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# pytorch-widedeep

A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch

Documentation: https://pytorch-widedeep.readthedocs.io

Companion posts and tutorials: infinitoml

Experiments and comparison with `LightGBM`: TabularDL vs LightGBM

Slack: if you want to contribute or just want to chat with us, join slack

The content of this document is organized as follows:

- pytorch-widedeep
  - Introduction
  - Architectures
  - The `deeptabular` component
  - The `rec` module
  - Text and Images
  - Acknowledgments
  - License
  - Cite
    - BibTex
    - APA

### Introduction

`pytorch-widedeep` is based on Google's Wide and Deep Algorithm, adjusted for multi-modal datasets.

In general terms, `pytorch-widedeep` is a package to use deep learning with tabular data. In particular, is intended to facilitate the combination of text and images with corresponding tabular data using wide and deep models. With that in mind there are a number of architectures that can be implemented with the library. The main components of those architectures are shown in the Figure below:

In math terms, and following the notation in the paper, the expression for the architecture without a `deephead` component can be formulated as:

Where σ is the sigmoid function, 'W' are the weight matrices applied to the wide model and to the final activations of the deep models, 'a' are these final activations, φ(x) are the cross product transformations of the original features 'x', and , and 'b' is the bias term. In case you are wondering what are "cross product transformations", here is a quote taken directly from the paper: "For binary features, a cross-product transformation (e.g., “AND(gender=female, language=en)”) is 1 if and only if the constituent features (“gender=female” and “language=en”) are all 1, and 0 otherwise".

It is perfectly possible to use custom models (and not necessarily those in the library) as long as the the custom models have a property called `output_dim` with the size of the last layer of activations, so that `WideDeep` can be constructed. Examples on how to use custom components can be found in the Examples folder and the section below.

### Architectures

The `pytorch-widedeep` library offers a number of different architectures. In this section we will show some of them in their simplest form (i.e. with default param values in most cases) with their corresponding code snippets. Note that all the snippets below shoud run locally. For a more detailed explanation of the different components and their parameters, please refer to the documentation.

For the examples below we will be using a toy dataset generated as follows:

    import os
    import random

    import numpy as np
    import pandas as pd
    from PIL import Image
    from faker import Faker

    def create_and_save_random_image(image_number, size=(32, 32)):

        if not os.path.exists("images"):
            os.makedirs("images")

        array = np.random.randint(0, 256, (size[0], size[1], 3), dtype=np.uint8)

        image = Image.fromarray(array)

        image_name = f"image_{image_number}.png"
        image.save(os.path.join("images", image_name))

        return image_name

    fake = Faker()

    cities = ["New York", "Los Angeles", "Chicago", "Houston"]
    names = ["Alice", "Bob", "Charlie", "David", "Eva"]

    data = {
        "city": [random.choice(cities) for _ in range(100)],
        "name": [random.choice(names) for _ in range(100)],
        "age": [random.uniform(18, 70) for _ in range(100)],
        "height": [random.uniform(150, 200) for _ in range(100)],
        "sentence": [fake.sentence() for _ in range(100)],
        "other_sentence": [fake.sentence() for _ in range(100)],
        "image_name": [create_and_save_random_image(i) for i in range(100)],
        "target": [random.choice([0, 1]) for _ in range(100)],
    }

    df = pd.DataFrame(data)

This will create a 100 rows dataframe and a dir in your local folder, called `images` with 100 random images (or images with just noise).

Perhaps the simplest architecture would be just one component, `wide`, `deeptabular`, `deeptext` or `deepimage` on their own, which is also possible, but let's start the examples with a standard Wide and Deep architecture. From there, how to build a model comprised only of one component will be straightforward.

Note that the examples shown below would be almost identical using any of the models available in the library. For example, `TabMlp` can be replaced by `TabResnet`, `TabNet`, `TabTransformer`, etc. Similarly, `BasicRNN` can be replaced by `AttentiveRNN`, `StackedAttentiveRNN`, or `HFModel` with their corresponding parameters and preprocessor in the case of the Hugging Face models.

1. Wide and Tabular component (aka deeptabular)

    from pytorch_widedeep.preprocessing import TabPreprocessor, WidePreprocessor
    from pytorch_widedeep.models import Wide, TabMlp, WideDeep
    from pytorch_widedeep.training import Trainer

    # Wide
    wide_cols = ["city"]
    crossed_cols = [("city", "name")]
    wide_preprocessor = WidePreprocessor(wide_cols=wide_cols, crossed_cols=crossed_cols)
    X_wide = wide_preprocessor.fit_transform(df)
    wide = Wide(input_dim=np.unique(X_wide).shape[0])

    # Tabular
    tab_preprocessor = TabPreprocessor(
        embed_cols=["city", "name"], continuous_cols=["age", "height"]
    )
    X_tab = tab_preprocessor.fit_transform(df)
    tab_mlp = TabMlp(
        column_idx=tab_preprocessor.column_idx,
        cat_embed_input=tab_preprocessor.cat_embed_input,
        continuous_cols=tab_preprocessor.continuous_cols,
        mlp_hidden_dims=[64, 32],
    )

    # WideDeep
    model = WideDeep(wide=wide, deeptabular=tab_mlp)

    # Train
    trainer = Trainer(model, objective="binary")

    trainer.fit(
        X_wide=X_wide,
        X_tab=X_tab,
        target=df["target"].values,
        n_epochs=1,
        batch_size=32,
    )

2. Tabular and Text data

    from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor
    from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep
    from pytorch_widedeep.training import Trainer

    # Tabular
    tab_preprocessor = TabPreprocessor(
        embed_cols=["city", "name"], continuous_cols=["age", "height"]
    )
    X_tab = tab_preprocessor.fit_transform(df)
    tab_mlp = TabMlp(
        column_idx=tab_preprocessor.column_idx,
        cat_embed_input=tab_preprocessor.cat_embed_input,
        continuous_cols=tab_preprocessor.continuous_cols,
        mlp_hidden_dims=[64, 32],
    )

    # Text
    text_preprocessor = TextPreprocessor(
        text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
    )
    X_text = text_preprocessor.fit_transform(df)
    rnn = BasicRNN(
        vocab_size=len(text_preprocessor.vocab.itos),
        embed_dim=16,
        hidden_dim=8,
        n_layers=1,
    )

    # WideDeep
    model = WideDeep(deeptabular=tab_mlp, deeptext=rnn)

    # Train
    trainer = Trainer(model, objective="binary")

    trainer.fit(
        X_tab=X_tab,
        X_text=X_text,
        target=df["target"].values,
        n_epochs=1,
        batch_size=32,
    )

3. Tabular and text with a FC head on top via the `head_hidden_dims` param in `WideDeep`

    from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor
    from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep
    from pytorch_widedeep.training import Trainer

    # Tabular
    tab_preprocessor = TabPreprocessor(
        embed_cols=["city", "name"], continuous_cols=["age", "height"]
    )
    X_tab = tab_preprocessor.fit_transform(df)
    tab_mlp = TabMlp(
        column_idx=tab_preprocessor.column_idx,
        cat_embed_input=tab_preprocessor.cat_embed_input,
        continuous_cols=tab_preprocessor.continuous_cols,
        mlp_hidden_dims=[64, 32],
    )

    # Text
    text_preprocessor = TextPreprocessor(
        text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
    )
    X_text = text_preprocessor.fit_transform(df)
    rnn = BasicRNN(
        vocab_size=len(text_preprocessor.vocab.itos),
        embed_dim=16,
        hidden_dim=8,
        n_layers=1,
    )

    # WideDeep
    model = WideDeep(deeptabular=tab_mlp, deeptext=rnn, head_hidden_dims=[32, 16])

    # Train
    trainer = Trainer(model, objective="binary")

    trainer.fit(
        X_tab=X_tab,
        X_text=X_text,
        target=df["target"].values,
        n_epochs=1,
        batch_size=32,
    )

4. Tabular and multiple text columns that are passed directly to `WideDeep`

    from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor
    from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep
    from pytorch_widedeep.training import Trainer

    # Tabular
    tab_preprocessor = TabPreprocessor(
        embed_cols=["city", "name"], continuous_cols=["age", "height"]
    )
    X_tab = tab_preprocessor.fit_transform(df)
    tab_mlp = TabMlp(
        column_idx=tab_preprocessor.column_idx,
        cat_embed_input=tab_preprocessor.cat_embed_input,
        continuous_cols=tab_preprocessor.continuous_cols,
        mlp_hidden_dims=[64, 32],
    )

    # Text
    text_preprocessor_1 = TextPreprocessor(
        text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
    )
    X_text_1 = text_preprocessor_1.fit_transform(df)
    text_preprocessor_2 = TextPreprocessor(
        text_col="other_sentence", maxlen=20, max_vocab=100, n_cpus=1
    )
    X_text_2 = text_preprocessor_2.fit_transform(df)
    rnn_1 = BasicRNN(
        vocab_size=len(text_preprocessor_1.vocab.itos),
        embed_dim=16,
        hidden_dim=8,
        n_layers=1,
    )
    rnn_2 = BasicRNN(
        vocab_size=len(text_preprocessor_2.vocab.itos),
        embed_dim=16,
        hidden_dim=8,
        n_layers=1,
    )

    # WideDeep
    model = WideDeep(deeptabular=tab_mlp, deeptext=[rnn_1, rnn_2])

    # Train
    trainer = Trainer(model, objective="binary")

    trainer.fit(
        X_tab=X_tab,
        X_text=[X_text_1, X_text_2],
        target=df["target"].values,
        n_epochs=1,
        batch_size=32,
    )

5. Tabular data and multiple text columns that are fused via a the library's `ModelFuser` class

    from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor
    from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep, ModelFuser
    from pytorch_widedeep import Trainer

    # Tabular
    tab_preprocessor = TabPreprocessor(
        embed_cols=["city", "name"], continuous_cols=["age", "height"]
    )
    X_tab = tab_preprocessor.fit_transform(df)
    tab_mlp = TabMlp(
        column_idx=tab_preprocessor.column_idx,
        cat_embed_input=tab_preprocessor.cat_embed_input,
        continuous_cols=tab_preprocessor.continuous_cols,
        mlp_hidden_dims=[64, 32],
    )

    # Text
    text_preprocessor_1 = TextPreprocessor(
        text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
    )
    X_text_1 = text_preprocessor_1.fit_transform(df)
    text_preprocessor_2 = TextPreprocessor(
        text_col="other_sentence", maxlen=20, max_vocab=100, n_cpus=1
    )
    X_text_2 = text_preprocessor_2.fit_transform(df)

    rnn_1 = BasicRNN(
        vocab_size=len(text_preprocessor_1.vocab.itos),
        embed_dim=16,
        hidden_dim=8,
        n_layers=1,
    )
    rnn_2 = BasicRNN(
        vocab_size=len(text_preprocessor_2.vocab.itos),
        embed_dim=16,
        hidden_dim=8,
        n_layers=1,
    )

    models_fuser = ModelFuser(models=[rnn_1, rnn_2], fusion_method="mult")

    # WideDeep
    model = WideDeep(deeptabular=tab_mlp, deeptext=models_fuser)

    # Train
    trainer = Trainer(model, objective="binary")

    trainer.fit(
        X_tab=X_tab,
        X_text=[X_text_1, X_text_2],
        target=df["target"].values,
        n_epochs=1,
        batch_size=32,
    )

6. Tabular and multiple text columns, with an image column. The text columns are fused via the library's `ModelFuser` and then all fused via the deephead paramenter in `WideDeep` which is a custom `ModelFuser` coded by the user

This is perhaps the less elegant solution as it involves a custom component by the user and slicing the 'incoming' tensor. In the future, we will include a `TextAndImageModelFuser` to make this process more straightforward. Still, is not really complicated and it is a good example of how to use custom components in `pytorch-widedeep`.

Note that the only requirement for the custom component is that it has a property called `output_dim` that returns the size of the last layer of activations. In other words, it does not need to inherit from `BaseWDModelComponent`. This base class simply checks the existence of such property and avoids some typing errors internally.

    import torch

    from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor, ImagePreprocessor
    from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep, ModelFuser, Vision
    from pytorch_widedeep.models._base_wd_model_component import BaseWDModelComponent
    from pytorch_widedeep import Trainer

    # Tabular
    tab_preprocessor = TabPreprocessor(
        embed_cols=["city", "name"], continuous_cols=["age", "height"]
    )
    X_tab = tab_preprocessor.fit_transform(df)
    tab_mlp = TabMlp(
        column_idx=tab_preprocessor.column_idx,
        cat_embed_input=tab_preprocessor.cat_embed_input,
        continuous_cols=tab_preprocessor.continuous_cols,
        mlp_hidden_dims=[16, 8],
    )

    # Text
    text_preprocessor_1 = TextPreprocessor(
        text_col="sentence", maxlen=20, max_vocab=100, n_cpus=1
    )
    X_text_1 = text_preprocessor_1.fit_transform(df)
    text_preprocessor_2 = TextPreprocessor(
        text_col="other_sentence", maxlen=20, max_vocab=100, n_cpus=1
    )
    X_text_2 = text_preprocessor_2.fit_transform(df)
    rnn_1 = BasicRNN(
        vocab_size=len(text_preprocessor_1.vocab.itos),
        embed_dim=16,
        hidden_dim=8,
        n_layers=1,
    )
    rnn_2 = BasicRNN(
        vocab_size=len(text_preprocessor_2.vocab.itos),
        embed_dim=16,
        hidden_dim=8,
        n_layers=1,
    )
    models_fuser = ModelFuser(
        models=[rnn_1, rnn_2],
        fusion_method="mult",
    )

    # Image
    image_preprocessor = ImagePreprocessor(img_col="image_name", img_path="images")
    X_img = image_preprocessor.fit_transform(df)
    vision = Vision(pretrained_model_setup="resnet18", head_hidden_dims=[16, 8])

    # deephead (custom model fuser)
    class MyModelFuser(BaseWDModelComponent):
        """
        Simply a Linear + Relu sequence on top of the text + images followed by a
        Linear -> Relu -> Linear for the concatenation of tabular slice of the
        tensor and the output of the text and image sequential model
        """
        def __init__(
            self,
            tab_incoming_dim: int,
            text_incoming_dim: int,
            image_incoming_dim: int,
            output_units: int,
        ):

            super(MyModelFuser, self).__init__()

            self.tab_incoming_dim = tab_incoming_dim
            self.text_incoming_dim = text_incoming_dim
            self.image_incoming_dim = image_incoming_dim
            self.output_units = output_units
            self.text_and_image_fuser = torch.nn.Sequential(
                torch.nn.Linear(text_incoming_dim + image_incoming_dim, output_units),
                torch.nn.ReLU(),
            )
            self.out = torch.nn.Sequential(
                torch.nn.Linear(output_units + tab_incoming_dim, output_units * 4),
                torch.nn.ReLU(),
                torch.nn.Linear(output_units * 4, output_units),
            )

        def forward(self, X: torch.Tensor) -> torch.Tensor:
            tab_slice = slice(0, self.tab_incoming_dim)
            text_slice = slice(
                self.tab_incoming_dim, self.tab_incoming_dim + self.text_incoming_dim
            )
            image_slice = slice(
                self.tab_incoming_dim + self.text_incoming_dim,
                self.tab_incoming_dim + self.text_incoming_dim + self.image_incoming_dim,
            )
            X_tab = X[:, tab_slice]
            X_text = X[:, text_slice]
            X_img = X[:, image_slice]
            X_text_and_image = self.text_and_image_fuser(torch.cat([X_text, X_img], dim=1))
            return self.out(torch.cat([X_tab, X_text_and_image], dim=1))

        @property
        def output_dim(self):
            return self.output_units

    deephead = MyModelFuser(
        tab_incoming_dim=tab_mlp.output_dim,
        text_incoming_dim=models_fuser.output_dim,
        image_incoming_dim=vision.output_dim,
        output_units=8,
    )

    # WideDeep
    model = WideDeep(
        deeptabular=tab_mlp,
        deeptext=models_fuser,
        deepimage=vision,
        deephead=deephead,
    )

    # Train
    trainer = Trainer(model, objective="binary")

    trainer.fit(
        X_tab=X_tab,
        X_text=[X_text_1, X_text_2],
        X_img=X_img,
        target=df["target"].values,
        n_epochs=1,
        batch_size=32,
    )

7. A two-tower model

This is a popular model in the context of recommendation systems. Let's say we have a tabular dataset formed my triples (user features, item features, target). We can create a two-tower model where the user and item features are passed through two separate models and then "fused" via a dot product.

    import numpy as np
    import pandas as pd

    from pytorch_widedeep import Trainer
    from pytorch_widedeep.preprocessing import TabPreprocessor
    from pytorch_widedeep.models import TabMlp, WideDeep, ModelFuser

    # Let's create the interaction dataset
    # user_features dataframe
    np.random.seed(42)
    user_ids = np.arange(1, 101)
    ages = np.random.randint(18, 60, size=100)
    genders = np.random.choice(["male", "female"], size=100)
    locations = np.random.choice(["city_a", "city_b", "city_c", "city_d"], size=100)
    user_features = pd.DataFrame(
        {"id": user_ids, "age": ages, "gender": genders, "location": locations}
    )

    # item_features dataframe
    item_ids = np.arange(1, 101)
    prices = np.random.uniform(10, 500, size=100).round(2)
    colors = np.random.choice(["red", "blue", "green", "black"], size=100)
    categories = np.random.choice(["electronics", "clothing", "home", "toys"], size=100)

    item_features = pd.DataFrame(
        {"id": item_ids, "price": prices, "color": colors, "category": categories}
    )

    # Interactions dataframe
    interaction_user_ids = np.random.choice(user_ids, size=1000)
    interaction_item_ids = np.random.choice(item_ids, size=1000)
    purchased = np.random.choice([0, 1], size=1000, p=[0.7, 0.3])
    interactions = pd.DataFrame(
        {
            "user_id": interaction_user_ids,
            "item_id": interaction_item_ids,
            "purchased": purchased,
        }
    )
    user_item_purchased = interactions.merge(
        user_features, left_on="user_id", right_on="id"
    ).merge(item_features, left_on="item_id", right_on="id")

    # Users
    tab_preprocessor_user = TabPreprocessor(
        cat_embed_cols=["gender", "location"],
        continuous_cols=["age"],
    )
    X_user = tab_preprocessor_user.fit_transform(user_item_purchased)
    tab_mlp_user = TabMlp(
        column_idx=tab_preprocessor_user.column_idx,
        cat_embed_input=tab_preprocessor_user.cat_embed_input,
        continuous_cols=["age"],
        mlp_hidden_dims=[16, 8],
        mlp_dropout=[0.2, 0.2],
    )

    # Items
    tab_preprocessor_item = TabPreprocessor(
        cat_embed_cols=["color", "category"],
        continuous_cols=["price"],
    )
    X_item = tab_preprocessor_item.fit_transform(user_item_purchased)
    tab_mlp_item = TabMlp(
        column_idx=tab_preprocessor_item.column_idx,
        cat_embed_input=tab_preprocessor_item.cat_embed_input,
        continuous_cols=["price"],
        mlp_hidden_dims=[16, 8],
        mlp_dropout=[0.2, 0.2],
    )

    two_tower_model = ModelFuser([tab_mlp_user, tab_mlp_item], fusion_method="dot")

    model = WideDeep(deeptabular=two_tower_model)

    trainer = Trainer(model, objective="binary")

    trainer.fit(
        X_tab=[X_user, X_item],
        target=interactions.purchased.values,
        n_epochs=1,
        batch_size=32,
    )

8. Tabular with a multi-target loss

This one is "a bonus" to illustrate the use of multi-target losses, more than actually a different architecture.

    from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor, ImagePreprocessor
    from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep, ModelFuser, Vision
    from pytorch_widedeep.losses_multitarget import MultiTargetClassificationLoss
    from pytorch_widedeep.models._base_wd_model_component import BaseWDModelComponent
    from pytorch_widedeep import Trainer

    # let's add a second target to the dataframe
    df["target2"] = [random.choice([0, 1]) for _ in range(100)]

    # Tabular
    tab_preprocessor = TabPreprocessor(
        embed_cols=["city", "name"], continuous_cols=["age", "height"]
    )
    X_tab = tab_preprocessor.fit_transform(df)
    tab_mlp = TabMlp(
        column_idx=tab_preprocessor.column_idx,
        cat_embed_input=tab_preprocessor.cat_embed_input,
        continuous_cols=tab_preprocessor.continuous_cols,
        mlp_hidden_dims=[64, 32],
    )

    # 'pred_dim=2' because we have two binary targets. For other types of targets,
    #  please, see the documentation
    model = WideDeep(deeptabular=tab_mlp, pred_dim=2).

    loss = MultiTargetClassificationLoss(binary_config=[0, 1], reduction="mean")

    # When a multi-target loss is used, 'custom_loss_function' must not be None.
    # See the docs
    trainer = Trainer(model, objective="multitarget", custom_loss_function=loss)

    trainer.fit(
        X_tab=X_tab,
        target=df[["target", "target2"]].values,
        n_epochs=1,
        batch_size=32,
    )

### The `deeptabular` component

It is important to emphasize again that each individual component, `wide`, `deeptabular`, `deeptext` and `deepimage`, can be used independently and in isolation. For example, one could use only `wide`, which is in simply a linear model. In fact, one of the most interesting functionalities in`pytorch-widedeep` would be the use of the `deeptabular` component on its own, i.e. what one might normally refer as Deep Learning for Tabular Data. Currently, `pytorch-widedeep` offers the following different models for that component:

1. Wide: a simple linear model where the nonlinearities are captured via cross-product transformations, as explained before.
2. TabMlp: a simple MLP that receives embeddings representing the categorical features, concatenated with the continuous features, which can also be embedded.
3. TabResnet: similar to the previous model but the embeddings are passed through a series of ResNet blocks built with dense layers.
4. TabNet: details on TabNet can be found in TabNet: Attentive Interpretable Tabular Learning

Two simpler attention based models that we call:

1. ContextAttentionMLP: MLP with at attention mechanism "on top" that is based on Hierarchical Attention Networks for Document Classification
2. SelfAttentionMLP: MLP with an attention mechanism that is a simplified version of a transformer block that we refer as "query-key self-attention".

The `Tabformer` family, i.e. Transformers for Tabular data:

1. TabTransformer: details on the TabTransformer can be found in TabTransformer: Tabular Data Modeling Using Contextual Embeddings.
2. SAINT: Details on SAINT can be found in SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.
3. FT-Transformer: details on the FT-Transformer can be found in Revisiting Deep Learning Models for Tabular Data.
4. TabFastFormer: adaptation of the FastFormer for tabular data. Details on the Fasformer can be found in FastFormers: Highly Efficient Transformer Models for Natural Language Understanding
5. TabPerceiver: adaptation of the Perceiver for tabular data. Details on the Perceiver can be found in Perceiver: General Perception with Iterative Attention

And probabilistic DL models for tabular data based on Weight Uncertainty in Neural Networks:

1. BayesianWide: Probabilistic adaptation of the `Wide` model.
2. BayesianTabMlp: Probabilistic adaptation of the `TabMlp` model

Note that while there are scientific publications for the TabTransformer, SAINT and FT-Transformer, the TabFasfFormer and TabPerceiver are our own adaptation of those algorithms for tabular data.

In addition, Self-Supervised pre-training can be used for all `deeptabular` models, with the exception of the `TabPerceiver`. Self-Supervised pre-training can be used via two methods or routines which we refer as: encoder-decoder method and constrastive-denoising method. Please, see the documentation and the examples for details on this functionality, and all other options in the library.

### The `rec` module

This module was introduced as an extension to the existing components in the library, addressing questions and issues related to recommendation systems. While still under active development, it currently includes a select number of powerful recommendation models.

It's worth noting that this library already supported the implementation of various recommendation algorithms using existing components. For example, models like Wide and Deep, Two-Tower, or Neural Collaborative Filtering could be constructed using the library's core functionalities.

The recommendation algorithms in the `rec` module are:

1. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
2. (Deep) Field Aware Factorization Machine (FFM): a Deep Learning version of the algorithm presented in Field-aware Factorization Machines in a Real-world Online Advertising System
3. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
4. Deep Interest Network for Click-Through Rate Prediction

These can all be used as the `deeptabular` component in the `WideDeep` model. See the examples for more details.

### Text and Images

For the text component, `deeptext`, the library offers the following models:

1. BasicRNN: a simple RNN 2. AttentiveRNN: a RNN with an attention mechanism based on the Hierarchical Attention Networks for DocumentClassification
2. StackedAttentiveRNN: a stack of AttentiveRNNs
3. HFModel: a wrapper around Hugging Face Transfomer-based models. At the moment only models from the families BERT, RoBERTa, DistilBERT, ALBERT and ELECTRA are supported. This is because this library is designed to address classification and regression tasks and these are the most 'popular' encoder-only models, which have proved to be those that work best for these tasks. If there is demand for other models, they will be included in the future.

For the image component, `deepimage`, the library supports models from the following families: 'resnet', 'shufflenet', 'resnext', 'wide_resnet', 'regnet', 'densenet', 'mobilenetv3', 'mobilenetv2', 'mnasnet', 'efficientnet' and 'squeezenet'. These are offered via `torchvision` and wrapped up in the `Vision` class.

### Acknowledgments

This library takes from a series of other libraries, so I think it is just fair to mention them here in the README (specific mentions are also included in the code).

The `Callbacks` and `Initializers` structure and code is inspired by the `torchsample` library, which in itself partially inspired by `Keras`.

The `TextProcessor` class in this library uses the `fastai`'s `Tokenizer` and `Vocab`. The code at `utils.fastai_transforms` is a minor adaptation of their code so it functions within this library. To my experience their `Tokenizer` is the best in class.

The `ImageProcessor` class in this library uses code from the fantastic Deep Learning for Computer Vision (DL4CV) book by Adrian Rosebrock.

### License

This work is dual-licensed under Apache 2.0 and MIT (or any later version). You can choose between one of them if you use this work.

`SPDX-License-Identifier: Apache-2.0 AND MIT`

### Cite

#### BibTex

    @article{Zaurin_pytorch-widedeep_A_flexible_2023,
    author = {Zaurin, Javier Rodriguez and Mulinka, Pavol},
    doi = {10.21105/joss.05027},
    journal = {Journal of Open Source Software},
    month = jun,
    number = {86},
    pages = {5027},
    title = {{pytorch-widedeep: A flexible package for multimodal deep learning}},
    url = {https://joss.theoj.org/papers/10.21105/joss.05027},
    volume = {8},
    year = {2023}
    }

#### APA

    Zaurin, J. R., & Mulinka, P. (2023). pytorch-widedeep: A flexible package for
    multimodal deep learning. Journal of Open Source Software, 8(86), 5027.
    https://doi.org/10.21105/joss.05027