--- license: apache-2.0 tags: - generated_from_keras_callback widget: - text: Application needs to keep track of subtasks in a task. example_title: Requirment 1 - text: The system shall allow users to enter time in several different formats. example_title: Requirment 2 - text: The system shall allow users who hold any of the ORES/ORELSE/PROVIDER keys to be viewed as a clinical user and has full access privileges to all problem list options. example_title: Requirment 3 base_model: sentence-transformers/all-MiniLM-L6-v2 model-index: - name: kasrahabib/KM35NCDF results: [] --- # kasrahabib/KM35NCDF This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on Software Requirements Dataset (SWARD) for classifying 19 Non-functional requirements. Note that based on literature, two out of 19 classes are Data and Behavior, belong to types of Functional software requirements. It achieves the following results on the evaluation set: - Train Loss: 0.1691 - Validation Loss: 0.7548 - Epoch: 14 - Final Macro F1-score: 0.79 Labels: 0 or A -> Availability; 1 or AC -> Access Control; 2 or AU -> Audit; 3 or B -> Behaviour; 4 or D -> Data; 5 or FT -> Fault Tolerance; 6 or I -> Interface/Interoperability; 7 or LE -> Legal; 8 or LF -> Look and Feel; 9 or MN -> Maintainability; 10 or O -> Operational; 11 or PE -> Performance; 12 or PO -> Portability; 13 or RL -> Reliability; 14 or SA -> Safety; 15 or SC -> Scalability; 16 or SE -> Security; 17 or ST -> Stability; 18 or US -> Usability; ## Usage Pipeline ```python from transformers import pipeline frame_work = 'tf' task = 'text-classification' model_ckpt = 'kasrahabib/KM35NCDF ' software_requirment_cls = pipeline(task = task, model = model_ckpt, framework = frame_work) example_1_US = 'Application needs to keep track of subtasks in a task.' example_2_PE = 'The system shall allow users to enter time in several different formats.' example_3_AC = 'The system shall allow users who hold any of the ORES/ORELSE/PROVIDER keys to be viewed as a clinical user and has full access privileges to all problem list options.' software_requirment_cls([example_1_US, example_2_PE, example_3_AC]) ``` ``` [{'label': 'US', 'score': 0.9712953567504883}, {'label': 'PE', 'score': 0.9457865953445435}, {'label': 'AC', 'score': 0.9639136791229248}] ``` ## Model Inference: ```python import numpy as np from transformers import AutoTokenizer, TFAutoModelForSequenceClassification model_ckpt = 'kasrahabib/KM35NCDF ' tokenizer = AutoTokenizer.from_pretrained(model_ckpt) model = TFAutoModelForSequenceClassification.from_pretrained(model_ckpt) example_1_US = 'Application needs to keep track of subtasks in a task.' example_2_PE = 'The system shall allow users to enter time in several different formats.' example_3_AC = 'The system shall allow users who hold any of the ORES/ORELSE/PROVIDER keys to be viewed as a clinical user and has full access privileges to all problem list options.' requirements = [example_1_US, example_2_PE, example_3_AC] encoded_requirements = tokenizer(requirements, return_tensors = 'np', padding = 'longest') y_pred = model(encoded_requirements).logits classifications = np.argmax(y_pred, axis = 1) classifications = [model.config.id2label[output] for output in classifications] print(classifications) ``` ``` ['US', 'PE', 'AC'] ``` ## Usage Locally Downloaded (e.g., GitHub): 1 - Clone the repository: ```shell git lfs install git clone url_of_repo ``` 2 - Locate the path to the downloaded directory
3 - Write the link to the path in the ```model_ckpt``` variable
Then modify the code as below: ```python import numpy as np from transformers import AutoTokenizer, TFAutoModelForSequenceClassification model_ckpt = 'rest_of_the_path/KM35NCDF ' tokenizer = AutoTokenizer.from_pretrained(model_ckpt) model = TFAutoModelForSequenceClassification.from_pretrained(model_ckpt) example_1_US = 'Application needs to keep track of subtasks in a task.' example_2_PE = 'The system shall allow users to enter time in several different formats.' example_3_AC = 'The system shall allow users who hold any of the ORES/ORELSE/PROVIDER keys to be viewed as a clinical user and has full access privileges to all problem list options.' requirements = [example_1_US, example_2_PE, example_3_AC] encoded_requirements = tokenizer(requirements, return_tensors = 'np', padding = 'longest') y_pred = model(encoded_requirements).logits classifications = np.argmax(y_pred, axis = 1) classifications = [model.config.id2label[output] for output in classifications] print(classifications) ``` ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6735, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.0 - Tokenizers 0.13.2