Models¶

In Hezar, models are the typical PyTorch modules with some extra features for loading, saving, exporting, etc. Let’s dive into some of the most important ones!

Models Basics¶

Building Models¶

Like any other package, you can import any model from hezar.models that you want.

from hezar.models import BertMaskFilling, BertMaskFillingConfig

bert = BertMaskFilling(BertMaskFillingConfig())

You can also configure the architecture by changing the properties in a model’s config like so:

config = BertMaskFillingConfig(num_hidden_layers=8, num_attention_heads=8)
bert = BertMaskFilling(config)

Every model in Hezar, can be pushed to or downloaded from the Hub.

Loading pre-trained models¶

Loading a model from Hub is as easy as:

from hezar.models import Model

bert = Model.load("hezarai/bert-base-fa")

The load methods takes the following steps to build the model:

Load the config file model_config.yaml and figure out the model’s class using the name config parameter. (bert_mask_filling in this snippet)
Build the model with random weights from the corresponding class. (BertMaskFilling in this snippet)
Download the weights file (model.pt) and load the state dict into to the model.
If the path contains any preprocessor, the preprocessor (WordPieceTokenizer in this snippet) will be loaded too. (You can disable loading preprocessors by setting Model.load(path, load_preprocessor=False))

Inference & Prediction¶

Now that you have loaded a model along with its preprocessors, feature extractors, etc. you can perform an end-to-end inference in a single line of code using Model.predict method.

A sequence labeling example would be like this:

from hezar.models import Model

pos_model = Model.load("hezarai/bert-fa-pos-lscp-500k")  # Part-of-speech
inputs = ["شرکت هوش مصنوعی هزار"]
pos_outputs = pos_model.predict(inputs)
print(f"POS: {pos_outputs}")

POS: [[{'token': 'شرکت', 'tag': 'Ne'}, {'token': 'هوش', 'tag': 'Ne'}, {'token': 'مصنوعی', 'tag': 'AJe'}, {'token': 'هزار', 'tag': 'NUM'}]]

Saving Models¶

You can save any model along with its config and preprocessor and other files on disk like:

from hezar.models import RobertaMaskFilling, RobertaMaskFillingConfig

roberta = RobertaMaskFilling(RobertaMaskFillingConfig(vocab_size=60000))
roberta.save("my-roberta")

Pushing to the Hub¶

Every model can be pushed to the Hub.

from hezar.models import RobertaTextClassification, RobertaTextClassificationConfig

roberta = RobertaTextClassification(RobertaTextClassificationConfig(num_labels=2))
roberta.push_to_hub("arxyzan/roberta-sentiment")

INFO: Uploaded:`RobertaTextClassificationConfig(name=roberta_text_classification)` --> `arxyzan/roberta-sentiment/model_config.yaml`
INFO: Uploaded: `RobertaTextClassification(name=roberta_text_classification)` --> `arxyzan/roberta-sentiment/model.pt`

Custom Models¶

Every Hezar model is a subclass of the base model class Model and the Model itself is a subclass of PyTorch nn.Module with some extra features. So if you’re familiar with PyTorch, this should feel like home!

A Sample Perceptron¶

from dataclasses import dataclass

from torch import Tensor, nn

from hezar.models import Model, ModelConfig
from hezar.registry import register_model


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


@register_model("perceptron", config_class=PerceptronConfig)
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, model_outputs, **kwargs):
        return model_outputs.numpy()

The only point here is that you have to pass a ModelConfig to your model and read everything from the config and the rest is just typical PyTorch stuff.

Now you have access to all the features of a Hezar model.

model = Perceptron(PerceptronConfig())
inputs = [1, 2, 3, 4]
outputs = model.predict(inputs)
print(outputs)
model.save("my-perceptron")
model.push_to_hub("hezarai/perceptron")

[[-1.0953112 -1.9854667]]
INFO: Uploaded:`PerceptronConfig(name=perceptron)` --> `hezarai/perceptron/model_config.yaml`
INFO: Uploaded: `Perceptron(name=perceptron)` --> `hezarai/perceptron/model.pt`

To learn more about the internals of the models in Hezar take a look at the models in-depth guide