tasknet 1.20.0

Seamless integration of tasks with huggingface models

tasknet : simple huggingface dataset fine-tuning with multi-task transformers Trainer

tasknet is an interface between Huggingface datasets and Huggingface Trainer.

Task templates

tasknet relies on task templates to avoid boilerplate codes. The task templates correspond to Transformers AutoClasses:

• SequenceClassification
• TokenClassification
• MultipleChoice
• Seq2SeqLM (experimental support)

The task templates follow the same interface. They implement preprocess_function, a data collator and compute_metrics. Look at tasks.py and use existing templates as a starting point to implement a custom task template.

Task instances

Each task template is associated with specific fields. Classification has two text fields s1,s2, and a label y. Pass a dataset to a template, and fill-in the mapping between the dataset fields and the template fields to instanciate a task.

import tasknet as tn
from datasets import load_dataset

rte = tn.Classification(
    dataset=load_dataset("glue", "rte"),
    s1="sentence1", s2="sentence2", y="label"
)

class args:
  model_name='roberta-base'
  learning_rate = 3e-5 
  # see https://huggingface.co/docs/transformers/v4.24.0/en/main_classes/trainer#transformers.TrainingArguments

 
tasks = [rte]
model = tn.Model(tasks, args)
trainer = tn.Trainer(model, tasks, args)
trainer.train()

As you can see, tasknet is multitask by design. It works with list of tasks and the model creates a task_models_list attribute.

Installation

pip install tasknet

Additional examples:

Colab:

https://colab.research.google.com/drive/15Xf4Bgs3itUmok7XlAK6EEquNbvjD9BD?usp=sharing

tasknet vs jiant

jiant is another library comparable to tasknet. tasknet is a minimal extension of Trainer centered on task templates, while jiant builds a custom analog of Trainer from scratch called runner. tasknet is leaner and easier to extend. jiant is config-based while tasknet is designed for interative use and scripting.

Credit

This code uses some part of the examples of the transformers library and some code from multitask-learning-transformers.

Contact

You can request features on github or reach me at [e-mail]

@misc{sileod22-tasknet,
  author = {Sileo, Damien},
  doi = {10.5281/zenodo.561225781},
  month = {11},
  title = {{tasknet, multitask interface between Trainer and datasets}},
  url = {https://github.com/sileod/tasknet},
  version = {1.5.0},
  year = {2022}}