Delete project.yml

project.yml

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-# Title and description of the project
-title: "Citations of ECFR Banking Regulation in a spaCy pipeline."
-description: "Custom text classification project for spaCy v3 adapted from the spaCy v3"
-
-vars:
-  lang: "en"
-  train: corpus/train.spacy
-  dev: corpus/dev.spacy
-  version: "0.1.0"
-  gpu_id: -1
-  vectors_model: "en_core_web_lg"
-  name: ecfr_ner
-  prodigy:
-    ner_labels: ecfr_initial_ner
-    ner_manual_labels: ecfr_manual_ner
-    senter_labels: ecfr_labeled_sents
-    ner_labeled_dataset: ecfr_labeled_ner
-  assets:
-    ner_labels: assets/ecfr_ner_labels.jsonl
-    senter_labels: assets/ecfr_senter_labels.jsonl
-    ner_patterns: assets/patterns.jsonl
-    corpus_labels: corpus/labels
-    data_files: data
-    trained_model: my_trained_model
-    trained_model_textcat: my_trained_model/textcat_multilabel
-    output_models: output
-    python_code: python_Code
-
-directories: [ "data", "python_Code"]
-
-assets:
-  - dest: "data/firstStep_file.jsonl"
-    description: "JSONL file containing formatted data from the first step"
-  - dest: "data/five_examples_annotated5.jsonl"
-    description: "JSONL file containing five annotated examples"
-  - dest: "data/goldenEval.jsonl"
-    description: "JSONL file containing golden evaluation data"
-  - dest: "data/thirdStep_file.jsonl"
-    description: "JSONL file containing classified data from the third step"
-  - dest: "data/train.jsonl"
-    description: "JSONL file containing training data"
-  - dest: "data/train200.jsonl"
-    description: "JSONL file containing initial training data"
-  - dest: "data/train4465.jsonl"
-    description: "JSONL file containing formatted and labeled training data"
-  - dest: "python_Code/finalStep-formatLabel.py"
-    description: "Python script for formatting labeled data in the final step"
-  - dest: "python_Code/firstStep-format.py"
-    description: "Python script for formatting data in the first step"
-  - dest: "python_Code/five_examples_annotated.ipynb"
-    description: "Jupyter notebook containing five annotated examples"
-  - dest: "python_Code/secondStep-score.py"
-    description: "Python script for scoring data in the second step"
-  - dest: "python_Code/thirdStep-label.py"
-    description: "Python script for labeling data in the third step"
-  - dest: "python_Code/train_eval_split.ipynb"
-    description: "Jupyter notebook for training and evaluation data splitting"
-  - dest: "data/firstStep_file.jsonl"
-    description: "Python script for evaluating the trained model"
-  - dest: "README.md"
-    description: "Markdown file containing project documentation"
-
-workflows:
-  train:
-    - preprocess
-    - train-text-classification-model
-    - classify-unlabeled-data
-    - format-labeled-data
-    # - review-evaluation-data
-    # - export-reviewed-evaluation-data
-    # - import-training-data
-    # - import-golden-evaluation-data
-    # - train-model-experiment1
-    # - convert-data-to-spacy-format
-  evaluate:
-    - evaluate-model
-
-commands:
-  - name: "preprocess"
-    help: |
-      Execute the Python script `firstStep-format.py`, which performs the initial formatting of a dataset file for the first step of the project. This script extracts text and labels from a dataset file in JSONL format and writes them to a new JSONL file in a specific format.
-      
-      Usage:
-      ```
-      spacy project run preprocess
-      ```
-
-      Explanation:
-      - The script `firstStep-format.py` reads data from the file specified in the `dataset_file` variable (`data/train200.jsonl` by default).
-      - It extracts text and labels from each JSON object in the dataset file.
-      - If both text and at least one label are available, it writes a new JSON object to the output file specified in the `output_file` variable (`data/firstStep_file.jsonl` by default) with the extracted text and label.
-      - If either text or label is missing in a JSON object, a warning message is printed.
-      - Upon completion, the script prints a message confirming the processing and the path to the output file.
-    script:
-      - "python3 python_Code/firstStep-format.py"
-
-  - name: "train-text-classification-model"
-    help: |
-      Train the text classification model for the second step of the project using the `secondStep-score.py` script. This script loads a blank English spaCy model and adds a text classification pipeline to it. It then trains the model using the processed data from the first step.
-      
-      Usage:
-      ```
-      spacy project run train-text-classification-model
-      ```
-
-      Explanation:
-      - The script `secondStep-score.py` loads a blank English spaCy model and adds a text classification pipeline to it.
-      - It reads processed data from the file specified in the `processed_data_file` variable (`data/firstStep_file.jsonl` by default).
-      - The processed data is converted to spaCy format for training the model.
-      - The model is trained using the converted data for a specified number of iterations (`n_iter`).
-      - Losses are printed for each iteration during training.
-      - Upon completion, the trained model is saved to the specified output directory (`./my_trained_model` by default).
-    script:
-      - "python3 python_Code/secondStep-score.py"
-
-  - name: "classify-unlabeled-data"
-    help: |
-      Classify the unlabeled data for the third step of the project using the `thirdStep-label.py` script. This script loads the trained spaCy model from the previous step and classifies each record in the unlabeled dataset.
-      
-      Usage:
-      ```
-      spacy project run classify-unlabeled-data
-      ```
-
-      Explanation:
-      - The script `thirdStep-label.py` loads the trained spaCy model from the specified model directory (`./my_trained_model` by default).
-      - It reads the unlabeled data from the file specified in the `unlabeled_data_file` variable (`data/train.jsonl` by default).
-      - Each record in the unlabeled data is classified using the loaded model.
-      - The predicted labels for each record are extracted and stored along with the text.
-      - The classified data is optionally saved to a file specified in the `output_file` variable (`data/thirdStep_file.jsonl` by default).
-    script:
-      - "python3 python_Code/thirdStep-label.py"
-
-  - name: "format-labeled-data"
-    help: |
-      Format the labeled data for the final step of the project using the `finalStep-formatLabel.py` script. This script processes the classified data from the third step and transforms it into a specific format, considering a threshold for label acceptance.
-      
-      Usage:
-      ```
-      spacy project run format-labeled-data
-      ```
-
-      Explanation:
-      - The script `finalStep-formatLabel.py` reads classified data from the file specified in the `input_file` variable (`data/thirdStep_file.jsonl` by default).
-      - For each record, it determines accepted categories based on a specified threshold.
-      - It constructs an output record containing the text, predicted labels, accepted categories, answer (accept/reject), and options with meta information.
-      - The transformed data is written to the file specified in the `output_file` variable (`data/train4465.jsonl` by default).
-    script:
-      - "python3 python_Code/finalStep-formatLabel.py"
-
-  - name: "evaluate-model"
-    help: |
-      Evaluate the trained model using the evaluation data and print the metrics.
-
-      Usage:
-      ```
-      spacy project run evaluate-model
-      ```
-
-      Explanation:
-      - The script `evaluate_model.py` loads the trained model and evaluates it using the golden evaluation data.
-      - It calculates evaluation metrics such as accuracy, precision, recall, and F1-score.
-      - The metrics are printed to the console.
-    script:
-      - "python python_Code/evaluate_model.py"
-      
-  # - name: "review-evaluation-data"
-  #   help: |
-  #     Review the evaluation data in Prodigy and automatically accept annotations.
-      
-  #     Usage:
-  #     ```
-  #     spacy project run review-evaluation-data
-  #     ```
-
-  #     Explanation:
-  #     - The command reviews the evaluation data in Prodigy.
-  #     - It automatically accepts annotations made during the review process.
-  #     - Only sessions allowed by the environment variable PRODIGY_ALLOWED_SESSIONS are permitted to review data. In this case, the session 'reviwer' is allowed.
-  #   script:
-  #     - "PRODIGY_ALLOWED_SESSIONS=reviwer python3 -m prodigy review project3eval-review project3eval --auto-accept"
-
-  # - name: "export-reviewed-evaluation-data"
-  #   help: |
-  #     Export the reviewed evaluation data from Prodigy to a JSONL file named 'goldenEval.jsonl'.
-      
-  #     Usage:
-  #     ```
-  #     spacy project run export-reviewed-evaluation-data
-  #     ```
-
-  #     Explanation:
-  #     - The command exports the reviewed evaluation data from Prodigy to a JSONL file.
-  #     - The data is exported from the Prodigy database associated with the project named 'project3eval-review'.
-  #     - The exported data is saved to the file 'goldenEval.jsonl'.
-  #     - This command helps in preserving the reviewed annotations for further analysis or processing.
-  #   script:
-  #     - "prodigy db-out project3eval-review > goldenEval.jsonl"
-
-  # - name: "import-training-data"
-  #   help: |
-  #     Import the training data into Prodigy from a JSONL file named 'train200.jsonl'.
-      
-  #     Usage:
-  #     ```
-  #     spacy project run import-training-data
-  #     ```
-
-  #     Explanation:
-  #     - The command imports the training data into Prodigy from the specified JSONL file.
-  #     - The data is imported into the Prodigy database associated with the project named 'prodigy3train'.
-  #     - This command prepares the training data for annotation and model training in Prodigy.
-  #   script:
-  #     - "prodigy db-in prodigy3train train200.jsonl"
-
-  # - name: "import-golden-evaluation-data"
-  #   help: |
-  #     Import the golden evaluation data into Prodigy from a JSONL file named 'goldeneval.jsonl'.
-      
-  #     Usage:
-  #     ```
-  #     spacy project run import-golden-evaluation-data
-  #     ```
-
-  #     Explanation:
-  #     - The command imports the golden evaluation data into Prodigy from the specified JSONL file.
-  #     - The data is imported into the Prodigy database associated with the project named 'golden3'.
-  #     - This command prepares the golden evaluation data for further analysis and model evaluation in Prodigy.
-  #   script:
-  #     - "prodigy db-in golden3 goldeneval.jsonl"
-
-  # - name: "train-model-experiment1"
-  #   help: |
-  #     Train a text classification model using Prodigy with the 'prodigy3train' dataset and evaluating on 'golden3'.
-      
-  #     Usage:
-  #     ```
-  #     spacy project run train-model-experiment1
-  #     ```
-
-  #     Explanation:
-  #     - The command trains a text classification model using Prodigy.
-  #     - It uses the 'prodigy3train' dataset for training and evaluates the model on the 'golden3' dataset.
-  #     - The trained model is saved to the './output/experiment1' directory.
-  #   script:
-  #     - "python3 -m prodigy train --textcat-multilabel prodigy3train,eval:golden3 ./output/experiment1"
-
-  # - name: "download-model"
-  #   help: |
-  #     Download the English language model 'en_core_web_lg' from spaCy.
-      
-  #     Usage:
-  #     ```
-  #     spacy project run download-model
-  #     ```
-
-  #     Explanation:
-  #     - The command downloads the English language model 'en_core_web_lg' from spaCy.
-  #     - This model is used as the base model for further data processing and training in the project.
-  #   script:
-  #     - "python3 -m spacy download en_core_web_lg"
-
-  # - name: "convert-data-to-spacy-format"
-  #   help: |
-  #     Convert the annotated data from Prodigy to spaCy format using the 'prodigy3train' and 'golden3' datasets.
-      
-  #     Usage:
-  #     ```
-  #     spacy project run convert-data-to-spacy-format
-  #     ```
-
-  #     Explanation:
-  #     - The command converts the annotated data from Prodigy to spaCy format.
-  #     - It uses the 'prodigy3train' and 'golden3' datasets for conversion.
-  #     - The converted data is saved to the './corpus' directory with the base model 'en_core_web_lg'.
-  #   script:
-  #     - "python3 -m prodigy data-to-spacy --textcat-multilabel prodigy3train,eval:golden3 ./corpus --base-model en_core_web_lg"
-
-  # - name: "train-custom-model"
-  #   help: |
-  #     Train a custom text classification model using spaCy with the converted data in spaCy format.
-      
-  #     Usage:
-  #     ```
-  #     spacy project run train-custom-model
-  #     ```
-
-  #     Explanation:
-  #     - The command trains a custom text classification model using spaCy.
-  #     - It uses the converted data in spaCy format located in the './corpus' directory.
-  #     - The model is trained using the configuration defined in 'corpus/config.cfg'.
-  #   script:
-  #     - "python -m spacy train corpus/config.cfg --paths.train corpus/train.spacy --paths.dev corpus/dev.spacy"
-