readme: ---
  tags:
  - question-answering, multi-step-reasoning, multi-hop-reasoning
  thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png
  license: cc-by-4.0
  ---
  
  # 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".](https://arxiv.org/abs/2205.12496).
  
  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](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
  
  model_name = "StonyBrookNLP/teabreac-nt5-small-drop"
  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"]
  ```