Edit model card

T5-small for QA

Google's T5-small pre-trained on the C4 dataset, fine-tuned for Question-Answering on SQuAD v2 with the following hyperparameters:

  optimizer=adamw_hf
  learning_rate=3e-5
  adam_beta1=0.9
  adam_beta2=0.999
  adam_epsilon=1e-08
  num_train_epochs=2
  per_device_train_batch_size=12

Usage

The input [context and question] has to be prepared in a specific way as follows:

from transformers import pipeline

def prep_input(_context, _question):
    return " ".join(["question:", _question.strip(), "context:", _context.strip()])

t5qa = pipeline("text2text-generation", "fgaim/t5-small-squad-v2")

context = """
Oxygen is a chemical element with symbol O and atomic number 8. It is a member of the chalcogen group on the periodic table and is a highly reactive nonmetal and oxidizing agent that readily forms compounds (notably oxides) with most elements. By mass, oxygen is the third-most abundant element in the universe, after hydrogen and helium. At standard temperature and pressure, two atoms of the element bind to form dioxygen, a colorless and odorless diatomic gas with the formula O.
"""

t5qa(prep_input(context, "How many atoms combine to form dioxygen?"))
# [{'generated_text': 'two'}]

t5qa(prep_input(context, "What element makes up almost half of the earth's crust by mass?"))
# [{'generated_text': 'oxygen'}]

t5qa(prep_input(context, "What are the most abundent elements of the universe by mass?"))
# [{'generated_text': 'hydrogen and helium'}]
Downloads last month
9
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Datasets used to train fgaim/t5-small-squad-v2