Commit 50e99bd7 authored by Herbie Bradley's avatar Herbie Bradley
Browse files

Merge remote-tracking branch 'origin/big-refactor' into calibration

parents 3d4c4cd6 a3252ed7
# Generated by utils.py
dataset_name: sw
doc_to_target: '{% if answer is not none %}{{answer[24+1]}}{% else %}{{answer_number|string}}{%
endif %}'
doc_to_text: '{% if answer is not none %}{{question+"\nJibu la Hatua kwa Hatua:"}}{%
else %}{{"Swali: "+question+"\nJibu la Hatua kwa Hatua:"}}{% endif %}'
include: cot_yaml
task: mgsm_sw_direct
# Generated by utils.py
dataset_name: te
doc_to_target: '{% if answer is not none %}{{answer[18+1]}}{% else %}{{answer_number|string}}{%
endif %}'
doc_to_text: '{% if answer is not none %}{{question+"\nదశలవారీగా సమాధానం:"}}{% else
%}{{"ప్రశ్న: "+question+"\nదశలవారీగా సమాధానం:"}}{% endif %}'
include: cot_yaml
task: mgsm_te_direct
# Generated by utils.py
dataset_name: th
doc_to_target: '{% if answer is not none %}{{answer[17+1]}}{% else %}{{answer_number|string}}{%
endif %}'
doc_to_text: '{% if answer is not none %}{{question+"\nคำตอบทีละขั้นตอน:"}}{% else
%}{{"โจทย์: "+question+"\nคำตอบทีละขั้นตอน:"}}{% endif %}'
include: cot_yaml
task: mgsm_th_direct
# Generated by utils.py
dataset_name: zh
doc_to_target: '{% if answer is not none %}{{answer[5+1]}}{% else %}{{answer_number|string}}{%
endif %}'
doc_to_text: '{% if answer is not none %}{{question+"\n逐步解答:"}}{% else %}{{"问题: "+question+"\n逐步解答:"}}{%
endif %}'
include: cot_yaml
task: mgsm_zh_direct
# MuTual
### Paper
Title: `MuTual: A Dataset for Multi-Turn Dialogue Reasoning`
Abstract: https://www.aclweb.org/anthology/2020.acl-main.130/
MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is
modified from Chinese high school English listening comprehension test data.
Homepage: https://github.com/Nealcly/MuTual
### Citation
```
@inproceedings{mutual,
title = "MuTual: A Dataset for Multi-Turn Dialogue Reasoning",
author = "Cui, Leyang and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming" ,
booktitle = "Proceedings of the 58th Conference of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
}
```
### Groups and Tasks
#### Groups
* Not part of a group yet.
#### Tasks
* `mutual`
* `mutual_plus`
### Checklist
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
include: mutual.yaml
task: mutual_plus
dataset_name: mutual_plus
task: mutual
dataset_path: "EleutherAI/mutual"
dataset_name: mutual
output_type: multiple_choice
training_split: train
validation_split: validation
doc_to_text: "{{article}}"
doc_to_target: "{{['A', 'B', 'C', 'D'].index(answers)}}"
doc_to_choice: "{{options}}"
process_docs: !function utils.process_docs
process_results: !function utils.process_results
should_decontaminate: true
doc_to_decontamination_query: "{{article}}"
metric_list:
- metric: r@1
aggregation: mean
higher_is_better: true
- metric: r@2
aggregation: mean
higher_is_better: true
- metric: mrr
aggregation: mean
higher_is_better: true
import numpy as np
def process_docs(dataset):
def _detokenize(text):
text = text.replace(" '", "'")
text = text.replace(" \n", "\n")
text = text.replace("\n ", "\n")
text = text.replace(" n't", "n't")
text = text.replace("`` ", '"')
text = text.replace("''", '"')
# punctuation
text = text.replace(" :", ":")
text = text.replace(" ;", ";")
text = text.replace(" !", "!")
text = text.replace(" ?", "?")
text = text.replace(" ,", ",")
text = text.replace(" .", ".")
return text
def _process(doc):
return {
"article": _detokenize(doc["article"]),
"options": [_detokenize(option) for option in doc["options"]],
}
return dataset.map(_process)
def process_results(doc, results):
gold = ["A", "B", "C", "D"].index(doc["answers"])
r4_1 = np.argmax(results) == gold # r4_1 = accuracy
ranks = sorted(results, reverse=True)
r4_2 = (ranks.index(results[gold]) == 1) + r4_1
mrr = 1.0 / (ranks.index(results[gold]) + 1) # `+ 1` for index offset
return {"r@1": r4_1, "r@2": r4_2, "mrr": mrr}
task: nq_open
dataset_path: nq_open
output_type: greedy_until
training_split: train
validation_split: validation
description: "Answer these questions:\n"
doc_to_text: "Q: {{question}}?\nA:"
doc_to_target: "{{answer}}" # TODO: should be multi-target
fewshot_delimiter: "\n"
generation_kwargs:
until:
- "\n"
- "."
- ","
do_sample: false
temperature: 0.0
filter_list:
- name: remove_whitespace
filter:
- function: remove_whitespace
- function: take_first
target_delimiter: " "
metric_list:
- metric: exact_match
aggregation: mean
higher_is_better: true
ignore_case: true
ignore_punctuation: true
regexes_to_ignore:
- "\ban|a|the\b"
# QASPER
### Paper
Title: `A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers`
Abstract: https://arxiv.org/abs/2105.03011
QASPER is a dataset of 5,049 questions over 1,585 Natural Language Processing papers.
Each question is written by an NLP practitioner who read only the title and abstract
of the corresponding paper, and the question seeks information present in the full
text. The questions are then answered by a separate set of NLP practitioners who also
provide supporting evidence to answers.
Homepage: https://allenai.org/data/qasper
### Citation
```
@article{DBLP:journals/corr/abs-2105-03011,
author = {Pradeep Dasigi and
Kyle Lo and
Iz Beltagy and
Arman Cohan and
Noah A. Smith and
Matt Gardner},
title = {A Dataset of Information-Seeking Questions and Answers Anchored in
Research Papers},
journal = {CoRR},
volume = {abs/2105.03011},
year = {2021},
url = {https://arxiv.org/abs/2105.03011},
eprinttype = {arXiv},
eprint = {2105.03011},
timestamp = {Fri, 14 May 2021 12:13:30 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2105-03011.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Groups and Tasks
#### Groups
* `qasper`: executes both `qasper_bool` and `qasper_freeform`
#### Tasks
* `qasper_bool`: Multiple choice task that evaluates the task with `answer_type="bool"`
* `qasper_freeform`: Greedy generation task that evaluates the samples from the task with `answer_type="free form answer"`
### Checklist
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
group: qasper
task: qasper_bool
dataset_path: qasper
output_type: multiple_choice
training_split: train
validation_split: validation
process_docs: !function utils.process_docs_bool
doc_to_text: "TITLE: {{title}}\nABSTRACT: {{abstract}}\n\nQ: {{question}}\n\nA:"
doc_to_target: 1
doc_to_choice: ["no", "yes"]
metric_list:
- metric: f1
group: qasper
task: qasper_freeform
dataset_path: qasper
output_type: greedy_until
training_split: train
validation_split: validation
process_docs: !function utils.process_docs_freeform
doc_to_text: "TITLE: {{title}}\nABSTRACT: {{abstract}}\n\nQ: {{question}}\n\nA:"
doc_to_target: answer
generation_kwargs:
until:
- "\n"
metric_list:
- metric: !function metrics.f1_abstractive
aggregation: mean
higher_is_better: true
import re
import string
from collections import Counter
def normalize_answer(s):
"""
Taken from the official evaluation script for v1.1 of the SQuAD dataset.
Lower text and remove punctuation, articles and extra whitespace.
"""
def remove_articles(text):
return re.sub(r"\b(a|an|the)\b", " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def f1_abstractive(predictions, references):
"""
Taken from the official evaluation script for v1.1 of the SQuAD dataset.
"""
prediction_tokens = normalize_answer(predictions[0]).split()
references_tokens = normalize_answer(references[0]).split()
common = Counter(prediction_tokens) & Counter(references_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(references_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
from datasets import Dataset
from functools import partial
def process_docs(dataset, set_answer_type="bool"):
FEATURES = ["title", "abstract", "question", "answer", "answer_type"]
def _categorise_answer(answer_blob):
if answer_blob["unanswerable"]:
answer = "unanswerable"
answer_type = "unanswerable"
return answer, answer_type
elif answer_blob["yes_no"]:
answer = "yes"
answer_type = "bool"
return answer, answer_type
elif answer_blob["free_form_answer"]:
answer = answer_blob["free_form_answer"]
answer_type = "free form answer"
return answer, answer_type
elif answer_blob["extractive_spans"]:
answer = answer_blob["extractive_spans"]
answer_type = "extractive_spans"
return answer, answer_type
elif answer_blob["yes_no"] is False:
answer = "no"
answer_type = "bool"
return answer, answer_type
def _flatten(doc):
"""Given a `doc`, flatten it out so that each JSON blob
contains exactly one question and one answer. Logic taken from
the reference implementation available at
https://github.com/allenai/qasper-led-baseline/blob/main/scripts/evaluator.py
"""
obs_list = {
"title": [],
"abstract": [],
"question": [],
"answer": [],
"answer_type": [],
}
title = doc.pop("title")
abstract = doc.pop("abstract")
for question, answer_list in zip(doc["qas"]["question"], doc["qas"]["answers"]):
for answer_blob in answer_list["answer"]:
answer, answer_type = _categorise_answer(answer_blob)
if answer_type == set_answer_type:
obs_list["title"].append(title)
obs_list["abstract"].append(abstract)
obs_list["question"].append(question)
obs_list["answer_type"].append(answer_type)
if type(answer) == list:
answer = ", ".join(answer)
obs_list["answer"].append(answer)
return obs_list
dataset = dataset.map(
_flatten,
remove_columns=[key for key in dataset.features.keys() if key not in FEATURES],
)
new_dataset = {}
for key in dataset.features.keys():
new_dataset[key] = [x for row in dataset[key] for x in row]
return Dataset.from_dict(new_dataset)
process_docs_bool = partial(process_docs, set_answer_type="bool")
process_docs_freeform = partial(process_docs, set_answer_type="free form answer")
# Task-name
### Paper
Title: `paper title goes here`
Abstract: `link to paper PDF or arXiv abstract goes here`
`Short description of paper / benchmark goes here:`
Homepage: `homepage to the benchmark's website goes here, if applicable`
### Citation
```
BibTeX-formatted citation goes here
```
### Subtasks
List or describe tasks defined in this folder, and their names here:
* `task_name`: `1-sentence description of what this particular task does`
* `task_name2`: .....
### Checklist
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
task: squadv2
dataset_path: squad_v2
output_type: greedy_until
training_split: train
validation_split: validation
doc_to_text: "Title: {{title}}\n\nBackground: {{context}}\n\nQuestion: {{question}}\n\n Answer:"
doc_to_target: "{% if answers.text| length > 0 %}{{answers.text}}{% else %}{{['']}}{% endif %}"
target_delimiter: ""
should_decontaminate: true
doc_to_decontamination_query: context
generation_kwargs:
until:
- "\n"
# filter_list:
# - name: remove_whitespace
# filter:
# - function: remove_whitespace
# - function: take_first
metric_list:
- metric: !function utils.exact
aggregation: mean
higher_is_better: true
- metric: !function utils.f1
aggregation: mean
higher_is_better: true
include: default.yaml
task: squadv2_noans_loglikelihood
dataset_path: squad_v2
output_type: loglikelihood
training_split: train
validation_split: validation
doc_to_target: " unanswerable"
metric_list:
- metric: perplexity
import re
import string
import collections
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
return re.sub(regex, " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def get_tokens(s):
if not s:
return []
return normalize_answer(s).split()
# Exact match (the normalized answer exactly match the gold answer)
def exact(predictions, references):
return int(normalize_answer(references[0]) == normalize_answer(predictions[0]))
# The F-score of predicted tokens versus the gold answer
def f1(predictions, references):
gold_toks = get_tokens(references[0])
pred_toks = get_tokens(predictions[0])
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if len(gold_toks) == 0 or len(pred_toks) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
group: squadv2_complete
task:
- squadv2
- squadv2_noans_loglikelihood
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