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# requirements_ambiguity_v2

This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on a private dataset with 2,523 labeled software requirements for ambiguity detection in Dutch.

Please contact me via [LinkedIn](https://www.linkedin.com/in/denizayhan/) if you have any questions about this model or the dataset used. 

The dataset and this model were created as part of the final project assignment of the Natural Language Understanding course (XCS224U) from the Professional AI Program of the Stanford School of Engineering. 

It achieves the following results on the evaluation set:
- Loss: 0.7485
- Accuracy: 0.8458
- F1: 0.8442
- Recall: 0.7474

## Intended uses & limitations

The model performs automated ambiguity detection through binary text classification. Its intended use is as a tool voor requirements engineers to detect spurious and ambiguous formulations. 

## Training and evaluation data

The model was trained on ReqAmbi dataset. This dataset is private and contains 2,523 requirement formulations. Each requirement is manually 
labeled 0 (unambiguous) or 1 (ambiguous). The dataset is split 2,019/253/253 into train, validation and test. The reported metrics are from the evaluation on the test set. The validation set was used for cross-validation during training.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
| 0.5268        | 1.0   | 36   | 0.5424          | 0.8063   | 0.8057 | 0.7263 |
| 0.318         | 2.0   | 72   | 0.4688          | 0.8182   | 0.8182 | 0.7579 |
| 0.1244        | 3.0   | 108  | 0.6019          | 0.8379   | 0.8366 | 0.7474 |
| 0.0308        | 4.0   | 144  | 0.7485          | 0.8458   | 0.8442 | 0.7474 |


### Framework versions

- Transformers 4.24.0
- Pytorch 2.0.0
- Datasets 2.9.0
- Tokenizers 0.11.0

{"language": ["nl"], "license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "In de Zaaktypeconfiguratie kan per fase een andere behandelaar worden geconfigureerd waardoor bij de overgang naar de volgende status de behandelaar automatisch wordt gewijzigd. De behandelaar/groep behandelaren kan automatisch worden bepaald op basis van een kenmerk."}, {"text": "Er kan informatie aan het digitale formulier worden toegevoegd (gespreksverslagen en resultaatafspraken bijvoorbeeld) door medewerker en/of leidinggevende, dit kan tussentijds opgeslagen en/of afgesloten worden voordat het wordt vrijgegeven voor de andere partij."}, {"text": "De Oplossing ondersteunt parafering en het plaatsen van een gecertificeerde elektronische handtekening."}, {"text": "De Aangeboden oplossing biedt de functionaliteit om individuele en bulkmutaties te verwerken met ingangsdatum op elke willekeurige datum in de maand, zowel in het verleden als in de toekomst, binnen een lopend kalenderjaar."}], "model-index": [{"name": "requirements_ambiguity_v2", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer nl license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #nl #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

requirements\_ambiguity\_v2
===========================


This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on a private dataset with 2,523 labeled software requirements for ambiguity detection in Dutch.


Please contact me via LinkedIn if you have any questions about this model or the dataset used.


The dataset and this model were created as part of the final project assignment of the Natural Language Understanding course (XCS224U) from the Professional AI Program of the Stanford School of Engineering.


It achieves the following results on the evaluation set:


* Loss: 0.7485
* Accuracy: 0.8458
* F1: 0.8442
* Recall: 0.7474


Intended uses & limitations
---------------------------


The model performs automated ambiguity detection through binary text classification. Its intended use is as a tool voor requirements engineers to detect spurious and ambiguous formulations.


Training and evaluation data
----------------------------


The model was trained on ReqAmbi dataset. This dataset is private and contains 2,523 requirement formulations. Each requirement is manually
labeled 0 (unambiguous) or 1 (ambiguous). The dataset is split 2,019/253/253 into train, validation and test. The reported metrics are from the evaluation on the test set. The validation set was used for cross-validation during training.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0
* Datasets 2.9.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0
* Datasets 2.9.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #nl #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0
* Datasets 2.9.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #nl #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4### Training results### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0
* Datasets 2.9.0
* Tokenizers 0.11.0