---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: paraphrase-MiniLM-L12-v2-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: 0.5057060886900621

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. -->

# paraphrase-MiniLM-L12-v2-CoLA

This model is a fine-tuned version of [sentence-transformers/paraphrase-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4636
- Matthews Correlation: 0.5057

## 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: 8e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 30198
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5747        | 1.0   | 67   | 0.5394          | 0.3455               |
| 0.5025        | 2.0   | 134  | 0.4999          | 0.4270               |
| 0.3698        | 3.0   | 201  | 0.4636          | 0.5057               |
| 0.2969        | 4.0   | 268  | 0.5309          | 0.4751               |
| 0.2275        | 5.0   | 335  | 0.6238          | 0.4775               |
| 0.1859        | 6.0   | 402  | 0.6315          | 0.4867               |
| 0.1517        | 7.0   | 469  | 0.7783          | 0.4695               |
| 0.1016        | 8.0   | 536  | 0.6762          | 0.4901               |
| 0.1017        | 9.0   | 603  | 0.7412          | 0.5046               |
| 0.0898        | 10.0  | 670  | 0.7719          | 0.4877               |
| 0.0527        | 11.0  | 737  | 0.8627          | 0.4955               |
| 0.0582        | 12.0  | 804  | 0.8986          | 0.4738               |
| 0.074         | 13.0  | 871  | 0.9469          | 0.4942               |
| 0.0508        | 14.0  | 938  | 0.9436          | 0.4918               |
| 0.024         | 15.0  | 1005 | 0.9391          | 0.4919               |
| 0.0458        | 16.0  | 1072 | 0.9375          | 0.4946               |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.1