tasknet 1.60

tasknet : simple modernBERT fine-tuning, with multi-task support

tasknet is an interface between Huggingface datasets and Huggingface transformers Trainer.

Tasknet should work with all recent versions of Transformers.

Installation and example

pip install tasknet

Each task template has fields that should be matched with specific dataset columns. Classification has two text fields s1,s2, and a label y. Pass a dataset to a template, and fill in the mapping between the template fields and the dataset columns to instantiate 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") #s2 is optional for classification, used to represent text pairs
 # See AutoTask for shorter code

class hparams:
  model_name = 'tasksource/ModernBERT-base-nli' # better performance for most tasks
  learning_rate = 3e-5 # see hf.co/docs/transformers/en/main_classes/trainer#transformers.TrainingArguments
 
model, trainer = tn.Model_Trainer(tasks=[rte],hparams)
trainer.train(), trainer.evaluate()
p = trainer.pipeline()
p([{'text':'premise here','text_pair': 'hypothesis here'}]) # HuggingFace pipeline for inference

Tasknet is multitask by design. model.task_models_list contains one model per task, with a shared encoder.

Task templates

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

• SequenceClassification(s1, s2, y)
• TokenClassification(tokens, labels) (tokens and labels are lists of words and assigned labels)
• MultipleChoice(s1, choices) (s1 is a prompt/qusetion, choices is a list of texts, y is the index of the correct choice)
• QuestionAnswering(context, question, answers) (extractive QA in the SQuAD format; empty answers mean unanswerable)
• SpanTagging(question, context, spans) (query-conditioned extraction: character spans of the context answering the question, any number, none for no answer)
• 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.

AutoTask

You can also leverage tasksource with tn.AutoTask and have one-line access to 600+ datasets, see implemented tasks.

rte = tn.AutoTask("glue/rte", nrows=5000)

AutoTask guesses a template based on the dataset structure. It also accepts a dataset as input, if it fits the template (e.g. after tasksource custom preprocessing).

Balancing dataset sizes

tn.Classification(dataset, nrows=5000, nrows_eval=500, oversampling=2)

You can balance multiple datasets with nrows and oversampling. nrows is the maximal number of examples. If a dataset has less than nrows, it will be oversampled at most oversampling times.

Colab examples

Minimal-ish example:

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

More complex example, where tasknet was scaled to 600 tasks:

https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing

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 damien.sileo@inria.fr

@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}}