Muennighoff
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Create README.md
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README.md
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Creation (Copied & adapted from https://github.com/stanford-crfm/helm/blob/0eaaa62a2263ddb94e9850ee629423b010f57e4a/src/helm/benchmark/scenarios/babi_qa_scenario.py):
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```python
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!wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz
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!tar -xf tasks_1-20_v1-2.tar.gz
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import json
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from typing import List
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tasks = list(range(1, 20))
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splits = ["train", "valid", "test"]
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def process_path(path: str) -> str:
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"""Turn a path string (task 19) from the original format 's,w' to a verbal model-friendly format 'south west'"""
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steps: List[str] = path.split(",")
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directions = {"s": "south", "n": "north", "e": "east", "w": "west"}
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path = " ".join([directions[step] for step in steps])
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return path
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for split in splits:
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with open(f"babi_{split}.jsonl", "w") as f_base:
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for task in tasks:
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split_path: str = f"./tasks_1-20_v1-2/en-valid/qa{task}_{split}.txt"
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with open(split_path, "r") as f:
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facts = list(f)
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story: List[str] = []
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for fact in facts:
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fid = int(fact.split(" ")[0])
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if fid == 1:
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story = []
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fact = " ".join(fact.split(" ")[1:])
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is_question = "?" in fact
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if is_question:
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question, answer = fact.split("\t")[:2]
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question, answer = question.strip(), answer.strip()
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# All tasks except task 19 have a verbal single-word answer (e.g. kitchen, apple, yes).
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# Task 19 (path finding) has a non verbal answer format (
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if task == 19:
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answer = process_path(answer)
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f_base.write(json.dumps({
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"passage": "".join(story),
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"question": question,
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"answer": answer,
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"task": task,
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}) + "\n")
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if "?" in story:
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print("STORY", "".join(story))
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else:
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story.append(fact)
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```
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