Metadata-Version: 2.1
Name: hezar
Version: 0.33.2
Summary: Hezar: The all-in-one AI library for Persian, supporting a wide variety of tasks and modalities!
Home-page: https://github.com/hezarai
License: MIT
Keywords: packaging,poetry
Author: Aryan Shekarlaban
Author-email: arxyzan@gmail.com
Maintainer: Aryan Shekarlaban
Maintainer-email: arxyzan@gmail.com
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Provides-Extra: all
Provides-Extra: audio
Provides-Extra: docs
Provides-Extra: embeddings
Provides-Extra: linting
Provides-Extra: nlp
Provides-Extra: vision
Requires-Dist: datasets (>=2.9.0)
Requires-Dist: gensim (==4.3.2) ; extra == "embeddings" or extra == "all"
Requires-Dist: huggingface_hub (>=0.12.0)
Requires-Dist: jiwer (>=3) ; extra == "nlp" or extra == "audio" or extra == "all"
Requires-Dist: librosa (>=0.10.0) ; extra == "audio" or extra == "all"
Requires-Dist: nltk (>=3.8.0) ; extra == "nlp" or extra == "all"
Requires-Dist: omegaconf (>=2.3.0)
Requires-Dist: pillow (>=9,<=10) ; extra == "vision" or extra == "all"
Requires-Dist: scikit-learn (>=1.0.0)
Requires-Dist: seqeval (>=1.2.0) ; extra == "nlp" or extra == "all"
Requires-Dist: soundfile (>=0.12.0) ; extra == "audio" or extra == "all"
Requires-Dist: tensorboard (>=2.10.0)
Requires-Dist: tokenizers (>=0.13.0)
Requires-Dist: torch (>=1.10.0)
Requires-Dist: transformers (>=4.28.0)
Project-URL: Documentation, https://hezarai.github.io/hezar/
Project-URL: Repository, https://github.com/hezarai/hezar
Description-Content-Type: text/markdown


<p align="center">
  <img src="https://raw.githubusercontent.com/hezarai/hezar/main/hezar.png"/>
</p>

_<p align="center"> The all-in-one AI library for Persian_
<br><br>
![PyPI Version](https://img.shields.io/pypi/v/hezar?color=blue)
[![PyPi Downloads](https://static.pepy.tech/badge/hezar)](https://pepy.tech/project/hezar)
![PyPI License](https://img.shields.io/pypi/l/hezar)
![GitHub Workflow Status (with event)](https://img.shields.io/github/actions/workflow/status/hezarai/hezar/.github%2Fworkflows%2Fdocs-deploy.yml?label=docs)<br>
[![Static Badge](https://img.shields.io/badge/Hugging_Face_Hub-yellow?label=%F0%9F%A4%97&labelColor=yellow&link=https%3A%2F%2Fhuggingface.co%2Fhezarai)](https://huggingface.co/hezarai)
[![Static Badge](https://img.shields.io/badge/Telegram_Channel-blue?logo=telegram&link=https%3A%2F%2Ft.me%2Fhezarai)](https://t.me/hezarai)
</p>

**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 Hugging Face 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
```
## Documentation
Explore Hezar to learn more on the [docs](https://hezarai.github.io/hezar/index.html) page or explore the key concepts:
- [Getting Started](https://hezarai.github.io/hezar/get_started/overview.html)
- [Quick Tour](https://hezarai.github.io/hezar/get_started/quick_tour.html)
- [Tutorials](https://hezarai.github.io/hezar/tutorial/models.html)
- [Developer Guides](https://hezarai.github.io/hezar/guide/hezar_architecture.html)
- [Contribution](https://hezarai.github.io/hezar/contribute/contribute_to_hezar.html)
- [Reference API](https://hezarai.github.io/hezar/source/index.html)

## Quick Tour
### Models
There's a bunch of ready to use trained models for different tasks on the Hub!

**🤗Hugging Face Hub Page**: [https://huggingface.co/hezarai](https://huggingface.co/hezarai)

Let's walk you through some examples!

- **Text Classification (sentiment analysis, categorization, etc)**
```python
from hezar.models import Model

example = ["هزار، کتابخانه‌ای کامل برای به کارگیری آسان هوش مصنوعی"]
model = Model.load("hezarai/bert-fa-sentiment-dksf")
outputs = model.predict(example)
print(outputs)
```
```
[[{'label': 'positive', 'score': 0.812910258769989}]]
```
- **Sequence Labeling (POS, NER, etc.)**
```python
from hezar.models import Model

pos_model = Model.load("hezarai/bert-fa-pos-lscp-500k")  # Part-of-speech
ner_model = Model.load("hezarai/bert-fa-ner-arman")  # Named entity recognition
inputs = ["شرکت هوش مصنوعی هزار"]
pos_outputs = pos_model.predict(inputs)
ner_outputs = ner_model.predict(inputs)
print(f"POS: {pos_outputs}")
print(f"NER: {ner_outputs}")
```
```
POS: [[{'token': 'شرکت', 'label': 'Ne'}, {'token': 'هوش', 'label': 'Ne'}, {'token': 'مصنوعی', 'label': 'AJe'}, {'token': 'هزار', 'label': 'NUM'}]]
NER: [[{'token': 'شرکت', 'label': 'B-org'}, {'token': 'هوش', 'label': 'I-org'}, {'token': 'مصنوعی', 'label': 'I-org'}, {'token': 'هزار', 'label': 'I-org'}]]
```
- **Language Modeling (Mask Filling)**
```python
from hezar.models import Model

roberta_mlm = Model.load("hezarai/roberta-fa-mlm")
inputs = ["سلام بچه ها حالتون <mask>"]
outputs = roberta_mlm.predict(inputs, top_k=1)
print(outputs)
```
```
[[{'token': 'چطوره', 'sequence': 'سلام بچه ها حالتون چطوره', 'token_id': 34505, 'score': 0.2230483442544937}]]
```
- **Speech Recognition**
```python
from hezar.models import Model

whisper = Model.load("hezarai/whisper-small-fa")
transcripts = whisper.predict("examples/assets/speech_example.mp3")
print(transcripts)
```
```
[{'text': 'و این تنها محدود به محیط کار نیست'}]
```
- **Image to Text (OCR)**
```python
from hezar.models import Model
# OCR with TrOCR
model = Model.load("hezarai/trocr-base-fa-v2")
texts = model.predict(["examples/assets/ocr_example.jpg"])
print(f"TrOCR Output: {texts}")

# OCR with CRNN
model = Model.load("hezarai/crnn-fa-printed-96-long")
texts = model.predict("examples/assets/ocr_example.jpg")
print(f"CRNN Output: {texts}")
```
```
TrOCR Output: [{'text': 'چه میشه کرد، باید صبر کنیم'}]
CRNN Output: [{'text': 'چه میشه کرد، باید صبر کنیم'}]
```
![](https://raw.githubusercontent.com/hezarai/hezar/main/examples/assets/ocr_example.jpg)

- **Image to Text (License Plate Recognition)**
```python
from hezar.models import Model

model = Model.load("hezarai/crnn-fa-64x256-license-plate-recognition")
plate_text = model.predict("assets/license_plate_ocr_example.jpg")
print(plate_text)  # Persian text of mixed numbers and characters might not show correctly in the console
```
```
[{'text': '۵۷س۷۷۹۷۷'}]
```
![](https://raw.githubusercontent.com/hezarai/hezar/main/examples/assets/license_plate_ocr_example.jpg)

- **Image to Text (Image Captioning)**
```python
from hezar.models import Model

model = Model.load("hezarai/vit-roberta-fa-image-captioning-flickr30k")
texts = model.predict("examples/assets/image_captioning_example.jpg")
print(texts)
```
```
[{'text': 'سگی با توپ تنیس در دهانش می دود.'}]
```
![](https://raw.githubusercontent.com/hezarai/hezar/main/examples/assets/image_captioning_example.jpg)

We constantly keep working on adding and training new models and this section will hopefully be expanding over time ;)
### Word Embeddings
- **FastText**
```python
from hezar.embeddings import Embedding

fasttext = Embedding.load("hezarai/fasttext-fa-300")
most_similar = fasttext.most_similar("هزار")
print(most_similar)
```
```
[{'score': 0.7579, 'word': 'میلیون'},
 {'score': 0.6943, 'word': '21هزار'},
 {'score': 0.6861, 'word': 'میلیارد'},
 {'score': 0.6825, 'word': '26هزار'},
 {'score': 0.6803, 'word': '٣هزار'}]
```
- **Word2Vec (Skip-gram)**
```python
from hezar.embeddings import Embedding

word2vec = Embedding.load("hezarai/word2vec-skipgram-fa-wikipedia")
most_similar = word2vec.most_similar("هزار")
print(most_similar)
```
```
[{'score': 0.7885, 'word': 'چهارهزار'},
 {'score': 0.7788, 'word': '۱۰هزار'},
 {'score': 0.7727, 'word': 'دویست'},
 {'score': 0.7679, 'word': 'میلیون'},
 {'score': 0.7602, 'word': 'پانصد'}]
```
- **Word2Vec (CBOW)**
```python
from hezar.embeddings import Embedding

word2vec = Embedding.load("hezarai/word2vec-cbow-fa-wikipedia")
most_similar = word2vec.most_similar("هزار")
print(most_similar)
```
```
[{'score': 0.7407, 'word': 'دویست'},
 {'score': 0.7400, 'word': 'میلیون'},
 {'score': 0.7326, 'word': 'صد'},
 {'score': 0.7276, 'word': 'پانصد'},
 {'score': 0.7011, 'word': 'سیصد'}]
```
For a full guide on the embeddings module, see the [embeddings tutorial](https://hezarai.github.io/hezar/tutorial/embeddings.html).
### Datasets
You can load any of the datasets on the [Hub](https://huggingface.co/hezarai) like below:
```python
from hezar.data import Dataset

sentiment_dataset = Dataset.load("hezarai/sentiment-dksf")  # A TextClassificationDataset instance
lscp_dataset = Dataset.load("hezarai/lscp-pos-500k")  # A SequenceLabelingDataset instance
xlsum_dataset = Dataset.load("hezarai/xlsum-fa")  # A TextSummarizationDataset instance
alpr_ocr_dataset = Dataset.load("hezarai/persian-license-plate-v1")  # An OCRDataset instance
...
```
The returned dataset objects from `load()` are PyTorch Dataset wrappers for specific tasks and can be used by a data loader out-of-the-box!

You can also load Hezar's datasets using 🤗Datasets:
```python
from datasets import load_dataset

dataset = load_dataset("hezarai/sentiment-dksf")
```
For a full guide on Hezar's datasets, see the [datasets tutorial](https://hezarai.github.io/hezar/tutorial/datasets.html).
### Training
Hezar makes it super easy to train models using out-of-the-box models and datasets provided in the library.

```python
from hezar.models import BertSequenceLabeling, BertSequenceLabelingConfig
from hezar.data import Dataset
from hezar.trainer import Trainer, TrainerConfig
from hezar.preprocessors import Preprocessor

base_model_path = "hezarai/bert-base-fa"
dataset_path = "hezarai/lscp-pos-500k"

train_dataset = Dataset.load(dataset_path, split="train", tokenizer_path=base_model_path)
eval_dataset = Dataset.load(dataset_path, split="test", tokenizer_path=base_model_path)

model = BertSequenceLabeling(BertSequenceLabelingConfig(id2label=train_dataset.config.id2label))
preprocessor = Preprocessor.load(base_model_path)

train_config = TrainerConfig(
    task="sequence_labeling",
    device="cuda",
    init_weights_from=base_model_path,
    batch_size=8,
    num_epochs=5,
    checkpoints_dir="checkpoints/",
    metrics=["seqeval"],
)

trainer = Trainer(
    config=train_config,
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    data_collator=train_dataset.data_collator,
    preprocessor=preprocessor,
)
trainer.train()

trainer.push_to_hub("bert-fa-pos-lscp-500k")  # push model, config, preprocessor, trainer files and configs
```
You can actually go way deeper with the Trainer. See more details [here](https://hezarai.github.io/hezar/tutorial/training.html).

## Going Deeper
Hezar's primary focus is on providing ready to use models (implementations & pretrained weights) for different casual tasks
not by reinventing the wheel, but by being built on top of
**[PyTorch](https://github.com/pytorch/pytorch),
🤗[Transformers](https://github.com/huggingface/transformers),
🤗[Tokenizers](https://github.com/huggingface/tokenizers),
🤗[Datasets](https://github.com/huggingface/datasets),
[Scikit-learn](https://github.com/scikit-learn/scikit-learn),
[Gensim](https://github.com/RaRe-Technologies/gensim),** etc.
Besides, it's deeply integrated with the **🤗[Hugging Face Hub](https://github.com/huggingface/huggingface_hub)** and
almost any module e.g, models, datasets, preprocessors, trainers, etc. can be uploaded to or downloaded from the Hub!

More specifically, here's a simple summary of the core modules in Hezar:
- **Models**:  Every model is a `hezar.models.Model` instance which is in fact, a PyTorch `nn.Module` wrapper with extra features for saving, loading, exporting, etc.
- **Datasets**: Every dataset is a `hezar.data.Dataset` instance which is a PyTorch Dataset implemented specifically for each task that can load the data files from the Hugging Face Hub.
- **Preprocessors**: All preprocessors are preferably backed by a robust library like Tokenizers, pillow, etc.
- **Embeddings**: All embeddings are developed on top of Gensim and can be easily loaded from the Hub and used in just 2 lines of code!
- **Trainer**: Trainer is the base class for training almost any model in Hezar or even your own custom models backed by Hezar. The Trainer comes with a lot of features and is also exportable to the Hub!
- **Metrics**: Metrics are also another configurable and portable modules backed by Scikit-learn, seqeval, etc. and can be easily used in the trainers!

For more info, check the [tutorials](https://hezarai.github.io/hezar/tutorial/)

## 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 ❤️

                                Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright 2022 Hezar AI

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

