hezar 0.14.3

Hezar: A seamless AI framework & library for Persian

A seamless AI library for Persian

Hezar (meaning thousand in Persian) is a multipurpose AI library built to make AI easy for the Persian community!

Hezar is a library that:

• brings together all the best works in AI for Persian
• makes using AI models as easy as a couple of lines of code
• seamlessly integrates with HuggingFace Hub for all of its models
• has a highly developer-friendly interface
• has a task-based model interface which is more convenient for general users.
• is packed with additional tools like word embeddings, tokenizers, feature extractors, etc.
• comes with a lot of supplementary ML tools for deployment, benchmarking, optimization, etc.
• and more!

Installation

Hezar is available on PyPI and can be installed with pip:

pip install hezar

You can also install the latest version from the source. Clone the repo and execute the following commands:

git clone https://github.com/hezarai/hezar.git
pip install ./hezar

Quick Tour

Ready-to-use models from Hub

There's a bunch of ready-to-use trained models for different tasks on the Hub. See them here!

For example, you can grab a BERT-based model for sentiment analysis like so:

from hezar import Model, Tokenizer

# this is our Hub repo
model_path = "hezarai/bert-fa-sentiment-digikala-snappfood"
# load model and tokenizer
model = Model.load(model_path)
tokenizer = Tokenizer.load(model_path)
# tokenize inputs
example = ["هزار، کتابخانه‌ای کامل برای به کارگیری آسان هوش مصنوعی"]
inputs = tokenizer(example, return_tensors="pt")
# inference
outputs = model.predict(inputs)
# print outputs
print(outputs)

{'labels': ['positive'], 'probs': [0.812910258769989]}

Build models from scratch

Wanna use models without any pretrained weights? Easy!

Build a raw BERT-based model for text classification with a single line of code!

from hezar import build_model

model = build_model("bert_text_classification", id2label={0: "negative", 1: "positive"})
print(model)

You can also import model directly:

from hezar import BertTextClassification, BertTextClassificationConfig

bert_tc = BertTextClassification(BertTextClassificationConfig(num_labels=2))

Write your own model

It's fairly easy to extend this library or add your own model. Hezar has its own Model base class that is simply a normal PyTorch nn.Module but with some extra features!

Here's a simple example:

from dataclasses import dataclass

from torch import Tensor, nn

from hezar import Model, ModelConfig


@dataclass
class PerceptronConfig(ModelConfig):
    name: str = "perceptron"
    input_shape: int = 4
    output_shape: int = 2


class Perceptron(Model):
    """
    A simple single layer network
    """

    def __init__(self, config, **kwargs):
        super().__init__(config, **kwargs)
        self.nn = nn.Linear(
            in_features=self.config.input_shape,
            out_features=self.config.output_shape,
        )

    def forward(self, inputs: list, **kwargs):
        inputs = Tensor(inputs).reshape(1, -1)
        x = self.nn(inputs)
        return x

    def post_process(self, inputs, **kwargs):
        # post-process forward outputs (optional method)
        return inputs.numpy()  # convert torch tensor to numpy array


model = Perceptron(PerceptronConfig())
inputs = [1, 2, 3, 4]
outputs = model.predict(inputs)
print(outputs)

[[-0.13248837  0.7039478 ]]

As you can see, defining a new model is just like a typical PyTorch module, but comes with some amazing functionalities out-of-the-box like pushing to the Hub!

hub_repo = "<your_hf_username>/my-awesome-perceptron"
model.push_to_hub(hub_repo)

INFO: Uploaded:`PerceptronConfig(name=preceptron)` --> `your_hf_username/my-awesome-perceptron/model_config.yaml`
INFO: Uploaded: `Perceptron(name=preceptron)` --> `your_hf_username/my-awesome-perceptron/model.pt`

Documentation

Refer to the docs for a full documentation.

Contribution

This is a really heavy project to be maintained by a couple of developers. The idea isn't novel at all but actually doing it is really difficult hence being the only one in the whole history of the Persian open source! So any contribution is appreciated ❤️

MIT License

Copyright (c) 2022 Hezar AI

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.