semvec/pypi-packages huggingface dataset:
wagtail-dynamic-stream-block 4.1.0
A Wagtail stream block whose child blocks are determined at runtime.
License: BSD-3-Clause
Requires Python: >=3.8

wagtail-dynamic-stream-block
============================

A Wagtail stream block whose child blocks are determined at runtime. Please bear with us while we prepare more detailed documentation.

Compatibility
-------------

`wagtail-dynamic-stream-block`' major.minor version number indicates the Wagtail release it is compatible with. Currently this is Wagtail 4.1.x

Installation
------------

1. Install using `pip`:
  ```shell
  pip install wagtail-dynamic-stream-block
  ```
2. Add
   `wagtail_dynamic_stream_block` to your `INSTALLED_APPS` setting:
   ```python
   INSTALLED_APPS = [
     # ...
     'wagtail_dynamic_stream_block'
     # ...
   ]
   ```
wagtail-dynamic-choice 4.1.0
A collection of useful Wagtail blocks for selecting items.
License: BSD-3-Clause
Requires Python: >=3.8

wagtail-dynamic-choice
======================

A collection of useful Wagtail blocks for selecting items. Please bear with us while we prepare more detailed documentation.

Compatibility
-------------

`wagtail-dynamic-choice`' major.minor version number indicates the Wagtail release it is compatible with. Currently this is Wagtail 4.1.x

Installation
------------

1. Install using `pip`:
  ```shell
  pip install wagtail-dynamic-choice
  ```
2. Add
   `wagtail_dynamic_choice` to your `INSTALLED_APPS` setting:
   ```python
   INSTALLED_APPS = [
     # ...
     'wagtail_dynamic_choice'
     # ...
   ]
   ```
pear-xai 0.0.10
PEAR: Post-hoc Explainer Agreement Regularization
License: BSD-3-Clause
Requires Python: >=3.9
Keywords: pytorch,XAI,machine learning

# :pear: PEAR: Post-hoc Explainer Agreement Regularization

A repo for training neural networks for post hoc explainer agreement. More details about the results can be found in our paper titled [Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective](https://arxiv.org/pdf/2303.13299.pdf).


## Getting Started

If you are intersted in using the :pear: PEAR package to train your own models, you can install the package through pip.

```$ pip install pear-xai```

You should then be able to run an example in a python interpreter:

```
$ python
>>> import pear
>>> pear.run_example()
```

If you are interested in cloning this repository and reproducing or building on our experiments, you can install the requirements manually as follows. (This code was developed and tested with Python 3.9.5)

After downloading the repository (or cloning it), install the requirements:

```$ pip install -r requirements.txt```

You can then open and run the Jupyter Notebook [tutorials](notebooks) on reproducing our results.
________________________________________________________________________________________________

## Contributing

We believe in open-source community driven software development. Please open issues and pull requests with any questions or improvements you have.


## Citing Our Work

To cite our work, please reference the paper linked above with the following citation.

```
@article{schwarzschild2023reckoning,
  title={Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective},
  author={Schwarzschild, Avi and Cembalest, Max and Rao, Karthik and Hines, Keegan and Dickerson, John},
  journal={arXiv preprint arXiv:2303.13299},
  year={2023}
}
```
wagtail-content-block 4.1.1
Content block primitives for Wagtail, used by wagtail-content-admin.
License: BSD-3-Clause
Requires Python: >=3.8

wagtail-content-block
=====================

Content block primitives for Wagtail, used by wagtail-content-admin. Please bear with us while we prepare more detailed documentation.

Compatibility
-------------

`wagtail-content-block`' major.minor version number indicates the Wagtail release it is compatible with. Currently this is Wagtail 4.1.x

Installation
------------

1. Install using `pip`:
  ```shell
  pip install wagtail-content-block
  ```
2. Add
   `wagtail_content_block` to your `INSTALLED_APPS` setting:
   ```python
   INSTALLED_APPS = [
     # ...
     'wagtail_content_block'
     # ...
   ]
   ```
wagtail-block-model-field 4.1.1
Use streamfield blocks as model fields.
License: BSD-3-Clause
Requires Python: >=3.8

wagtail-block-model-field
=========================

Provides a model field for encapsulating arbitrary stream blocks. Please bear with us while we prepare more detailed documentation.

Compatibility
-------------

`wagtail-block-model-field`' major.minor version number indicates the Wagtail release it is compatible with. Currently this is Wagtail 4.1.x

Installation
------------

1. Install using `pip`:
  ```shell
  pip install wagtail-block-model-field
  ```
2. Add
   `wagtail_block_model_field` to your `INSTALLED_APPS` setting:
   ```python
   INSTALLED_APPS = [
     # ...
     'wagtail_block_model_field'
     # ...
   ]
   ```