What's this?

This is one of the models reported in the paper: "Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts"..

This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.

We release the following models:

A: Base Models finetuned on target datasets: {base_model}-{target_dataset}
B: Base models pretrained on TeaBReaC: teabreac-{base_model}
C: Base models pretrained on TeaBReaC and then finetuned on target datasets: teabreac-{base_model}-{target_dataset}

The base_model above can be from: bart-large, t5-large, t5-3b, nt5-small, preasm-large. The target_dataset above can be from: drop, tatqa, iirc-gold, iirc-retrieved, numglue.

The A models are only released for completeness / reproducibility. In your end application you probably just want to use either B or C.

How to use it?

Please checkout the details in our github repository, but in a nutshell:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac

model_name = "StonyBrookNLP/nt5-small-numglue"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
    "answer_me: Who scored the first touchdown of the game?" +
    "context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
    # Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
    input_texts, return_tensors="pt",
    truncation=True, max_length=800,
    add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1,  max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
    tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]