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# userutterance_classification_ver1

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2898
- Accuracy: 0.9539

## 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 130
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.8334        | 0.15  | 200  | 4.7254          | 0.0748   |
| 3.4798        | 0.3   | 400  | 3.4244          | 0.2971   |
| 2.319         | 0.45  | 600  | 2.4423          | 0.5184   |
| 1.5683        | 0.6   | 800  | 1.7401          | 0.6310   |
| 0.9625        | 0.75  | 1000 | 1.2750          | 0.7265   |
| 0.6922        | 0.9   | 1200 | 0.9717          | 0.7761   |
| 0.5019        | 1.05  | 1400 | 0.8036          | 0.8284   |
| 0.3538        | 1.2   | 1600 | 0.6690          | 0.8471   |
| 0.2413        | 1.35  | 1800 | 0.5585          | 0.8713   |
| 0.2623        | 1.5   | 2000 | 0.4840          | 0.8874   |
| 0.2103        | 1.66  | 2200 | 0.4261          | 0.9126   |
| 0.1456        | 1.81  | 2400 | 0.3872          | 0.9152   |
| 0.1276        | 1.96  | 2600 | 0.3329          | 0.9290   |
| 0.09          | 2.11  | 2800 | 0.2925          | 0.9432   |
| 0.0534        | 2.26  | 3000 | 0.2996          | 0.9361   |
| 0.0588        | 2.41  | 3200 | 0.2951          | 0.9403   |
| 0.044         | 2.56  | 3400 | 0.3324          | 0.9403   |
| 0.0535        | 2.71  | 3600 | 0.3155          | 0.9432   |
| 0.0537        | 2.86  | 3800 | 0.3206          | 0.9419   |
| 0.1325        | 3.01  | 4000 | 0.2945          | 0.9465   |
| 0.0611        | 3.16  | 4200 | 0.2903          | 0.9442   |
| 0.0077        | 3.31  | 4400 | 0.3052          | 0.9477   |
| 0.0187        | 3.46  | 4600 | 0.2774          | 0.95     |
| 0.0125        | 3.61  | 4800 | 0.2851          | 0.9513   |
| 0.0157        | 3.76  | 5000 | 0.2883          | 0.9523   |
| 0.0414        | 3.91  | 5200 | 0.3163          | 0.9497   |
| 0.0025        | 4.06  | 5400 | 0.2998          | 0.9494   |
| 0.0019        | 4.21  | 5600 | 0.2925          | 0.9513   |
| 0.0013        | 4.36  | 5800 | 0.2872          | 0.9526   |
| 0.0014        | 4.51  | 6000 | 0.2906          | 0.9532   |
| 0.0015        | 4.67  | 6200 | 0.2862          | 0.9529   |
| 0.0281        | 4.82  | 6400 | 0.2863          | 0.9535   |
| 0.0287        | 4.97  | 6600 | 0.2898          | 0.9539   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "userutterance_classification_ver1", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "config": "imbalanced", "split": "validation", "args": "imbalanced"}, "metrics": [{"type": "accuracy", "value": 0.9538709677419355, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard deberta-v2 text-classification generated_from_trainer dataset:clinc_oos license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

userutterance\_classification\_ver1
===================================


This model is a fine-tuned version of microsoft/deberta-v3-base on the clinc\_oos dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2898
* Accuracy: 0.9539


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: 4e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 130
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 130
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 130
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 130
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2