GeNRe

GitHub Project for GeNRe (Gender-Neutral Rewriter Using French Collective Nouns).

Project Structure

• /data: contains the data of the project
  • /collective_nouns: contains the collective nouns dictionary
    • /scripts: contains Python scripts used to further populate the dictionary
      • /output: contains output of said scripts
  • /dev: contains development set of sentences used to build the RBS (rule-based system)
  • /europarl: contains Europarl corpus file used to extract sentences for fine-tuning and evaluation
  • /eval: contains evaluation data for both corpora (Wikipedia/Europarl) and for each component of the RBS (dependency detection/generation)
  • /ft: contains train and valid sets used for fine-tuning
  • /sentence_gathering: contains Python scripts used to filter the Wikipedia and Europarl corpora, get sentences with member nouns in our dictionary and annotate them accordingly
    • /output: contains said sentences for both corpora
  • /tests: contains test sets for each component of the RBS (dependency detection/generation)
• /instruction_models: contains Python scripts used to communicate with Claude 3 Opus' and Mixtral 8x7B's APIs, for comparison with the RBS and fine-tuned models. Also contains a config.ini file for configuration purposes
• /neural: contains the IPYNB file used to fine-tune T5 and M2M100 models
• /rbs: contains Python scripts for the RBS (rule-based system) components
  • /tests: contains component testing Python scripts (see Testing)

Usage

pip install -r requirements.txt

python -m spacy download fr_core_news_sm

Rule-based system (RBS)

Run python main.py to instantiate the required spaCy and inflecteur models. From then, you can input as many sentences to convert as you want.

Before using the script, check the Testing section to make sure everything works correctly in order to have the best usage experience.

Testing

Run python -m rbs.tests.tests to execute a series of tests for the two components of the RBS: the dependency detection component and the generation component. Test data can be found in the data/tests folder.

If the default tests are failing, it probably means that there is something wrong with your spaCy installation. Please run python -m spacy info in your environment and make sure of the following:

• your local spaCy version is equal to 3.7.4,
• your local fr_core_news_sm spaCy pipeline version is equal to 3.7.0.

If the issue persists, please open an issue detailing your environment and the steps that you have taken so far.

Fine-tuned models

GeNRe-FT-T5

GeNRe-FT-T5 is made available on HuggingFace.

For inference, use the following code:

from transformers import T5ForConditionalGeneration, T5Tokenizer

model = T5ForConditionalGeneration.from_pretrained("spidersouris/genre-t5-small-60k")
tokenizer = T5Tokenizer.from_pretrained("spidersouris/genre-t5-small-60k")

GeNRe-FT-M2M100

GeNRe-FT-M2M100 is made available on HuggingFace.

For inference, use the following code:

from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer

model = M2M100ForConditionalGeneration.from_pretrained("spidersouris/genre-m2m100_418M")
tokenizer = M2M100Tokenizer.from_pretrained("spidersouris/genre-m2m100_418M")

Instruction models

instruction_models/main.py is nothing more than a Python wrapper around the Mistral and Claude APIs.

Prompts and few-shot examples can be modified in instruction_models/utils.py.

Configuration

You can configure your model API keys as well as additional options by editing the included config.ini file.

The specified input file in config.ini should be a CSV file with one sentence per row (header excluded).

Evaluation

Run python eval.py with arguments to run evaluation.

Usage: python eval.py inp_file out_file [-h] [--component {gen,deps}] [--devset DEVSET] [--evalset EVALSET] [--print_error_types] [--autocol AUTOCOL] [--maxsents MAXSENTS] [--annotated] [--extract] [--corpus {wiki,eupr}]

Arguments

Positional arguments correspond to the input file (inp_file) and the output file (out_file).

Optional arguments are listed below:

• -c, --component (str): The component to evaluate (either "gen" or "deps").
• -ds, --devset (str): The path to the development set file.
• -es, --evalset (str): The path to the evaluation set file.
• -p, --print_error_types: Print the error types.
• -ac, --autocol (int): The column number of the automatically generated sentences to evaluate. Default: 3.
• -ms, --maxsents (int): The maximum number of sentences to evaluate. Default: 250.
• -a, --annotated: Indicates whether the data is annotated with <n> tags.
• -e, --extract: Extract evaluation data from the input file.
• -cp, --corpus (str): The corpus type (either "wiki" or "eupr").

Examples

Coming soon.