semvec/pypi-packages huggingface dataset:
DannyBuffet-lotr-sdk 0.0.2
SDK for interacting with the Lord of the Rings API
Requires Python: >=3.6

# Lord of the Rings SDK

The Lord of the Rings SDK is a Python package that provides easy access to the Lord of the Rings API. It includes modules for interacting with the movies, and quotes from the Lord of the Rings movies.

## Installation

To get started, you'll need to install the DR_lotr_sdk package.

```bash
pip install DannyBuffet-lotr-sdk
```

This will install all the necessary dependencies.



## Importing the SDK

You can import the Lord of the Rings SDK in your Python script using the following import statement:

```python
from lordoftheringssdk.movie import Movie
from lordoftheringssdk.quote import Quote
```

This will allow you to use the Movie, and Quote classes from the SDK in your code.



## Usage

Here are some basic examples of how to use the Lord of the Rings SDK:

```python
from lordoftheringssdk.movie import Movie
from lordoftheringssdk.quote import Quote

# Get all the movies
movies = Movie.get()
print(movies[1])
print()

# Get a specific quote
print(movies[5].quotes[2])
print()

# Get the names of all the movies
movies_list = Movie.get()
for movie in movies:
    print(movie.name)
print()

# Get all the quotes
quotes = Quote.get()
for quote in quotes:
    print(quote)
print()

# Get a specific quote
print(quotes[3])
print()

# Get information about a specific movie
movie_id = Movie.get(id="5cd95395de30eff6ebccde5d")
print(movie_id.name)
print()

# Get all the quotes from a specific movie
print(movie_id.quotes)
print()
```

## Endpoints

The SDK interacts with the following endpoints:

- `/movies`: This endpoint returns a list of all the movies from the Lord of the Rings series.

- `/movies/<id>`: This endpoint returns information about a specific movie, identified by the id parameter.

- `/movie/<id>/quotes`: This endpoint returns a list of all the quotes from a specific movie, identified by the id parameter.

- `/movie/<id>/quotes/<id>`: This endpoint returns a specific quote from a specific movie, identified by the id parameter.

- `/quotes`: This endpoint returns a list of all the quotes from the Lord of the Rings movies.

- `/quotes/<id>`: This endpoint returns information about a specific quote, identified by the id parameter.
statcan-dialogue-dataset 0.2.0
The Statcan Dialogue Dataset
Requires Python: >=3.7

# Statcan Dialogue Dataset

<div align="center">

[**💻Code**](https://github.com/mcGill-NLP/statcan-dialogue-dataset) | [**📄Paper**](https://arxiv.org/abs/2304.01412) | [**🌐Homepage**](https://mcgill-nlp.github.io/statcan-dialogue-dataset) |
| :--: | :--: | :--: |

> **[The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents](https://arxiv.org/abs/2304.01412)**\
> *[Xing Han Lu](https://xinghanlu.com), [Siva Reddy](https://sivareddy.in), [Harm de Vries](https://www.harmdevries.com/)*\
> EACL 2023

![Banner Image showing a sample conversation between a user and an agent](/images/banner.svg)


</div>

This repository contains the code for our project. For detailed information, instructions on requesting access, API user guide and documentation, please visit the [website](https://mcgill-nlp.github.io/statcan-dialogue-dataset). You can find useful links above. A quickstart is provided below.

## Quickstart

First, you need to request access (please go to the main [webpage](https://mcgill-nlp.github.io/statcan-dialogue-dataset) for more information). 

Then, you can install our Python library to load the dataset:

```python
pip install statcan-dialogue-dataset
```

Now, inside Python, you can use various functions to work with the data:

```python
import statcan_dialogue_dataset as sdd

# Download the dataset (from dataverse)
sdd.download_dataverse("your_api_token")
# Alternatively, Download from huggingface
sdd.download_huggingface("your_api_token")

# Extract ZIP into ~/.statcan_dialogue_dataset/
sdd.extract_task_data_zip(remove_zip=True)

# Load task specific split for a specific language
train_ret = sdd.load_task_data(task="retrieval", split="train", lang="en")

# Load all task conversations
task_conversations = sdd.load_task_conversations()

# Load table metadata
table = sdd.metadata.Table.from_code(10100002)
# Get subjects or surveys
subjects = table.get_subjects()
surveys = table.get_surveys()
```

For more information, head to the [Core API Reference](https://mcgill-nlp.github.io/statcan-dialogue-dataset/docs/core/) page.


## Reproduce paper results

The full `eacl_code` directory contains the code used to produce the results in the paper. To reproduce the results, start by reading the `README.md` file in the `eacl_code` directory.


## StatCan resources

You may find the following external resources useful:
* [Web Data Service User Guide](https://www.statcan.gc.ca/eng/developers/wds/user-guide)
* [Web Data Service API](https://www.statcan.gc.ca/eng/developers/wds)
* [Developer homepage](https://www.statcan.gc.ca/eng/developers)
DR-lord-sdk 0.0.3
SDK for interacting with the Lord of the Rings API
Requires Python: >=3.6

# Lord of the Rings SDK

The Lord of the Rings SDK is a Python package that provides easy access to the Lord of the Rings API. It includes modules for interacting with the movies, and quotes from the Lord of the Rings movies.

## Installation

To get started, you'll need to install the DR_lotr_sdk package.

```bash
pip install DR_lotr_sdk
```

This will install all the necessary dependencies.



## Importing the SDK

You can import the Lord of the Rings SDK in your Python script using the following import statement:

```python
from lordoftheringssdk.movie import Movie
from lordoftheringssdk.quote import Quote
```

This will allow you to use the Movie, and Quote classes from the SDK in your code.



## Usage

Here are some basic examples of how to use the Lord of the Rings SDK:

```python
from lordoftheringssdk.movie import Movie
from lordoftheringssdk.quote import Quote

# Get all the movies
movies = Movie.get()
print(movies[1])
print()

# Get a specific quote
print(movies[5].quotes[2])
print()

# Get the names of all the movies
movies_list = Movie.get()
for movie in movies:
    print(movie.name)
print()

# Get all the quotes
quotes = Quote.get()
for quote in quotes:
    print(quote)
print()

# Get a specific quote
print(quotes[3])
print()

# Get information about a specific movie
movie_id = Movie.get(id="5cd95395de30eff6ebccde5d")
print(movie_id.name)
print()

# Get all the quotes from a specific movie
print(movie_id.quotes)
print()
```

## Endpoints

The SDK interacts with the following endpoints:

- `/movies`: This endpoint returns a list of all the movies from the Lord of the Rings series.

- `/movies/<id>`: This endpoint returns information about a specific movie, identified by the id parameter.

- `/movie/<id>/quotes`: This endpoint returns a list of all the quotes from a specific movie, identified by the id parameter.

- `/movie/<id>/quotes/<id>`: This endpoint returns a specific quote from a specific movie, identified by the id parameter.

- `/quotes`: This endpoint returns a list of all the quotes from the Lord of the Rings movies.

- `/quotes/<id>`: This endpoint returns information about a specific quote, identified by the id parameter.
NetGsm 0.0.7
NetGsm Sms Client Python package
Requires Python: >=3.6

[![PyPI version](https://img.shields.io/pypi/v/NetGsm.svg)](https://pypi.python.org/pypi/NetGsm)

# NetGsm Python PyPackage

NetGsm is the most popular sms provider at Turkey. NetGsm client is a Python library to access services quickly.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.

```bash
pip install NetGsm
```
## Environment Variables

```bash
NETGSM_USER_CODE: net gsm user code
NETGSM_USER_PASSWORD: net gsm user password
```
### Note
If you don't want to set this variables from global environment you can pass them to class.
You can see usage below
## Usage

```python
from netgsm import SmsService

kwargs = {
    # you can also set user code from environment.
    'user_code': 'net gsm user code', # Default value : None
    # you can also set user password from environment.
    'user_password': 'net gsm user password',  # Default value : None
    'api_url': 'net gsm api url'  # Default value : 'https://api.netgsm.com.tr/sms/send/get'
}
sms_service = SmsService(**kwargs)
sms_service.send_sms(phone='Phone Number (5551234567)', message='Your Message', header='Your header')
# header default value : None if you don't pass this value your header is your user code
```

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License

[MIT](https://choosealicense.com/licenses/mit/)
madgrad 1.3
A general purpose PyTorch Optimizer
Requires Python: >=3.6


# MADGRAD Optimization Method

A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization

Documentation availiable at https://madgrad.readthedocs.io/en/latest/.


``` pip install madgrad ```

Try it out! A best-of-both-worlds optimizer with the generalization performance of SGD and at least as fast convergence as that of Adam, often faster. A drop-in torch.optim implementation `madgrad.MADGRAD` is provided, as well as a FairSeq wrapped instance. For FairSeq, just import madgrad anywhere in your project files and use the `--optimizer madgrad` command line option, together with `--weight-decay`, `--momentum`, and optionally `--madgrad_eps`.

The madgrad.py file containing the optimizer can be directly dropped into any PyTorch project if you don't want to install via pip. If you are using fairseq, you need the acompanying fairseq_madgrad.py file as well.

## Things to note:
 - You may need to use a lower weight decay than you are accustomed to. Often 0.
 - You should do a full learning rate sweep as the optimal learning rate will be different from SGD or Adam. Best LR values we found were 2.5e-4 for 152 layer PreActResNet on CIFAR10, 0.001 for ResNet-50 on ImageNet, 0.025 for IWSLT14 using `transformer_iwslt_de_en` and 0.005 for RoBERTa training on BookWiki using `BERT_BASE`. On NLP models gradient clipping also helped.

# Mirror MADGRAD
The mirror descent version of MADGRAD is also included as `madgrad.MirrorMADGRAD`. This version works extremely well, even better than MADGRAD, on large-scale transformer training. This version is recommended for any problem where the datasets are big enough that generalization gap is not an issue.

As the mirror descent version does not implicitly regularize, you can usually use weight
decay values that work well with other optimizers.

# Tech Report

[Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization](https://arxiv.org/abs/2101.11075)

We introduce MADGRAD, a novel optimization method in the family of AdaGrad adaptive gradient methods. MADGRAD shows excellent performance on deep learning optimization problems from multiple fields, including classification and image-to-image tasks in vision, and recurrent and bidirectionally-masked models in natural language processing. For each of these tasks, MADGRAD matches or outperforms both SGD and ADAM in test set performance, even on problems for which adaptive methods normally perform poorly.


```BibTeX
@misc{defazio2021adaptivity,
      title={Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization}, 
      author={Aaron Defazio and Samy Jelassi},
      year={2021},
      eprint={2101.11075},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```

# Results

![vision](https://github.com/facebookresearch/madgrad/blob/main/figures/vision.png?raw=true)
![nlp](https://github.com/facebookresearch/madgrad/blob/main/figures/nlp.png?raw=true)

# License

MADGRAD is licensed under the [MIT License](LICENSE).