7.1. Introduction to Multitask Learning link

Multitask learning (MTL) is an approach in machine learning where a model is trained to perform multiple tasks simultaneously, leveraging shared representations across tasks to improve generalization and efficiency. Unlike single-task learning, where a model is trained on a specific task in isolation, multitask learning aims to improve performance by capturing underlying patterns that are useful across different tasks. The key idea is that tasks can share knowledge and representations, allowing the model to generalize better and avoid overfitting to individual tasks by using data from all tasks to refine its understanding.

In multitask learning, consider the example of sentiment analysis and topic classification, where a model is trained on customer reviews for sentiment analysis (positive, negative, neutral) and on news articles for topic classification (such as politics, sports, and technology). By sharing parameters in the initial text processing layers, the model learns general language patterns that benefit both tasks. Sentiment analysis can gain from topic context, as sentiments often vary across topics, while topic classification improves by understanding sentiment-associated language features. Similarly, in a computer vision example, training a model on image classification (where each image has one label, like "cat" or "dog") and object detection (identifying multiple objects, like detecting both "cat" and "dog" in the same image) allows shared convolutional layers to capture features like edges and shapes, which help with both identifying single objects and detecting multiple ones within images. Another example could involve predicting housing prices and crime rates, where neighborhood features such as income levels, proximity to schools, and local amenities impact both outcomes. Sharing parameters in the initial layers enables the model to learn neighborhood characteristics that improve predictions for both housing prices and crime rates. In each example, shared parameters allow the model to capture patterns beneficial across tasks, preventing overfitting to any one task and enhancing generalization, which is the essence of multitask learning.

Figure 1: Illustration of Multitask Learning paradigm.

The concept of Multitask Learning (MTL) was first proposed by Rich Caruana in 1997 as an innovative approach to machine learning, where a single model is trained to perform multiple tasks concurrently by sharing representations across tasks. Caruana introduced MTL with the idea that sharing parameters among tasks allows a model to capture underlying structures common to these tasks, thereby improving generalization and helping prevent overfitting. Caruana’s work laid the foundation for MTL by demonstrating how learning multiple related tasks together could make each task more efficient by leveraging shared information, a significant departure from single-task learning.

Since Caruana's proposal, MTL has evolved through a series of advancements, particularly in the areas of regularization, decomposition, branching, propagation, optimization, and unification. In the early 2000s, researchers focused on developing better regularization techniques to control how shared representations influence individual tasks, enabling better balance between learning shared patterns and task-specific details. Decomposition techniques emerged, allowing the model to separate general and task-specific knowledge by selectively sharing parameters and effectively disentangling features that are only useful for particular tasks. Branching architectures further refined this by incorporating task-specific layers at later stages in the network, allowing different tasks to diverge in ways that improved performance on specialized subtasks while still using shared representations in the earlier layers.

Figure 2: Historical journey of Multitask Learning paradigm.

By the 2010s, propagation methods became integral to MTL, enabling more efficient ways to share information across tasks. Researchers explored techniques like cross-stitch networks and conditional computation, where only relevant features were shared between specific tasks, enhancing generalization by allowing knowledge to propagate selectively rather than uniformly across all tasks. Optimization strategies for MTL were also crucial in this period, particularly for designing gradient-based methods that could handle the complex interactions between tasks. Techniques such as task weighting were developed to adjust the learning emphasis on each task dynamically, ensuring that none dominated or hindered the progress of others.

Approaching 2022, MTL research turned towards unifying various approaches to develop flexible architectures capable of dynamically adapting to multiple tasks, even as task complexity and diversity increased. Unification involved blending different strategies, such as combining regularization, decomposition, and propagation techniques in a single model framework that could accommodate a variety of tasks with minimal fine-tuning. This era of MTL brought in frameworks that allowed a model to allocate resources based on task requirements dynamically, making MTL more robust and adaptable across various domains, from natural language processing to computer vision. These advancements have redefined MTL, building on Caruana’s vision by creating models that leverage shared information to improve both efficiency and adaptability in complex, multi-task environments

Mathematically, multitask learning can be framed as an optimization problem where the model minimizes a weighted combination of loss functions across multiple tasks. For a set of tasks $T_1, T_2, \dots, T_n$, with corresponding loss functions $\mathcal{L}_1, \mathcal{L}_2, \dots, \mathcal{L}_n$, the overall loss function in multitask learning can be expressed as:

$$ \mathcal{L}_{\text{MTL}} = \sum_{i=1}^{n} \lambda_i \mathcal{L}_i, $$

where $\lambda_i$ are the weights that control the relative importance of each task's loss. The model must balance minimizing these losses in a way that improves performance across all tasks, while also ensuring that no single task dominates the learning process. One of the primary advantages of multitask learning is that it allows the model to benefit from auxiliary tasks, which can provide additional data and regularization that help improve performance on the primary tasks of interest.

The difference between single-task learning and multitask learning lies in the structure of the model and how information is shared across tasks. In single-task learning, the model typically consists of task-specific layers that focus on learning representations tailored to the target task. In multitask learning, the model is divided into shared layers, which learn representations common across all tasks, and task-specific layers, which focus on learning features unique to each task. The shared layers enable the model to capture more generalizable features, reducing the risk of overfitting to any one task. However, the challenge lies in determining the right balance between shared and task-specific layers. Too much sharing can lead to task interference, where learning representations for one task negatively impacts the performance of others, while too little sharing limits the benefits of multitask learning.

One key application where multitask learning has shown significant success is in natural language understanding (NLU). Models like T5 (Text-To-Text Transfer Transformer) utilize multitask learning to handle a wide range of tasks, including translation, summarization, and question-answering, within a single framework. The shared representations learned by the model across these tasks allow it to transfer knowledge from one task to another, improving performance and data efficiency. For example, a multitask model trained on both translation and summarization may learn to represent sentence structures and semantic relations more effectively, which benefits both tasks.

Balancing shared and task-specific layers is critical in multitask learning. Shared layers are responsible for learning common features across tasks, while task-specific layers capture unique characteristics of individual tasks. This balance can be formalized by splitting the model into a shared backbone $f_{\text{shared}}(x)$ and task-specific heads $f_{\text{task}_i}(x)$. The shared backbone is trained using data from all tasks, while each task-specific head is optimized using data relevant to its specific task. This structure can be expressed as:

$$ y_i = f_{\text{task}_i}(f_{\text{shared}}(x)), $$

where $x$ represents the input, $f_{\text{shared}}$ represents the shared layers, and $f_{\text{task}_i}$ represents the task-specific layer for task $i$. The effectiveness of multitask learning depends on how well the shared layers generalize across tasks while the task-specific layers handle the nuances of each task. One benefit of this approach is that it reduces overfitting, as the model is less likely to memorize task-specific details when it is forced to learn general representations that apply across tasks.

In terms of data efficiency, multitask learning allows models to leverage data from multiple tasks, which is particularly useful in low-data regimes. Since the model can learn shared representations from all tasks, it can perform better on tasks with limited labeled data by transferring knowledge from related tasks with more abundant data. This cross-task data sharing helps improve the model's ability to generalize, especially when tasks are similar or complementary.

Figure 3: Illustration of context aware Q&A system.

In a multitask learning (MTL) architecture designed for Question Answering (Q&A) and Context Learning tasks, the model begins with independent encoding, where separate encoders process the question and context inputs for each task. This separation allows for initial feature extraction without immediate task interference, setting a strong foundation for shared learning. An alignment encoding layer then follows, aligning question and context representations to capture associations that link the two tasks, thereby preparing the inputs for the dual co-attention layer. In the dual co-attention layer, the model performs cross-attention between questions and contexts, allowing each task to focus on its relevant parts: Q&A narrows in on potential answer locations, while Context Learning emphasizes question-relevant areas to enhance its comprehension of the context. After co-attention, a self-attention layer further refines each task’s representations by capturing internal dependencies across the input sequence, helping the model gain a deeper understanding of long-range relationships. These refined outputs then pass through a final encoding layer, producing task-specific representations in question and context attentions. The question attention prioritizes answer spans in the context for Q&A, while context attention enables Context Learning to focus on structural and semantic comprehension. Finally, a Feed-Forward Network (FFN) and multi-head attention combine these attentions, enabling the model to generate precise answers for Q&A and well-contextualized responses for Context Learning. This structure effectively balances shared learning with task-specific focus, enabling both tasks to benefit from each other without diminishing individual performance.

Despite these advantages, multitask learning also faces challenges, especially in managing task interference, where optimizing for one task can conflict with the goals of another, leading to reduced performance. Task interference becomes particularly problematic when tasks are dissimilar or have conflicting objectives, as excessive parameter sharing or using identical representations can result in suboptimal learning for specific tasks. Balancing the loss functions across tasks, often termed loss balancing, is another critical challenge in MTL. Choosing the appropriate task loss weights ($\lambda_i$) is essential to prevent any one task from overshadowing the others. Techniques such as dynamic task weighting and uncertainty-based weighting have been introduced to address this challenge, enabling the model to adjust task weights adaptively throughout training and thereby optimize for multiple tasks without sacrificing the performance of individual tasks. Together, these architectural design choices and solutions to MTL’s challenges contribute to achieving a model that balances task interactions while maximizing overall efficiency and accuracy.

In a multitask learning (MTL) architecture inspired by models like ELECTRA, input token representations follow a dual processing path through a generator, a discriminator, and task-specific layers. This approach begins by encoding input tokens using embeddings that capture the semantic and syntactic properties of each token. These embeddings are then fed into the generator, which functions differently from traditional masked language models like BERT. Instead of predicting masked tokens, the generator in ELECTRA replaces certain tokens with plausible alternatives. The generator’s goal is to produce realistic, contextually appropriate replacements rather than just filling in masks, thereby enhancing the model’s understanding of context and token semantics.

Figure 4: Example of ELECTRA-based MTL model architecture.

Following the generator, the processed tokens are passed through a discriminator, whose role is to identify whether each token in the input sequence is a genuine (real) token or one that was replaced by the generator. The discriminator doesn’t reprocess the embeddings sequentially but rather evaluates the likelihood of each token being "real" or "fake," based on the context. This step teaches the model to recognize context-sensitive token replacements, effectively training it to discern nuanced contextual relationships. This distinction allows ELECTRA to capture generalizable language patterns that can benefit multiple tasks.

Once tokens are processed by the discriminator, the resulting representations can be passed to task-specific language model layers tailored for various linguistic tasks, such as Part-of-Speech (POS) scoring, which assigns syntactic tags (e.g., noun, verb) to each token; Dependency Head scoring, which identifies the syntactic head for each word to clarify relationships between tokens; Constituent Span scoring, defining phrase boundaries like noun or verb phrases; and Semantic Role scoring, which assigns functional roles within sentences, such as identifying agents, patients, or instruments. Each of these tasks utilizes a final Feed-Forward Network (FFN), typically followed by task-specific softmax or scoring layers, to transform embeddings into the desired outputs, such as POS tags or dependency heads.

ELECTRA-based MTL models are highly efficient in performing multiple linguistic tasks simultaneously because each task leverages the shared representations from both generator and discriminator layers. This structure allows the model to capture broad, general language patterns in early layers and then specialize representations for specific linguistic tasks in later layers. By leveraging the unique generator-discriminator setup, this architecture achieves strong performance across diverse tasks without needing separate models for each, enhancing both efficiency and accuracy across a range of language processing tasks.

In term of practical implementation, the rust-bert library provides Rust bindings to various pretrained Transformer models, enabling developers to perform advanced natural language processing tasks like summarization, translation, text generation, and question answering. In this example, we use the rust-bert library to load a small T5 model for text summarization. The T5 model is highly adaptable and performs well on summarization tasks, making it a good choice for applications that need concise summaries of longer texts. Using the pretrained T5 model, this code demonstrates how rust-bert can be configured and used in Rust, offering an efficient, Rust-native interface to interact with cutting-edge NLP models.

[dependencies]
anyhow = "1.0.90"
rust-bert = "0.19.0"

use anyhow::Result;
use rust_bert::pipelines::summarization::{SummarizationConfig, SummarizationModel};
use rust_bert::resources::RemoteResource;
use rust_bert::t5::{T5ConfigResources, T5ModelResources, T5VocabResources};

fn main() -> Result<()> {
    // Define the resources needed for the summarization model
    let config_resource = Box::new(RemoteResource::from_pretrained(T5ConfigResources::T5_SMALL));
    let vocab_resource = Box::new(RemoteResource::from_pretrained(T5VocabResources::T5_SMALL));
    let weights_resource = Box::new(RemoteResource::from_pretrained(T5ModelResources::T5_SMALL));

    // Provide a dummy merges resource as required by the struct
    let dummy_merges_resource = Box::new(RemoteResource::new("https://example.com", "dummy"));

    // Set up the summarization model configuration
    let summarization_config = SummarizationConfig {
        model_type: rust_bert::pipelines::common::ModelType::T5,
        model_resource: weights_resource,
        config_resource,
        vocab_resource,
        merges_resource: dummy_merges_resource, // Using dummy resource here
        min_length: 10,
        max_length: 512,
        ..Default::default()
    };

    // Initialize the summarization model
    let summarization_model = SummarizationModel::new(summarization_config)?;

    // Input text for summarization
    let input = ["In findings published Tuesday in Cornell University's arXiv by a team of scientists \
from the University of Montreal and a separate report published Wednesday in Nature Astronomy by a team \
from University College London (UCL), the presence of water vapour was confirmed in the atmosphere of K2-18b, \
a planet circling a star in the constellation Leo. This is the first such discovery in a planet in its star's \
habitable zone — not too hot and not too cold for liquid water to exist. The Montreal team, led by Björn Benneke, \
used data from the NASA's Hubble telescope to assess changes in the light coming from K2-18b's star as the planet \
passed between it and Earth. They found that certain wavelengths of light, which are usually absorbed by water, \
weakened when the planet was in the way, indicating not only does K2-18b have an atmosphere, but the atmosphere \
contains water in vapour form. The team from UCL then analyzed the Montreal team's data using their own software \
and confirmed their conclusion. This was not the first time scientists have found signs of water on an exoplanet, \
but previous discoveries were made on planets with high temperatures or other pronounced differences from Earth. \
\"This is the first potentially habitable planet where the temperature is right and where we now know there is water,\" \
said UCL astronomer Angelos Tsiaras. \"It's the best candidate for habitability right now.\" \"It's a good sign\", \
said Ryan Cloutier of the Harvard–Smithsonian Center for Astrophysics, who was not one of either study's authors. \
\"Overall,\" he continued, \"the presence of water in its atmosphere certainly improves the prospect of K2-18b being \
a potentially habitable planet, but further observations will be required to say for sure. \" \
K2-18b was first identified in 2015 by the Kepler space telescope. It is about 110 light-years from Earth and larger \
but less dense. Its star, a red dwarf, is cooler than the Sun, but the planet's orbit is much closer, such that a year \
on K2-18b lasts 33 Earth days. According to The Guardian, astronomers were optimistic that NASA's James Webb space \
telescope — scheduled for launch in 2021 — and the European Space Agency's 2028 ARIEL program, could reveal more \
about exoplanets like K2-18b."];

    // Summarize the input text
    let output = summarization_model.summarize(&input);
    for sentence in output {
        println!("{sentence}");
    }

    Ok(())
}

The code first configures and loads the T5 model with specific resources for configuration, vocabulary, and weights. These resources are downloaded from Hugging Face's Model Hub using RemoteResource. Since T5 does not require merges (typically used for BPE tokenization in models like GPT), a dummy resource is provided to fulfill the struct's requirements. Once the model is set up, the code inputs a sample article about the discovery of water vapor in the atmosphere of exoplanet K2-18b and uses the model's summarize function to generate a concise summary. Finally, the output is printed, showcasing how Rust can efficiently handle complex NLP tasks by leveraging pretrained models through rust-bert. This approach streamlines access to NLP tools in Rust and facilitates tasks requiring state-of-the-art language models in a production-ready environment.

Let see other practical example from the following code that demonstrates the setup and use of a pre-trained T5 language model for multilingual translation tasks using the rust-bert library, which provides high-level abstractions for working with state-of-the-art NLP models in Rust. This example showcases how organizations can leverage T5 for real-time translations across multiple languages by utilizing pre-trained models available through Hugging Face’s model hub. Such multilingual capabilities are valuable in applications requiring efficient and accurate translation of content for international audiences, such as global customer support systems, real-time language interpretation, and cross-lingual content generation.

[dependencies]
anyhow = "1.0.90"
rust-bert = "0.19.0"
tch = "0.8.0"

use anyhow;
use rust_bert::pipelines::common::ModelType;
use rust_bert::pipelines::translation::{Language, TranslationConfig, TranslationModel};
use rust_bert::resources::RemoteResource;
use rust_bert::t5::{T5ConfigResources, T5ModelResources, T5VocabResources};
use tch::Device;

fn main() -> anyhow::Result<()> {
    // Define resources for model configuration, vocabulary, and weights
    let model_resource = RemoteResource::from_pretrained(T5ModelResources::T5_BASE);
    let config_resource = RemoteResource::from_pretrained(T5ConfigResources::T5_BASE);
    let vocab_resource = RemoteResource::from_pretrained(T5VocabResources::T5_BASE);

    // Placeholder resource for merges (not needed for T5 but required by the API)
    let merges_resource = RemoteResource::from_pretrained(T5VocabResources::T5_BASE);

    // Define source and target languages for translation
    let source_languages = vec![
        Language::English,
        Language::French,
        Language::German,
        Language::Indonesian,
    ];
    let target_languages = vec![
        Language::English,
        Language::French,
        Language::German,
        Language::Indonesian,
    ];

    // Configure translation model
    let translation_config = TranslationConfig::new(
        ModelType::T5,
        model_resource.into(),
        config_resource,
        vocab_resource,
        merges_resource, // Placeholder resource, not actively used
        source_languages,
        target_languages,
        Device::cuda_if_available(),
    );

    // Initialize the model
    let model = TranslationModel::new(translation_config)?;

    // Define a source sentence for translation
    let source_sentence = "This sentence will be translated into multiple languages.";

    // Translate the sentence into multiple languages
    let mut outputs = Vec::new();
    outputs.push(model.translate(&[source_sentence], Language::English, Language::French)?);
    outputs.push(model.translate(&[source_sentence], Language::English, Language::German)?);
    outputs.push(model.translate(&[source_sentence], Language::English, Language::Indonesian)?);

    // Print out the translated sentences
    for output in outputs {
        for sentence in output {
            println!("{sentence}");
        }
    }

    Ok(())
}

In the code, essential resources for the T5 model—including configuration, vocabulary, and model weights—are loaded from remote resources. Since the T5 model doesn't require a merges resource (used typically for certain tokenizers), a placeholder is provided to satisfy the library's API requirements. The translation configuration is set to work with English, French, German, and Romanian as both source and target languages. The code initializes a TranslationModel with this configuration and then translates a sample sentence from English to French, German, and Romanian. The translated sentences are then printed out, demonstrating the T5 model’s ability to handle various translation tasks seamlessly within a Rust application.

The T5 model family on [Hugging Face ](https://huggingface.co/docs/transformers/index)offers a range of variants tailored to meet different needs, from efficiency to performance, across diverse NLP tasks. The original T5 model comes in multiple sizes, each designed to fit specific computational requirements. These include t5-small, with 60 million parameters, suitable for lightweight applications, and t5-base, which has 220 million parameters and is optimal for moderately demanding tasks. Larger versions like t5-large (770 million parameters) and t5-3b (3 billion parameters) cater to more intensive tasks, while t5-11b, with 11 billion parameters, is the largest model, ideal for high-capacity NLP applications where accuracy is prioritized over computational efficiency.

The Flan-T5 series introduces an instruction-tuned approach to T5, allowing it to better understand and follow specific instructions, making it especially useful in tasks requiring guided responses, such as question-answering and summarization. The Flan-T5 models come in various sizes: flan-t5-small, flan-t5-base, flan-t5-large, flan-t5-xl, and flan-t5-xxl, each correlating with the parameter count and capacities of the original T5 variants. Flan-T5's tuning with additional instruction-based data makes it more adaptable for interactive applications where nuanced understanding of instructions improves user experience.

The T5 v1.1 models represent an improved version of the original T5 models, refined with architectural optimizations. Available in t5-v1_1-small, t5-v1_1-base, t5-v1_1-large, t5-v1_1-xl, and t5-v1_1-xxl, these models incorporate better training strategies and adjustments, resulting in more efficient performance with lower computational demands. T5 v1.1 is known for providing higher quality results with reduced resource requirements, making it an attractive choice for applications where cost-efficiency and quality need to be balanced.

MT5, the multilingual version of T5, addresses the needs of applications requiring support across languages. With models like mt5-small, mt5-base, mt5-large, mt5-xl, and mt5-xxl, MT5 serves multilingual tasks effectively, offering flexibility for developers needing translation, multilingual understanding, and generation capabilities. MT5’s architecture is optimized for multilingual NLP, making it well-suited for global applications, including translation services, cross-lingual search, and chatbots supporting diverse languages.

Lastly, the mT0 models, based on T5 and developed by BigScience, are instruction-tuned for multilingual tasks. These include mt0-small, mt0-base, mt0-large, mt0-xl, and mt0-xxl. mT0 is specifically designed to handle multilingual prompts and responses, offering benefits similar to Flan-T5 but optimized for diverse languages. These models excel in international NLP applications where understanding instructions in multiple languages is critical, such as in cross-lingual customer support and automated question-answering for global users.

Together, these T5 variants allow for versatility in NLP, from lightweight models for mobile and edge devices to large-scale models for high-performance tasks, enabling developers to select the best-suited model based on task complexity, language requirements, and computational constraints. The hf-hub crate in Rust makes it easy to integrate these Hugging Face T5 models directly into Rust applications by accessing Hugging Face’s extensive model hub. Using hf-hub, developers can fetch, download, and manage model resources from the Hugging Face Hub, streamlining the deployment of T5 models and other transformers in Rust projects. This capability enables Rust developers to efficiently leverage T5’s powerful NLP functions, such as summarization, translation, and question-answering, while also benefiting from the range of model sizes and multilingual support. By supporting seamless access to pretrained models, the hf-hub crate provides a flexible and scalable approach for incorporating state-of-the-art NLP directly into Rust applications.

Here's an example of how to use the hf-hub crate to download several T5 model variants from Hugging Face. This code will download the model files for t5-small, t5-base, and t5-large. The hf-hub crate provides an API for accessing models, datasets, and other resources hosted on the Hugging Face Hub, making it convenient to download and use models directly in Rust applications.

To run this example, ensure you have the hf-hub crate installed in your Cargo.toml:

[dependencies]
hf-hub = "0.9.0"  # Check for the latest version on crates.io
anyhow = "1.0"

use anyhow;
use hf_hub::api::sync::Api;
use std::fs;
use std::path::Path;

fn main() -> anyhow::Result<()> {
    let api = Api::new()?;

    // List of T5 model variants to download
    let models = ["t5-small", "t5-base", "t5-large"];

    for &model in &models {
        // Create model directory if it doesn't exist
        let model_dir = Path::new("models").join(model);
        fs::create_dir_all(&model_dir)?;

        // Download config.json
        let config_path = api.model(model.to_string()).get("config.json")?;
        fs::copy(config_path, model_dir.join("config.json"))?;

        // Download spiece.model (instead of vocab.json)
        let spiece_path = api.model(model.to_string()).get("spiece.model")?;
        fs::copy(spiece_path, model_dir.join("spiece.model"))?;

        // Download pytorch_model.bin
        let weights_path = api.model(model.to_string()).get("pytorch_model.bin")?;
        fs::copy(weights_path, model_dir.join("pytorch_model.bin"))?;

        println!("Downloaded {} model files to {:?}", model, model_dir);
    }

    Ok(())
}

This Rust code uses the hf-hub crate to download various T5 model variants from Hugging Face, specifically t5-small, t5-base, and t5-large, for use in NLP applications. The Api::new() function initializes a Hugging Face API client, enabling access to model repositories on the Hub. The code then defines a vector of model names and iterates over each one, specifying the repository and downloading essential files like config.json, vocab.json, and pytorch_model.bin for each model variant. These files are necessary for configuring, tokenizing, and loading the model's weights in downstream tasks. Each file is saved in a dedicated models directory with a unique name that corresponds to the specific model variant. By organizing the models in this way, the code enables efficient management and loading of T5 models for various NLP use cases in Rust, demonstrating how to leverage the Hugging Face Hub directly from Rust applications.

Recent trends in multitask learning focus on reducing task interference and enhancing scalability. Unified models, such as T5 and mT5, represent the forefront of multitask learning, designed to handle a wide range of tasks across multiple languages and modalities. These models use sophisticated architectures that balance shared and task-specific representations, often employing adaptive attention mechanisms to allocate resources dynamically based on task demands. Moreover, integrating multitask learning with large-scale pre-training and fine-tuning frameworks has become the predominant approach in NLP, enabling models to generalize more effectively across tasks and domains.

In industry, multitask learning is widely applied in scenarios requiring simultaneous handling of multiple related tasks within a unified framework. For instance, in customer service automation, a multitask model can handle varied requests such as answering FAQs, processing transactions, and providing product recommendations. By sharing knowledge across these tasks, the model improves performance on each task individually while streamlining training and deployment efficiency.

Benchmarking the effectiveness of multitask learning requires performance comparison against single-task models. This involves training single-task models independently on each task and evaluating them with standard metrics such as accuracy, BLEU score, or ROUGE score, depending on the specific task. The multitask model should demonstrate improvements from knowledge transfer between tasks and exhibit better data efficiency when trained on smaller datasets.

In conclusion, multitask learning enhances model generalization and data efficiency by leveraging shared representations across tasks. While it introduces challenges such as task interference and balancing of losses, multitask learning has proven effective in numerous real-world applications, particularly in NLP. Implementing multitask learning models in Rust presents an opportunity to explore these techniques in a high-performance, memory-efficient environment, allowing developers to optimize and deploy models capable of handling multiple tasks concurrently. As the field progresses, advancements in architectures and dynamic task management will further improve the scalability and efficacy of multitask learning systems.

7.2. The T5 Architecture link

The T5 architecture represents a significant innovation in the realm of multitask learning for natural language processing (NLP). Developed by Google, T5 was designed as a unified model that frames every NLP task—from translation to summarization and question answering—within the same text-to-text format. This approach simplifies the model architecture and training process, as it treats both inputs and outputs as strings of text, regardless of the task. By using this standardized format, T5 allows for consistent learning across a wide range of NLP tasks, leveraging a shared architecture that adapts to various challenges without requiring task-specific modifications.

Figure 5: Illustration of T5’s encoder-decoder model architecture.

At its core, the T5 architecture is based on the Transformer model’s encoder-decoder structure. The input text is first passed through the encoder, which generates a contextualized representation of the input sequence. This representation is then fed into the decoder, which generates the output text token by token. The key innovation of T5 lies in how it reformulates every task into this text-to-text paradigm. For example, a translation task might take the input "translate English to French: What is your name?" and expect the output "Quel est votre nom?", while a summarization task would take an article as input and return its summary as output. Mathematically, this can be represented as a mapping from the input text $X$ to the output text $Y$, where the model learns the conditional probability distribution:

$$ P(Y | X) = \prod_{t=1}^{T} P(y_t | y_1, y_2, \dots, y_{t-1}, X), $$

where $y_t$ represents the token generated at each step, conditioned on both the input text $X$ and the previously generated tokens $y_1, y_2, \dots, y_{t-1}$. This formulation allows T5 to handle various tasks within the same framework, making it highly versatile.

One of the key advantages of the T5 model is its text-to-text format, which simplifies task definitions. In traditional multitask learning models, different tasks often require distinct architectures or output heads. However, T5 eliminates this need by using a single sequence-to-sequence architecture for all tasks, making it easier to train and fine-tune the model across a diverse set of tasks. The model’s ability to represent everything in the same format enhances its generalization capabilities, as the shared encoder-decoder layers can learn patterns that transfer well across tasks. This design also facilitates large-scale pre-training on massive datasets, enabling the model to acquire a broad understanding of language before fine-tuning on task-specific data.

Pre-training plays a critical role in the success of T5. During pre-training, the model is exposed to vast amounts of text data, where it learns to predict masked tokens in a text sequence, a task known as “span corruption.” This pre-training objective helps the model capture the underlying structure and relationships in language, making it highly effective when transferred to downstream tasks like translation or summarization. Pre-training in T5 is crucial for achieving high performance, as it provides the model with a strong foundation that can be fine-tuned for specific tasks with relatively little labeled data. The pre-training loss function can be expressed as:

$$ \mathcal{L}_{\text{pretrain}} = - \sum_{i=1}^{N} \log P(y_i | X_{\setminus i}), $$

where $y_i$ represents the masked tokens, and $X_{\setminus i}$ denotes the input sequence with certain spans masked out. This pre-training step ensures that T5 learns robust representations that can be efficiently adapted to a variety of NLP tasks.

T5’s ability to handle diverse tasks is a result of its flexible architecture and shared representation learning. By framing every task as a sequence-to-sequence problem, T5 ensures that the same model can be used for tasks as different as question answering and sentiment analysis. The encoder-decoder structure allows the model to generalize across tasks by learning common features in the input text while still generating task-specific outputs. This flexibility contrasts with other models that might require separate fine-tuning heads for each task, making T5 particularly efficient in multitask settings.

Scaling T5 has shown a significant impact on its performance across tasks. The original T5 model was released in various sizes, from small models with tens of millions of parameters to the largest models with billions of parameters. Empirical results have demonstrated that scaling up the number of parameters improves performance on almost every NLP task. This trend aligns with the general observation in deep learning that larger models, when properly trained, tend to generalize better and achieve superior results. However, scaling also introduces challenges related to computational resources, training time, and inference latency, which need to be carefully managed in real-world applications.

In Rust, implementing the T5 model involves defining its encoder-decoder architecture using frameworks like tch-rs, which provides Rust bindings to PyTorch. The encoder consists of a series of self-attention layers, where each token in the input attends to all other tokens to learn contextual representations. The decoder, also composed of self-attention layers, generates the output sequence based on the encoded representation and the tokens generated so far. This encoder-decoder framework is shared across all tasks, making the implementation of T5 in Rust straightforward for multitask learning.

use tch::{nn, nn::Module, nn::OptimizerConfig, Device, Tensor};

/// Define a basic T5 Encoder Block with self-attention and feed-forward layers
fn encoder_block(p: &nn::Path, n_embd: i64, n_heads: i64) -> impl Module {
    let self_attn = nn::multi_head_attention(p / "self_attn", n_embd, n_heads);
    let layer_norm1 = nn::layer_norm(p / "layer_norm1", vec![n_embd], Default::default());
    let feed_forward = nn::seq()
        .add(nn::linear(p / "lin1", n_embd, 4 * n_embd, Default::default()))
        .add_fn(|x| x.relu())
        .add(nn::linear(p / "lin2", 4 * n_embd, n_embd, Default::default()));
    let layer_norm2 = nn::layer_norm(p / "layer_norm2", vec![n_embd], Default::default());

    nn::func(move |xs| {
        let attn_output = xs.apply(&self_attn);
        let x = xs + attn_output;
        let x = x.apply(&layer_norm1);

        let ff_output = x.apply(&feed_forward);
        x + ff_output.apply(&layer_norm2)
    })
}

/// Define the T5 Encoder
fn encoder(p: &nn::Path, n_embd: i64, n_layers: i64, n_heads: i64) -> impl Module {
    let embedding = nn::embedding(p / "embedding", 32000, n_embd, Default::default());
    let encoder_blocks: Vec<_> = (0..n_layers)
        .map(|i| encoder_block(&p / format!("block_{}", i), n_embd, n_heads))
        .collect();

    nn::func(move |xs| {
        let mut x = xs.apply(&embedding);
        for block in &encoder_blocks {
            x = x.apply(block);
        }
        x
    })
}

/// Define a T5 Decoder Block with self-attention, encoder-decoder attention, and feed-forward layers
fn decoder_block(p: &nn::Path, n_embd: i64, n_heads: i64) -> impl Module {
    let self_attn = nn::multi_head_attention(p / "self_attn", n_embd, n_heads);
    let enc_dec_attn = nn::multi_head_attention(p / "enc_dec_attn", n_embd, n_heads);
    let layer_norm1 = nn::layer_norm(p / "layer_norm1", vec![n_embd], Default::default());
    let layer_norm2 = nn::layer_norm(p / "layer_norm2", vec![n_embd], Default::default());
    let layer_norm3 = nn::layer_norm(p / "layer_norm3", vec![n_embd], Default::default());

    let feed_forward = nn::seq()
        .add(nn::linear(p / "lin1", n_embd, 4 * n_embd, Default::default()))
        .add_fn(|x| x.relu())
        .add(nn::linear(p / "lin2", 4 * n_embd, n_embd, Default::default()));

    nn::func(move |xs| {
        let self_attn_output = xs.apply(&self_attn);
        let x = xs + self_attn_output;
        let x = x.apply(&layer_norm1);

        let enc_dec_attn_output = x.apply(&enc_dec_attn);
        let x = x + enc_dec_attn_output;
        let x = x.apply(&layer_norm2);

        let ff_output = x.apply(&feed_forward);
        x + ff_output.apply(&layer_norm3)
    })
}

/// Define the T5 Decoder
fn decoder(p: &nn::Path, n_embd: i64, n_layers: i64, n_heads: i64) -> impl Module {
    let embedding = nn::embedding(p / "embedding", 32000, n_embd, Default::default());
    let decoder_blocks: Vec<_> = (0..n_layers)
        .map(|i| decoder_block(&p / format!("block_{}", i), n_embd, n_heads))
        .collect();

    nn::func(move |xs, encoder_output| {
        let mut x = xs.apply(&embedding);
        for block in &decoder_blocks {
            x = x.apply(block);
        }
        x
    })
}

/// Define the T5 model, combining encoder and decoder
fn t5_model(vs: &nn::Path, n_embd: i64, n_layers: i64, n_heads: i64) -> impl Module {
    let encoder = encoder(vs / "encoder", n_embd, n_layers, n_heads);
    let decoder = decoder(vs / "decoder", n_embd, n_layers, n_heads);
    
    nn::func(move |src, tgt| {
        let encoder_output = encoder.forward(&src);
        decoder.forward(&tgt, &encoder_output)
    })
}

/// Example function to train the T5 model with a dataset
fn train_t5_model() -> Result<(), Box<dyn std::error::Error>> {
    let vs = nn::VarStore::new(Device::Cpu);
    let t5 = t5_model(&vs.root(), 512, 6, 8);
    let mut opt = nn::Adam::default().build(&vs, 1e-4)?;

    // Example data loading step (replace with actual dataset loading)
    let src = Tensor::randn(&[64, 128], (tch::Kind::Int64, Device::Cpu));  // dummy input
    let tgt = Tensor::randn(&[64, 128], (tch::Kind::Int64, Device::Cpu));  // dummy target

    for epoch in 0..100 {
        let output = t5.forward(&src, &tgt);
        
        // Example loss calculation (replace with actual criterion)
        let loss = output.mean(tch::Kind::Float);
        
        // Optimize
        opt.backward_step(&loss);
        
        println!("Epoch: {} | Loss: {:?}", epoch, f64::from(loss));
    }

    Ok(())
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    train_t5_model()
}

Training the T5 model on multiple NLP tasks simultaneously can be done by first pre-training the model on a large corpus of unlabeled data and then fine-tuning it on task-specific datasets. The multitask learning capability of T5 allows it to perform well on a variety of tasks, and training can be done by alternating between different tasks or by using a task-specific loss function that balances the contributions of each task. For example, in a multitask setup involving translation, summarization, and sentiment analysis, the loss function might be a weighted sum of the individual task losses:

$$ \mathcal{L}_{\text{multitask}} = \lambda_1 \mathcal{L}_{\text{translation}} + \lambda_2 \mathcal{L}_{\text{summarization}} + \lambda_3 \mathcal{L}_{\text{sentiment}}, $$

where $\lambda_1, \lambda_2, \lambda_3$ are the weights that control the importance of each task. Fine-tuning T5 in Rust can be done by adjusting these weights and optimizing the model using gradient descent.

Fine-tuning T5 on specific tasks using Rust involves loading pre-trained weights into the T5 model and further training it on task-specific data. Fine-tuning enables the model to adapt to the nuances of a particular task, such as generating summaries that are concise and informative or translating sentences with high accuracy. The performance of the fine-tuned model can be evaluated by comparing it with task-specific models that have been trained from scratch. In most cases, the fine-tuned T5 model outperforms task-specific models, as it benefits from the general language understanding it gained during pre-training.

In industry, T5 has been applied to a wide range of tasks, from machine translation and summarization to question answering and dialogue generation. Its unified framework makes it particularly attractive for companies that need a versatile model capable of handling multiple NLP tasks with minimal modification. By using T5, organizations can simplify their NLP pipelines, as the same model can be fine-tuned and deployed for different use cases without needing to retrain separate models for each task.

Recent trends in multitask learning and unified models, such as T5, emphasize the importance of large-scale pre-training and efficient fine-tuning strategies. Researchers are increasingly focused on scaling up these models and finding ways to reduce their computational footprint, making them more accessible for real-time applications. Techniques like model distillation, quantization, and sparse training are being explored to make large models like T5 more resource-efficient while maintaining their strong performance across diverse tasks.

Lets see a practical example. This code initializes a language model based on the T5 architecture, specifically configured for translation and text generation tasks. It loads pre-trained model weights and tokenizer configurations from Hugging Face's hub, processes a specified prompt, and generates output either by encoding the prompt or by decoding it for conditional generation. Key parameters, such as temperature and repeat penalties, help guide the generation style, and the code uses hardcoded options for flexibility in configuring the device, model type, and other settings.

[dependencies]
anyhow = "1.0"
serde_json = "1.0.132"
tch = "0.12.0"
reqwest = { version = "0.12.8", features = ["blocking"] }
candle-transformers = "0.7.2"
candle-core = "0.7.2"
candle-nn = "0.7.2"
hf-hub = "0.3.2"
tokenizers = "0.20.1"
accelerate-src = "0.3.2"

use candle_core::backend::BackendDevice;
use std::io::Write;
use std::path::PathBuf;

use candle_transformers::models::t5;
use anyhow::{anyhow, Result};
use candle_core::{DType, Device, Tensor, CudaDevice};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;

const DTYPE: DType = DType::F32;

// Hardcoded configurations
const USE_CPU: bool = true;
const MODEL_ID: &str = "t5-small";
const REVISION: &str = "main";
const PROMPT: &str = "Translate English to French: How are you?";
const DECODE: bool = true;
const DISABLE_CACHE: bool = false;
const TEMPERATURE: f64 = 0.8;
const TOP_P: Option<f64> = None;
const REPEAT_PENALTY: f32 = 1.1;
const REPEAT_LAST_N: usize = 64;
const MAX_TOKENS: usize = 512;

struct T5ModelBuilder {
    device: Device,
    config: t5::Config,
    weights_filename: Vec<PathBuf>,
}

impl T5ModelBuilder {
    pub fn load() -> Result<(Self, Tokenizer)> {
        let device = if USE_CPU { Device::Cpu } else { Device::Cuda(CudaDevice::new(0)?) };
        let model_id = MODEL_ID.to_string();
        let revision = REVISION.to_string();

        let repo = Repo::with_revision(model_id.clone(), RepoType::Model, revision);
        let api = Api::new()?;
        let repo = api.repo(repo);
        let config_filename = repo.get("config.json")?;
        let tokenizer_filename = repo.get("tokenizer.json")?;
        let weights_filename = vec![repo.get("model.safetensors")?];

        let config = std::fs::read_to_string(config_filename)?;
        let mut config: t5::Config = serde_json::from_str(&config)?;
        config.use_cache = !DISABLE_CACHE;

        // Load the tokenizer without additional modifications.
        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(|e| anyhow!(e))?;
        Ok((
            Self {
                device,
                config,
                weights_filename,
            },
            tokenizer,
        ))
    }

    pub fn build_encoder(&self) -> Result<t5::T5EncoderModel> {
        let vb = unsafe {
            VarBuilder::from_mmaped_safetensors(&self.weights_filename, DTYPE, &self.device)?
        };
        Ok(t5::T5EncoderModel::load(vb, &self.config)?)
    }

    pub fn build_conditional_generation(&self) -> Result<t5::T5ForConditionalGeneration> {
        let vb = unsafe {
            VarBuilder::from_mmaped_safetensors(&self.weights_filename, DTYPE, &self.device)?
        };
        Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
    }
}

fn main() -> Result<()> {
    let (builder, tokenizer) = T5ModelBuilder::load()?;
    let device = &builder.device;

    // Tokenize with padding and truncation applied directly here.
    let tokens = tokenizer
        .encode(PROMPT, true)
        .map_err(|e| anyhow!("Tokenization failed: {}", e))?
        .get_ids()
        .to_vec();

    let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;

    if !DECODE {
        let mut model = builder.build_encoder()?;
        let ys = model.forward(&input_token_ids)?;
        println!("{ys}");
    } else {
        let mut model = builder.build_conditional_generation()?;
        let mut output_token_ids = vec![
            builder.config.decoder_start_token_id.unwrap_or(builder.config.pad_token_id) as u32,
        ];

        let temperature = if TEMPERATURE <= 0.0 { None } else { Some(TEMPERATURE) };
        let mut logits_processor = LogitsProcessor::new(299792458, temperature, TOP_P);
        let encoder_output = model.encode(&input_token_ids)?;

        for index in 0.. {
            if output_token_ids.len() > MAX_TOKENS {
                break;
            }
            let decoder_token_ids = if index == 0 || !builder.config.use_cache {
                Tensor::new(output_token_ids.as_slice(), device)?.unsqueeze(0)?
            } else {
                let last_token = *output_token_ids.last().unwrap();
                Tensor::new(&[last_token], device)?.unsqueeze(0)?
            };

            let logits = model.decode(&decoder_token_ids, &encoder_output)?.squeeze(0)?;
            let logits = if REPEAT_PENALTY == 1.0 {
                logits
            } else {
                let start_at = output_token_ids.len().saturating_sub(REPEAT_LAST_N);
                candle_transformers::utils::apply_repeat_penalty(
                    &logits,
                    REPEAT_PENALTY,
                    &output_token_ids[start_at..],
                )?
            };

            let next_token_id = logits_processor.sample(&logits)?;
            if next_token_id as usize == builder.config.eos_token_id {
                break;
            }
            output_token_ids.push(next_token_id);
            if let Some(text) = tokenizer.id_to_token(next_token_id) {
                let text = text.replace('▁', " ").replace("<0x0A>", "\n");
                print!("{text}");
                std::io::stdout().flush()?;
            }
        }
        println!("\n{} tokens generated", output_token_ids.len());
    }
    Ok(())
}

pub fn normalize_l2(v: &Tensor) -> Result<Tensor> {
    Ok(v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)?)
}

The program begins by defining and loading the model and tokenizer files. If set to decode, it uses a loop to iteratively generate tokens based on the prompt, with logits adjustments applied to manage repetition and randomness in the output. In encoding mode, the code simply processes the input through the encoder and displays the output embeddings. The tokenizer is configured with padding and truncation options, and final token outputs are decoded back into text for display. Constants control the model's behavior, such as device allocation (CPU/GPU), generation temperature, and token penalties, allowing a flexible setup without command-line input.

In conclusion, the T5 architecture is a powerful example of a multitask learning framework that can handle a variety of NLP tasks within a unified text-to-text paradigm. Its encoder-decoder structure and ability to share knowledge across tasks make it an ideal candidate for multitask learning scenarios. Implementing and fine-tuning T5 in Rust offers a highly efficient and scalable approach to solving NLP problems, leveraging Rust’s performance advantages while ensuring the model can generalize across different tasks effectively.u

7.3. Unified Models for Multitask Learning link

Unified models in multitask learning represent a significant advancement in the field of natural language processing, where the goal is to design a single model architecture that can handle a variety of tasks without requiring modifications or task-specific components. These models offer a flexible and scalable solution, enabling developers to deploy a single model across multiple tasks, simplifying both deployment and maintenance. Unlike traditional models that are often trained separately for individual tasks, unified models leverage shared knowledge and representations, making them more efficient and adaptable. The T5 model is one example of a successful unified model, but others, such as mT5, BART, and UnifiedQA, demonstrate the versatility and power of task-agnostic architectures.

The fundamental principle behind unified models is the use of a shared architecture that remains consistent across tasks. In such models, tasks are treated in a similar way, with a common architecture handling diverse inputs and outputs. This approach reduces the complexity of training and deploying multiple models for different tasks, as the same architecture can be fine-tuned or adapted to perform well on each task. For example, mT5 extends the T5 framework to support multiple languages, treating multilingual translation, summarization, and question answering as variants of the same underlying task structure. This task-agnostic design is particularly useful in environments where models need to handle a broad range of tasks with minimal adjustments.

One of the key advantages of unified models is that they simplify model deployment and maintenance. In traditional systems, deploying separate models for different tasks requires maintaining multiple versions of the model, each trained and optimized individually. This introduces challenges in scaling and updating the models as new tasks emerge. By contrast, a unified model can be deployed once and fine-tuned as needed for additional tasks. This reduces the computational and operational overhead associated with maintaining task-specific architectures and makes scaling much easier. The uniform structure of unified models also makes it possible to apply the same optimization and efficiency techniques, such as quantization and pruning, across all tasks.

However, designing a unified model that performs well across a wide range of tasks presents significant challenges. One of the primary difficulties is ensuring that the model does not overfit to one task while underperforming on others, a problem known as task interference. When tasks are too dissimilar, the shared layers of the model may struggle to represent all tasks effectively, leading to suboptimal performance. Mathematically, this challenge can be expressed through the multitask loss function, where the model must minimize a combined loss across multiple tasks:

$$ \mathcal{L}_{\text{unified}} = \sum_{i=1}^{n} \lambda_i \mathcal{L}_i, $$

where $\mathcal{L}_i$ represents the loss for task $i$, and $\lambda_i$ controls the relative importance of each task. Finding the right balance for the task weights $\lambda_i$ is crucial to prevent one task from dominating the optimization process. In practice, techniques such as dynamic task weighting or task-specific layer modulation are often employed to mitigate these issues, allowing the model to adjust its parameters dynamically based on the current task.

Modular architectures have emerged as a solution to some of the challenges posed by unified models. In a modular framework, the model consists of shared components that are used across tasks, but also task-specific modules that can be activated when necessary. This allows the model to maintain a degree of specialization while benefiting from the generalization provided by the shared components. For example, a unified model might use the same encoder across all tasks but employ different decoders depending on the task, allowing for greater flexibility while still sharing the majority of the parameters. This modular approach can be formalized as:

$$ y_i = f_{\text{task}_i}(f_{\text{shared}}(x)), $$

where $f_{\text{shared}}(x)$ represents the shared encoder, and $f_{\text{task}_i}(x)$ represents the task-specific decoder for task $i$. By isolating the task-specific components, modular architectures offer a way to handle task interference while preserving the benefits of a unified model.

Parameter-efficient training is another key concept in the design of unified models. Given the large size of models like T5 or BART, training and fine-tuning these models across multiple tasks can be computationally expensive. Techniques like parameter-efficient fine-tuning (PEFT) and low-rank adaptation (LoRA) have been developed to reduce the number of parameters that need to be updated during training. In LoRA, for example, the model's weight matrices are factorized into lower-rank matrices, reducing the number of trainable parameters while maintaining the model's expressive power. The objective function for training under LoRA can be written as:

$$ W = W_0 + \Delta W = W_0 + A B, $$

where $W_0$ is the original pre-trained weight matrix, $\Delta W$ is the learned update, and $A$ and $B$ are the low-rank matrices. This technique allows unified models to be fine-tuned efficiently on new tasks without the need to retrain the entire model, making it particularly useful in multitask learning.

Unified models like BART (Bidirectional and Auto-Regressive Transformers) take a different approach by combining bidirectional encoding and autoregressive decoding, which makes the model versatile in handling both generative tasks (like summarization) and discriminative tasks (like classification). BART uses a similar encoder-decoder framework as T5, but with a focus on reconstructing corrupted input sequences, which helps the model learn strong representations for both understanding and generating text. This structure enables BART to perform well across a variety of tasks while maintaining a unified model architecture.

The provided Rust code is a text summarization script using Hugging Face's Rust-BERT library and the DistilBART model, specifically designed to run on CPU using the tch crate for PyTorch bindings in Rust. This script imports necessary resources and dependencies, such as model configuration, vocabulary, and merges files, which it retrieves remotely from Hugging Face’s pre-trained model repository. The code sets up a summarization configuration, defining parameters like beam search, length penalty, minimum and maximum token lengths, and then processes a long input text to produce a summary output. The main function also prints each summarized sentence to the console.

use anyhow;
use rust_bert::bart::{
    BartConfigResources, BartMergesResources, BartModelResources, BartVocabResources,
};
use rust_bert::pipelines::common::ModelResource;
use rust_bert::pipelines::summarization::{SummarizationConfig, SummarizationModel};
use rust_bert::resources::RemoteResource;
use tch::Device;

fn main() -> anyhow::Result<()> {
    let config_resource = Box::new(RemoteResource::from_pretrained(
        BartConfigResources::DISTILBART_CNN_6_6,
    ));
    let vocab_resource = Box::new(RemoteResource::from_pretrained(
        BartVocabResources::DISTILBART_CNN_6_6,
    ));
    let merges_resource = Box::new(RemoteResource::from_pretrained(
        BartMergesResources::DISTILBART_CNN_6_6,
    ));
    let model_resource = Box::new(RemoteResource::from_pretrained(
        BartModelResources::DISTILBART_CNN_6_6,
    ));

    let summarization_config = SummarizationConfig {
        model_resource: ModelResource::Torch(model_resource),
        config_resource,
        vocab_resource,
        merges_resource: Some(merges_resource),
        num_beams: 1,
        length_penalty: 1.0,
        min_length: 56,
        max_length: Some(142),
        device: Device::Cpu,
        ..Default::default()
    };

    let summarization_model = SummarizationModel::new(summarization_config)?;

    let input = ["In findings published Tuesday in Cornell University's arXiv by a team of scientists \
from the University of Montreal and a separate report published Wednesday in Nature Astronomy by a team \
from University College London (UCL), the presence of water vapour was confirmed in the atmosphere of K2-18b, \
a planet circling a star in the constellation Leo. This is the first such discovery in a planet in its star's \
habitable zone — not too hot and not too cold for liquid water to exist. The Montreal team, led by Björn Benneke, \
used data from the NASA's Hubble telescope to assess changes in the light coming from K2-18b's star as the planet \
passed between it and Earth. They found that certain wavelengths of light, which are usually absorbed by water, \
weakened when the planet was in the way, indicating not only does K2-18b have an atmosphere, but the atmosphere \
contains water in vapour form. The team from UCL then analyzed the Montreal team's data using their own software \
and confirmed their conclusion. This was not the first time scientists have found signs of water on an exoplanet, \
but previous discoveries were made on planets with high temperatures or other pronounced differences from Earth. \
\"This is the first potentially habitable planet where the temperature is right and where we now know there is water,\" \
said UCL astronomer Angelos Tsiaras. \"It's the best candidate for habitability right now.\" \"It's a good sign\", \
said Ryan Cloutier of the Harvard–Smithsonian Center for Astrophysics, who was not one of either study's authors. \
\"Overall,\" he continued, \"the presence of water in its atmosphere certainly improves the prospect of K2-18b being \
a potentially habitable planet, but further observations will be required to say for sure. \" \
K2-18b was first identified in 2015 by the Kepler space telescope. It is about 110 light-years from Earth and larger \
but less dense. Its star, a red dwarf, is cooler than the Sun, but the planet's orbit is much closer, such that a year \
on K2-18b lasts 33 Earth days. According to The Guardian, astronomers were optimistic that NASA's James Webb space \
telescope — scheduled for launch in 2021 — and the European Space Agency's 2028 ARIEL program, could reveal more \
about exoplanets like K2-18b."];

    //    Credits: WikiNews, CC BY 2.5 license (https://en.wikinews.org/wiki/Astronomers_find_water_vapour_in_atmosphere_of_exoplanet_K2-18b)
    let _output = summarization_model.summarize(&input)?;
    for sentence in _output {
        println!("{sentence}");
    }

    Ok(())
}

In this code, SummarizationModel::new() initializes the model with the specified SummarizationConfig, leveraging DistilBART, a distilled version of the BART model fine-tuned for the CNN/DailyMail summarization task. The input text on exoplanet K2-18b is processed to condense its content, focusing on essential information. By using a high-level configuration structure, the script controls the summarization process, such as by limiting the summary length with min_length and max_length and adjusting num_beams for result diversity.

Experimenting with different task combinations in a unified model framework is an essential part of understanding the model’s ability to generalize. By training the model on diverse tasks—such as translation and summarization, or question answering and text classification—it is possible to evaluate how well the shared representations transfer between tasks. In some cases, tasks with similar structures or objectives will benefit from multitask learning, while dissimilar tasks may suffer from interference. Benchmarking these models against task-specific models can provide insights into the trade-offs between generalization and task specialization.

Lets see another code sample. This Rust code performs machine translation using the MBART-50 model from Hugging Face’s rust-bert library. MBART-50 is a multilingual sequence-to-sequence model capable of translating across numerous language pairs. The code imports resources for the model configuration, vocabulary, and language options for both source and target languages, all of which are retrieved remotely from Hugging Face's model repository. The main function initializes a translation configuration and sets up a TranslationModel with MBART-50, using GPU if available. The code then defines a source sentence in English and translates it into multiple languages, including French, Spanish, and Hindi, printing each translation to the console.

use anyhow;
use rust_bert::mbart::{
    MBartConfigResources, MBartModelResources, MBartSourceLanguages, MBartTargetLanguages,
    MBartVocabResources,
};
use rust_bert::pipelines::common::{ModelResource, ModelType};
use rust_bert::pipelines::translation::{Language, TranslationConfig, TranslationModel};
use rust_bert::resources::RemoteResource;
use tch::Device;

fn main() -> anyhow::Result<()> {
    let model_resource = RemoteResource::from_pretrained(MBartModelResources::MBART50_MANY_TO_MANY);
    let config_resource =
        RemoteResource::from_pretrained(MBartConfigResources::MBART50_MANY_TO_MANY);
    let vocab_resource = RemoteResource::from_pretrained(MBartVocabResources::MBART50_MANY_TO_MANY);

    let source_languages = MBartSourceLanguages::MBART50_MANY_TO_MANY;
    let target_languages = MBartTargetLanguages::MBART50_MANY_TO_MANY;

    let translation_config = TranslationConfig::new(
        ModelType::MBart,
        ModelResource::Torch(Box::new(model_resource)),
        config_resource,
        vocab_resource,
        None,
        source_languages,
        target_languages,
        Device::cuda_if_available(),
    );
    let model = TranslationModel::new(translation_config)?;

    let source_sentence = "This sentence will be translated in multiple languages.";

    let mut outputs = Vec::new();
    outputs.extend(model.translate(&[source_sentence], Language::English, Language::French)?);
    outputs.extend(model.translate(&[source_sentence], Language::English, Language::Spanish)?);
    outputs.extend(model.translate(&[source_sentence], Language::English, Language::Indonesian)?);

    for sentence in outputs {
        println!("{sentence}");
    }
    Ok(())
}

This script showcases TranslationConfig's flexibility in specifying source and target languages, allowing dynamic, language-to-language translation with MBART-50. By leveraging Language enums, the model can quickly switch between translations, showcasing the model's many-to-many translation capabilities. The setup includes device configuration to use GPU if available, enhancing translation speed. This script highlights efficient multilingual processing by outputting each translated sentence sequentially, demonstrating MBART's multilingual proficiency in a straightforward Rust implementation.

Unified models have seen increasing use in industry, where the ability to deploy a single model that can handle multiple tasks simplifies the deployment pipeline and reduces operational costs. For example, in customer service automation, a unified model could handle both intent classification and response generation, reducing the need to maintain separate models for each task. Similarly, in e-commerce platforms, unified models can be used for both product recommendation and customer sentiment analysis, streamlining the workflow and improving efficiency.

Recent trends in unified models emphasize the development of more efficient architectures that can handle larger numbers of tasks while maintaining strong performance. Advances in hardware acceleration, such as the use of TPUs and GPUs, have made it possible to train and deploy large-scale models like mT5 and UnifiedQA across multiple languages and tasks. Additionally, techniques like multitask pre-training and modular design are becoming more common, allowing models to handle an even wider range of tasks without sacrificing performance or efficiency.

In conclusion, unified models represent a powerful approach to multitask learning, offering the ability to handle diverse tasks within a single architecture. While challenges such as task interference and efficient fine-tuning remain, techniques like modular architectures and parameter-efficient training have made it possible to design flexible and scalable models. Implementing these models in Rust provides a performance-efficient way to explore multitask learning, allowing developers to optimize and deploy models that can generalize across a broad range of tasks. As the field evolves, unified models are likely to play a central role in the development of multitask learning systems.

7.4. Fine-Tuning Multitask Models for Specific Applications link

Fine-tuning multitask models, such as T5, for specific applications is a crucial step in adapting pre-trained models to new domains or tasks while retaining their previously learned capabilities. Multitask models are typically pre-trained on large datasets spanning various tasks, enabling them to capture general language patterns and representations. However, for real-world applications like summarization or translation in specific domains (e.g., legal, medical), fine-tuning is essential to adapt the model to the nuances of the target domain. The process of fine-tuning takes advantage of the knowledge the model has already acquired during its pre-training and uses it to specialize in the new tasks with relatively less data and training time compared to training a model from scratch.

Mathematically, fine-tuning involves continuing to minimize the loss function of the model, but on a new task-specific dataset. Suppose Lpretrain\\mathcal{L}\_{\\text{pretrain}}Lpretrain represents the loss function used during the pre-training phase, which covers multiple tasks. During fine-tuning, we introduce a new task-specific loss Lnew\\mathcal{L}\_{\\text{new}}Lnew, and the objective is to minimize this new loss while maintaining the model’s performance on previously learned tasks. The new objective can be formulated as a combination of the pre-trained loss and the new task-specific loss:

$$ \mathcal{L}_{\text{fine-tune}} = \lambda_1 \mathcal{L}_{\text{new}} + \lambda_2 \mathcal{L}_{\text{pretrain}}, $$

where $\lambda_1$ and $\lambda_2$ are hyperparameters that control the balance between adapting the model to the new task and preserving the knowledge from pre-training. This formulation ensures that while the model is specialized for the new task, it does not completely forget its previous training, thus addressing one of the key challenges in fine-tuning: catastrophic forgetting.

Catastrophic forgetting occurs when a model fine-tuned on a new task loses its ability to perform well on tasks it previously learned. This happens because the model’s weights are updated too aggressively on the new task, overwriting the representations that were useful for other tasks. Techniques such as Elastic Weight Consolidation (EWC) or regularization-based methods help mitigate this issue. EWC introduces a regularization term to the loss function that penalizes significant changes in the weights that are important for previous tasks. This can be expressed as:

$$ \mathcal{L}_{\text{ewc}} = \mathcal{L}_{\text{new}} + \frac{\lambda}{2} \sum_i F_i (\theta_i - \theta_i^*)^2, $$

where $\theta_i$ represents the model's current weights, $\theta_i^*$ are the weights learned during pre-training, and $F_i$ is the Fisher information matrix, which measures the importance of each weight. This term penalizes changes to weights that are critical for previously learned tasks, helping the model retain its multitask capabilities.

Transfer learning plays a key role in the fine-tuning process. By leveraging the knowledge from pre-trained multitask models, transfer learning allows the model to quickly adapt to new tasks, even when the available data is limited. In low-resource scenarios, where only a small amount of domain-specific data is available, the model’s ability to transfer its general knowledge is particularly valuable. Fine-tuning in these cases typically involves using a much smaller learning rate, ensuring that the model does not drastically alter its pre-trained parameters but instead fine-tunes them to capture the specific nuances of the new task. This approach is especially effective when the new task is related to the tasks the model was pre-trained on, allowing for efficient knowledge transfer.

When fine-tuning multitask models on domain-specific data, one of the challenges is handling the variability and complexity of the data. Domain-specific tasks often introduce new vocabulary, specialized terminology, or unique sentence structures that the pre-trained model may not have encountered before. As a result, the model’s embeddings may need to be fine-tuned to handle the new distribution of text. In such cases, techniques like domain-adaptive pre-training (DAPT) can be employed, where the model undergoes an additional phase of pre-training on a domain-specific corpus before being fine-tuned on the target task. This helps the model better capture the specific characteristics of the domain and improves performance on domain-specific tasks.

This Rust code provides a structure for fine-tuning a simplified T5 model, specifically managing pre-trained weights by downloading them if they don’t already exist locally. It defines a t5_model with encoder and decoder blocks and sets up multi-head attention within these blocks. A download_weights function is incorporated to retrieve model weights from a specified URL and save them locally if not already present. The code then initializes and loads these weights into a VarStore, configures an Adam optimizer, and fine-tunes the model using placeholder data over multiple epochs. Loss values are output to track the training progress, and the final model is saved.

[dependencies]
anyhow = "1.0"
serde_json = "1.0.132"
tch = "0.17.0"
reqwest = { version = "0.12.8", features = ["blocking"] }

use reqwest::blocking::get;
use std::{fs, io::Write, path::Path};
use tch::{nn, nn::Module, nn::OptimizerConfig, Device, Tensor};

/// Download the pre-trained weights file if it doesn't exist locally
fn download_weights(url: &str, output_path: &str) -> Result<(), Box<dyn std::error::Error>> {
    if Path::new(output_path).exists() {
        println!("Weights file already exists at '{}'", output_path);
        return Ok(());
    }

    println!("Downloading weights from {}...", url);
    let response = get(url)?;
    let mut out = fs::File::create(output_path)?;
    out.write_all(&response.bytes()?)?;
    println!("Downloaded weights to '{}'", output_path);

    Ok(())
}

/// Define a simplified multi-head attention structure
fn multi_head_attention(p: &nn::Path, n_embd: i64, _n_heads: i64) -> impl Module {
    nn::seq()
        .add(nn::linear(p / "query", n_embd, n_embd, Default::default()))
        .add(nn::linear(p / "key", n_embd, n_embd, Default::default()))
        .add(nn::linear(p / "value", n_embd, n_embd, Default::default()))
        .add(nn::linear(p / "out", n_embd, n_embd, Default::default()))
}

/// Define a simplified T5 model structure for fine-tuning
fn t5_model(vs: &nn::Path, n_embd: i64, n_layers: i64, n_heads: i64) -> impl Module {
    let encoder = encoder(&(vs / "encoder"), n_embd, n_layers, n_heads);
    let decoder = decoder(&(vs / "decoder"), n_embd, n_layers, n_heads);

    nn::func(move |src| {
        let encoder_output = encoder.forward(&src);
        decoder.forward(&encoder_output)
    })
}

/// Load pre-trained weights for the T5 model
fn load_pretrained_weights(vs: &mut nn::VarStore, weight_path: &str) -> Result<(), Box<dyn std::error::Error>> {
    vs.load(weight_path)?;
    Ok(())
}

/// Fine-tune the T5 model with domain-specific data
fn fine_tune_t5_model(weight_path: &str) -> Result<(), Box<dyn std::error::Error>> {
    // Check if CUDA is available and use it; otherwise, default to CPU
    let device = if tch::Cuda::is_available() {
        Device::Cuda(0)
    } else {
        Device::Cpu
    };
    let mut vs = nn::VarStore::new(device);
    let t5 = t5_model(&vs.root(), 512, 6, 8);

    // Load pre-trained weights into the model
    load_pretrained_weights(&mut vs, weight_path)?;

    // Configure optimizer and hyperparameters
    let mut opt = nn::Adam::default().build(&vs, 1e-5)?;
    let batch_size = 16;
    let epochs = 3;

    // Placeholder for domain-specific data loading
    let src = Tensor::randn(&[batch_size, 128], (tch::Kind::Int64, device)); // Dummy input
    let tgt = Tensor::randn(&[batch_size, 128], (tch::Kind::Int64, device)); // Dummy target

    // Fine-tuning loop
    for epoch in 0..epochs {
        let output = t5.forward(&src);

        // Calculate loss (using Cross-Entropy as a placeholder)
        let loss = output.cross_entropy_for_logits(&tgt);

        // Perform backpropagation and optimization step
        opt.backward_step(&loss);

        println!("Epoch: {} | Loss: {:?}", epoch, loss.double_value(&[]));
    }

    // Save the fine-tuned model
    vs.save("fine_tuned_t5.ot")?;

    Ok(())
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Define the URL and output path for the pre-trained weights
    let weights_url = "https://example.com/path/to/weights.ot"; // Replace with the actual URL
    let pretrained_weight_path = "weights.ot";

    // Download the weights file if not already present
    download_weights(weights_url, pretrained_weight_path)?;

    // Run the fine-tuning function
    fine_tune_t5_model(pretrained_weight_path)?;

    Ok(())
}

/// Define the Encoder structure
fn encoder(p: &nn::Path, n_embd: i64, n_layers: i64, n_heads: i64) -> impl Module {
    let embedding = nn::embedding(p / "embedding", 32000, n_embd, Default::default());
    let encoder_blocks: Vec<_> = (0..n_layers)
        .map(|i| encoder_block(&(p / format!("block_{}", i)), n_embd, n_heads))
        .collect();

    nn::func(move |xs| {
        let mut x = xs.apply(&embedding);
        for block in &encoder_blocks {
            x = x.apply(block);
        }
        x
    })
}

/// Define the Decoder structure
fn decoder(p: &nn::Path, n_embd: i64, n_layers: i64, n_heads: i64) -> impl Module {
    let embedding = nn::embedding(p / "embedding", 32000, n_embd, Default::default());
    let decoder_blocks: Vec<_> = (0..n_layers)
        .map(|i| decoder_block(&(p / format!("block_{}", i)), n_embd, n_heads))
        .collect();

    nn::func(move |xs| {
        let mut x = xs.apply(&embedding);
        for block in &decoder_blocks {
            x = x.apply(block);
        }
        x
    })
}

/// Define an Encoder Block with Self-Attention and Feed-Forward layers
fn encoder_block(p: &nn::Path, n_embd: i64, n_heads: i64) -> impl Module {
    let self_attn = multi_head_attention(&(p / "self_attn"), n_embd, n_heads);
    let layer_norm1 = nn::layer_norm(p / "layer_norm1", vec![n_embd], Default::default());
    let feed_forward = nn::seq()
        .add(nn::linear(p / "lin1", n_embd, 4 * n_embd, Default::default()))
        .add_fn(|x| x.relu())
        .add(nn::linear(p / "lin2", 4 * n_embd, n_embd, Default::default()));
    let layer_norm2 = nn::layer_norm(p / "layer_norm2", vec![n_embd], Default::default());

    nn::func(move |xs| {
        let attn_output = xs.apply(&self_attn);
        let x = xs + attn_output;
        let x = x.apply(&layer_norm1);

        let ff_output = x.apply(&feed_forward);
        x + ff_output.apply(&layer_norm2)
    })
}

/// Define a Decoder Block with Self-Attention, Cross-Attention, and Feed-Forward layers
fn decoder_block(p: &nn::Path, n_embd: i64, n_heads: i64) -> impl Module {
    let self_attn = multi_head_attention(&(p / "self_attn"), n_embd, n_heads);
    let enc_dec_attn = multi_head_attention(&(p / "enc_dec_attn"), n_embd, n_heads);
    let layer_norm1 = nn::layer_norm(p / "layer_norm1", vec![n_embd], Default::default());
    let layer_norm2 = nn::layer_norm(p / "layer_norm2", vec![n_embd], Default::default());
    let layer_norm3 = nn::layer_norm(p / "layer_norm3", vec![n_embd], Default::default());

    let feed_forward = nn::seq()
        .add(nn::linear(p / "lin1", n_embd, 4 * n_embd, Default::default()))
        .add_fn(|x| x.relu())
        .add(nn::linear(p / "lin2", 4 * n_embd, n_embd, Default::default()));

    nn::func(move |xs| {
        let self_attn_output = xs.apply(&self_attn);
        let x = xs + self_attn_output;
        let x = x.apply(&layer_norm1);

        let enc_dec_attn_output = x.apply(&enc_dec_attn);
        let x = x + enc_dec_attn_output;
        let x = x.apply(&layer_norm2);

        let ff_output = x.apply(&feed_forward);
        x + ff_output.apply(&layer_norm3)
    })
}

The code works by first checking if CUDA is available, allowing GPU use if present; otherwise, it defaults to the CPU. In main, it checks for the pre-trained weights file, downloading it from a given URL if missing, and then proceeds with fine-tuning. The fine-tuning function loads the weights into a model instance and performs a training loop where the model is optimized using the Adam optimizer. Within each epoch, a forward pass generates output from the input tensors, and a placeholder cross-entropy loss is calculated and used to update model parameters through backpropagation. After training, the model is saved, preserving the updated weights for future use.

Fine-tuning strategies can vary depending on the target task and the available data. One approach is full fine-tuning, where all the parameters of the pre-trained model are updated during the fine-tuning process. While this method provides maximum flexibility in adapting the model, it can be computationally expensive and risks overfitting if the domain-specific dataset is small. An alternative approach is selective fine-tuning, where only the top layers of the model (or specific task-specific layers) are fine-tuned, while the rest of the model’s weights are kept frozen. This technique reduces the risk of overfitting and lowers the computational cost of fine-tuning, making it suitable for low-resource tasks.

Evaluating the effectiveness of fine-tuning involves testing the model’s performance on both the new task and previously learned tasks. For example, a model fine-tuned for legal document summarization should not only perform well on summarization tasks but should also retain its capabilities in translation or question answering if it was originally trained for multitask learning. This evaluation can be done using task-specific metrics such as ROUGE for summarization or BLEU for translation, as well as by measuring the model’s ability to generalize across tasks. Cross-validation and testing on held-out datasets are crucial for ensuring that the fine-tuning process has not caused overfitting or degraded performance on other tasks.

In industry, fine-tuning multitask models like T5 has become increasingly common for domain-specific applications. For example, in the healthcare sector, fine-tuning models on medical texts allows them to provide accurate summarizations of clinical reports or generate domain-specific translations for medical documents. Similarly, in legal applications, models fine-tuned on legal corpora are able to generate high-quality summaries or perform document classification with greater accuracy. The ability to fine-tune pre-trained multitask models on specific applications reduces the need for training models from scratch and accelerates the deployment of AI-driven solutions across various industries.

Recent trends in fine-tuning large-scale multitask models include the exploration of techniques like adapters and LoRA (Low-Rank Adaptation), which allow for parameter-efficient fine-tuning. These methods update only a small portion of the model's parameters, significantly reducing the computational cost while maintaining high performance on the target task. This approach is particularly useful for fine-tuning models in low-resource or real-time applications, where computational resources are limited but high performance is still required.

This Rust code defines a simplified T5 model with LoRA to enable efficient fine-tuning by updating only a small portion of the model's parameters. Designed to work with low-resource environments, the code incorporates a download_weights function that retrieves pre-trained weights from a specified URL if they’re not already saved locally. The t5_model function builds the model architecture with encoder and decoder blocks, each containing custom multi-head attention layers enhanced by LoRA, allowing adjustments with reduced computational demand. The fine-tuning function loads the downloaded weights, configures an optimizer, and trains the model using a sample dataset. Finally, it saves the fine-tuned model parameters for later use.

[dependencies]
anyhow = "1.0"
serde_json = "1.0.132"
tch = "0.17.0"
reqwest = { version = "0.12.8", features = ["blocking"] }

use reqwest::blocking::get;
use std::{fs, io::Write, path::Path};
use tch::{nn, nn::Module, nn::OptimizerConfig, Device, Tensor};

/// LoRA module for low-rank adaptation
fn lora_layer(p: &nn::Path, input_dim: i64, rank: i64) -> impl Module {
    let low_rank_matrix = nn::linear(p / "low_rank", input_dim, rank, Default::default());
    let high_rank_matrix = nn::linear(p / "high_rank", rank, input_dim, Default::default());

    nn::func(move |x| x.apply(&low_rank_matrix).relu().apply(&high_rank_matrix))
}

/// Custom multi-head attention implementation with LoRA
fn multi_head_attention_with_lora(p: &nn::Path, n_embd: i64, _n_heads: i64, rank: i64) -> impl Module {
    // Define attention projections
    let query_proj = nn::linear(p / "query_proj", n_embd, n_embd, Default::default());
    let key_proj = nn::linear(p / "key_proj", n_embd, n_embd, Default::default());
    let value_proj = nn::linear(p / "value_proj", n_embd, n_embd, Default::default());
    let output_proj = nn::linear(p / "output_proj", n_embd, n_embd, Default::default());
    
    let lora_adaptation = lora_layer(&(p / "lora_adapt"), n_embd, rank);

    nn::func(move |x| {
        // Compute queries, keys, and values
        let queries = x.apply(&query_proj);
        let keys = x.apply(&key_proj);
        let values = x.apply(&value_proj);

        // Scaled dot-product attention
        let scores = queries.matmul(&keys.transpose(-2, -1)) / (n_embd as f64).sqrt();
        let attn_weights = scores.softmax(-1, tch::Kind::Float);
        let attn_output = attn_weights.matmul(&values);

        // Apply LoRA adaptation to the output
        let adapted_output = attn_output.apply(&lora_adaptation);

        // Project the final output
        adapted_output.apply(&output_proj)
    })
}

/// Download the pre-trained weights file if it doesn't exist locally
fn download_weights(url: &str, output_path: &str) -> Result<(), Box<dyn std::error::Error>> {
    if Path::new(output_path).exists() {
        println!("Weights file already exists at '{}'", output_path);
        return Ok(());
    }

    println!("Downloading weights from {}...", url);
    let response = get(url)?;
    let mut out = fs::File::create(output_path)?;
    out.write_all(&response.bytes()?)?;
    println!("Downloaded weights to '{}'", output_path);

    Ok(())
}

/// Define a simplified T5 model structure with LoRA for fine-tuning
fn t5_model(vs: &nn::Path, n_embd: i64, n_layers: i64, n_heads: i64, rank: i64) -> impl Module {
    let encoder = encoder(&(vs / "encoder"), n_embd, n_layers, n_heads, rank);
    let decoder = decoder(&(vs / "decoder"), n_embd, n_layers, n_heads, rank);

    nn::func(move |src| {
        let encoder_output = encoder.forward(&src);
        decoder.forward(&encoder_output)
    })
}

/// Load pre-trained weights for the T5 model
fn load_pretrained_weights(vs: &mut nn::VarStore, weight_path: &str) -> Result<(), Box<dyn std::error::Error>> {
    vs.load(weight_path)?;
    Ok(())
}

/// Fine-tune the T5 model with domain-specific data
fn fine_tune_t5_model(weight_path: &str) -> Result<(), Box<dyn std::error::Error>> {
    let device = if tch::Cuda::is_available() {
        Device::Cuda(0)
    } else {
        Device::Cpu
    };
    let mut vs = nn::VarStore::new(device);
    let t5 = t5_model(&vs.root(), 512, 6, 8, 4); // Adding rank parameter for LoRA

    load_pretrained_weights(&mut vs, weight_path)?;

    let mut opt = nn::Adam::default().build(&vs, 1e-5)?;
    let batch_size = 16;
    let epochs = 3;

    let src = Tensor::randn(&[batch_size, 128], (tch::Kind::Int64, device));
    let tgt = Tensor::randn(&[batch_size, 128], (tch::Kind::Int64, device));

    for epoch in 0..epochs {
        let output = t5.forward(&src);

        let loss = output.cross_entropy_for_logits(&tgt);
        opt.backward_step(&loss);

        println!("Epoch: {} | Loss: {:?}", epoch, loss.double_value(&[]));
    }

    vs.save("fine_tuned_t5_lora.ot")?;
    Ok(())
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let weights_url = "https://example.com/path/to/weights.ot"; // Replace with actual URL
    let pretrained_weight_path = "weights.ot";

    download_weights(weights_url, pretrained_weight_path)?;
    fine_tune_t5_model(pretrained_weight_path)?;

    Ok(())
}

/// Define the Encoder structure with LoRA applied in attention blocks
fn encoder(p: &nn::Path, n_embd: i64, n_layers: i64, n_heads: i64, rank: i64) -> impl Module {
    let embedding = nn::embedding(p / "embedding", 32000, n_embd, Default::default());
    let encoder_blocks: Vec<_> = (0..n_layers)
        .map(|i| encoder_block(&(p / format!("block_{}", i)), n_embd, n_heads, rank))
        .collect();

    nn::func(move |xs| {
        let mut x = xs.apply(&embedding);
        for block in &encoder_blocks {
            x = x.apply(block);
        }
        x
    })
}

/// Define the Decoder structure with LoRA applied in attention blocks
fn decoder(p: &nn::Path, n_embd: i64, n_layers: i64, n_heads: i64, rank: i64) -> impl Module {
    let embedding = nn::embedding(p / "embedding", 32000, n_embd, Default::default());
    let decoder_blocks: Vec<_> = (0..n_layers)
        .map(|i| decoder_block(&(p / format!("block_{}", i)), n_embd, n_heads, rank))
        .collect();

    nn::func(move |xs| {
        let mut x = xs.apply(&embedding);
        for block in &decoder_blocks {
            x = x.apply(block);
        }
        x
    })
}

/// Define an Encoder Block with LoRA-applied Self-Attention and Feed-Forward layers
fn encoder_block(p: &nn::Path, n_embd: i64, n_heads: i64, rank: i64) -> impl Module {
    let self_attn = multi_head_attention_with_lora(&(p / "self_attn"), n_embd, n_heads, rank);
    let layer_norm1 = nn::layer_norm(p / "layer_norm1", vec![n_embd], Default::default());
    let feed_forward = nn::seq()
        .add(nn::linear(p / "lin1", n_embd, 4 * n_embd, Default::default()))
        .add_fn(|x| x.relu())
        .add(nn::linear(p / "lin2", 4 * n_embd, n_embd, Default::default()));
    let layer_norm2 = nn::layer_norm(p / "layer_norm2", vec![n_embd], Default::default());

    nn::func(move |xs| {
        let attn_output = xs.apply(&self_attn);
        let x = xs + attn_output;
        let x = x.apply(&layer_norm1);

        let ff_output = x.apply(&feed_forward);
        x + ff_output.apply(&layer_norm2)
    })
}

/// Define a Decoder Block with LoRA-applied Self-Attention, Cross-Attention, and Feed-Forward layers
fn decoder_block(p: &nn::Path, n_embd: i64, n_heads: i64, rank: i64) -> impl Module {
    let self_attn = multi_head_attention_with_lora(&(p / "self_attn"), n_embd, n_heads, rank);
    let enc_dec_attn = multi_head_attention_with_lora(&(p / "enc_dec_attn"), n_embd, n_heads, rank);
    let layer_norm1 = nn::layer_norm(p / "layer_norm1", vec![n_embd], Default::default());
    let layer_norm2 = nn::layer_norm(p / "layer_norm2", vec![n_embd], Default::default());
    let layer_norm3 = nn::layer_norm(p / "layer_norm3", vec![n_embd], Default::default());

    let feed_forward = nn::seq()
        .add(nn::linear(p / "lin1", n_embd, 4 * n_embd, Default::default()))
        .add_fn(|x| x.relu())
        .add(nn::linear(p / "lin2", 4 * n_embd, n_embd, Default::default()));

    nn::func(move |xs| {
        let self_attn_output = xs.apply(&self_attn);
        let x = xs + self_attn_output;
        let x = x.apply(&layer_norm1);

        let enc_dec_attn_output = x.apply(&enc_dec_attn);
        let x = x + enc_dec_attn_output;
        let x = x.apply(&layer_norm2);

        let ff_output = x.apply(&feed_forward);
        x + ff_output.apply(&layer_norm3)
    })
}

The code begins by checking if CUDA is available to enable GPU usage; otherwise, it defaults to CPU processing. In the main function, it downloads pre-trained weights if they aren’t present locally. The model architecture utilizes custom multi-head attention, where low-rank adaptations are added through LoRA layers that require fewer parameters, applied within each attention head’s output projection. These low-rank matrices, defined by the lora_layer function, capture task-specific changes with minimal overhead. During fine-tuning, the model processes a batch of sample data over multiple epochs, calculating the loss after each forward pass. The optimizer updates only the LoRA layers, optimizing memory and speed. After training, the code saves the model’s adapted parameters, making it suitable for deployment in resource-constrained environments while preserving accuracy on the target task.

In conclusion, fine-tuning multitask models like T5 for specific applications is a powerful strategy for adapting pre-trained models to new tasks and domains. By leveraging techniques such as transfer learning, regularization, and domain-adaptive pre-training, fine-tuning allows models to specialize without sacrificing their generalization capabilities. Rust offers a performance-optimized environment for implementing fine-tuning pipelines, enabling efficient adaptation of multitask models for real-world applications. As the field of multitask learning evolves, the ability to fine-tune large-scale models in a resource-efficient manner will remain a key focus, driving the development of more advanced and scalable solutions.

7.5. Evaluating and Benchmarking Multitask Learning Models link

Evaluating and benchmarking multitask learning models is a critical aspect of understanding their performance and generalization capabilities across diverse tasks. In multitask learning, models are trained to perform multiple tasks simultaneously, and their success depends not only on how well they perform individual tasks but also on how they balance the trade-offs between these tasks. Evaluation requires a combination of task-specific metrics and overall performance measures that assess the model’s ability to generalize across tasks of varying complexity and importance.

Each task within a multitask framework often comes with its own set of evaluation metrics. For example, in natural language processing (NLP), tasks like translation, summarization, and question answering are typically evaluated using metrics such as BLEU, ROUGE, and F1-score, respectively. For a multitask model $M$ performing tasks $T_1, T_2, \dots, T_n$, let $\mathcal{M}_i$ represent the evaluation metric for task $T_i$. The overall performance of the model can be captured as an aggregate of these individual metrics. Mathematically, this can be represented as:

$$ \mathcal{M}_{\text{overall}} = \sum_{i=1}^{n} w_i \mathcal{M}_i, $$

where $w_i$ represents the weight assigned to task $T_i$, and $\mathcal{M}_i$ is the evaluation metric for that task. These weights $w_i$ are critical, as they reflect the importance of each task in the context of the overall performance. For instance, in a multitask model trained on both translation and summarization, the weight for each task would determine the relative priority of these tasks in the final evaluation. It is important to carefully set these weights, particularly in real-world applications where certain tasks may be more impactful or require higher accuracy than others.

Benchmarking multitask models requires testing them across a diverse set of tasks to assess their ability to generalize and handle varying levels of difficulty. Multitask models are often evaluated on benchmarks like GLUE or SuperGLUE in NLP, which include tasks such as sentiment analysis, textual entailment, and coreference resolution. These benchmarks provide a standardized way to measure the performance of models on multiple tasks, allowing for direct comparisons between multitask and single-task models. The key to benchmarking in multitask learning lies in evaluating the model’s performance on each task while also considering its ability to perform well across the board.

A unique challenge in evaluating multitask models is that tasks can vary significantly in difficulty, both in terms of the data they require and the complexity of the problem. For example, a task like summarization, which requires understanding and generating coherent text, may be inherently more difficult than a simpler classification task like sentiment analysis. Therefore, when evaluating a multitask model, it is essential to ensure that the evaluation framework accounts for these differences in task difficulty. One way to address this is through task-specific weighting, where more challenging tasks are given higher weight in the overall evaluation, ensuring that the model is not penalized for performing slightly worse on more difficult tasks.

Designing robust evaluation frameworks for multitask models also involves accounting for the role of task weighting and loss balancing. During training, multitask models optimize a combined loss function that balances the performance across different tasks. The weights assigned to each task’s loss can significantly affect the model’s learning trajectory, leading to different levels of performance on each task. In the evaluation stage, it is essential to ensure that the evaluation metrics align with these training weights. This prevents scenarios where the model is evaluated more heavily on tasks that were given less priority during training, which could lead to misleading conclusions about the model's performance.

Mathematically, the loss function during training is often represented as a weighted sum of individual task losses:

$$ \mathcal{L}_{\text{multi}} = \sum_{i=1}^{n} \lambda_i \mathcal{L}_i, $$

where $\mathcal{L}_i$ represents the loss for task $T_i$, and $\lambda_i$ is the weight assigned to that task. The challenge in evaluation is to ensure that the task weights used in the loss function during training are reflected in the evaluation process. Inconsistent weighting between training and evaluation can lead to discrepancies, where a model performs well during training but underperforms when evaluated on certain tasks. Careful design of task-specific evaluation metrics and appropriate weighting strategies can mitigate these issues.

Ethical considerations are increasingly important when evaluating multitask models, particularly when tasks have different social or practical impacts. For instance, if a multitask model is applied in a healthcare setting where one task involves medical diagnosis and another involves sentiment analysis of patient feedback, the evaluation framework must ensure that the performance on the medical diagnosis task is prioritized, as it directly impacts patient outcomes. This highlights the need for task-aware evaluation, where tasks with significant ethical or societal implications are given higher priority during both training and evaluation. Additionally, ensuring that the model does not propagate biases or perform unfairly across different demographic groups is crucial, especially when tasks involve sensitive data like healthcare or finance.

In Rust, implementing evaluation and benchmarking tools for multitask learning models involves designing systems that can compute a wide range of task-specific metrics and aggregate them into an overall performance score. The tch-rs crate, which provides Rust bindings to PyTorch, allows developers to efficiently implement evaluation pipelines that assess models across multiple tasks. For instance, a benchmarking tool in Rust could load a pre-trained multitask model, run it on multiple test datasets corresponding to different tasks, and compute metrics such as BLEU, ROUGE, or accuracy. These metrics can then be weighted and combined to provide a comprehensive evaluation of the model’s performance across tasks.

[dependencies]
anyhow = "1.0"
serde_json = "1.0.132"
tch = "0.17.0"
reqwest = { version = "0.12.8", features = ["blocking"] }

use tch::{nn, Device, Tensor};
use std::collections::HashMap;
use std::{fs::File, io::Write, path::Path};
use reqwest::blocking::get;

// Placeholder function to compute BLEU score for a task (you can integrate a more comprehensive library for exact calculations)
fn compute_bleu(predictions: &Tensor, references: &Tensor) -> f64 {
    // Placeholder BLEU computation; Replace with an actual algorithm for real use cases
    let matches = predictions.eq_tensor(&references).sum(tch::Kind::Float);
    let total = predictions.size()[0] as f64;
    matches.double_value(&[]) / total
}

// Placeholder function to compute ROUGE score for a task
fn compute_rouge(predictions: &Tensor, references: &Tensor) -> f64 {
    // Placeholder ROUGE computation
    let matches = predictions.eq_tensor(&references).sum(tch::Kind::Float);
    let total = predictions.size()[0] as f64;
    matches.double_value(&[]) / total
}

// Placeholder function to compute accuracy for a classification task
fn compute_accuracy(predictions: &Tensor, labels: &Tensor) -> f64 {
    let correct = predictions.eq_tensor(labels).sum(tch::Kind::Float);
    let total = predictions.size()[0] as f64;
    correct.double_value(&[]) / total
}

// Function to download the pre-trained weights file if it doesn't exist locally
fn download_weights(url: &str, output_path: &str) -> Result<(), Box<dyn std::error::Error>> {
    if Path::new(output_path).exists() {
        println!("Weights file already exists at '{}'", output_path);
        return Ok(());
    }

    println!("Downloading weights from {}...", url);
    let response = get(url)?;
    let mut out = File::create(output_path)?;
    out.write_all(&response.bytes()?)?;
    println!("Downloaded weights to '{}'", output_path);

    Ok(())
}

// Load a pre-trained multitask model
fn load_model(vs: &nn::VarStore) -> impl nn::Module {
    // Define or load your model architecture here
    // For demonstration, assume it's a simple encoder-decoder structure
    nn::seq()
        .add(nn::linear(&vs.root() / "layer1", 512, 256, Default::default()))
        .add_fn(|x| x.relu()) // Apply ReLU activation
        .add(nn::linear(&vs.root() / "layer2", 256, 128, Default::default()))
}

// Run model on test datasets and evaluate metrics
fn evaluate_model(
    model: &impl nn::Module,
    test_data: &HashMap<String, (Tensor, Tensor)>,  // Dictionary of task names and (input, target) data
) -> HashMap<String, f64> {
    let mut scores = HashMap::new();

    for (task_name, (input, target)) in test_data.iter() {
        let predictions = model.forward(&input);

        let score = match task_name.as_str() {
            "translation" => compute_bleu(&predictions, target),  // BLEU score for translation tasks
            "summarization" => compute_rouge(&predictions, target),  // ROUGE score for summarization tasks
            "classification" => compute_accuracy(&predictions, target),  // Accuracy for classification tasks
            _ => 0.0,
        };

        scores.insert(task_name.clone(), score);
    }

    scores
}

// Aggregates task-specific metrics into a final overall score
fn aggregate_scores(scores: &HashMap<String, f64>, weights: &HashMap<String, f64>) -> f64 {
    scores.iter().map(|(task, score)| score * weights.get(task).unwrap_or(&1.0)).sum()
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Define path and URL for the model weights
    let pretrained_weight_path = "path/to/pretrained/model.ot";
    let weights_url = "https://example.com/path/to/weights.ot";  // Replace with actual URL

    // Download the weights file if it doesn't exist
    download_weights(weights_url, pretrained_weight_path)?;

    // Initialize model and load pre-trained weights
    let mut vs = nn::VarStore::new(Device::Cpu);  // Change to Device::Cuda(0) if GPU is desired and available
    let model = load_model(&vs);
    vs.load(pretrained_weight_path)?;

    // Sample test data for different tasks
    let translation_data = (Tensor::randn(&[32, 128], (tch::Kind::Float, Device::Cpu)), Tensor::randn(&[32, 128], (tch::Kind::Float, Device::Cpu)));
    let summarization_data = (Tensor::randn(&[32, 128], (tch::Kind::Float, Device::Cpu)), Tensor::randn(&[32, 128], (tch::Kind::Float, Device::Cpu)));
    let classification_data = (Tensor::randn(&[32, 128], (tch::Kind::Float, Device::Cpu)), Tensor::randn(&[32, 128], (tch::Kind::Int64, Device::Cpu)));

    let mut test_data = HashMap::new();
    test_data.insert("translation".to_string(), translation_data);
    test_data.insert("summarization".to_string(), summarization_data);
    test_data.insert("classification".to_string(), classification_data);

    // Evaluate model on each task and calculate scores
    let task_scores = evaluate_model(&model, &test_data);

    // Define weights for each task for aggregation
    let mut weights = HashMap::new();
    weights.insert("translation".to_string(), 0.4);
    weights.insert("summarization".to_string(), 0.3);
    weights.insert("classification".to_string(), 0.3);

    // Aggregate scores into an overall evaluation metric
    let overall_score = aggregate_scores(&task_scores, &weights);

    println!("Task-specific Scores: {:?}", task_scores);
    println!("Overall Model Performance Score: {:?}", overall_score);

    Ok(())
}

Rust’s high performance and memory safety make it particularly suitable for handling large-scale benchmarking of multitask models. Since multitask models typically involve processing multiple datasets and tasks concurrently, Rust’s concurrency features can be leveraged to parallelize the evaluation process, speeding up the computation of metrics. Additionally, custom evaluation metrics can be implemented in Rust to reflect the specific goals of multitask learning applications. For example, in a multitask system handling both translation and sentiment analysis, a custom metric could be designed to prioritize the fluency and accuracy of translations while ensuring that sentiment classification remains consistent across different languages.

Conducting comparative studies in Rust to benchmark multitask models against single-task and unified models is another important aspect of evaluating multitask learning systems. By comparing the performance of a multitask model with single-task models trained on the same tasks, researchers and developers can assess the benefits of shared representation learning. Typically, multitask models are expected to perform better on tasks with limited data, as they can leverage knowledge learned from other tasks. However, in some cases, single-task models may outperform multitask models on specific tasks where specialized learning is crucial. These comparative studies provide valuable insights into the trade-offs between multitask learning and task specialization.

In industry, evaluating multitask models is essential for deploying them in real-world applications where multiple tasks need to be handled simultaneously. For example, in customer service automation, a multitask model might need to handle sentiment analysis, entity recognition, and response generation within a single framework. By evaluating such a model across these tasks, companies can ensure that the model performs well in all areas, providing a seamless customer experience. Benchmarking multitask models against single-task models also helps businesses decide whether a multitask approach is the most efficient solution for their needs, or whether task-specific models would yield better results.

Recent trends in multitask learning emphasize the development of more robust and scalable evaluation frameworks, especially as models like T5, BART, and GPT-3 are increasingly applied to multiple tasks across domains. These models are evaluated not only on standard NLP benchmarks but also on cross-domain tasks that test their generalization capabilities in areas like legal, medical, and technical document processing. As the field evolves, the design of more sophisticated evaluation frameworks that account for task difficulty, ethical considerations, and real-world impacts will be key to advancing multitask learning.

In conclusion, evaluating and benchmarking multitask models involves balancing task-specific performance with overall generalization across tasks. The use of appropriate task weights, robust evaluation metrics, and fair comparison frameworks is essential for accurately assessing the effectiveness of multitask learning models. Rust’s performance and concurrency features make it an ideal platform for implementing these evaluation tools, providing a powerful environment for assessing multitask models in large-scale applications. As multitask learning continues to grow, the need for comprehensive, scalable, and ethically aware evaluation frameworks will remain central to its success.

7.6. Scaling and Optimizing Multitask Learning Models link

Scaling multitask learning (MTL) models presents unique challenges, especially as these models increase in size and complexity to accommodate diverse tasks. Key issues involve computational costs and memory usage, which can become prohibitive when deploying MTL models in production environments. When scaling MTL models, developers must balance the desire for high task performance with real-world resource constraints, such as limited memory, processing power, and latency requirements. Model complexity directly impacts these aspects, often leading to slower inference times and higher energy consumption. These constraints necessitate advanced optimization techniques, such as model pruning, quantization, and distributed training. Each of these techniques contributes to reducing the model's operational footprint while preserving task-specific performance. In Rust, implementing such techniques efficiently is particularly advantageous due to Rust’s memory safety and performance optimization capabilities, making it an excellent choice for building scalable MTL applications.

In large-scale multitask learning models, scaling laws—empirical observations of how model size affects performance—provide insights into the balance between task coverage and computational efficiency. These laws reveal that as model parameters grow, the performance of MTL models on individual tasks tends to improve, but with diminishing returns relative to the increase in computational costs. This presents a fundamental trade-off, particularly when using large MTL models across tasks with varying levels of complexity and resource requirements. For example, tasks like translation, summarization, and question answering each have different latency tolerances and accuracy requirements, demanding a careful balance between model size and task performance. Developers must consider the interaction between model complexity, training time, and inference speed, particularly when these models are deployed in environments with hardware limitations or real-time performance requirements. Hardware acceleration, such as using GPUs or TPUs, and distributed computing, where tasks are split across multiple devices, are increasingly vital to making large-scale MTL models feasible for deployment. These approaches allow the model to handle higher data throughput and reduce latency, thus optimizing performance and resource usage.

To address the scaling needs of multitask learning models, quantization and model pruning are two leading techniques that reduce the computational burden without sacrificing significant performance. Quantization involves reducing the precision of the model’s parameters, typically from 32-bit floating-point (FP32) to lower-precision formats, such as 8-bit integers (INT8). This approach not only saves memory but also accelerates computations, as lower-precision operations are faster on modern hardware. Model pruning, on the other hand, involves systematically removing unimportant parameters, such as those with near-zero weights, from the network. By reducing the model’s parameter count, pruning allows the model to run more efficiently, especially when combined with quantization. In Rust, both quantization and pruning can be implemented at various levels of the model. By directly working with the model’s parameters, developers can control the degree of quantization and pruning dynamically, tailoring it to specific hardware or application constraints.

This Rust program loads and fine-tunes a T5 model using a quantized variant provided by the candle_transformers library, allowing efficient text generation and transformation. The code includes specific features for tracing, model configuration, and text generation, utilizing pre-trained model weights from the Hugging Face Hub. Hard-coded values replace the need for command-line arguments, simplifying model setup and text generation based on a predefined prompt and model parameters. It incorporates LoRA with user-specified sampling techniques like temperature and top-p sampling, applying repeat penalties to reduce token repetition in output sequences.

[dependencies]
anyhow = "1.0"
serde_json = "1.0.132"
tch = "0.17.0"
reqwest = { version = "0.12.8", features = ["blocking"] }
candle-transformers = "0.7.2"
candle-core = "0.7.2"
candle-nn = "0.7.2"
hf-hub = "0.3.2"
tokenizers = "0.20.1"
accelerate-src = "0.3.2"
langchain-rust = "4.6.0"
rust-bert = "0.23.0"

use std::io::Write;
use std::path::PathBuf;
use candle_transformers::models::quantized_t5 as t5;
use anyhow::{Error as E, Result};
use candle_core::{Device, Tensor};
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;


struct T5ModelBuilder {
    device: Device,
    config: t5::Config,
    weights_filename: PathBuf,
}

impl T5ModelBuilder {
    pub fn load() -> Result<(Self, Tokenizer)> {
        let device = Device::Cpu;
        let model_id = "lmz/candle-quantized-t5".to_string();
        let revision = "main".to_string();

        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
        let api = Api::new()?;
        let api = api.repo(repo);

        // Define the default model configurations
        let config_filename = api.get("config.json")?;
        let tokenizer_filename = api.get("tokenizer.json")?;
        let weights_filename = api.get("model.gguf")?;

        let config = std::fs::read_to_string(config_filename)?;
        let mut config: t5::Config = serde_json::from_str(&config)?;
        config.use_cache = true;

        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
        Ok((
            Self {
                device,
                config,
                weights_filename,
            },
            tokenizer,
        ))
    }

    pub fn build_model(&self) -> Result<t5::T5ForConditionalGeneration> {
        let vb = t5::VarBuilder::from_gguf(&self.weights_filename, &self.device)?;
        Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
    }
}

fn main() -> Result<()> {
    let prompt = "Translate the following text to French: 'Hello, how are you?'";
    let temperature = 0.8;
    let top_p = Some(0.9);
    let repeat_penalty = 1.1;
    let repeat_last_n = 64;

    let (builder, mut tokenizer) = T5ModelBuilder::load()?;
    let device = &builder.device;
    let tokenizer = tokenizer
        .with_padding(None)
        .with_truncation(None)
        .map_err(E::msg)?;
    let tokens = tokenizer
        .encode(prompt, true)
        .map_err(E::msg)?
        .get_ids()
        .to_vec();
    let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
    let mut model = builder.build_model()?;
    let mut output_token_ids = [builder
        .config
        .decoder_start_token_id
        .unwrap_or(builder.config.pad_token_id) as u32]
        .to_vec();
    let mut logits_processor = LogitsProcessor::new(299792458, Some(temperature), top_p);
    let encoder_output = model.encode(&input_token_ids)?;
    let start = std::time::Instant::now();

    for index in 0.. {
        if output_token_ids.len() > 512 {
            break;
        }
        let decoder_token_ids = if index == 0 || !builder.config.use_cache {
            Tensor::new(output_token_ids.as_slice(), device)?.unsqueeze(0)?
        } else {
            let last_token = *output_token_ids.last().unwrap();
            Tensor::new(&[last_token], device)?.unsqueeze(0)?
        };
        let logits = model
            .decode(&decoder_token_ids, &encoder_output)?
            .squeeze(0)?;
        let logits = if repeat_penalty == 1. {
            logits
        } else {
            let start_at = output_token_ids.len().saturating_sub(repeat_last_n);
            candle_transformers::utils::apply_repeat_penalty(
                &logits,
                repeat_penalty,
                &output_token_ids[start_at..],
            )?
        };

        let next_token_id = logits_processor.sample(&logits)?;
        if next_token_id as usize == builder.config.eos_token_id {
            break;
        }
        output_token_ids.push(next_token_id);
        if let Some(text) = tokenizer.id_to_token(next_token_id) {
            let text = text.replace('▁', " ").replace("<0x0A>", "\n");
            print!("{text}");
            std::io::stdout().flush()?;
        }
    }
    let dt = start.elapsed();
    println!(
        "\n{} tokens generated ({:.2} token/s)\n",
        output_token_ids.len(),
        output_token_ids.len() as f64 / dt.as_secs_f64(),
    );
    Ok(())
}

The code begins by initializing tracing if enabled, then configures and loads a quantized T5 model using the T5ModelBuilder. This builder downloads or loads a pre-specified configuration file, tokenizer, and weights for a predefined model type. The tokenizer processes the prompt into input tokens, which are passed into the model’s encoder. During decoding, the model iteratively generates output tokens, applying specified temperature and top-p sampling probabilities to modulate randomness, and uses repeat penalties to minimize redundancy. The program prints tokens progressively, producing a final generated sequence and calculating the time elapsed, allowing users to gauge model performance and text generation speed.

Beyond model optimization, distributed training has become crucial for scaling large MTL models across clusters of GPUs or computing nodes. Distributed training allows the model to learn from extensive data across multiple tasks simultaneously, which is essential for achieving robust multitask generalization. In Rust, developers can implement distributed training pipelines by leveraging frameworks such as tch-rs or through integration with backend systems like gRPC for inter-node communication. These frameworks allow data parallelism, where each node processes a subset of the data, and model parallelism, where portions of the model are trained on separate nodes. With effective distributed training, MTL models can scale efficiently across large datasets, reducing training time and improving task performance, especially when trained on heterogeneous tasks with distinct data characteristics.

In practical deployments, the trade-offs between model accuracy, training time, and inference speed become apparent, as industry applications demand a balance between precision and real-time performance. For instance, in the healthcare industry, a multitask model used for diagnosis must prioritize high accuracy while maintaining reasonable inference times for rapid clinical decisions. Meanwhile, in financial services, MTL models used for risk assessment and fraud detection benefit from optimizations that reduce latency, ensuring timely decision-making processes. By employing techniques like model pruning, quantization, and distributed training, these industries can deploy scalable, efficient multitask models without compromising on performance or real-time requirements. Recent trends indicate a growing adoption of hardware-accelerated multitask models, with GPUs, TPUs, and ASICs playing a significant role in supporting real-time inference in resource-constrained environments. Rust’s ability to operate close to the hardware allows developers to leverage these advancements fully, building optimized MTL solutions that meet industry-specific latency and accuracy demands.

To implement and evaluate these optimization techniques, Rust’s performance and concurrency benefits can be leveraged to handle high-throughput data streams and rapid model inference. Techniques such as quantization and pruning can be implemented directly on model weights, reducing model complexity and increasing throughput, which is particularly beneficial for applications requiring low-latency processing. Distributed training in Rust enables models to scale across large data sets or clusters of computing nodes, which is essential for training models that generalize well across multiple tasks. Finally, benchmarking these optimized multitask models in real-world deployment scenarios allows developers to quantify gains in efficiency and accuracy, demonstrating the impact of Rust’s efficient memory management and concurrency capabilities in handling large-scale MTL systems.

In summary, scaling and optimizing multitask learning models requires a multi-faceted approach that balances model complexity with deployment efficiency. Techniques like model pruning, quantization, and distributed training, especially when implemented in Rust, allow MTL models to be scaled to real-world, resource-constrained environments. The latest trends in hardware acceleration and distributed computing provide further avenues for optimizing MTL deployment, while Rust’s performance advantages ensure that these models run efficiently and reliably. By combining these techniques, developers can create multitask learning models that achieve high accuracy across diverse tasks while remaining computationally feasible for large-scale, real-time applications.

7.7. Future Directions in Multitask Learning and Unified Models link

The field of multitask learning (MTL) is rapidly advancing, with trends pointing towards larger, more versatile models capable of handling a diverse range of tasks simultaneously. Current research focuses on extending multitask models to incorporate multimodal learning—the integration of various data types such as text, images, and audio—allowing for a broader range of applications beyond text-only tasks. Multimodal MTL models can leverage data from different sources to develop richer, more contextually aware representations, improving task performance in fields that rely on varied data inputs, such as autonomous driving (where vision, sound, and textual data are combined) and healthcare (where text records, medical images, and sensor data are analyzed together). Additionally, there is a growing interest in continual learning approaches, which enable models to adapt to new tasks and data without the need to retrain from scratch. Continual learning allows models to retain previously learned knowledge while integrating new information, creating systems that evolve over time—a critical feature for applications where data continuously updates, such as personalized recommendation systems or real-time language translation.

In integrating multimodal capabilities, multimodal learning introduces new possibilities for multitask models by enabling them to process and analyze data from different sources in a unified architecture. Mathematically, this involves creating cross-modal representations that enable information from one modality, such as image data, to inform and enhance understanding in another modality, like text. Cross-modal alignment functions, such as contrastive loss functions, enable the model to learn relationships across modalities. For example, a contrastive objective could encourage embeddings of related text and image pairs to be closer in representation space than unrelated pairs, allowing the model to understand relationships between different data types. This cross-modal learning enhances the model's ability to perform tasks that require an understanding of both text and images, such as image captioning or video summarization. The challenge, however, lies in efficiently training these models while maintaining memory and computational efficiency, as multimodal data can significantly increase both data dimensionality and model complexity.

As multitask learning models continue to scale, continual learning becomes increasingly essential, allowing models to handle new tasks and integrate new data without forgetting previous knowledge. Continual learning is mathematically challenging due to the risk of "catastrophic forgetting," where models lose previous task knowledge while learning new tasks. Techniques such as Elastic Weight Consolidation (EWC) address this by selectively regularizing important weights that contribute significantly to prior tasks, preventing drastic updates during new training phases. Another technique, Progressive Neural Networks, avoids overwriting previous knowledge by introducing task-specific pathways that grow with each new task. For instance, a multitask model in financial services might initially be trained for fraud detection and then adapted to handle customer sentiment analysis without losing its fraud detection capabilities.

In Rust, implementing continual learning techniques can leverage the tch-rs library for tensor operations, and the language’s memory safety and efficiency can support scalable, robust implementation. As these models grow and adapt, ethical considerations become critical. Models that handle multiple tasks across domains or continuously update with new information require transparent decision-making processes to mitigate risks related to bias and fairness.

use tch::{nn, nn::Module, nn::OptimizerConfig, Device, Tensor};

/// Define a simple model for multitask learning with a single hidden layer
fn create_model(vs: &nn::Path, input_dim: i64, hidden_dim: i64, output_dim: i64) -> impl Module {
    nn::seq()
        .add(nn::linear(vs / "layer1", input_dim, hidden_dim, Default::default()))
        .add_fn(|x| x.relu())
        .add(nn::linear(vs / "layer2", hidden_dim, output_dim, Default::default()))
}

/// Train on Task A and calculate Fisher Information Matrix for EWC
fn train_task_a(
    model: &impl Module,
    vs: &nn::VarStore,
    data: (Tensor, Tensor),
    epochs: i64,
    learning_rate: f64,
) -> Vec<Tensor> {
    let (inputs, targets) = data;
    let mut opt = nn::Adam::default().build(&vs, learning_rate).unwrap();

    for epoch in 0..epochs {
        let predictions = model.forward(&inputs);
        let loss = predictions.cross_entropy_for_logits(&targets);
        opt.backward_step(&loss);
        println!("Task A Epoch {}: Loss = {:?}", epoch, loss.double_value(&[]));
    }

    // Calculate Fisher Information Matrix
    let predictions = model.forward(&inputs);
    let loss = predictions.cross_entropy_for_logits(&targets);
    loss.backward();

    // Estimate Fisher Information for each parameter
    vs.trainable_variables()
        .iter()
        .map(|param| {
            if param.requires_grad() {
                param.grad().square()
            } else {
                Tensor::zeros_like(param)
            }
        })
        .collect()
}

/// Fine-tune on Task B using EWC regularization
/// Fine-tune on Task B using EWC regularization
fn train_task_b_with_ewc(
    model: &impl Module,
    vs: &nn::VarStore,
    fisher_info: &[Tensor],
    data: (Tensor, Tensor),
    epochs: i64,
    learning_rate: f64,
    ewc_lambda: f64,
) {
    let (inputs, targets) = data;
    let mut opt = nn::Adam::default().build(&vs, learning_rate).unwrap();

    // Save initial parameters for EWC regularization
    let initial_params: Vec<Tensor> = vs.trainable_variables()
        .iter()
        .map(|param| param.detach())
        .collect();

    for epoch in 0..epochs {
        let predictions = model.forward(&inputs);
        let loss = predictions.cross_entropy_for_logits(&targets);

        // EWC regularization term with reshaping to ensure shape consistency
        let ewc_loss = vs.trainable_variables()
            .iter()
            .zip(initial_params.iter())
            .zip(fisher_info.iter())
            .map(|((param, init), fisher)| {
                let fisher_resized = fisher.reshape(&param.size()); // Reshape fisher to match param's shape
                let init_resized = init.reshape(&param.size());      // Reshape init to match param's shape
                ((param - init_resized).square() * fisher_resized * ewc_lambda).sum(tch::Kind::Float)
            })
            .fold(Tensor::zeros(&[], (tch::Kind::Float, Device::Cpu)), |acc, term| acc + term);

        let total_loss = loss + ewc_loss;
        opt.backward_step(&total_loss);
        
        println!("Task B Epoch {}: Loss = {:?}", epoch, total_loss.double_value(&[]));
    }
}


fn main() -> Result<(), Box<dyn std::error::Error>> {
    let device = Device::Cpu;
    let vs = nn::VarStore::new(device);
    let model = create_model(&vs.root(), 10, 20, 3);  // Example model with input dim=10, hidden dim=20, output dim=3

    // Dummy data for Task A and Task B
    let task_a_data = (Tensor::randn(&[64, 10], (tch::Kind::Float, device)), Tensor::randint(3, &[64], (tch::Kind::Int64, device)));
    let task_b_data = (Tensor::randn(&[64, 10], (tch::Kind::Float, device)), Tensor::randint(3, &[64], (tch::Kind::Int64, device)));

    // Step 1: Train on Task A and calculate Fisher Information Matrix
    let fisher_info = train_task_a(&model, &vs, task_a_data, 10, 1e-3);

    // Step 2: Fine-tune on Task B with EWC regularization
    train_task_b_with_ewc(&model, &vs, &fisher_info, task_b_data, 10, 1e-3, 0.4);

    Ok(())
}

The societal implications of increasingly powerful multitask models extend beyond technical challenges, as these models grow in capacity and complexity to potentially perform tasks that can influence human decisions and interactions. Unified models operating across multiple domains or modalities can amass substantial data about user behavior, presenting privacy risks and raising questions about responsible AI use. Additionally, fairness is a central issue when multitask models apply similar representations across tasks; without careful oversight, these models can inadvertently propagate biases from one task to another. Techniques such as fairness-aware regularization and transparency mechanisms, which clarify decision boundaries, are crucial in balancing the power of multitask models with ethical responsibility. Researchers and industry leaders advocate for a framework that guides the development of fair, transparent, and accountable multitask models, particularly as these systems are deployed in sensitive areas like healthcare, hiring, and law enforcement.

On a practical level, Rust offers a promising foundation for integrating early multimodal and continual learning capabilities into multitask learning models. Using tch-rs for model definition and tensor manipulation, developers can experiment with prototype multimodal architectures that leverage multiple data sources. For instance, a multimodal model could be implemented in Rust to handle both textual and image-based inputs, allowing developers to explore applications like product recommendation systems that analyze customer reviews alongside product images. Furthermore, continual learning techniques such as knowledge distillation can be implemented in Rust to periodically transfer learned knowledge from one model to a smaller, more efficient version, ensuring that performance is maintained even as the model grows. Distributed training setups, supported by Rust’s concurrency model, can enable these complex multitask models to be trained and evaluated across multiple hardware nodes, making it feasible to scale experiments with multimodal and continual learning without incurring substantial computational overhead.

Case studies from industry highlight how multitask learning is already beginning to evolve toward these future directions. In autonomous vehicles, for example, multitask models must process multiple data streams—like camera images, LiDAR scans, and traffic signs—in real time. Here, multimodal learning enables simultaneous analysis across data types to make informed driving decisions. Likewise, in virtual assistants, continual learning helps models adapt to changing user preferences and language without frequent retraining, ensuring more natural and personalized interactions over time. As multitask models become more adaptable and versatile, they are increasingly tailored to domain-specific requirements, underscoring the need for optimized, scalable implementation techniques. Rust's performance, safety, and control over system resources make it a strong candidate for building future-ready multitask learning applications that incorporate multimodal data processing, efficient memory management, and continual learning.

Future research and development in multitask learning and unified models will likely continue to explore methods for increasing model adaptability, efficiency, and fairness. By embedding multimodal and continual learning capabilities within multitask models, researchers are setting the stage for more powerful AI systems that better understand context across different domains and evolve in response to new data. As Rust continues to grow as a systems language for machine learning, it has the potential to support the high-performance demands of these applications, pushing the boundaries of multitask learning and unified models in both research and industry settings. This section ultimately highlights the intersection of technical innovation and ethical responsibility, illustrating the importance of Rust-based solutions that are both cutting-edge and conscientious.

7.8. Conclusion link

Chapter 7 provides a thorough exploration of multitask learning and unified models, offering insights into how these approaches can enhance the performance and efficiency of NLP models. By mastering these concepts and their implementation in Rust, readers will be equipped to develop advanced models that can tackle a wide range of tasks, paving the way for more versatile and scalable AI systems.

7.8.1. Further Learning with GenAI link

These prompts are designed to be comprehensive and technically challenging, pushing readers to deepen their understanding of how these models work, how they can be optimized, and how they can be applied to solve a variety of natural language processing tasks.

• Explain the fundamental concepts of multitask learning and how it differs from single-task learning. What are the key advantages of multitask learning, particularly in terms of data efficiency and model generalization? Provide examples of how multitask learning can be applied in natural language processing (NLP).

• Describe the T5 (Text-To-Text Transfer Transformer) architecture and explain how it frames every NLP task as a text-to-text problem. How does this unified approach benefit the model’s ability to handle diverse tasks? Implement the T5 architecture in Rust, focusing on its encoder-decoder design.

• Discuss the trade-offs between multitask learning and task specialization. How can multitask learning models avoid task interference and ensure that they perform well across all tasks? Implement a multitask learning model in Rust and evaluate its performance on multiple NLP tasks.

• Explore the concept of shared and task-specific layers in multitask learning models. How does the balance between these layers affect the model’s ability to generalize across tasks? Implement a multitask learning model in Rust that includes both shared and task-specific layers, and analyze its performance.

• Explain the process of pre-training and fine-tuning in the T5 model. How does pre-training on a large corpus of text enable T5 to excel in diverse tasks, and what are the challenges of fine-tuning T5 on specific tasks? Implement a fine-tuning pipeline in Rust for T5 and evaluate its performance on a specialized NLP task.

• Compare the T5 model with other unified models, such as BART and UnifiedQA. What are the key architectural differences and similarities between these models, and how do they influence the models’ performance across tasks? Implement key architectural features of these models in Rust and compare their performance on a common NLP task.

• Discuss the challenges of designing unified models that perform well across a wide range of tasks. What strategies can be used to ensure that these models maintain high performance while handling diverse tasks? Implement a unified model in Rust and experiment with different task combinations to assess its generalization capabilities.

• Explain the importance of transfer learning in the context of multitask models like T5. How does transfer learning enhance the model’s ability to adapt to new tasks, and what are the best practices for implementing transfer learning in Rust?

• Explore techniques to prevent catastrophic forgetting during the fine-tuning of multitask models. How can models like T5 retain knowledge from previous tasks while adapting to new ones? Implement these techniques in Rust and analyze their effectiveness in maintaining task performance.

• Discuss the role of evaluation metrics in assessing the performance of multitask learning models. How can we design evaluation frameworks that accurately reflect the strengths and weaknesses of these models across different tasks? Implement custom evaluation metrics in Rust for a multitask learning model and apply them to assess performance.

• Analyze the impact of task weighting and loss balancing in multitask learning models. How do these factors influence the overall performance of the model, and what strategies can be used to optimize them? Implement task weighting and loss balancing techniques in Rust and evaluate their effects on model performance.

• Explore the scalability challenges of multitask learning models, particularly in terms of computational cost and memory usage. How can techniques like model pruning, quantization, and distributed training help manage these challenges? Implement these optimization techniques in Rust for a large multitask model and assess their impact on performance and efficiency.

• Discuss the potential of multimodal learning in multitask models. How can integrating different data modalities (e.g., text, images, audio) enhance the capabilities of multitask models? Implement a simple multimodal learning feature in Rust and explore its impact on task performance.

• Examine the trade-offs between model size, flexibility, and performance in unified multitask learning models. How can we optimize these models to achieve a balance between these factors? Implement a unified multitask model in Rust with different configurations and compare their performance.

• Explore the concept of lifelong learning in multitask models. How can these models be designed to continually adapt to new tasks and data without retraining from scratch? Implement an early-stage lifelong learning feature in Rust and test its ability to handle sequential learning tasks.

• Discuss the ethical considerations of deploying multitask learning models, particularly when tasks have different social or practical impacts. How can we ensure that these models operate fairly and transparently across all tasks? Implement a bias detection framework in Rust to evaluate the fairness of a multitask model’s outputs.

• Analyze the effects of scaling laws on multitask learning models. How do model size, dataset size, and computational resources interact to influence the performance of these models? Implement experiments in Rust to explore these scaling laws and derive insights for optimizing multitask models.

• Explore the use of modular architectures in multitask learning. How can modular components be used to handle diverse NLP tasks more efficiently? Implement a modular multitask learning model in Rust and evaluate its performance across different task sets.

• Discuss the potential of distributed computing in optimizing the training of multitask learning models. How can distributed training help scale these models across multiple GPUs or nodes? Implement a distributed training pipeline in Rust for a multitask learning model and evaluate its scalability.

• Examine the future directions of multitask learning and unified models. What are the emerging trends and challenges in this field, and how can they shape the evolution of AI? Implement a prototype model in Rust that integrates cutting-edge ideas in multitask learning and evaluate its potential for future applications.

By engaging with these prompts, readers will gain valuable insights into the nuances of multitask learning and unified models, while also developing practical skills in implementing and fine-tuning these models using Rust.

7.8.2. Hands On Practices link

Self-Exercise 7.1: Multitask Learning with Shared and Task-Specific Layers link

Objective: To understand the balance between shared and task-specific layers in multitask learning models by implementing and evaluating a multitask model that handles multiple NLP tasks.

Tasks:

• Implement a multitask learning model in Rust that incorporates both shared layers for common representations and task-specific layers for individual tasks.

• Train the model on a combination of related NLP tasks, such as text classification, sentiment analysis, and named entity recognition.

• Evaluate the model’s performance on each task, comparing it with single-task models to analyze the benefits and potential drawbacks of shared representations.

• Experiment with different configurations of shared and task-specific layers to determine the optimal balance for maximizing performance across all tasks.

Deliverables:

• A Rust codebase implementing a multitask learning model with both shared and task-specific layers.

• A detailed performance report comparing the multitask model with single-task models, including metrics such as accuracy, precision, and recall for each task.

• An analysis of the effects of different layer configurations on the model’s ability to generalize across tasks, with insights into the trade-offs involved.

Self-Exercise 7.2: Fine-Tuning T5 for Specialized NLP Tasks link

Objective: To practice fine-tuning the T5 model for specific NLP tasks, such as summarization or translation, and to evaluate its performance on these specialized tasks.

Tasks:

• Load a pre-trained T5 model and prepare it for fine-tuning on a specialized NLP task, such as abstractive summarization or machine translation.

• Implement the fine-tuning process in Rust, focusing on adapting the model’s parameters to the new task while preserving its ability to perform previously learned tasks.

• Train the fine-tuned T5 model on a domain-specific dataset, monitoring for issues such as overfitting and data imbalance.

• Compare the performance of the fine-tuned model with a baseline model trained from scratch on the same task, analyzing the benefits of transfer learning.

Deliverables:

• A Rust codebase that fine-tunes a T5 model on a specialized NLP task.

• A training report that includes the steps taken to fine-tune the model, the challenges encountered, and the strategies used to overcome them.

• A comparative analysis report showing the performance of the fine-tuned T5 model versus a baseline model, with detailed metrics on accuracy, fluency, and task-specific outcomes.

Self-Exercise 7.3: Task Weighting and Loss Balancing in Multitask Models link

Objective: To understand the impact of task weighting and loss balancing on the performance of multitask learning models by implementing and optimizing these techniques in Rust.

Tasks:

• Implement a multitask learning model in Rust that supports task weighting and loss balancing to prioritize certain tasks over others.

• Experiment with different weighting strategies, adjusting the loss contributions of each task based on their importance or difficulty.

• Train the multitask model on a set of diverse NLP tasks, monitoring how task weighting and loss balancing affect overall model performance.

• Analyze the trade-offs between different weighting strategies, evaluating the impact on both individual task performance and the model’s generalization capabilities.

Deliverables:

• A Rust implementation of a multitask learning model with task weighting and loss balancing features.

• A report detailing the experiments conducted with different weighting strategies, including performance metrics for each task and the overall model.

• An analysis of the trade-offs involved in task weighting and loss balancing, with recommendations for optimizing multitask model performance in different scenarios.

Self-Exercise 7.4: Scaling Multitask Models with Distributed Training link

Objective: To explore the scalability of multitask learning models by implementing distributed training in Rust and analyzing its impact on model performance and efficiency.

Tasks:

• Implement a distributed training pipeline in Rust for a large multitask learning model, enabling it to scale across multiple GPUs or nodes.

• Train the multitask model on a large dataset, leveraging distributed computing to reduce training time and improve resource utilization.

• Experiment with different distributed training configurations, such as varying the number of GPUs or adjusting the data distribution strategy, to optimize training efficiency.

• Evaluate the scalability of the model by comparing training times, resource usage, and model performance across different distributed setups.

Deliverables:

• A Rust codebase implementing distributed training for a multitask learning model.

• A performance report comparing the results of different distributed training configurations, including metrics on training speed, resource consumption, and model accuracy.

• An analysis of the scalability of multitask models in distributed environments, with insights into best practices for optimizing distributed training in real-world applications.

Self-Exercise 7.5: Designing and Implementing Custom Evaluation Metrics for Multitask Models link

Objective: To develop and implement custom evaluation metrics for multitask learning models that reflect the specific goals and challenges of multitask learning.

Tasks:

• Identify the key performance criteria for multitask learning models, considering both individual task performance and overall model generalization.

• Design custom evaluation metrics in Rust that address the unique challenges of multitask learning, such as task interference, loss balancing, and data efficiency.

• Implement these custom metrics in a Rust-based evaluation framework, integrating them with an existing multitask learning model.

• Evaluate the multitask model using the custom metrics, analyzing how well the metrics capture the strengths and weaknesses of the model across different tasks.

Deliverables:

• A Rust implementation of custom evaluation metrics tailored for multitask learning models.

• A detailed report on the design and rationale behind the custom metrics, including how they address specific challenges in multitask learning.

• An evaluation report using the custom metrics to assess the performance of a multitask model, with insights into the model’s strengths, weaknesses, and areas for improvement.