deploy: 613fd34

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 46dac3e782f238b726e25024d3fa6112
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/contributing.md.txt

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+# Contributing to Hezar
+Welcome to Hezar! We greatly appreciate your interest in contributing to this project and helping us make it even more
+valuable to the Persian community. Whether you're a developer, researcher, or enthusiast, your contributions are
+invaluable in helping us grow and improve Hezar.
+
+Before you start contributing, please take a moment to review the following guidelines.
+
+## Code of Conduct
+
+This project and its community adhere to
+the [Contributor Code of Conduct](https://github.com/hezarai/hezar/blob/main/CODE_OF_CONDUCT.md).
+
+## How to Contribute
+
+### Reporting Bugs
+
+If you come across a bug or unexpected behavior, please help us by reporting it.
+Use the [GitHub Issue Tracker](https://github.com/hezarai/hezar/issues) to create a detailed bug report.
+Include information such as:
+
+- A clear and descriptive title.
+- Steps to reproduce the bug.
+- Expected behavior.
+- Actual behavior.
+- Your operating system and Python version.
+
+### Adding features
+
+Have a great idea for a new feature or improvement? We'd love to hear it. You can open an issue and add your suggestion
+with a clear description and further suggestions on how it can be implemented. Also, if you already can implement it
+yourself, just follow the instructions on how you can send a PR.
+
+### Adding/Improving documents
+
+Have a suggestion to enhance our documentation or want to contribute entirely new sections? We welcome your input!<br>
+Here's how you can get involved:<br>
+Docs website is deployed here: [https://hezarai.github.io/hezar](https://hezarai.github.io/hezar) and the source for the
+docs are located at the [docs](https://github.com/hezarai/hezar/tree/main/docs) folder in the root of the repo. Feel
+free to apply your changes or add new docs to this section. Notice that docs are written in Markdown format. In case you have
+added new files to this section, you must include them in the `index.md` file in the same folder. For example, if you've
+added the file `new_doc.md` to the `get_started` folder, you have to modify `get_started/index.md` and put your file
+name there.
+
+### Commit guidelines
+
+#### Functional best practices
+
+- Ensure only one "logical change" per commit for efficient review and flaw identification.
+- Smaller code changes facilitate quicker reviews and easier troubleshooting using Git's bisect capability.
+- Avoid mixing whitespace changes with functional code changes.
+- Avoid mixing two unrelated functional changes.
+- Refrain from sending large new features in a single giant commit.
+
+#### Styling best practices
+
+- Use imperative mood in the subject (e.g., "Add support for ..." not "Adding support or added support") .
+- Keep the subject line short and concise, preferably less than 50 characters.
+- Capitalize the subject line and do not end it with a period.
+- Wrap body lines at 72 characters.
+- Use the body to explain what and why a change was made.
+- Do not explain the "how" in the commit message; reserve it for documentation or code.
+- For commits referencing an issue or pull request, write the proper commit subject followed by the reference in
+  parentheses (e.g., "Add NFKC normalizer (#9999)").
+- Reference codes & paths in back quotes (e.g., `variable`, `method()`, `Class()`, `file.py`).
+- Preferably use the following [gitmoji](https://gitmoji.dev/) compatible codes at the beginning of your commit message:
+
+| Emoji Code           | Emoji | Description                                  | Example Commit                                                 |
+|----------------------|-------|----------------------------------------------|----------------------------------------------------------------|
+| `:bug:`              | 🐛    | Fix a bug or issue                           | `:bug: Fix issue with image loading in DataLoader`             |
+| `:sparkles:`         | ✨     | Add feature or improvements                  | `:sparkles: Introduce support for text summarization`          |
+| `:recycle:`          | ♻️    | Refactor code (backward compatible refactor) | `:recycle: Refactor data preprocessing utilities`              |
+| `:memo:`             | 📝    | Add or change docs                           | `:memo: Update documentation for text classification`          |
+| `:pencil2:`          | ✏️    | Minor change or improvement                  | `:pencil2: Improve logging in Trainer`                         |
+| `:fire:`             | 🔥    | Remove code or file                          | `:fire: Remove outdated utility function`                      |
+| `:boom:`             | 💥    | Introduce breaking changes                   | `:boom: Update API, requires modification in existing scripts` |
+| `:test_tube:`        | 🧪    | Test-related changes                         | `:test_tube: Add unit tests for data loading functions`        |
+| `:bookmark:`         | 🔖    | Version release                              | `:bookmark: Release v1.0.0`                                    |
+| `:adhesive_bandage:` | 🩹    | Non-critical fix                             | `:adhesive_bandage: Fix minor issue in BPE tokenizer`          |
+
+## Sending a PR
+
+In order to apply any change to the repo, you have to follow these step:
+
+1. Fork the Hezar repository.
+2. Create a new branch for your feature, bug fix, etc.
+3. Make your changes.
+4. Update the documentation to reflect your changes.
+5. Ensure your code adheres to the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html).
+6. Format the code using `ruff` (`ruff check --fix .`)
+7. Write tests to ensure the functionality if needed.
+8. Run tests and make sure all of them pass. (Skip this step if your changes do not involve codes)
+9. Open a pull request from your fork and the PR template will be automatically loaded to help you do the rest.
+10. Be responsive to feedback and comments during the review process.
+11. Thanks for contributing to the Hezar project.😉❤️
+
+## License
+
+By contributing to Hezar, you agree that your contributions will be licensed under
+the [Apache 2.0 License](https://github.com/hezarai/hezar/blob/main/LICENSE).
+
+We look forward to your contributions and appreciate your efforts in making Hezar a powerful AI tool for the Persian
+community!

_sources/get_started/index.md.txt

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+# Get Started
+```{toctree}
+:maxdepth: 1
+
+overview.md
+installation.md
+quick_tour.md
+```

_sources/get_started/installation.md.txt

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+# Installation
+
+## Install from PyPi
+Installing Hezar is as easy as any other Python library! Most of the requirements are cross-platform and installing
+them on any machine is a piece of cake!
+
+```
+pip install hezar
+```
+### Installation variations
+Hezar is packed with a lot of tools that are dependent on other packages. Most of the
+time you might not want everything to be installed, hence, providing multiple variations of
+Hezar so that the installation is light and fast for general use.
+
+You can install optional dependencies for each mode like so:
+```
+pip install hezar[nlp]  # For natural language processing
+pip install hezar[vision]  # For computer vision and image processing
+pip install hezar[audio]  # For audio and speech processing
+pip install hezar[embeddings]  # For word embeddings
+```
+Or you can also install everything using:
+```
+pip install hezar[all]
+```
+## Install from source
+Also, you can install the dev version of the library using the source:
+```
+pip install git+https://github.com/hezarai/hezar.git
+```
+
+## Test installation
+From a Python console or in CLI just import `hezar` and check the version:
+```python
+import hezar
+
+print(hezar.__version__)
+```
+```
+0.23.1
+```

_sources/get_started/overview.md.txt

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+# Overview
+
+Welcome to Hezar! A library that makes state-of-the-art machine learning as easy as possible aimed for the Persian
+language, built by the Persian community!
+
+In Hezar, the primary goal is to provide plug-and-play AI/ML utilities so that you don't need to know much about what's
+going on under the hood. Hezar is not just a model library, but instead it's packed with every aspect you need for any
+ML pipeline like datasets, trainers, preprocessors, feature extractors, etc.
+
+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!
+
+To find out more, just take the [quick tour](quick_tour.md)!

_sources/get_started/quick_tour.md.txt

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+# 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_mask_filling = Model.load("hezarai/roberta-fa-mask-filling")
+inputs = ["سلام بچه ها حالتون <mask>"]
+outputs = roberta_mask_filling.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(
+    output_dir="bert-fa-pos-lscp-500k",
+    task="sequence_labeling",
+    device="cuda",
+    init_weights_from=base_model_path,
+    batch_size=8,
+    num_epochs=5,
+    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
+```
+
+Want to go deeper? Check out the [guides](../guide/index.md).

_sources/guide/advanced_training.md.txt

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+# Advanced Training
+Docs coming soon, stay tuned!