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# distilbert-base-uncased-distilled-clinc

This model is a fine-tuned with knowledge distillation version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb).

It achieves the following results on the evaluation set:
- Loss: 0.1005
- Accuracy: 0.9394

## 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: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9031        | 1.0   | 318  | 0.5745          | 0.7365   |
| 0.4481        | 2.0   | 636  | 0.2856          | 0.8748   |
| 0.2528        | 3.0   | 954  | 0.1798          | 0.9187   |
| 0.176         | 4.0   | 1272 | 0.1398          | 0.9294   |
| 0.1416        | 5.0   | 1590 | 0.1211          | 0.9348   |
| 0.1243        | 6.0   | 1908 | 0.1116          | 0.9348   |
| 0.1133        | 7.0   | 2226 | 0.1062          | 0.9377   |
| 0.1075        | 8.0   | 2544 | 0.1035          | 0.9387   |
| 0.1039        | 9.0   | 2862 | 0.1014          | 0.9381   |
| 0.1018        | 10.0  | 3180 | 0.1005          | 0.9394   |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"}, "metrics": [{"type": "accuracy", "value": 0.9393548387096774, "name": "Accuracy"}]}]}]}
transformers pytorch distilbert text-classification generated_from_trainer dataset:clinc_oos license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-distilled-clinc
=======================================


This model is a fine-tuned with knowledge distillation version of distilbert-base-uncased on the clinc\_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the NLP with Transformers book. You can find the full code in the accompanying Github repository.


It achieves the following results on the evaluation set:


* Loss: 0.1005
* Accuracy: 0.9394


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: 2e-05
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1+cu102
* Datasets 1.13.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1+cu102
* Datasets 1.13.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1+cu102
* Datasets 1.13.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.1+cu102
* Datasets 1.13.0
* Tokenizers 0.10.3