


# CAP_coded_UK_statutory_instruments

This model predicts the CAP code of parliamentary bills/instruments (https://www.comparativeagendas.net/pages/master-codebook)

The model is trained on ~40k UK Parliamentary Statutory Instruments from the UK House of Commons and the Scottish Parliament. 
The model is cased (case sensitive)

Any questions on the model and training data feel free to message me on twitter - @sachary_


- Train Loss: 0.1188
- Train Sparse Categorical Accuracy: 0.9688
- Validation Loss: 0.2032
- Validation Sparse Categorical Accuracy: 0.9556




The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.2167     | 0.9474                            | 0.2351          | 0.9444                                 | 0     |
| 0.1539     | 0.9592                            | 0.2076          | 0.9536                                 | 1     |
| 0.1188     | 0.9688                            | 0.2032          | 0.9556                                 | 2     |


### Framework versions

- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1

{"tags": ["generated_from_keras_callback"], "widget": [{"text": "The National Health Service (Charges for Drugs and Appliances) (Scotland) Regulations 2007", "example_title": "example 1"}, {"text": "The Inshore Fishing (Prohibited Methods of Fishing) (Luce Bay) Order 2015", "example_title": "example 2"}], "model-index": [{"name": "CAP_coded_UK_statutory_instruments", "results": []}]}
transformers pytorch tf bert text-classification generated_from_keras_callback autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 

CAP\_coded\_UK\_statutory\_instruments
======================================


This model predicts the CAP code of parliamentary bills/instruments (URL


The model is trained on ~40k UK Parliamentary Statutory Instruments from the UK House of Commons and the Scottish Parliament.
The model is cased (case sensitive)


Any questions on the model and training data feel free to message me on twitter - @sachary\_


* Train Loss: 0.1188
* Train Sparse Categorical Accuracy: 0.9688
* Validation Loss: 0.2032
* Validation Sparse Categorical Accuracy: 0.9556


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 5e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.19.2
* TensorFlow 2.8.2
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training results ### Framework versions


* Transformers 4.19.2
* TensorFlow 2.8.2
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
 ### Training results ### Framework versions


* Transformers 4.19.2
* TensorFlow 2.8.2
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us 
### Training results### Framework versions


* Transformers 4.19.2
* TensorFlow 2.8.2
* Datasets 2.2.2
* Tokenizers 0.12.1