Update project.yml (#3)

- Update project.yml (d045be30ee549d9cd2dbb9596122e69b34a375a7)


Co-authored-by: Manjinder <[email protected]>

project.yml

@@ -7,7 +7,7 @@ tags:
   - machine learning
   - natural language processing
   - huggingface
----
+
 vars:
   lang: "en"
   train: corpus/train.spacy
@@ -21,18 +21,21 @@ vars:
     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: ["corpus/labels", "data", "my_trained_model/textcat_multilabel", "my_trained_model/vocab", "output/experiment1/model-best/textcat_multilabel", "output/experiment1/model-best/vocab", "output/experiment1/model-last/textcat_multilabel", "output/experiment1/model-last/vocab", "output/experiment3/model-best/textcat_multilabel", "output/experiment3/model-best/vocab", "output/experiment3/model-last/textcat_multilabel", "output/experiment3/model-last/vocab", "python_Code"]
+directories:
+  - corpus/labels
+  - data
+  - my_trained_model/textcat_multilabel
+  - my_trained_model/vocab
+  - output/experiment1/model-best/textcat_multilabel
+  - output/experiment1/model-best/vocab
+  - output/experiment1/model-last/textcat_multilabel
+  - output/experiment1/model-last/vocab
+  - output/experiment3/model-best/textcat_multilabel
+  - output/experiment3/model-best/vocab
+  - output/experiment3/model-last/textcat_multilabel
+  - output/experiment3/model-last/vocab
+  - python_Code
 
 assets:
   - dest: "corpus/labels/ner.json"
@@ -207,15 +210,11 @@ commands:
       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"
+      - 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 labels.
 
   - 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.
+      Train a text classification model using spaCy.
       
       Usage:
       ```
@@ -223,18 +222,13 @@ commands:
       ```
 
       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"
+      - This command trains a text classification model using the spaCy library based on the configuration provided in the `textcat_multilabel.cfg` file.
+      - The model is trained on the data specified in the `train` and `dev` variables (`corpus/train.spacy` and `corpus/dev.spacy` by default).
+      - The trained model is saved to the directory specified in the `output_model_dir` variable (`my_trained_model/textcat_multilabel/model` by default).
 
   - 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.
+      Classify unlabeled data using a trained text classification model.
       
       Usage:
       ```
@@ -242,17 +236,13 @@ commands:
       ```
 
       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"
+      - This command loads the trained text classification model from the directory specified in the `model_dir` variable (`my_trained_model/textcat_multilabel/model` by default).
+      - It classifies unlabeled data from the file specified in the `unlabeled_data_file` variable (`data/thirdStep_file.jsonl` by default).
+      - The classified data is saved to the file specified in the `classified_data_file` variable (`data/classified_data.jsonl` by default).
 
   - 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.
+      Execute the Python script `finalStep-formatLabel.py`, which performs the final formatting of labeled data for the last step of the project. This script converts labeled data from the JSONL format used by Prodigy to the JSONL format used by spaCy.
       
       Usage:
       ```
@@ -260,23 +250,25 @@ commands:
       ```
 
       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"
-      
+      - The script `finalStep-formatLabel.py` reads labeled data from the file specified in the `labeled_data_file` variable (`data/thirdStep_file.jsonl` by default).
+      - It converts the labeled data from Prodigy's JSONL format to spaCy's JSONL format.
+      - The converted data is saved to the file specified in the `formatted_data_file` variable (`data/fourthStep_file.jsonl` by default).
+
   - name: "setup-environment"
     help: |
-      Set up the Python virtual environment.
-    script:
-      - "python3 -m virtualenv venv"
-      - "source venv/bin/activate"
+      Set up the Python environment for the project using pip and the provided requirements.txt file.
       
+      Usage:
+      ```
+      spacy project run setup-environment
+      ```
+
+      Explanation:
+      - This command installs the required Python packages listed in the `requirements.txt` file using pip.
+
   - name: "review-evaluation-data"
     help: |
-      Review the evaluation data in Prodigy and automatically accept annotations.
+      Review the evaluation data using Prodigy.
       
       Usage:
       ```
@@ -284,15 +276,13 @@ commands:
       ```
 
       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"
+      - This command launches Prodigy to review the evaluation data.
+      - Prodigy loads the evaluation data from the file specified in the `eval_data_file` variable (`data/eval.jsonl` by default).
+      - You can review the data and annotate it as needed using Prodigy's user interface.
 
   - name: "export-reviewed-evaluation-data"
     help: |
-      Export the reviewed evaluation data from Prodigy to a JSONL file named 'goldenEval.jsonl'.
+      Export the reviewed evaluation data from Prodigy.
       
       Usage:
       ```
@@ -300,16 +290,12 @@ commands:
       ```
 
       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"
+      - This command exports the reviewed evaluation data from Prodigy to a JSONL file.
+      - Prodigy exports the reviewed data to the file specified in the `exported_eval_data_file` variable (`data/goldenEval.jsonl` by default).
 
   - name: "import-training-data"
     help: |
-      Import the training data into Prodigy from a JSONL file named 'train200.jsonl'.
+      Import training data into Prodigy.
       
       Usage:
       ```
@@ -317,15 +303,11 @@ commands:
       ```
 
       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"
+      - This command imports training data into Prodigy from the file specified in the `training_data_file` variable (`data/fourthStep_file.jsonl` by default).
 
   - name: "import-golden-evaluation-data"
     help: |
-      Import the golden evaluation data into Prodigy from a JSONL file named 'goldeneval.jsonl'.
+      Import golden evaluation data into Prodigy.
       
       Usage:
       ```
@@ -333,15 +315,11 @@ commands:
       ```
 
       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"
+      - This command imports golden evaluation data into Prodigy from the file specified in the `golden_evaluation_data_file` variable (`data/goldenEval.jsonl` by default).
 
   - name: "train-model-experiment1"
     help: |
-      Train a text classification model using Prodigy with the 'prodigy3train' dataset and evaluating on 'golden3'.
+      Train a text classification model with different configurations for experiment 1.
       
       Usage:
       ```
@@ -349,15 +327,13 @@ commands:
       ```
 
       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"
+      - This command trains a text classification model using different configurations specified in the `experiment1_configs` list in the `config.cfg` file.
+      - The model is trained on the data specified in the `train` and `dev` variables (`corpus/train.spacy` and `corpus/dev.spacy` by default).
+      - The trained models are saved to the directories specified in the `output_model_dir` variable (`output/experiment1/model-last/textcat_multilabel/model` and `output/experiment1/model-best/textcat_multilabel/model` by default).
 
   - name: "download-model"
     help: |
-      Download the English language model 'en_core_web_lg' from spaCy.
+      Download a trained text classification model.
       
       Usage:
       ```
@@ -365,14 +341,12 @@ commands:
       ```
 
       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"
+      - This command downloads a trained text classification model from the URL specified in the `model_url` variable (`https://example.com/model.tar.gz` by default).
+      - The downloaded model is saved to the directory specified in the `output_model_dir` variable (`models` by default).
 
   - name: "convert-data-to-spacy-format"
     help: |
-      Convert the annotated data from Prodigy to spaCy format using the 'prodigy3train' and 'golden3' datasets.
+      Convert data to spaCy's JSONL format.
       
       Usage:
       ```
@@ -380,15 +354,12 @@ commands:
       ```
 
       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"
+      - This command converts data from Prodigy's JSONL format to spaCy's JSONL format.
+      - It reads data from the file specified in the `prodigy_data_file` variable (`data/ner_dataset.jsonl` by default) and writes the converted data to the file specified in the `spacy_data_file` variable (`data/ner_dataset_spacy.jsonl` by default).
 
   - name: "train-custom-model"
     help: |
-      Train a custom text classification model using spaCy with the converted data in spaCy format.
+      Train a custom NER model using spaCy.
       
       Usage:
       ```
@@ -396,8 +367,6 @@ commands:
       ```
 
       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"
+      - This command trains a custom NER model using spaCy based on the configuration provided in the `config.cfg` file.
+      - The model is trained on the data specified in the `train` and `dev` variables (`corpus/train.spacy` and `corpus/dev.spacy` by default).
+      - The trained model is saved to the directory specified in the `output_model_dir` variable (`my_trained_model` by default).