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natural language processing
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ecfr-textcat
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<!-- WEASEL: AUTO-GENERATED DOCS START (do not remove) -->

# 🪐 Weasel Project: Citations of ECFR Banking Regulation in a spaCy pipeline.

Custom text classification project for spaCy v3 adapted from the spaCy v3

## 📋 project.yml

The [`project.yml`](project.yml) defines the data assets required by the
project, as well as the available commands and workflows. For details, see the
[Weasel documentation](https://github.com/explosion/weasel).

### ⏯ Commands

The following commands are defined by the project. They
can be executed using [`weasel run [name]`](https://github.com/explosion/weasel/tree/main/docs/cli.md#rocket-run).
Commands are only re-run if their inputs have changed.

| Command | Description |
| --- | --- |
| `format-script` | 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.
 |
| `train-text-classification-model` | 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).
 |
| `classify-unlabeled-data` | 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).
 |
| `format-labeled-data` | 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).
 |
| `setup-environment` | Set up the Python virtual environment.
 |
| `review-evaluation-data` | 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.
 |
| `export-reviewed-evaluation-data` | 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.
 |
| `import-training-data` | 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.
 |
| `import-golden-evaluation-data` | 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.
 |
| `train-model-experiment1` | 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.
 |
| `download-model` | 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.
 |
| `convert-data-to-spacy-format` | 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'.

 |

| `train-custom-model` | 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'.

 |



### ⏭ Workflows



The following workflows are defined by the project. They

can be executed using [`weasel run [name]`](https://github.com/explosion/weasel/tree/main/docs/cli.md#rocket-run)

and will run the specified commands in order. Commands are only re-run if their

inputs have changed.



| Workflow | Steps |

| --- | --- |

| `all` | `format-script` &rarr; `train-text-classification-model` &rarr; `classify-unlabeled-data` &rarr; `format-labeled-data` &rarr; `setup-environment` &rarr; `review-evaluation-data` &rarr; `export-reviewed-evaluation-data` &rarr; `import-training-data` &rarr; `import-golden-evaluation-data` &rarr; `train-model-experiment1` &rarr; `download-model` &rarr; `convert-data-to-spacy-format` &rarr; `train-custom-model` |



### 🗂 Assets



The following assets are defined by the project. They can

be fetched by running [`weasel assets`](https://github.com/explosion/weasel/tree/main/docs/cli.md#open_file_folder-assets)

in the project directory.



| File | Source | Description |

| --- | --- | --- |

| [`corpus/labels/ner.json`](corpus/labels/ner.json) | Local | JSON file containing NER labels |

| [`corpus/labels/parser.json`](corpus/labels/parser.json) | Local | JSON file containing parser labels |

| [`corpus/labels/tagger.json`](corpus/labels/tagger.json) | Local | JSON file containing tagger labels |

| [`corpus/labels/textcat_multilabel.json`](corpus/labels/textcat_multilabel.json) | Local | JSON file containing multilabel text classification labels |

| [`data/eval.jsonl`](data/eval.jsonl) | Local | JSONL file containing evaluation data |

| [`data/firstStep_file.jsonl`](data/firstStep_file.jsonl) | Local | JSONL file containing formatted data from the first step |

| `data/five_examples_annotated5.jsonl` | Local | JSONL file containing five annotated examples |

| [`data/goldenEval.jsonl`](data/goldenEval.jsonl) | Local | JSONL file containing golden evaluation data |

| [`data/thirdStep_file.jsonl`](data/thirdStep_file.jsonl) | Local | JSONL file containing classified data from the third step |

| [`data/train.jsonl`](data/train.jsonl) | Local | JSONL file containing training data |

| [`data/train200.jsonl`](data/train200.jsonl) | Local | JSONL file containing initial training data |

| [`data/train4465.jsonl`](data/train4465.jsonl) | Local | JSONL file containing formatted and labeled training data |

| [`my_trained_model/textcat_multilabel/cfg`](my_trained_model/textcat_multilabel/cfg) | Local | Configuration files for the text classification model |

| [`my_trained_model/textcat_multilabel/model`](my_trained_model/textcat_multilabel/model) | Local | Trained model files for the text classification model |

| [`my_trained_model/vocab/key2row`](my_trained_model/vocab/key2row) | Local | Mapping from keys to row indices in the vocabulary |

| [`my_trained_model/vocab/lookups.bin`](my_trained_model/vocab/lookups.bin) | Local | Binary lookups file for the vocabulary |

| [`my_trained_model/vocab/strings.json`](my_trained_model/vocab/strings.json) | Local | JSON file containing string representations of the vocabulary |

| [`my_trained_model/vocab/vectors`](my_trained_model/vocab/vectors) | Local | Directory containing vector files for the vocabulary |

| [`my_trained_model/vocab/vectors.cfg`](my_trained_model/vocab/vectors.cfg) | Local | Configuration file for vectors in the vocabulary |

| [`my_trained_model/config.cfg`](my_trained_model/config.cfg) | Local | Configuration file for the trained model |

| [`my_trained_model/meta.json`](my_trained_model/meta.json) | Local | JSON file containing metadata for the trained model |

| [`my_trained_model/tokenizer`](my_trained_model/tokenizer) | Local | Tokenizer files for the trained model |

| [`output/experiment1/model-best/textcat_multilabel/cfg`](output/experiment1/model-best/textcat_multilabel/cfg) | Local | Configuration files for the best model in experiment 1 |

| [`output/experiment1/model-best/textcat_multilabel/model`](output/experiment1/model-best/textcat_multilabel/model) | Local | Trained model files for the best model in experiment 1 |

| [`output/experiment1/model-best/vocab/key2row`](output/experiment1/model-best/vocab/key2row) | Local | Mapping from keys to row indices in the vocabulary for the best model in experiment 1 |

| [`output/experiment1/model-best/vocab/lookups.bin`](output/experiment1/model-best/vocab/lookups.bin) | Local | Binary lookups file for the vocabulary for the best model in experiment 1 |

| [`output/experiment1/model-best/vocab/strings.json`](output/experiment1/model-best/vocab/strings.json) | Local | JSON file containing string representations of the vocabulary for the best model in experiment 1 |

| [`output/experiment1/model-best/vocab/vectors`](output/experiment1/model-best/vocab/vectors) | Local | Directory containing vector files for the vocabulary for the best model in experiment 1 |

| [`output/experiment1/model-best/vocab/vectors.cfg`](output/experiment1/model-best/vocab/vectors.cfg) | Local | Configuration file for vectors in the vocabulary for the best model in experiment 1 |

| [`output/experiment1/model-best/config.cfg`](output/experiment1/model-best/config.cfg) | Local | Configuration file for the best model in experiment 1 |

| [`output/experiment1/model-best/meta.json`](output/experiment1/model-best/meta.json) | Local | JSON file containing metadata for the best model in experiment 1 |

| [`output/experiment1/model-best/tokenizer`](output/experiment1/model-best/tokenizer) | Local | Tokenizer files for the best model in experiment 1 |

| [`output/experiment1/model-last/textcat_multilabel/cfg`](output/experiment1/model-last/textcat_multilabel/cfg) | Local | Configuration files for the last model in experiment 1 |

| [`output/experiment1/model-last/textcat_multilabel/model`](output/experiment1/model-last/textcat_multilabel/model) | Local | Trained model files for the last model in experiment 1 |

| [`output/experiment1/model-last/vocab/key2row`](output/experiment1/model-last/vocab/key2row) | Local | Mapping from keys to row indices in the vocabulary for the last model in experiment 1 |

| [`output/experiment1/model-last/vocab/lookups.bin`](output/experiment1/model-last/vocab/lookups.bin) | Local | Binary lookups file for the vocabulary for the last model in experiment 1 |

| [`output/experiment1/model-last/vocab/strings.json`](output/experiment1/model-last/vocab/strings.json) | Local | JSON file containing string representations of the vocabulary for the last model in experiment 1 |

| [`output/experiment1/model-last/vocab/vectors`](output/experiment1/model-last/vocab/vectors) | Local | Directory containing vector files for the vocabulary for the last model in experiment 1 |

| [`output/experiment1/model-last/vocab/vectors.cfg`](output/experiment1/model-last/vocab/vectors.cfg) | Local | Configuration file for vectors in the vocabulary for the last model in experiment 1 |

| [`output/experiment1/model-last/config.cfg`](output/experiment1/model-last/config.cfg) | Local | Configuration file for the last model in experiment 1 |

| [`output/experiment1/model-last/meta.json`](output/experiment1/model-last/meta.json) | Local | JSON file containing metadata for the last model in experiment 1 |

| [`output/experiment1/model-last/tokenizer`](output/experiment1/model-last/tokenizer) | Local | Tokenizer files for the last model in experiment 1 |

| [`output/experiment3/model-best/textcat_multilabel/cfg`](output/experiment3/model-best/textcat_multilabel/cfg) | Local | Configuration files for the best model in experiment 3 |

| [`output/experiment3/model-best/textcat_multilabel/model`](output/experiment3/model-best/textcat_multilabel/model) | Local | Trained model files for the best model in experiment 3 |

| [`output/experiment3/model-best/vocab/key2row`](output/experiment3/model-best/vocab/key2row) | Local | Mapping from keys to row indices in the vocabulary for the best model in experiment 3 |

| [`output/experiment3/model-best/vocab/lookups.bin`](output/experiment3/model-best/vocab/lookups.bin) | Local | Binary lookups file for the vocabulary for the best model in experiment 3 |

| [`output/experiment3/model-best/vocab/strings.json`](output/experiment3/model-best/vocab/strings.json) | Local | JSON file containing string representations of the vocabulary for the best model in experiment 3 |

| [`output/experiment3/model-best/vocab/vectors`](output/experiment3/model-best/vocab/vectors) | Local | Directory containing vector files for the vocabulary for the best model in experiment 3 |

| [`output/experiment3/model-best/vocab/vectors.cfg`](output/experiment3/model-best/vocab/vectors.cfg) | Local | Configuration file for vectors in the vocabulary for the best model in experiment 3 |

| [`output/experiment3/model-best/config.cfg`](output/experiment3/model-best/config.cfg) | Local | Configuration file for the best model in experiment 3 |

| [`output/experiment3/model-best/meta.json`](output/experiment3/model-best/meta.json) | Local | JSON file containing metadata for the best model in experiment 3 |

| [`output/experiment3/model-best/tokenizer`](output/experiment3/model-best/tokenizer) | Local | Tokenizer files for the best model in experiment 3 |

| [`output/experiment3/model-last/textcat_multilabel/cfg`](output/experiment3/model-last/textcat_multilabel/cfg) | Local | Configuration files for the last model in experiment 3 |

| [`output/experiment3/model-last/textcat_multilabel/model`](output/experiment3/model-last/textcat_multilabel/model) | Local | Trained model files for the last model in experiment 3 |

| [`output/experiment3/model-last/vocab/key2row`](output/experiment3/model-last/vocab/key2row) | Local | Mapping from keys to row indices in the vocabulary for the last model in experiment 3 |

| [`output/experiment3/model-last/vocab/lookups.bin`](output/experiment3/model-last/vocab/lookups.bin) | Local | Binary lookups file for the vocabulary for the last model in experiment 3 |

| [`output/experiment3/model-last/vocab/strings.json`](output/experiment3/model-last/vocab/strings.json) | Local | JSON file containing string representations of the vocabulary for the last model in experiment 3 |

| [`output/experiment3/model-last/vocab/vectors`](output/experiment3/model-last/vocab/vectors) | Local | Directory containing vector files for the vocabulary for the last model in experiment 3 |

| [`output/experiment3/model-last/vocab/vectors.cfg`](output/experiment3/model-last/vocab/vectors.cfg) | Local | Configuration file for vectors in the vocabulary for the last model in experiment 3 |

| [`output/experiment3/model-last/config.cfg`](output/experiment3/model-last/config.cfg) | Local | Configuration file for the last model in experiment 3 |

| [`output/experiment3/model-last/meta.json`](output/experiment3/model-last/meta.json) | Local | JSON file containing metadata for the last model in experiment 3 |

| [`output/experiment3/model-last/tokenizer`](output/experiment3/model-last/tokenizer) | Local | Tokenizer files for the last model in experiment 3 |

| [`python_Code/finalStep-formatLabel.py`](python_Code/finalStep-formatLabel.py) | Local | Python script for formatting labeled data in the final step |

| [`python_Code/firstStep-format.py`](python_Code/firstStep-format.py) | Local | Python script for formatting data in the first step |

| [`python_Code/five_examples_annotated.ipynb`](python_Code/five_examples_annotated.ipynb) | Local | Jupyter notebook containing five annotated examples |

| [`python_Code/secondStep-score.py`](python_Code/secondStep-score.py) | Local | Python script for scoring data in the second step |

| [`python_Code/thirdStep-label.py`](python_Code/thirdStep-label.py) | Local | Python script for labeling data in the third step |

| [`python_Code/train_eval_split.ipynb`](python_Code/train_eval_split.ipynb) | Local | Jupyter notebook for training and evaluation data splitting |

| [`TerminalCode.txt`](TerminalCode.txt) | Local | Text file containing terminal code |

| [`README.md`](README.md) | Local | Markdown file containing project documentation |

| [`prodigy.json`](prodigy.json) | Local | JSON file containing Prodigy configuration |



<!-- WEASEL: AUTO-GENERATED DOCS END (do not remove) -->