|
--- |
|
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. |
|
base_model: GroNLP/bert-base-dutch-cased |
|
model-index: |
|
- name: requirements_ambiguity_v2 |
|
results: [] |
|
--- |
|
|
|
|
|
|
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# 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 |
|
|
