semvec/pypi-packages huggingface dataset:
FinancialMaths 0.0.1
Calculator for financial mathematics
Keywords: python,finance,financial mathematics,annuity,interest rate,discount rate


# FinancialMaths

- A simple financial mathematics calculator.
- Contents are mainly from the actuarial notes (CT1)

## Folder structure:

1. **InterestRateMethods** &rarr; contains the methods from converting {simple, compunding} {interest, discounting} {norminal, effective} rates
2. **InterestRates.py** &rarr; contains the Rate class which is responsible to the common interest methods.
3. **Annuities** &rarr; contains Annuity class with annuity functions.
4. **VaryAnnuity** &rarr; contains Annuity class with annuity functions. Also contiains varying annuity methods.
compassionai-garland 0.2.1
CompassionAI Project Garland - machine translation for classical Tibetan
Requires Python: >=3.6

# CompassionAI project Garland - neural machine translation from classical Tibetan

Machine translation from classical literary Tibetan. Current focus is on:

- Custom neural machine translation models that build on Hugging Face Transformers.
- Using short sentence translation models published by the big research labs, such as FAIR, as backbone models for translating long texts.

Eventually these techniques may generalize to other low resource languages.

## Installation

There are two modes for this library - inference and research. We provide instructions for Linux.

 - Inference should work on MacOS and Windows _mutatis mutandis_.
 - We *very strongly* recommend doing research *only* on Linux. We will not provide any support to people trying to perform research tasks without installing Linux.

### Virtual environment

We strongly recommend using a virtual environment for all your Python package installations, including anything from CompassionAI. To facilitate this, we provide a simple Conda environment YAML file in the CompassionAI/common repo. We recommend first installing miniconda, see <https://docs.conda.io/en/main/miniconda.html>. We then recommend installing Mamba, see <https://github.com/mamba-org/mamba>.

```bash
bash Miniconda3-latest-Linux-x86_64.sh
conda install mamba -c conda-forge
cd compassionai/common
mamba env create -f env-minimal.yml -n my-env
conda activate my-env
```

### Inference

Just install with pip:

```bash
pip install compassionai-garland
```

### Research

Begin by installing for inference. Then install the CompassionAI data registry repo and set two environment variables:

```bash
$CAI_TEMP_PATH
$CAI_DATA_BASE_PATH
```

We strongly recommend setting them with conda in your virtual environment:

```bash
conda activate my-env
conda env config vars set CAI_TEMP_PATH=#directory on a mountpoint with plenty of space, does not need to be fast
conda env config vars set CAI_DATA_BASE_PATH=#absolute path to the CompassionAI data registry
```

Our code uses these environment variables to load datasets from the registry, output processed datasets and store training results.

## Usage

### Inference

This is a supporting library for our main inference repos, such as Lotsawa. You shouldn't need to use it directly.

### Research

This library implements neural machine translation models from classical Tibetan to English, with experiments for other target languages as well.

- Dataset preparation code, especially see `cai_garland/data/parallel_dataset_prep.py`.
- Implementation of modified tokenizers and neural model architectures that builds on Hugging Face Transformers.
- Training drivers to fine-tune models on tasks relevant to translation, such as translation itself or text segmentation.
- Utility code for the above, including simple libraries of preprocessors and segmenters, as well as a translation utility class that implements the core loops of our contextual translation algorithms.
compassionai-manas 0.2.2
CompassionAI Project Manas - a bidirectional Tibetan transformer
Requires Python: >=3.6

# CompassionAI project Manas - classical Tibetan language understanding models

Monolingual classical literary Tibetan modeling. The current focus is on pretrained transformer models for:

- Monolingual tasks that are useful for teaching to read Tibetan, especially word segmentation, part-of-speech tagging and named entity recognition.
- Use as an encoder for the machine translation model.

## Installation

There are two modes for this library - inference and research. We provide instructions for Linux.

 - Inference should work on MacOS and Windows _mutatis mutandis_.
 - We *very strongly* recommend doing research *only* on Linux. We will not provide any support to people trying to perform research tasks without installing Linux.

### Virtual environment

We strongly recommend using a virtual environment for all your Python package installations, including anything from CompassionAI. To facilitate this, we provide a simple Conda environment YAML file in the CompassionAI/common repo. We recommend first installing miniconda, see <https://docs.conda.io/en/main/miniconda.html>. We then recommend installing Mamba, see <https://github.com/mamba-org/mamba>.

```bash
bash Miniconda3-latest-Linux-x86_64.sh
conda install mamba -c conda-forge
cd compassionai/common
mamba env create -f env-minimal.yml -n my-env
conda activate my-env
```

### Inference

Just install with pip:

```bash
pip install compassionai-manas
```

### Research

Begin by installing for inference. Then install the CompassionAI data registry repo and set two environment variables:

```bash
$CAI_TEMP_PATH
$CAI_DATA_BASE_PATH
```

We strongly recommend setting them with conda in your virtual environment:

```bash
conda activate my-env
conda env config vars set CAI_TEMP_PATH=#directory on a mountpoint with plenty of space, does not need to be fast
conda env config vars set CAI_DATA_BASE_PATH=#absolute path to the CompassionAI data registry
```

Our code uses these environment variables to load datasets from the registry, output processed datasets and store training results.

## Usage

### Inference

This is a supporting library for our main inference repos, such as Lotsawa. You shouldn't need to use it directly.

### Research

This library implements language understanding for classical Tibetan.

- Tokenization.
- Pre-training code.
- Fine-tuning on language understanding tasks, such as word segmentation and part-of-speech tagging.
compassionai-common 0.2.2
Common components for CompassionAI projects
Requires Python: >=3.6

# CompassionAI common repository

Common utilities and dataset preparation tools in use by various CompassionAI projects.

## Installation

There are two modes for this library - inference and research. We provide instructions for Linux.

 - Inference should work on MacOS and Windows _mutatis mutandis_.
 - We *very strongly* recommend doing research *only* on Linux. We will not provide any support to people trying to perform research tasks without installing Linux.

### Virtual environment

We strongly recommend using a virtual environment for all your Python package installations, including anything from CompassionAI. To facilitate this, we provide a simple Conda environment YAML file. We recommend first installing miniconda, see <https://docs.conda.io/en/main/miniconda.html>. We then recommend installing Mamba, see <https://github.com/mamba-org/mamba>.

```bash
bash Miniconda3-latest-Linux-x86_64.sh
conda install mamba -c conda-forge
mamba env create -f env-minimal.yml -n my-env
conda activate my-env
```

### Inference

Just install with pip:

```bash
pip install compassionai-common
```

### Research

Begin by installing for inference. Then install the CompassionAI data registry repo and set two environment variables:

```bash
$CAI_TEMP_PATH
$CAI_DATA_BASE_PATH
```

We strongly recommend setting them with conda in your virtual environment:

```bash
conda activate my-env
conda env config vars set CAI_TEMP_PATH=#directory on a mountpoint with plenty of space, does not need to be fast
conda env config vars set CAI_DATA_BASE_PATH=#absolute path to the CompassionAI data registry
```

Our code uses these environment variables to load datasets from the registry, output processed datasets and store training results.

You probably also want to install CUDA and PyTorch (>=1.12) with CUDA support - follow the instructions here <https://pytorch.org/get-started/locally/>. You don't need torchvision or torchaudio but it is safe to install them if you like. You can reinstall CUDA-enabled PyTorch with `pip` in your conda environment after installing everything as above.

For fine-tuning, you will need a powerful NVidia GPU. A GTX 1080 might work. We recommend at least an RTX 3080 Ti in a home setup, or a V100 if using a cloud. We have not tested non-NVidia GPUs.

For pre-training, you will need a TPU on GCP. We do not recommend fine-tuning on GPUs. We do not expect it to work on anything less than a DGX-2 or a p3dn.24xlarge instance.

## Usage

### Inference

This is a supporting library for our main inference repos, such as Lotsawa. You shouldn't need to use it directly.

### Research

This library contains components that are common to the various tasks performed by the other libraries, such as Manas and Garland.

 - Implements data loader objects such as KangyurLoader, TengyurLoader and TibetanDict.
 - Implements common PyTorch dataset objects, such as TokenTagDataset.
 - Provides utility functions for models and data, such as Hydra-Huggingface adapters, PyTorch callbacks, model downloaders and configuration providers.
simple-json-utils 0.0.2
A simple json utilities package
Requires Python: >=3.7

# Json Utilities
This package has two small functions, `get_json` `save_json`,


`get_json(filePath: str) -> dict`

`save_json(data: dict, filePath: str) -> bool` will save whatever `data` contains into file at `filePath`, with default indentation of 4, returns True if the operation was successful.
python-io-wrapper 0.3.1
IO wrapper to add missing methods from io.BaseIO

python-io-wrapper
=================

Early version of a Python 3 wrapper for a file-like object. Currently
supports only raw (binary) objects, but text will come soon. Implement
any missing methods from io.RawIOBase.

# Usage

```
from io_wrapper import RawIOWrapper

...

new_stream = RawIOWrapper(my_file_like_object)
```

## License

This package is in the Public Domain. See UNLICENSE.md for details.