---
title: "Citations of ECFR Banking Regulation in a spaCy pipeline."
description: "Custom text classification project for spaCy v3 adapted from the spaCy v3"
author: Manjinder
date: 2024-05-01
tags:
- machine learning
- natural language processing
- huggingface
---
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: ["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"
description: "JSON file containing NER labels"
- dest: "corpus/labels/parser.json"
description: "JSON file containing parser labels"
- dest: "corpus/labels/tagger.json"
description: "JSON file containing tagger labels"
- dest: "corpus/labels/textcat_multilabel.json"
description: "JSON file containing multilabel text classification labels"
- dest: "data/eval.jsonl"
description: "JSONL file containing evaluation data"
- 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: "my_trained_model/textcat_multilabel/cfg"
description: "Configuration files for the text classification model"
- dest: "my_trained_model/textcat_multilabel/model"
description: "Trained model files for the text classification model"
- dest: "my_trained_model/vocab/key2row"
description: "Mapping from keys to row indices in the vocabulary"
- dest: "my_trained_model/vocab/lookups.bin"
description: "Binary lookups file for the vocabulary"
- dest: "my_trained_model/vocab/strings.json"
description: "JSON file containing string representations of the vocabulary"
- dest: "my_trained_model/vocab/vectors"
description: "Directory containing vector files for the vocabulary"
- dest: "my_trained_model/vocab/vectors.cfg"
description: "Configuration file for vectors in the vocabulary"
- dest: "my_trained_model/config.cfg"
description: "Configuration file for the trained model"
- dest: "my_trained_model/meta.json"
description: "JSON file containing metadata for the trained model"
- dest: "my_trained_model/tokenizer"
description: "Tokenizer files for the trained model"
- dest: "output/experiment1/model-best/textcat_multilabel/cfg"
description: "Configuration files for the best model in experiment 1"
- dest: "output/experiment1/model-best/textcat_multilabel/model"
description: "Trained model files for the best model in experiment 1"
- dest: "output/experiment1/model-best/vocab/key2row"
description: "Mapping from keys to row indices in the vocabulary for the best model in experiment 1"
- dest: "output/experiment1/model-best/vocab/lookups.bin"
description: "Binary lookups file for the vocabulary for the best model in experiment 1"
- dest: "output/experiment1/model-best/vocab/strings.json"
description: "JSON file containing string representations of the vocabulary for the best model in experiment 1"
- dest: "output/experiment1/model-best/vocab/vectors"
description: "Directory containing vector files for the vocabulary for the best model in experiment 1"
- dest: "output/experiment1/model-best/vocab/vectors.cfg"
description: "Configuration file for vectors in the vocabulary for the best model in experiment 1"
- dest: "output/experiment1/model-best/config.cfg"
description: "Configuration file for the best model in experiment 1"
- dest: "output/experiment1/model-best/meta.json"
description: "JSON file containing metadata for the best model in experiment 1"
- dest: "output/experiment1/model-best/tokenizer"
description: "Tokenizer files for the best model in experiment 1"
- dest: "output/experiment1/model-last/textcat_multilabel/cfg"
description: "Configuration files for the last model in experiment 1"
- dest: "output/experiment1/model-last/textcat_multilabel/model"
description: "Trained model files for the last model in experiment 1"
- dest: "output/experiment1/model-last/vocab/key2row"
description: "Mapping from keys to row indices in the vocabulary for the last model in experiment 1"
- dest: "output/experiment1/model-last/vocab/lookups.bin"
description: "Binary lookups file for the vocabulary for the last model in experiment 1"
- dest: "output/experiment1/model-last/vocab/strings.json"
description: "JSON file containing string representations of the vocabulary for the last model in experiment 1"
- dest: "output/experiment1/model-last/vocab/vectors"
description: "Directory containing vector files for the vocabulary for the last model in experiment 1"
- dest: "output/experiment1/model-last/vocab/vectors.cfg"
description: "Configuration file for vectors in the vocabulary for the last model in experiment 1"
- dest: "output/experiment1/model-last/config.cfg"
description: "Configuration file for the last model in experiment 1"
- dest: "output/experiment1/model-last/meta.json"
description: "JSON file containing metadata for the last model in experiment 1"
- dest: "output/experiment1/model-last/tokenizer"
description: "Tokenizer files for the last model in experiment 1"
- dest: "output/experiment3/model-best/textcat_multilabel/cfg"
description: "Configuration files for the best model in experiment 3"
- dest: "output/experiment3/model-best/textcat_multilabel/model"
description: "Trained model files for the best model in experiment 3"
- dest: "output/experiment3/model-best/vocab/key2row"
description: "Mapping from keys to row indices in the vocabulary for the best model in experiment 3"
- dest: "output/experiment3/model-best/vocab/lookups.bin"
description: "Binary lookups file for the vocabulary for the best model in experiment 3"
- dest: "output/experiment3/model-best/vocab/strings.json"
description: "JSON file containing string representations of the vocabulary for the best model in experiment 3"
- dest: "output/experiment3/model-best/vocab/vectors"
description: "Directory containing vector files for the vocabulary for the best model in experiment 3"
- dest: "output/experiment3/model-best/vocab/vectors.cfg"
description: "Configuration file for vectors in the vocabulary for the best model in experiment 3"
- dest: "output/experiment3/model-best/config.cfg"
description: "Configuration file for the best model in experiment 3"
- dest: "output/experiment3/model-best/meta.json"
description: "JSON file containing metadata for the best model in experiment 3"
- dest: "output/experiment3/model-best/tokenizer"
description: "Tokenizer files for the best model in experiment 3"
- dest: "output/experiment3/model-last/textcat_multilabel/cfg"
description: "Configuration files for the last model in experiment 3"
- dest: "output/experiment3/model-last/textcat_multilabel/model"
description: "Trained model files for the last model in experiment 3"
- dest: "output/experiment3/model-last/vocab/key2row"
description: "Mapping from keys to row indices in the vocabulary for the last model in experiment 3"
- dest: "output/experiment3/model-last/vocab/lookups.bin"
description: "Binary lookups file for the vocabulary for the last model in experiment 3"
- dest: "output/experiment3/model-last/vocab/strings.json"
description: "JSON file containing string representations of the vocabulary for the last model in experiment 3"
- dest: "output/experiment3/model-last/vocab/vectors"
description: "Directory containing vector files for the vocabulary for the last model in experiment 3"
- dest: "output/experiment3/model-last/vocab/vectors.cfg"
description: "Configuration file for vectors in the vocabulary for the last model in experiment 3"
- dest: "output/experiment3/model-last/config.cfg"
description: "Configuration file for the last model in experiment 3"
- dest: "output/experiment3/model-last/meta.json"
description: "JSON file containing metadata for the last model in experiment 3"
- dest: "output/experiment3/model-last/tokenizer"
description: "Tokenizer files for the last model in experiment 3"
- 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: "TerminalCode.txt"
description: "Text file containing terminal code"
- dest: "README.md"
description: "Markdown file containing project documentation"
- dest: "prodigy.json"
description: "JSON file containing Prodigy configuration"
workflows:
all:
- format-script
- train-text-classification-model
- classify-unlabeled-data
- format-labeled-data
- setup-environment
- review-evaluation-data
- export-reviewed-evaluation-data
- import-training-data
- import-golden-evaluation-data
- train-model-experiment1
- download-model
- convert-data-to-spacy-format
- train-custom-model
commands:
- name: "format-script"
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 execute-first-step-format-script
```
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"
#commands:
- name: "setup-environment"
help: |
Set up the Python virtual environment.
script:
- "python3 -m virtualenv venv"
- "source venv/bin/activate"
- 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"