The all-in-one 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 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!
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
Explore Hezar to learn more on the docs page or explore the key concepts:
There's a bunch of ready to use trained models for different tasks on the Hub!
🤗Hugging Face Hub Page: https://huggingface.co/hezarai
Let's walk you through some examples!
- Text Classification (sentiment analysis, categorization, etc)
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.)
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)
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
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)
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-base-fa-64x256")
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'}]
- Image to Text (License Plate Recognition)
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'}]
- Image to Text (Image Captioning)
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.'}]
We constantly keep working on adding and training new models and this section will hopefully be expanding over time ;)
- FastText
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)
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)
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'}]
You can load any of the datasets on the Hub like below:
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
...Hezar makes it super easy to train models using out-of-the-box models and datasets provided in the library.
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 configsYou can actually go way deeper with the trainers. Refer to the notebooks to see the examples!
Hezar's primary focus is on providing ready to use models (implementations & pretrained weights) for different casual tasks without reinventing the wheel, but by being built on top of PyTorch, 🤗Transformers, 🤗Tokenizers, 🤗Datasets, Scikit-learn, Gensim, etc. Besides, it's deeply integrated with the 🤗Hugging Face 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.Modelinstance which is in fact, a PyTorchnn.Modulewrapper with extra features for saving, loading, exporting, etc. - Datasets: Every dataset is a
hezar.data.Datasetinstance 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
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 ❤️