

# distilroberta-base-CoLA

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4974
- Matthews Correlation: 0.5678

## Model description

Mostly as a litmus test to see how it fares vs. the `textattack` one (should be similar) & associated metrics:

```json
{
    "epoch": 4.0,
    "eval_loss": 0.49744734168052673,
    "eval_matthews_correlation": 0.5678267214677118,
    "eval_runtime": 1.9223,
    "eval_samples": 1043,
    "eval_samples_per_second": 542.586,
    "eval_steps_per_second": 135.777
}
```

## 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: 6e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 32010
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- 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: 4.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4778        | 1.0   | 67   | 0.4630          | 0.5161               |
| 0.4356        | 2.0   | 134  | 0.4725          | 0.5287               |
| 0.2934        | 3.0   | 201  | 0.4974          | 0.5678               |
| 0.1998        | 4.0   | 268  | 0.5419          | 0.5584               |


### 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": "distilroberta-base-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": 0.5678267214677118, "name": "Matthews Correlation"}]}]}]}
transformers pytorch safetensors roberta text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilroberta-base-CoLA
=======================


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


* Loss: 0.4974
* Matthews Correlation: 0.5678


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


Mostly as a litmus test to see how it fares vs. the 'textattack' one (should be similar) & associated metrics:


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: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 4
* seed: 32010
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 4
* 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: 4.0
* 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: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 4
* seed: 32010
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 4
* 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: 4.0
* 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 #safetensors #roberta #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: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 4
* seed: 32010
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 4
* 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: 4.0
* 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 #safetensors #roberta #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: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 4
* seed: 32010
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 4
* 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: 4.0
* 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