
# electra-base-discriminator-CoLA

This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3542
- Matthews Correlation: 0.6580

## Model description

Trying to find a decent optimum between accuracy/quality and inference speed.


```json
{
    "epoch": 8.0,
    "eval_loss": 0.3541961908340454,
    "eval_matthews_correlation": 0.6579677841732349,
    "eval_runtime": 1.9552,
    "eval_samples": 1043,
    "eval_samples_per_second": 533.451,
    "eval_steps_per_second": 33.756
}
```
## 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: 128
- eval_batch_size: 16
- seed: 22165
- 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: 8.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4004        | 1.0   | 67   | 0.3569          | 0.6340               |
| 0.2843        | 2.0   | 134  | 0.3542          | 0.6580               |
| 0.1228        | 3.0   | 201  | 0.4201          | 0.6412               |
| 0.0989        | 4.0   | 268  | 0.4780          | 0.6757               |
| 0.0681        | 5.0   | 335  | 0.4900          | 0.6925               |
| 0.0506        | 6.0   | 402  | 0.5837          | 0.6785               |
| 0.0093        | 7.0   | 469  | 0.6298          | 0.6652               |
| 0.0244        | 8.0   | 536  | 0.6292          | 0.6750               |


### 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"}], "model-index": [{"name": "electra-base-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": 0.6579677841732349, "name": "Matthews Correlation"}]}]}]}
transformers pytorch onnx safetensors electra text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #safetensors #electra #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

electra-base-discriminator-CoLA
===============================


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


* Loss: 0.3542
* Matthews Correlation: 0.6580


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


Trying to find a decent optimum between accuracy/quality and inference 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: 128
* eval\_batch\_size: 16
* seed: 22165
* 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: 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: 128
* eval\_batch\_size: 16
* seed: 22165
* 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: 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 #onnx #safetensors #electra #text-classification #generated_from_trainer #en #dataset-glue #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: 128
* eval\_batch\_size: 16
* seed: 22165
* 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: 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 #onnx #safetensors #electra #text-classification #generated_from_trainer #en #dataset-glue #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: 128
* eval\_batch\_size: 16
* seed: 22165
* 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: 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