 | 0.8409 | 0.5128 |
| 0.0457 | 14.0 | 938 | 0.8760 | 0.4947 |
| 0.04 | 15.0 | 1005 | 0.8522 | 0.5103 |
| 0.0485 | 16.0 | 1072 | 0.8556 | 0.5076 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.1
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