semvec/pypi-packages huggingface dataset:
hypergen-translation-plugin 0.1.3
Translation plugin for Django Hypergen
Requires Python: >=3.7

Hypergen Translation Plugin
===========================

Translation plugin for `Django Hypergen <https://github.com/runekaagaard/django-hypergen/>`_. Processes the AST
of Hypergen template python files and inserts them into the database for translation.

Provides the `TranslationPlugin` that makes Hypergen liveviews and actions translatable based on the current
django translation language.

Usage
=====

Installation::

    pip install hypergen-translation-plugin

Change `settings.py`::

    # Add to installed apps:
    INSTALLED_APPS = [
        ...
        'hypergen_translation'
    ]

    # Select which importable modules to look for translatable strings in.
    HYPERGEN_TRANSLATION_MODULES = ["hypergen_first_app.views"]
    # The base dir of the git/whatever project.
    HYPERGEN_TRANSLATION_PROJECT_DIR = BASE_DIR.parent
    # Optionally display link directly to file and linenumber for each translation occurence. 
    HYPERGEN_TRANSLATION_GITHUB = "https://github.com/runekaagaard/hypergen-translation-plugin"
    # Optionally link to another branch than main.´ on github.
    HYPERGEN_TRANSLATION_BRANCH = "dev"

Collection collections and store it in the database::

    python manage.py hypergen_translation_collect

Then edit in the admin.

Add plugin to liveviews and actions::

    from hypergen_translation.plugins import TranslationPlugin

    @liveview(..., user_plugins=[TranslationPlugin()])
    def my_view(request):
        p("translate this!")
gt4sd-trainer-hf-pl 1.0.0
Transformers trainer submodule of GT4SD.

# GT4SD's trainer submodule for HF transformers and PyTorch Lightning

Train Language Models via HuggingFace transformers and PyTorch Lightning.


### Development setup & installation

Create any virtual or conda environment compatible with the specs in setup.cfg. Then run:
```sh
pip install -e ".[dev]" 
```



### Perform training via the CLI command

GT4SD provides a trainer client based on the `gt4sd-lm-trainer` CLI command. 
```console
$ gt4sd-trainer-lm --help
usage: gt4sd-trainer-lm [-h] [--configuration_file CONFIGURATION_FILE]

optional arguments:
  -h, --help            show this help message and exit
  --configuration_file CONFIGURATION_FILE
                        Configuration file for the trainining. It can be used
                        to completely by-pass pipeline specific arguments.
                        (default: None)
```

To launch a training you have two options.

You can either specify the path of a configuration file that contains the needed training parameters:

```sh
gt4sd-trainer-lm  --training_pipeline_name ${TRAINING_PIPELINE_NAME} --configuration_file ${CONFIGURATION_FILE}
```

Or you can provide directly the needed parameters as arguments:

```sh
gt4sd-trainer-lm --type mlm --model_name_or_path mlm --training_file /path/to/train_file.jsonl --validation_file /path/to/valid_file.jsonl
```


### Convert PyTorch Lightning checkpoints to HuggingFace model via the CLI command

Once a training pipeline has been run via the `gt4sd-lm-trainer`, it's possible to convert the PyTorch Lightning checkpoint
 to HugginFace model via `gt4sd-pl-to-hf`:

```sh
gt4sd-pl-to-hf --hf_model_path ${HF_MODEL_PATH} --training_type ${TRAINING_TYPE} --model_name_or_path ${MODEL_NAME_OR_PATH} --ckpt {CKPT} --tokenizer_name_or_path {TOKENIZER_NAME_OR_PATH}
```



### References

If you use `gt4sd` in your projects, please consider citing the following:

```bib
@article{manica2022gt4sd,
  title={GT4SD: Generative Toolkit for Scientific Discovery},
  author={Manica, Matteo and Cadow, Joris and Christofidellis, Dimitrios and Dave, Ashish and Born, Jannis and Clarke, Dean and Teukam, Yves Gaetan Nana and Hoffman, Samuel C and Buchan, Matthew and Chenthamarakshan, Vijil and others},
  journal={arXiv preprint arXiv:2207.03928},
  year={2022}
}
```

### License

The `gt4sd` codebase is under MIT license.
For individual model usage, please refer to the model licenses found in the original packages.
flowfunnel 0.1.7
Leveraging Bayesian hierarchical models to diagnose issues and proactively predict user flow stages in conversion funnels.
Requires Python: >=3.8

<div align="center"><img src="https://github.com/yinsn/FlowFunnel/blob/develop/docs/images/flowfunnel-logo.svg" width="800"/></div>

# FlowFunnel

Leveraging Bayesian hierarchical models to diagnose issues and proactively predict user flow stages in conversion funnels. A cutting-edge solution for optimizing user behavior in digital platforms.