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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: question |
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dtype: string |
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- name: context |
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dtype: string |
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- name: choices |
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sequence: string |
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- name: label |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 63920351 |
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num_examples: 2523 |
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- name: validation |
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num_bytes: 52064930 |
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num_examples: 2086 |
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download_size: 5955070 |
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dataset_size: 115985281 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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--- |
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This dataset is derived from `tau/scrolls` [dataset](tau/scrolls) by running the following script: |
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```python |
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import re |
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from datasets import load_dataset |
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def _normalize_answer(text): |
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return " ".join(text.split()).strip() |
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def _drop_duplicates_in_input(untokenized_dataset): |
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# from scrolls/evaluator/dataset_evaluator.py |
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indices_to_keep = [] |
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id_to_idx = {} |
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outputs = [] |
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for i, (id_, output) in enumerate( |
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zip(untokenized_dataset["id"], untokenized_dataset["output"]) |
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): |
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if id_ in id_to_idx: |
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outputs[id_to_idx[id_]].append(output) |
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continue |
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indices_to_keep.append(i) |
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id_to_idx[id_] = len(outputs) |
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outputs.append([output]) |
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untokenized_dataset = untokenized_dataset.select(indices_to_keep).flatten_indices() |
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untokenized_dataset = untokenized_dataset.remove_columns("output") |
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untokenized_dataset = untokenized_dataset.add_column("outputs", outputs) |
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return untokenized_dataset |
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def _process_doc_prepended_question(doc): |
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input = doc["input"] |
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split = input.find("\n\n") |
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return { |
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"id": doc["id"], |
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"pid": doc["pid"], |
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"input": input, |
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"outputs": doc["outputs"], |
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"question": input[0:split], |
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"text": input[split + 2 :], |
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} |
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def process_doc(doc): |
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quality_multiple_choice_pattern = re.compile(r" *\([A-D]\) *") |
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doc = _process_doc_prepended_question(doc) |
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split = doc["text"].find("\n\n", doc["text"].find("(D)")) |
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choices_text = doc["text"][:split] |
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doc["text"] = doc["text"][split:].strip() |
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doc["choices"] = [ |
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_normalize_answer(choice) |
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for choice in re.split(quality_multiple_choice_pattern, choices_text)[1:] |
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] |
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doc["gold"] = doc["choices"].index(_normalize_answer(doc["outputs"][0])) |
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return doc |
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def get_quality_dataset(): |
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""" |
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download and processes the quality dataset following the lm-evaluation-harness scrolls_quality task |
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The processed dataset has the following train & validation splits with 2523 & 2086 examples respectively. |
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fields to be used during evaluation: |
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- question: the question prompt |
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- text: the context |
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- choices: list of choices (4 in total) |
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- gold: index of the correct choice |
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""" |
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quality_dataset = load_dataset("tau/scrolls", "quality") |
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del quality_dataset["test"] # drop test split -> no ground truths |
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for split in quality_dataset: |
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quality_dataset[split] = _drop_duplicates_in_input(quality_dataset[split]) |
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quality_dataset = quality_dataset.map(process_doc) |
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return quality_dataset |
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quality_dataset = get_quality_dataset() |
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quality_dataset = quality_dataset.rename_columns({"text": "context", "gold": "label"}) |
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quality_dataset = quality_dataset.remove_columns(["pid", "input", "outputs"]) |
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train_ds = quality_dataset["train"] |
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validation_ds = quality_dataset["validation"] |
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``` |
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The processing code is adapted from [lm-evaluation-harness scrolls task](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/scrolls/task.py) |
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--- |
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Relevant sections from the [SCROLLS: Standardized CompaRison Over Long Language Sequences paper](https://arxiv.org/pdf/2201.03533) |
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``` |
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QuALITY (Pang et al., 2021): A multiplechoice question answering dataset over stories |
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and articles sourced from Project Gutenberg,10 the |
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Open American National Corpus (Fillmore et al., |
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1998; Ide and Suderman, 2004), and more. Experienced writers wrote questions and distractors, and |
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were incentivized to write answerable, unambiguous questions such that in order to correctly answer |
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them, human annotators must read large portions |
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of the given document. To measure the difficulty |
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of their questions, Pang et al. conducted a speed |
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validation process, where another set of annotators |
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were asked to answer questions given only a short |
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period of time to skim through the document. As |
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a result, 50% of the questions in QuALITY are |
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labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong |
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answer. |
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``` |
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