

# deberta-v3-xsmall-CoLA

This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4237
- Matthews Correlation: 0.5895

## Model description

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


```json
{
    "epoch": 3.0,
    "eval_loss": 0.423,
    "eval_matthews_correlation": 0.589,
    "eval_runtime": 5.0422,
    "eval_samples": 1043,
    "eval_samples_per_second": 206.853,
    "eval_steps_per_second": 51.763
}

```

## 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: 16105
- 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: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.3945        | 1.0   | 67   | 0.4323          | 0.5778               |
| 0.3214        | 2.0   | 134  | 0.4237          | 0.5895               |
| 0.3059        | 3.0   | 201  | 0.4636          | 0.5795               |


### Framework versions

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

{"language": ["en"], "license": "mit", "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": "microsoft/deberta-v3-xsmall", "model-index": [{"name": "deberta-v3-xsmall-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": 0.5894856058137782, "name": "Matthews Correlation"}]}]}]}
transformers pytorch onnx deberta-v2 text-classification generated_from_trainer en dataset:glue base_model:microsoft/deberta-v3-xsmall license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #base_model-microsoft/deberta-v3-xsmall #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

deberta-v3-xsmall-CoLA
======================


This model is a fine-tuned version of microsoft/deberta-v3-xsmall on the GLUE COLA dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4237
* Matthews Correlation: 0.5895


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: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 4
* seed: 16105
* 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: 3.0
* 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: 16105
* 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: 3.0
* 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 #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #base_model-microsoft/deberta-v3-xsmall #license-mit #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: 16105
* 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: 3.0
* 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 #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #base_model-microsoft/deberta-v3-xsmall #license-mit #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: 16105
* 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: 3.0
* 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