

# electra-small-discriminator-CoLA

This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4403
- Matthews Correlation: 0.5510

## Model description

trying to optimize accuracy/speed:

```json
{
    "epoch": 8.0,
    "eval_loss": 0.4402828514575958,
    "eval_matthews_correlation": 0.5510400717227824,
    "eval_runtime": 0.9341,
    "eval_samples": 1043,
    "eval_samples_per_second": 1116.545,
    "eval_steps_per_second": 70.654
}

```

## 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: 512
- eval_batch_size: 16
- seed: 32754
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 8.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6139        | 1.0   | 17   | 0.5997          | 0.0                  |
| 0.5315        | 2.0   | 34   | 0.4890          | 0.5154               |
| 0.4244        | 3.0   | 51   | 0.4469          | 0.5433               |
| 0.3568        | 4.0   | 68   | 0.4403          | 0.5510               |
| 0.319         | 5.0   | 85   | 0.4517          | 0.5654               |
| 0.2887        | 6.0   | 102  | 0.4656          | 0.5728               |
| 0.2771        | 7.0   | 119  | 0.4558          | 0.5883               |
| 0.2729        | 8.0   | 136  | 0.4569          | 0.5858               |


### Framework versions

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

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "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"}], "base_model": "google/electra-small-discriminator", "model-index": [{"name": "electra-small-discriminator-CoLA", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE COLA", "type": "glue", "config": "cola", "split": "validation", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5510400717227824, "name": "Matthews Correlation"}]}]}]}
transformers pytorch electra text-classification generated_from_trainer en dataset:glue base_model:google/electra-small-discriminator license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #text-classification #generated_from_trainer #en #dataset-glue #base_model-google/electra-small-discriminator #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

electra-small-discriminator-CoLA
================================


This model is a fine-tuned version of google/electra-small-discriminator on the GLUE COLA dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4403
* Matthews Correlation: 0.5510


Model description
-----------------


trying to optimize accuracy/speed:


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: 512
* eval\_batch\_size: 16
* seed: 32754
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 8.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* train\_batch\_size: 512
* eval\_batch\_size: 16
* seed: 32754
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 8.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #electra #text-classification #generated_from_trainer #en #dataset-glue #base_model-google/electra-small-discriminator #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* train\_batch\_size: 512
* eval\_batch\_size: 16
* seed: 32754
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 8.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.1
TAGS
#transformers #pytorch #electra #text-classification #generated_from_trainer #en #dataset-glue #base_model-google/electra-small-discriminator #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* train\_batch\_size: 512
* eval\_batch\_size: 16
* seed: 32754
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 8.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


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