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--- |
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tags: |
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- question-answering, multi-step-reasoning, multi-hop-reasoning |
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thumbnail: https://raw.githubusercontent.com/StonyBrookNLP/teabreac/main/teabreac_icon.png |
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license: cc-by-4.0 |
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--- |
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# What's this? |
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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). |
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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. |
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We release the following models: |
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- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}` |
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- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}` |
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- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}` |
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The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`. |
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The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`. |
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The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**. |
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# How to use it? |
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Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac |
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model_name = "StonyBrookNLP/bart-large-numglue" |
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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enable_digit_tokenization(tokenizer) |
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input_texts = [ |
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"answer_me: Who scored the first touchdown of the game?" + |
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"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..." |
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# Note: some models have slightly different qn/ctxt format. See the github repo. |
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] |
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input_ids = tokenizer( |
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input_texts, return_tensors="pt", |
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truncation=True, max_length=800, |
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add_special_tokens=True, padding=True, |
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)["input_ids"] |
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generated_ids = model.generate(input_ids, min_length=1, max_length=50) |
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generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False) |
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generated_predictions = [ |
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tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions |
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] |
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# => ["Chaz Schilens"] |
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``` |
