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superglue.py
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superglue.py
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"""
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
https://w4ngatang.github.io/static/papers/superglue.pdf
SuperGLUE is a benchmark styled after GLUE with a new set of more difficult language
understanding tasks.
Homepage: https://super.gluebenchmark.com/
TODO: WSC requires free-form generation.
"""
import numpy as np
import sklearn
import transformers.data.metrics.squad_metrics as squad_metrics
from lm_eval.base import rf, Task
from lm_eval.metrics import mean, acc_all, metric_max_over_ground_truths, yesno
from lm_eval.utils import general_detokenize
_CITATION = """
@inproceedings{NEURIPS2019_4496bf24,
author = {Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
url = {https://proceedings.neurips.cc/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf},
volume = {32},
year = {2019}
}
"""
class BoolQ(Task):
VERSION = 1
DATASET_PATH = "super_glue"
DATASET_NAME = "boolq"
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
if self._training_docs is None:
self._training_docs = list(self.dataset["train"])
return self._training_docs
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return f"{doc['passage']}\nQuestion: {doc['question']}?\nAnswer:"
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["passage"]
def doc_to_target(self, doc):
return " " + yesno(doc["label"])
def construct_requests(self, doc, ctx):
ll_yes, _ = rf.loglikelihood(ctx, " yes")
ll_no, _ = rf.loglikelihood(ctx, " no")
return ll_yes, ll_no
def process_results(self, doc, results):
ll_yes, ll_no = results
gold = doc["label"]
acc = 1.0 if (ll_yes > ll_no) == gold else 0.0
return {"acc": acc}
def higher_is_better(self):
return {"acc": True}
def aggregation(self):
return {"acc": mean}
class CommitmentBank(Task):
VERSION = 1
DATASET_PATH = "super_glue"
DATASET_NAME = "cb"
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
if self._training_docs is None:
self._training_docs = list(self.dataset["train"])
return self._training_docs
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "{}\nQuestion: {}. True, False or Neither?\nAnswer:".format(
doc["premise"],
doc["hypothesis"],
)
def doc_to_target(self, doc):
# True = entailment
# False = contradiction
# Neither = neutral
return " {}".format({0: "True", 1: "False", 2: "Neither"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_true, _ = rf.loglikelihood(ctx, " True")
ll_false, _ = rf.loglikelihood(ctx, " False")
ll_neither, _ = rf.loglikelihood(ctx, " Neither")
return ll_true, ll_false, ll_neither
def process_results(self, doc, results):
gold = doc["label"]
pred = np.argmax(results)
acc = 1.0 if pred == gold else 0.0
return {"acc": acc, "f1": (pred, gold)}
def higher_is_better(self):
return {"acc": True, "f1": True}
@classmethod
def cb_multi_fi(cls, items):
preds, golds = zip(*items)
preds = np.array(preds)
golds = np.array(golds)
f11 = sklearn.metrics.f1_score(y_true=golds == 0, y_pred=preds == 0)
f12 = sklearn.metrics.f1_score(y_true=golds == 1, y_pred=preds == 1)
f13 = sklearn.metrics.f1_score(y_true=golds == 2, y_pred=preds == 2)
avg_f1 = mean([f11, f12, f13])
return avg_f1
def aggregation(self):
return {
"acc": mean,
"f1": self.cb_multi_fi,
}
class Copa(Task):
VERSION = 0
DATASET_PATH = "super_glue"
DATASET_NAME = "copa"
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
if self._training_docs is None:
self._training_docs = list(self.dataset["train"])
return self._training_docs
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
# Drop the period
connector = {
"cause": "because",
"effect": "therefore",
}[doc["question"]]
return doc["premise"].strip()[:-1] + f" {connector}"
def doc_to_target(self, doc):
correct_choice = doc["choice1"] if doc["label"] == 0 else doc["choice2"]
# Connect the sentences
return " " + self.convert_choice(correct_choice)
def construct_requests(self, doc, ctx):
choice1 = " " + self.convert_choice(doc["choice1"])
choice2 = " " + self.convert_choice(doc["choice2"])
ll_choice1, _ = rf.loglikelihood(ctx, choice1)
ll_choice2, _ = rf.loglikelihood(ctx, choice2)
return ll_choice1, ll_choice2
def process_results(self, doc, results):
gold = doc["label"]
pred = np.argmax(results)
acc = 1.0 if pred == gold else 0.0
return {"acc": acc}
def higher_is_better(self):
return {"acc": True}
def aggregation(self):
return {"acc": mean}
@staticmethod
def convert_choice(choice):
return choice[0].lower() + choice[1:]
class MultiRC(Task):
VERSION = 1
DATASET_PATH = "super_glue"
DATASET_NAME = "multirc"
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
if self._training_docs is None:
self._training_docs = list(self.dataset["train"])
return self._training_docs
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return f"{doc['paragraph']}\nQuestion: {doc['question']}\nAnswer:"
def doc_to_target(self, doc):
return " " + self.format_answer(answer=doc["answer"], label=doc["label"])
@staticmethod
def format_answer(answer, label):
label_str = "yes" if label else "no"
return f"{answer}\nIs the answer correct? {label_str}"
def construct_requests(self, doc, ctx):
true_choice = self.format_answer(answer=doc["answer"], label=True)
false_choice = self.format_answer(answer=doc["answer"], label=False)
ll_true_choice, _ = rf.loglikelihood(ctx, f" {true_choice}")
ll_false_choice, _ = rf.loglikelihood(ctx, f" {false_choice}")
return ll_true_choice, ll_false_choice
def process_results(self, doc, results):
ll_true_choice, ll_false_choice = results
pred = ll_true_choice > ll_false_choice
return {"acc": (pred, doc)}
def higher_is_better(self):
return {"acc": True}
def aggregation(self):
return {"acc": acc_all}
class ReCoRD(Task):
VERSION = 0
DATASET_PATH = "super_glue"
DATASET_NAME = "record"
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
# In ReCoRD, each doc manifests multiple "examples" in the context of few shot example packing.
# Each doc consists of multiple answer candidates, each of which is scored yes/no.
if self._training_docs is None:
self._training_docs = []
for doc in self.dataset["train"]:
self._training_docs.append(self._process_doc(doc))
return self._training_docs
def validation_docs(self):
# See: training_docs
for doc in self.dataset["validation"]:
yield self._process_doc(doc)
@classmethod
def _process_doc(cls, doc):
return {
"passage": doc["passage"],
"query": doc["query"],
"entities": sorted(list(set(doc["entities"]))),
"answers": sorted(list(set(doc["answers"]))),
}
def doc_to_text(self, doc):
initial_text, *highlights = doc["passage"].strip().split("\n@highlight\n")
text = initial_text + "\n\n"
for highlight in highlights:
text += f" - {highlight}.\n"
return text
@classmethod
def format_answer(cls, query, entity):
return f" - {query}".replace("@placeholder", entity)
def doc_to_target(self, doc):
# We only output the first correct entity in a doc
return self.format_answer(query=doc["query"], entity=doc["answers"][0])
def construct_requests(self, doc, ctx):
requests = [
rf.loglikelihood(ctx, self.format_answer(query=doc["query"], entity=entity))
for entity in doc["entities"]
]
return requests
def process_results(self, doc, results):
# ReCoRD's evaluation is actually deceptively simple:
# - Pick the maximum likelihood prediction entity
# - Evaluate the accuracy and token F1 PER EXAMPLE
# - Average over all examples
max_idx = np.argmax(np.array([result[0] for result in results]))
prediction = doc["entities"][max_idx]
gold_label_set = doc["answers"]
f1 = metric_max_over_ground_truths(
squad_metrics.compute_f1, prediction, gold_label_set
)
em = metric_max_over_ground_truths(
squad_metrics.compute_exact, prediction, gold_label_set
)
return {
"f1": f1,
"em": em,
}
def higher_is_better(self):
return {
"f1": True,
"em": True,
}
def aggregation(self):
return {
"f1": mean,
"em": mean,
}
class WordsInContext(Task):
VERSION = 0
DATASET_PATH = "super_glue"
DATASET_NAME = "wic"
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
if self._training_docs is None:
self._training_docs = list(self.dataset["train"])
return self._training_docs
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return (
"Sentence 1: {}\nSentence 2: {}\nQuestion: Is the word '{}' used in the same way in the"
" two sentences above?\nAnswer:".format(
doc["sentence1"],
doc["sentence2"],
doc["sentence1"][doc["start1"] : doc["end1"]],
)
)
def doc_to_target(self, doc):
return " {}".format({0: "no", 1: "yes"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_yes, _ = rf.loglikelihood(ctx, " yes")
ll_no, _ = rf.loglikelihood(ctx, " no")
return ll_yes, ll_no
def process_results(self, doc, results):
ll_yes, ll_no = results
gold = doc["label"]
acc = 1.0 if (ll_yes > ll_no) == gold else 0.0
return {"acc": acc}
def higher_is_better(self):
return {"acc": True}
def aggregation(self):
return {"acc": mean}
class SGWinogradSchemaChallenge(Task):
VERSION = 0
# Note: This implementation differs from Fig G.32 because this is the SuperGLUE,
# binary version of the task.
DATASET_PATH = "super_glue"
DATASET_NAME = "wsc"
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
if self.has_training_docs():
if self._training_docs is None:
# GPT-3 Paper's format only uses positive examples for fewshot "training"
self._training_docs = [
doc for doc in self.dataset["train"] if doc["label"]
]
return self._training_docs
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
raw_passage = doc["text"]
# NOTE: HuggingFace span indices are word-based not character-based.
pre = " ".join(raw_passage.split()[: doc["span2_index"]])
post = raw_passage[len(pre) + len(doc["span2_text"]) + 1 :]
passage = general_detokenize(pre + " *{}*".format(doc["span2_text"]) + post)
noun = doc["span1_text"]
pronoun = doc["span2_text"]
text = (
f"Passage: {passage}\n"
+ f'Question: In the passage above, does the pronoun "*{pronoun}*" refer to "*{noun}*"?\n'
+ "Answer:"
)
return text
def doc_to_target(self, doc):
return " " + yesno(doc["label"])
def construct_requests(self, doc, ctx):
ll_yes, _ = rf.loglikelihood(ctx, " yes")
ll_no, _ = rf.loglikelihood(ctx, " no")
return ll_yes, ll_no
def process_results(self, doc, results):
ll_yes, ll_no = results
gold = doc["label"]
acc = 1.0 if (ll_yes > ll_no) == gold else 0.0
return {"acc": acc}
def higher_is_better(self):
return {"acc": True}
def aggregation(self):
return {"acc": mean}