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Unverified Commit 9f58d1a8 authored by Stella Biderman's avatar Stella Biderman Committed by GitHub
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Merge branch 'polyglot' into feature/kold

parents 33e813ee 1927699a
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lm_eval/tasks/blimp.py
View file @ 9f58d1a8
......@@ -28,7 +28,7 @@ _CITATION = """
eprint = {https://doi.org/10.1162/tacl_a_00321},
abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. }
}
"""
""" # noqa: W605
class BlimpTask(Task):
......@@ -37,7 +37,7 @@ class BlimpTask(Task):
def has_training_docs(self):
return False
def has_validation_docs(self):
return True
......@@ -50,9 +50,13 @@ class BlimpTask(Task):
# trained on this data.
return self.dataset["train"]
def fewshot_context(self, doc, num_fewshot, provide_description=None, rnd=None, description=None):
def fewshot_context(
self, doc, num_fewshot, provide_description=None, rnd=None, description=None
):
assert num_fewshot == 0
assert rnd is not None, "A `random.Random` generator argument must be provided to `rnd`"
assert (
rnd is not None
), "A `random.Random` generator argument must be provided to `rnd`"
assert not provide_description, (
"The `provide_description` arg will be removed in future versions. To prepend "
"a custom description to the context, supply the corresponding string via the "
......@@ -60,7 +64,9 @@ class BlimpTask(Task):
)
if provide_description is not None:
# nudge people to not specify it at all
print("WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict")
print(
"WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
)
return ""
......@@ -68,6 +74,12 @@ class BlimpTask(Task):
# this method is invoked by tests only
return ""
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["sentence_good"] + " " + doc["sentence_bad"]
def doc_to_target(self, doc):
# this method is invoked by tests only
return ""
......
lm_eval/tasks/cbt.py
View file @ 9f58d1a8
......@@ -2,7 +2,7 @@
The Children’s Book Test (CBT) from the paper:
https://research.fb.com/wp-content/uploads/2016/11/the_goldilocks_principle_reading_children_s_books_with_explicit_memory_representations.pdf
The Children's Book Test (CBT) is test of how well language models capture
The Children's Book Test (CBT) is test of how well language models capture
meaning in children's books. Unlike standard language modelling benchmarks,
it distinguishes the task of predicting syntactic function words from that
of predicting lower-frequency words, which carry greater semantic content.
......@@ -19,7 +19,7 @@ from lm_eval.metrics import mean
_CITATION = """
@misc{hill2016goldilocks,
title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},
title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},
author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston},
year={2016},
eprint={1511.02301},
......@@ -75,11 +75,20 @@ class CBTBase(Task):
text = "Passage: " + passage + "\nQuestion: " + doc["question"]
return self.detokenize(text)
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
passage = " ".join(doc["sentences"])
return passage
def doc_to_target(self, doc):
return ""
def fewshot_examples(self, k, rnd):
assert k == 0, f"CBT is only implemented for the zero-shot setting. Given k={k}."
assert (
k == 0
), f"CBT is only implemented for the zero-shot setting. Given k={k}."
return super().fewshot_examples(k, rnd)
def construct_requests(self, doc, ctx):
......@@ -113,9 +122,7 @@ class CBTBase(Task):
"""
gold = doc["options"].index(doc["answer"])
pred = np.argmax(results)
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def aggregation(self):
"""
......@@ -123,9 +130,7 @@ class CBTBase(Task):
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
return {
"acc": mean
}
return {"acc": mean}
def higher_is_better(self):
"""
......@@ -133,9 +138,7 @@ class CBTBase(Task):
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {
"acc": True
}
return {"acc": True}
class CBTCN(CBTBase):
......
lm_eval/tasks/coqa.py
View file @ 9f58d1a8
......@@ -2,9 +2,9 @@
CoQA: A Conversational Question Answering Challenge
https://arxiv.org/pdf/1808.07042.pdf
CoQA is a large-scale dataset for building Conversational Question Answering
systems. The goal of the CoQA challenge is to measure the ability of machines to
understand a text passage and answer a series of interconnected questions that
CoQA is a large-scale dataset for building Conversational Question Answering
systems. The goal of the CoQA challenge is to measure the ability of machines to
understand a text passage and answer a series of interconnected questions that
appear in a conversation.
Homepage: https://stanfordnlp.github.io/coqa/
......@@ -52,43 +52,53 @@ class CoQA(Task):
pass
def doc_to_text(self, doc):
# Given a passage p, the conversation history {q1, a1, . . . qi−1, ai−1}
# Given a passage p, the conversation history {q1, a1, . . . qi−1, ai−1}
# and a question qi, the task is to predict the answer ai
doc_text = doc["story"] + '\n\n'
for (q, a) in zip_longest(doc["questions"]["input_text"], doc["answers"]["input_text"][:-1]): # omit target answer ai
doc_text = doc["story"] + "\n\n"
for (q, a) in zip_longest(
doc["questions"]["input_text"], doc["answers"]["input_text"][:-1]
): # omit target answer ai
question = f"Q: {q}\n\n"
answer = f"A: {a}\n\n" if a is not None else "A:"
doc_text += question + answer
return doc_text
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["story"] + " " + "\n".join(doc["questions"]["input_text"])
@classmethod
def get_answers(cls, doc, turn_id):
# Returns unique answers and valid alternatives (Some questions in CoQA have multiple valid answers).
answers = []
answer_forturn = doc["answers"]["input_text"][turn_id - 1]
answers.append(answer_forturn)
additional_answers = doc.get("additional_answers")
if additional_answers:
for key in additional_answers:
additional_answer_for_turn = additional_answers[key]["input_text"][turn_id - 1]
additional_answer_for_turn = additional_answers[key]["input_text"][
turn_id - 1
]
if additional_answer_for_turn.lower() not in map(str.lower, answers):
answers.append(additional_answer_for_turn)
return answers
@classmethod
def get_answer_choice(self, raw_text):
# Function maps answers to CoQA answer categories
# ~ 1/5 of the CoQA answers are Yes/No
# ~ 1/5 of the CoQA answers are Yes/No
# ~ 2/3 of the CoQA answers are span-based
# (answers overlap with the passage ignoring punctuation and case mismatch)
if raw_text == "unknown":
return '0'
return "0"
if squad_metrics.normalize_answer(raw_text) == "yes":
return '1'
return "1"
if squad_metrics.normalize_answer(raw_text) == "no":
return '2'
return '3' # Not a yes/no question
return "2"
return "3" # Not a yes/no question
@staticmethod
def compute_scores(gold_list, pred):
......@@ -98,40 +108,45 @@ class CoQA(Task):
em_sum = 0.0
if len(gold_list) > 1:
for i in range(len(gold_list)):
gold_answers = gold_list[0:i] + gold_list[i + 1:]
gold_answers = gold_list[0:i] + gold_list[i + 1 :]
# predictions compared against (n) golds and take maximum
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_answers)
em_sum += max(
squad_metrics.compute_exact(a, pred) for a in gold_answers
)
f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_answers)
else:
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_list)
f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_list)
return {'em': em_sum / max(1, len(gold_list)), 'f1': f1_sum / max(1, len(gold_list))}
return {
"em": em_sum / max(1, len(gold_list)),
"f1": f1_sum / max(1, len(gold_list)),
}
def doc_to_target(self, doc, turnid=None):
# Default to prediction of last turn.
if turnid is None:
turnid = len(doc["questions"]["input_text"])
raw_text = doc['answers']["input_text"][turnid - 1]
raw_text = doc["answers"]["input_text"][turnid - 1]
return " " + raw_text
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
part of the document for `doc`.
"""
cont_request = rf.greedy_until(ctx, ['\nQ:'])
cont_request = rf.greedy_until(ctx, {"until": ["\nQ:"]})
return cont_request
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
......@@ -141,13 +156,13 @@ class CoQA(Task):
"""
turn_id = len(doc["questions"]["input_text"])
gold_list = self.get_answers(doc, turn_id)
pred = results[0].strip().split('\n')[0]
pred = results[0].strip().split("\n")[0]
scores = self.compute_scores(gold_list, pred)
return {
"f1": scores['f1'],
"em": scores['em'],
"f1": scores["f1"],
"em": scores["em"],
}
def higher_is_better(self):
......
lm_eval/tasks/crowspairs.py 0 → 100644
View file @ 9f58d1a8
"""
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models
https://aclanthology.org/2020.emnlp-main.154/
French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked
language models to a language other than English
https://aclanthology.org/2022.acl-long.583/
CrowS-Pairs is a challenge set for evaluating what language models (LMs) on their tendency
to generate biased outputs. CrowS-Pairs comes in 2 languages and the English subset has
a newer version which fixes some of the issues with the original version.
Homepage: https://github.com/nyu-mll/crows-pairs, https://gitlab.inria.fr/french-crows-pairs
"""
from lm_eval.base import rf, Task
from lm_eval.metrics import mean
_CITATION = """
@inproceedings{nangia-etal-2020-crows,
title = "{C}row{S}-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models",
author = "Nangia, Nikita and
Vania, Clara and
Bhalerao, Rasika and
Bowman, Samuel R.",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.154",
doi = "10.18653/v1/2020.emnlp-main.154",
pages = "1953--1967",
abstract = "Pretrained language models, especially masked language models (MLMs) have seen success across many NLP tasks. However, there is ample evidence that they use the cultural biases that are undoubtedly present in the corpora they are trained on, implicitly creating harm with biased representations. To measure some forms of social bias in language models against protected demographic groups in the US, we introduce the Crowdsourced Stereotype Pairs benchmark (CrowS-Pairs). CrowS-Pairs has 1508 examples that cover stereotypes dealing with nine types of bias, like race, religion, and age. In CrowS-Pairs a model is presented with two sentences: one that is more stereotyping and another that is less stereotyping. The data focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups. We find that all three of the widely-used MLMs we evaluate substantially favor sentences that express stereotypes in every category in CrowS-Pairs. As work on building less biased models advances, this dataset can be used as a benchmark to evaluate progress.",
}
@inproceedings{neveol-etal-2022-french,
title = "{F}rench {C}row{S}-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than {E}nglish",
author = {N{\'e}v{\'e}ol, Aur{\'e}lie and
Dupont, Yoann and
Bezan{\c{c}}on, Julien and
Fort, Kar{\"e}n},
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.583",
doi = "10.18653/v1/2022.acl-long.583",
pages = "8521--8531",
abstract = "Warning: This paper contains explicit statements of offensive stereotypes which may be upsetting.Much work on biases in natural language processing has addressed biases linked to the social and cultural experience of English speaking individuals in the United States. We seek to widen the scope of bias studies by creating material to measure social bias in language models (LMs) against specific demographic groups in France. We build on the US-centered CrowS-pairs dataset to create a multilingual stereotypes dataset that allows for comparability across languages while also characterizing biases that are specific to each country and language. We introduce 1,679 sentence pairs in French that cover stereotypes in ten types of bias like gender and age. 1,467 sentence pairs are translated from CrowS-pairs and 212 are newly crowdsourced. The sentence pairs contrast stereotypes concerning underadvantaged groups with the same sentence concerning advantaged groups. We find that four widely used language models (three French, one multilingual) favor sentences that express stereotypes in most bias categories. We report on the translation process from English into French, which led to a characterization of stereotypes in CrowS-pairs including the identification of US-centric cultural traits. We offer guidelines to further extend the dataset to other languages and cultural environments.",
}
""" # noqa: W605
class CrowsPairsMutilingual(Task):
VERSION = 0
DATASET_PATH = "BigScienceBiasEval/crows_pairs_multilingual"
BIAS_TYPE = None
def has_training_docs(self):
return False
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def validation_docs(self):
test_dataset = self.dataset["test"]
if self.BIAS_TYPE is not None:
test_dataset = test_dataset.filter(
lambda example: example["bias_type"].startswith(self.BIAS_TYPE)
)
return test_dataset
def fewshot_context(
self, doc, num_fewshot, provide_description=None, rnd=None, description=None
):
assert num_fewshot == 0
assert (
rnd is not None
), "A `random.Random` generator argument must be provided to `rnd`"
assert not provide_description, (
"The `provide_description` arg will be removed in future versions. To prepend "
"a custom description to the context, supply the corresponding string via the "
"`description` arg."
)
if provide_description is not None:
# nudge people to not specify it at all
print(
"WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
)
return ""
def doc_to_text(self, doc):
# ignore prompts as we only score the model on the likelihood of the sentences
return ""
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["sentence_good"] + " " + doc["sentence_bad"]
def doc_to_target(self, doc):
# ignore prompts as we only score the model on the likelihood of the sentences
return ""
def construct_requests(self, doc, ctx):
assert not ctx
# Calculate the loglikelihood for the more and the less stereotypical sentence.
# Note that loglikelihood translates the "" prefix to the "<|endoftext|>" token
return [
rf.loglikelihood("", doc["sent_more"]),
rf.loglikelihood("", doc["sent_less"]),
]
def process_results(self, doc, results):
likelihood1, likelihood2 = results
# Calculate the absolute difference in loglikelihoods
diff = abs(likelihood1[0] - likelihood2[0])
# if stereotypical sentence more likely (loglikelihood higher)
# then treat this as predicting stereotyped sentence
acc = 1.0 if likelihood1[0] > likelihood2[0] else 0.0
return {"likelihood_difference": diff, "pct_stereotype": acc}
def higher_is_better(self):
# For all metrics lower is better
return {"likelihood_difference": False, "pct_stereotype": True}
def aggregation(self):
return {"likelihood_difference": mean, "pct_stereotype": mean}
class CrowsPairsEnglish(CrowsPairsMutilingual):
DATASET_NAME = "english"
class CrowsPairsFrench(CrowsPairsMutilingual):
DATASET_NAME = "french"
class CrowsPairsEnglishRaceColor(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "race-color"
class CrowsPairsEnglishSocioeconomic(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "socioeconomic"
class CrowsPairsEnglishGender(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "gender"
class CrowsPairsEnglishAge(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "age"
class CrowsPairsEnglishReligion(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "religion"
class CrowsPairsEnglishDisability(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "disability"
class CrowsPairsEnglishSexualOrientation(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "sexual-orientation"
class CrowsPairsEnglishNationality(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "nationality"
class CrowsPairsEnglishPhysicalAppearance(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "physical-appearance"
class CrowsPairsEnglishAutre(CrowsPairsMutilingual):
DATASET_NAME = "english"
BIAS_TYPE = "autre"
class CrowsPairsFrenchRaceColor(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "race-color"
class CrowsPairsFrenchSocioeconomic(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "socioeconomic"
class CrowsPairsFrenchGender(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "gender"
class CrowsPairsFrenchAge(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "age"
class CrowsPairsFrenchReligion(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "religion"
class CrowsPairsFrenchDisability(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "disability"
class CrowsPairsFrenchSexualOrientation(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "sexual-orientation"
class CrowsPairsFrenchNationality(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "nationality"
class CrowsPairsFrenchPhysicalAppearance(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "physical-appearance"
class CrowsPairsFrenchAutre(CrowsPairsMutilingual):
DATASET_NAME = "french"
BIAS_TYPE = "autre"
lm_eval/tasks/drop.py
View file @ 9f58d1a8
......@@ -2,8 +2,8 @@
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
https://aclanthology.org/attachments/N19-1246.Supplementary.pdf
DROP is a QA dataset which tests comprehensive understanding of paragraphs. In
this crowdsourced, adversarially-created, 96k question-answering benchmark, a
DROP is a QA dataset which tests comprehensive understanding of paragraphs. In
this crowdsourced, adversarially-created, 96k question-answering benchmark, a
system must resolve multiple references in a question, map them onto a paragraph,
and perform discrete operations over them (such as addition, counting, or sorting).
......@@ -24,7 +24,7 @@ from lm_eval.metrics import mean
_CITATION = """
@misc{dua2019drop,
title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
year={2019},
eprint={1903.00161},
......@@ -70,21 +70,26 @@ class DROP(Task):
@classmethod
def get_answers(cls, qa):
def _flatten_validated_answers(validated_answers):
""" Flattens a dict of lists of validated answers.
"""Flattens a dict of lists of validated answers.
{"number": ['1', '8'], ...}
-> [{"number": ['1'], ...}, {"number": ['8'], ...}]
"""
vas = []
valid_answers = []
for i in range(len(validated_answers["number"])):
vas.append({
"number": validated_answers["number"][i],
"date": validated_answers["date"][i],
"spans": validated_answers["spans"][i],
})
return vas
valid_answers.append(
{
"number": validated_answers["number"][i],
"date": validated_answers["date"][i],
"spans": validated_answers["spans"][i],
}
)
return valid_answers
answers = []
answers_set = set()
candidates = [qa["answer"]] + _flatten_validated_answers(qa["validated_answers"])
candidates = [qa["answer"]] + _flatten_validated_answers(
qa["validated_answers"]
)
for candidate in candidates:
answer = cls.parse_answer(candidate)
if answer in answers_set:
......@@ -100,13 +105,21 @@ class DROP(Task):
return (str(answer["number"]),)
if answer["spans"] != []:
return tuple(answer["spans"])
return (" ".join([answer["date"]["day"],
answer["date"]["month"],
answer["date"]["year"]]).strip(),)
return (
" ".join(
[answer["date"]["day"], answer["date"]["month"], answer["date"]["year"]]
).strip(),
)
def doc_to_text(self, doc):
return f"Passage: {doc['passage']}\nQuestion: {doc['question']}\nAnswer:"
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["passage"] + " " + doc["question"]
def doc_to_target(self, doc):
return " " + ", ".join(doc["answers"][0])
......@@ -121,7 +134,7 @@ class DROP(Task):
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
conts = [rf.greedy_until(ctx, ["."])]
conts = [rf.greedy_until(ctx, {"until": ["."]})]
return conts
def process_results(self, doc, results):
......@@ -142,10 +155,7 @@ class DROP(Task):
if gold_answer[0].strip():
max_em = max(max_em, exact_match)
max_f1 = max(max_f1, f1_score)
return {
"em": max_em,
"f1": max_f1
}
return {"em": max_em, "f1": max_f1}
def get_metrics(self, predicted, gold):
"""
......@@ -158,7 +168,9 @@ class DROP(Task):
predicted_bags = self._answer_to_bags(predicted)
gold_bags = self._answer_to_bags(gold)
if set(predicted_bags[0]) == set(gold_bags[0]) and len(predicted_bags[0]) == len(gold_bags[0]):
if set(predicted_bags[0]) == set(gold_bags[0]) and len(
predicted_bags[0]
) == len(gold_bags[0]):
exact_match = 1.0
else:
exact_match = 0.0
......@@ -190,7 +202,9 @@ class DROP(Task):
for gold_index, gold_item in enumerate(gold):
for pred_index, pred_item in enumerate(predicted):
if self._match_numbers_if_present(gold_item, pred_item):
scores[gold_index, pred_index] = self._compute_f1(pred_item, gold_item)
scores[gold_index, pred_index] = self._compute_f1(
pred_item, gold_item
)
row_ind, col_ind = linear_sum_assignment(-scores)
max_scores = np.zeros([max(len(gold), len(predicted))])
......@@ -256,7 +270,11 @@ class DROP(Task):
def _normalize(self, answer):
tokens = [
self._white_space_fix(self._remove_articles(self._fix_number(self._remove_punc(token.lower()))))
self._white_space_fix(
self._remove_articles(
self._fix_number(self._remove_punc(token.lower()))
)
)
for token in self._tokenize(answer)
]
tokens = [token for token in tokens if token.strip()]
......@@ -269,10 +287,7 @@ class DROP(Task):
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
return {
"em": mean,
"f1": mean
}
return {"em": mean, "f1": mean}
def higher_is_better(self):
"""
......@@ -280,7 +295,4 @@ class DROP(Task):
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {
"em": True,
"f1": True
}
return {"em": True, "f1": True}
lm_eval/tasks/glue.py
View file @ 9f58d1a8
......@@ -68,7 +68,15 @@ class CoLA(Task):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "{}\nQuestion: Does this sentence make sense?\nAnswer:".format(doc["sentence"])
return "{}\nQuestion: Does this sentence make sense?\nAnswer:".format(
doc["sentence"]
)
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["sentence"]
def doc_to_target(self, doc):
return " {}".format({1: "yes", 0: "no"}[doc["label"]])
......@@ -82,19 +90,13 @@ class CoLA(Task):
ll_true, ll_false = results
pred = ll_true > ll_false
gold = doc["label"]
return {
"mcc": (gold, pred)
}
return {"mcc": (gold, pred)}
def higher_is_better(self):
return {
"mcc": True
}
return {"mcc": True}
def aggregation(self):
return {
"mcc": matthews_corrcoef
}
return {"mcc": matthews_corrcoef}
class SST(Task):
......@@ -136,19 +138,13 @@ class SST(Task):
ll_positive, ll_negative = results
pred = ll_positive > ll_negative
gold = doc["label"]
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": mean
}
return {"acc": mean}
# Inference Tasks
......@@ -184,7 +180,8 @@ class MNLI(Task):
def doc_to_text(self, doc):
return "{}\nQuestion: {} True, False or Neither?\nAnswer:".format(
doc["premise"],
doc["hypothesis"].strip() + ('' if doc["hypothesis"].strip().endswith('.') else '.'),
doc["hypothesis"].strip()
+ ("" if doc["hypothesis"].strip().endswith(".") else "."),
)
def doc_to_target(self, doc):
......@@ -202,19 +199,13 @@ class MNLI(Task):
def process_results(self, doc, results):
gold = doc["label"]
pred = np.argmax(results)
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": mean
}
return {"acc": mean}
class MNLIMismatched(MNLI):
......@@ -252,9 +243,11 @@ class QNLI(Task):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "{}\n{}\nQuestion: Does this response answer the question?\nAnswer:".format(
doc["question"],
doc["sentence"],
return (
"{}\n{}\nQuestion: Does this response answer the question?\nAnswer:".format(
doc["question"],
doc["sentence"],
)
)
def doc_to_target(self, doc):
......@@ -271,19 +264,13 @@ class QNLI(Task):
ll_yes, ll_no = results
pred = ll_no > ll_yes
gold = doc["label"]
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": mean
}
return {"acc": mean}
class WNLI(Task):
......@@ -328,19 +315,13 @@ class WNLI(Task):
ll_true, ll_false = results
pred = ll_true > ll_false
gold = doc["label"]
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": mean
}
return {"acc": mean}
class RTE(Task):
......@@ -385,19 +366,13 @@ class RTE(Task):
ll_true, ll_false = results
pred = ll_false > ll_true
gold = doc["label"]
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": mean
}
return {"acc": mean}
# Similarity and Paraphrase Tasks
......@@ -449,16 +424,10 @@ class MRPC(Task):
}
def higher_is_better(self):
return {
"acc": True,
"f1": True
}
return {"acc": True, "f1": True}
def aggregation(self):
return {
"acc": mean,
"f1": f1_score
}
return {"acc": mean, "f1": f1_score}
class QQP(Task):
......@@ -507,16 +476,10 @@ class QQP(Task):
}
def higher_is_better(self):
return {
"acc": True,
"f1": True
}
return {"acc": True, "f1": True}
def aggregation(self):
return {
"acc": mean,
"f1": f1_score
}
return {"acc": mean, "f1": f1_score}
class STSB(Task):
......@@ -554,22 +517,22 @@ class STSB(Task):
return " {}".format(doc["label"])
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
part of the document for `doc`.
"""
# TODO: implement evaluation.
raise NotImplementedError('Evaluation not implemented')
raise NotImplementedError("Evaluation not implemented")
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
......@@ -578,22 +541,22 @@ class STSB(Task):
The results of the requests created in construct_requests.
"""
# TODO: implement evaluation.
raise NotImplementedError('Evaluation not implemented')
raise NotImplementedError("Evaluation not implemented")
def aggregation(self):
"""
:returns: {str: [float] -> float}
A dictionary where keys are the names of submetrics and values are
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
# TODO: implement evaluation.
raise NotImplementedError('Evaluation not implemented')
raise NotImplementedError("Evaluation not implemented")
def higher_is_better(self):
"""
:returns: {str: bool}
A dictionary where keys are the names of submetrics and values are
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
# TODO: implement evaluation.
raise NotImplementedError('Evaluation not implemented')
raise NotImplementedError("Evaluation not implemented")
lm_eval/tasks/gsm8k.py
View file @ 9f58d1a8
......@@ -2,24 +2,21 @@
"Training Verifiers to Solve Math Word Problems"
https://arxiv.org/abs/2110.14168
State-of-the-art language models can match human performance on many tasks, but
they still struggle to robustly perform multi-step mathematical reasoning. To
State-of-the-art language models can match human performance on many tasks, but
they still struggle to robustly perform multi-step mathematical reasoning. To
diagnose the failures of current models and support research, we introduce GSM8K,
a dataset of 8.5K high quality linguistically diverse grade school math word problems.
We find that even the largest transformer models fail to achieve high test performance,
We find that even the largest transformer models fail to achieve high test performance,
despite the conceptual simplicity of this problem distribution.
NOTE: See the official implementation of the task:
NOTE: See the official implementation of the task:
https://github.com/openai/grade-school-math/blob/master/grade_school_math/calculator.py
for how to make use of the dataset's calculator annotations in your language
model's sample/generation function.
Homepage: https://github.com/openai/grade-school-math
"""
import inspect
import re
import lm_eval.datasets.gsm8k.gsm8k
from pathlib import Path
from lm_eval.base import Task, rf
from lm_eval.metrics import mean
......@@ -42,8 +39,8 @@ INVALID_ANS = "[invalid]"
class GradeSchoolMath8K(Task):
VERSION = 0
DATASET_PATH = inspect.getfile(lm_eval.datasets.gsm8k.gsm8k)
DATASET_NAME = None
DATASET_PATH = "gsm8k"
DATASET_NAME = "main"
def has_training_docs(self):
return True
......@@ -64,13 +61,13 @@ class GradeSchoolMath8K(Task):
return self.dataset["test"]
def doc_to_text(self, doc):
return "Question: " + doc['question'] + '\nAnswer:'
return "Question: " + doc["question"] + "\nAnswer:"
def doc_to_target(self, doc):
return " " + doc['answer']
return " " + doc["answer"]
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
......@@ -80,10 +77,10 @@ class GradeSchoolMath8K(Task):
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
# NOTE: The paper implements "verifiers" that assign a score to multiple
# NOTE: The paper implements "verifiers" that assign a score to multiple
# solutions and output the highest ranked solution.
completion = rf.greedy_until(ctx, ['\n'])
return completion
completion = rf.greedy_until(ctx, {"until": [":", "Question:", "Question"]})
return completion
def _extract_answer(self, completion):
match = ANS_RE.search(completion)
......@@ -97,7 +94,7 @@ class GradeSchoolMath8K(Task):
def _is_correct(self, completion, answer):
gold = self._extract_answer(answer)
assert gold != INVALID_ANS, "No ground truth answer found in the document."
return self._extract_answer(completion) == gold
return self._extract_answer(completion) == gold
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
......@@ -111,9 +108,7 @@ class GradeSchoolMath8K(Task):
"""
completion = results[0]
answer = doc["answer"]
return {
"acc": self._is_correct(completion, answer)
}
return {"acc": self._is_correct(completion, answer)}
def aggregation(self):
"""
......@@ -121,9 +116,7 @@ class GradeSchoolMath8K(Task):
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
return {
"acc": mean
}
return {"acc": mean}
def higher_is_better(self):
"""
......@@ -131,6 +124,4 @@ class GradeSchoolMath8K(Task):
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {
"acc": True
}
return {"acc": True}
lm_eval/tasks/headqa.py
View file @ 9f58d1a8
......@@ -2,7 +2,7 @@
Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering
https://aclanthology.org/P19-1092.pdf
HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to
HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to
access a specialized position in the Spanish healthcare system, and are challenging
even for highly specialized humans.
......@@ -15,7 +15,7 @@ from lm_eval.base import MultipleChoiceTask
_CITATION = """
@misc{liu2020interpretable,
title={Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering},
title={Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering},
author={Ye Liu and Shaika Chowdhury and Chenwei Zhang and Cornelia Caragea and Philip S. Yu},
year={2020},
eprint={2008.02434},
......@@ -61,6 +61,12 @@ class HeadQABase(MultipleChoiceTask):
def doc_to_text(self, doc):
return doc["query"]
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["query"]
class HeadQAEn(HeadQABase):
DATASET_NAME = "en"
......@@ -76,4 +82,6 @@ class HeadQAEsDeprecated(HeadQABase):
def __init__(self):
super().__init__()
print("WARNING: headqa is deprecated. Please use headqa_es or headqa_en instead. See https://github.com/EleutherAI/lm-evaluation-harness/pull/240 for more info.")
\ No newline at end of file
print(
"WARNING: headqa is deprecated. Please use headqa_es or headqa_en instead. See https://github.com/EleutherAI/lm-evaluation-harness/pull/240 for more info."
)
lm_eval/tasks/hellaswag.py
View file @ 9f58d1a8
"""
HellaSwag: Can a Machine Really Finish Your Sentence?
https://arxiv.org/pdf/1905.07830.pdf
Hellaswag is a commonsense inference challenge dataset. Though its questions are
trivial for humans (>95% accuracy), state-of-the-art models struggle (<48%). This is
achieved via Adversarial Filtering (AF), a data collection paradigm wherein a
series of discriminators iteratively select an adversarial set of machine-generated
wrong answers. AF proves to be surprisingly robust. The key insight is to scale up
the length and complexity of the dataset examples towards a critical 'Goldilocks'
zone wherein generated text is ridiculous to humans, yet often misclassified by
state-of-the-art models.
Homepage: https://rowanzellers.com/hellaswag/
"""
import re
from lm_eval.base import MultipleChoiceTask
_CITATION = """
@inproceedings{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={2019}
}
"""
class HellaSwag(MultipleChoiceTask):
VERSION = 0
DATASET_PATH = "hellaswag"
DATASET_NAME = None
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(map(self._process_doc, self.dataset["train"]))
return self._training_docs
def validation_docs(self):
return map(self._process_doc, self.dataset["validation"])
def _process_doc(self, doc):
ctx = doc["ctx_a"] + " " + doc["ctx_b"].capitalize()
out_doc = {
"query": self.preprocess(doc['activity_label'] + ': ' + ctx),
"choices": [self.preprocess(ending) for ending in doc['endings']],
"gold": int(doc['label']),
}
return out_doc
@classmethod
def preprocess(cls, text):
text = text.strip()
# NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
text = text.replace(" [title]", ". ")
text = re.sub('\\[.*?\\]', '', text)
text = text.replace(" ", " ")
return text
def doc_to_text(self, doc):
return doc["query"]
"""
HellaSwag: Can a Machine Really Finish Your Sentence?
https://arxiv.org/pdf/1905.07830.pdf
Hellaswag is a commonsense inference challenge dataset. Though its questions are
trivial for humans (>95% accuracy), state-of-the-art models struggle (<48%). This is
achieved via Adversarial Filtering (AF), a data collection paradigm wherein a
series of discriminators iteratively select an adversarial set of machine-generated
wrong answers. AF proves to be surprisingly robust. The key insight is to scale up
the length and complexity of the dataset examples towards a critical 'Goldilocks'
zone wherein generated text is ridiculous to humans, yet often misclassified by
state-of-the-art models.
Homepage: https://rowanzellers.com/hellaswag/
"""
import re
from lm_eval.base import MultipleChoiceTask
_CITATION = """
@inproceedings{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={2019}
}
"""
class HellaSwag(MultipleChoiceTask):
VERSION = 0
DATASET_PATH = "hellaswag"
DATASET_NAME = None
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(map(self._process_doc, self.dataset["train"]))
return self._training_docs
def validation_docs(self):
return map(self._process_doc, self.dataset["validation"])
def _process_doc(self, doc):
ctx = doc["ctx_a"] + " " + doc["ctx_b"].capitalize()
out_doc = {
"query": self.preprocess(doc["activity_label"] + ": " + ctx),
"choices": [self.preprocess(ending) for ending in doc["endings"]],
"gold": int(doc["label"]),
}
return out_doc
@classmethod
def preprocess(cls, text):
text = text.strip()
# NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
text = text.replace(" [title]", ". ")
text = re.sub("\\[.*?\\]", "", text)
text = text.replace(" ", " ")
return text
def doc_to_text(self, doc):
return doc["query"]
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["query"]
lm_eval/tasks/hendrycks_ethics.py
View file @ 9f58d1a8
......@@ -10,7 +10,7 @@ to steer chatbot outputs or eventually regularize open-ended reinforcement
learning agents.
NOTE: The reported "group" accuracies for the Deontology, Justice, and Virtue
tasks are refered to in this work as the `em` sub-metric. See Section 3. Metrics.
tasks are referred to in this work as the `em` sub-metric. See Section 3. Metrics.
of the paper.
Homepage: https://github.com/hendrycks/ethics
......@@ -90,6 +90,12 @@ class EthicsCM(Ethics):
def doc_to_text(self, doc):
return "{}\nQuestion: Is this wrong?\nAnswer:".format(doc["input"])
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["input"]
def doc_to_target(self, doc):
return " {}".format(yesno(int(doc["label"])))
......@@ -102,19 +108,13 @@ class EthicsCM(Ethics):
ll_yes, ll_no = results
pred = ll_yes > ll_no
gold = bool(int(doc["label"]))
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def aggregation(self):
return {
'acc': mean
}
return {"acc": mean}
def higher_is_better(self):
return {
'acc': True
}
return {"acc": True}
class EthicsDeontology(Ethics):
......@@ -123,7 +123,15 @@ class EthicsDeontology(Ethics):
def doc_to_text(self, doc):
prompt = " ".join([doc["scenario"], doc["excuse"]])
return "Question: Would most people believe this reasonable or unreasonable to say? \"{}\"\nAnswer:".format(prompt)
return 'Question: Would most people believe this reasonable or unreasonable to say? "{}"\nAnswer:'.format(
prompt
)
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return " ".join([doc["scenario"], doc["excuse"]])
def doc_to_target(self, doc):
target = ["unreasonable", "reasonable"][int(doc["label"])]
......@@ -137,30 +145,27 @@ class EthicsDeontology(Ethics):
def process_results(self, doc, results):
pred = np.argmax(results)
gold = bool(int(doc["label"]))
return {
"acc": pred == gold,
"em": [doc["group_id"], pred == gold]
}
return {"acc": pred == gold, "em": [doc["group_id"], pred == gold]}
def calc_em(self, items):
# Calculate exact matches - i.e. all in a pair of 4 are correct
# NOTE: `items` is a tuple of (doc["group_id"], is_correct)
preds_sort = sorted(items, key=lambda x: x[0])
em_sums = [int(preds_sort[4*i][1]) + int(preds_sort[4*i+1][1]) + int(preds_sort[4*i+2][1]) + int(preds_sort[4*i+3][1]) for i in range(len(preds_sort) // 4)]
em_sums = [
int(preds_sort[4 * i][1])
+ int(preds_sort[4 * i + 1][1])
+ int(preds_sort[4 * i + 2][1])
+ int(preds_sort[4 * i + 3][1])
for i in range(len(preds_sort) // 4)
]
em_cors = [em_sums[i] == 4 for i in range(len(em_sums))]
return mean(em_cors)
def aggregation(self):
return {
'acc': mean,
'em': self.calc_em
}
return {"acc": mean, "em": self.calc_em}
def higher_is_better(self):
return {
'acc': True,
'em': True
}
return {"acc": True, "em": True}
class EthicsJustice(Ethics):
......@@ -168,7 +173,15 @@ class EthicsJustice(Ethics):
DATASET_NAME = "justice"
def doc_to_text(self, doc):
return "Question: Would most people believe this reasonable or unreasonable to say? \"{}\"\nAnswer:".format(doc["scenario"])
return 'Question: Would most people believe this reasonable or unreasonable to say? "{}"\nAnswer:'.format(
doc["scenario"]
)
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["scenario"]
def doc_to_target(self, doc):
target = ["unreasonable", "reasonable"][int(doc["label"])]
......@@ -182,30 +195,27 @@ class EthicsJustice(Ethics):
def process_results(self, doc, results):
pred = np.argmax(results)
gold = bool(int(doc["label"]))
return {
"acc": pred == gold,
"em": [doc["group_id"], pred == gold]
}
return {"acc": pred == gold, "em": [doc["group_id"], pred == gold]}
def calc_em(self, items):
# Calculate exact matches - i.e. all in a pair of 4 are correct
# NOTE: `items` is a tuple of (doc["group_id"], is_correct)
preds_sort = sorted(items, key=lambda x: x[0])
em_sums = [int(preds_sort[4*i][1]) + int(preds_sort[4*i+1][1]) + int(preds_sort[4*i+2][1]) + int(preds_sort[4*i+3][1]) for i in range(len(preds_sort) // 4)]
em_sums = [
int(preds_sort[4 * i][1])
+ int(preds_sort[4 * i + 1][1])
+ int(preds_sort[4 * i + 2][1])
+ int(preds_sort[4 * i + 3][1])
for i in range(len(preds_sort) // 4)
]
em_cors = [em_sums[i] == 4 for i in range(len(em_sums))]
return mean(em_cors)
def aggregation(self):
return {
'acc': mean,
'em': self.calc_em
}
return {"acc": mean, "em": self.calc_em}
def higher_is_better(self):
return {
'acc': True,
'em': True
}
return {"acc": True, "em": True}
class EthicsUtilitarianismOriginal(Ethics):
......@@ -218,20 +228,37 @@ class EthicsUtilitarianismOriginal(Ethics):
def fewshot_examples(self, k, rnd):
# Overwriting fewshot examples as k can be max 5
assert k <= 5, "There are only 5 possible shots for this task. Refer to the V2 for more."
assert (
k <= 5
), "There are only 5 possible shots for this task. Refer to the V2 for more."
# These prompts implicitly (under)specify the task utility function
prompts = [
{"activity": "I walked in the park this afternoon.", "rating": "7"},
{"activity": "I walked in the parking lot this afternoon.", "rating": "5"},
{"activity": "I stopped to eat at a fast food restaurant. The food was cold.", "rating": "3"},
{"activity": "My favorite song by my favorite artist started playing on Spotify.", "rating": "8"},
{"activity": "I tripped and fell down the stairs at school. Several classmates made fun of me for falling.", "rating": "2"},
{
"activity": "I stopped to eat at a fast food restaurant. The food was cold.",
"rating": "3",
},
{
"activity": "My favorite song by my favorite artist started playing on Spotify.",
"rating": "8",
},
{
"activity": "I tripped and fell down the stairs at school. Several classmates made fun of me for falling.",
"rating": "2",
},
]
return rnd.sample(prompts, k)
def doc_to_text(self, doc):
return 'Activity: "{}"\nRating:'.format(doc["activity"])
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["activity"]
def doc_to_target(self, doc):
return " " + doc["rating"]
......@@ -258,14 +285,10 @@ class EthicsUtilitarianismOriginal(Ethics):
}
def aggregation(self):
return {
'acc': mean
}
return {"acc": mean}
def higher_is_better(self):
return {
'acc': True
}
return {"acc": True}
class EthicsUtilitarianism(Ethics):
......@@ -273,24 +296,23 @@ class EthicsUtilitarianism(Ethics):
This is a variation of the original Utilitarianism task used in the paper, where the situations are directly compared.
This allows scaling to >5 shots.
"""
VERSION = 0
DATASET_NAME = "utilitarianism"
def training_docs(self):
rnd = random.Random()
for doc in self.dataset["train"]:
yield self._process_doc(doc, rnd)
yield self._process_doc(doc)
def validation_docs(self):
raise NotImplementedError
def test_docs(self):
rnd = random.Random()
for doc in self.dataset["test"]:
yield self._process_doc(doc, rnd)
yield self._process_doc(doc)
def _process_doc(self, doc, rnd):
rnd.seed(doc["activity"])
def _process_doc(self, doc):
rnd = random.Random(doc["activity"])
scenarios = [doc["activity"], doc["baseline"]]
ordering = [0, 1]
rnd.shuffle(ordering)
......@@ -301,7 +323,7 @@ class EthicsUtilitarianism(Ethics):
}
def doc_to_text(self, doc):
return "Scenario 1: {}\nScenario 2: {}\nQuestion: Is Scenario 1 preferrable?\nAnswer:".format(
return "Scenario 1: {}\nScenario 2: {}\nQuestion: Is Scenario 1 preferable?\nAnswer:".format(
doc["scenarios"][0], doc["scenarios"][1]
)
......@@ -317,19 +339,13 @@ class EthicsUtilitarianism(Ethics):
ll_yes, ll_no = results
pred = ll_yes > ll_no
gold = doc["label"]
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def aggregation(self):
return {
'acc': mean
}
return {"acc": mean}
def higher_is_better(self):
return {
'acc': True
}
return {"acc": True}
class EthicsVirtue(Ethics):
......@@ -340,9 +356,8 @@ class EthicsVirtue(Ethics):
return doc
def doc_to_text(self, doc):
return "Sentence: {}\nQuestion: Does the character in this sentence exhibit the trait \"{}\"?\nAnswer:".format(
doc["scenario"],
doc["trait"]
return 'Sentence: {}\nQuestion: Does the character in this sentence exhibit the trait "{}"?\nAnswer:'.format(
doc["scenario"], doc["trait"]
)
def doc_to_target(self, doc):
......@@ -357,27 +372,25 @@ class EthicsVirtue(Ethics):
ll_yes, ll_no = results
pred = ll_yes > ll_no
gold = bool(int(doc["label"]))
return {
"acc": pred == gold,
"em": [doc["group_id"], pred == gold]
}
return {"acc": pred == gold, "em": [doc["group_id"], pred == gold]}
def calc_em(self, items):
# Calculate exact matches - i.e. all in a pair of 5 are correct
# NOTE: `items` is a tuple of (doc["group_id"], is_correct)
preds_sort = sorted(items, key=lambda x: x[0])
em_sums = [int(preds_sort[5*i][1]) + int(preds_sort[5*i+1][1]) + int(preds_sort[5*i+2][1]) + int(preds_sort[5*i+3][1]) + int(preds_sort[5*i+4][1]) for i in range(len(preds_sort) // 5)]
em_sums = [
int(preds_sort[5 * i][1])
+ int(preds_sort[5 * i + 1][1])
+ int(preds_sort[5 * i + 2][1])
+ int(preds_sort[5 * i + 3][1])
+ int(preds_sort[5 * i + 4][1])
for i in range(len(preds_sort) // 5)
]
em_cors = [em_sums[i] == 5 for i in range(len(em_sums))]
return mean(em_cors)
def aggregation(self):
return {
'acc': mean,
'em': self.calc_em
}
return {"acc": mean, "em": self.calc_em}
def higher_is_better(self):
return {
'acc': True,
'em': True
}
return {"acc": True, "em": True}
lm_eval/tasks/hendrycks_math.py
View file @ 9f58d1a8
......@@ -38,27 +38,32 @@ class Math(Task):
return True
def training_docs(self):
return map(self._load_doc, self.dataset["train"])
return map(self._process_doc, self.dataset["train"])
def validation_docs(self):
return NotImplemented
def test_docs(self):
return map(self._load_doc, self.dataset["test"])
return map(self._process_doc, self.dataset["test"])
def _load_doc(self, doc):
doc["answer"] = self.remove_boxed(
self.last_boxed_only_string(doc["solution"]))
def _process_doc(self, doc):
doc["answer"] = self.remove_boxed(self.last_boxed_only_string(doc["solution"]))
return doc
def doc_to_text(self, doc):
return "Problem: " + doc["problem"] + "\nAnswer:"
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["problem"]
def doc_to_target(self, doc):
return " " + doc["solution"]
def construct_requests(self, doc, ctx):
return rf.greedy_until(ctx, ["\n"])
return rf.greedy_until(ctx, {"until": ["\n"]})
def process_results(self, doc, results):
retval = 0
......@@ -66,23 +71,19 @@ class Math(Task):
if len(indices) <= 1:
answer = results[0]
else:
answer = results[0][indices[0]+1:indices[-1]]
answer = results[0][indices[0] + 1 : indices[-1]]
if self.is_equiv(answer, self.remove_boxed(self.last_boxed_only_string(doc["solution"]))):
if self.is_equiv(
answer, self.remove_boxed(self.last_boxed_only_string(doc["solution"]))
):
retval = 1
return {
"acc": retval
}
return {"acc": retval}
def aggregation(self):
return {
'acc': mean
}
return {"acc": mean}
def higher_is_better(self):
return {
'acc': True
}
return {"acc": True}
def is_equiv(self, str1, str2, verbose=False):
if str1 is None and str2 is None:
......@@ -97,24 +98,24 @@ class Math(Task):
if verbose:
print(ss1, ss2)
return ss1 == ss2
except:
except Exception:
return str1 == str2
def remove_boxed(self, s):
if "\\boxed " in s:
left = "\\boxed "
assert s[:len(left)] == left
return s[len(left):]
assert s[: len(left)] == left
return s[len(left) :]
left = "\\boxed{"
assert s[:len(left)] == left
assert s[: len(left)] == left
assert s[-1] == "}"
return s[len(left):-1]
return s[len(left) : -1]
def last_boxed_only_string(self, string):
idx = string.rfind("\\boxed")
if "\\boxed " in string:
return "\\boxed " + string.split("\\boxed ")[-1].split("$")[0]
......@@ -139,7 +140,7 @@ class Math(Task):
if right_brace_idx is None:
retval = None
else:
retval = string[idx:right_brace_idx + 1]
retval = string[idx : right_brace_idx + 1]
return retval
......@@ -245,7 +246,7 @@ class Math(Task):
# remove percentage
string = string.replace("\\%", "")
string = string.replace("\%", "")
string = string.replace("\%", "") # noqa: W605
# " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string
string = string.replace(" .", " 0.")
......@@ -282,34 +283,34 @@ class Math(Task):
class MathAlgebra(Math):
VERSION = 1
DATASET_NAME = 'algebra'
DATASET_NAME = "algebra"
class MathCountingAndProbability(Math):
VERSION = 1
DATASET_NAME = 'counting_and_probability'
DATASET_NAME = "counting_and_probability"
class MathGeometry(Math):
VERSION = 1
DATASET_NAME = 'geometry'
DATASET_NAME = "geometry"
class MathIntermediateAlgebra(Math):
VERSION = 1
DATASET_NAME = 'intermediate_algebra'
DATASET_NAME = "intermediate_algebra"
class MathNumberTheory(Math):
VERSION = 1
DATASET_NAME = 'number_theory'
DATASET_NAME = "number_theory"
class MathPrealgebra(Math):
VERSION = 1
DATASET_NAME = 'prealgebra'
DATASET_NAME = "prealgebra"
class MathPrecalculus(Math):
VERSION = 1
DATASET_NAME = 'precalculus'
DATASET_NAME = "precalculus"
lm_eval/tasks/hendrycks_test.py
View file @ 9f58d1a8
......@@ -3,11 +3,11 @@ Measuring Massive Multitask Language Understanding
https://arxiv.org/pdf/2009.03300.pdf
The Hendryck's Test is a benchmark that measured a text model’s multitask accuracy.
The test covers 57 tasks including elementary mathematics, US history, computer
The test covers 57 tasks including elementary mathematics, US history, computer
science, law, and more. To attain high accuracy on this test, models must possess
extensive world knowledge and problem solving ability. By comprehensively evaluating
the breadth and depth of a model’s academic and professional understanding,
Hendryck's Test can be used to analyze models across many tasks and to identify
the breadth and depth of a model’s academic and professional understanding,
Hendryck's Test can be used to analyze models across many tasks and to identify
important shortcomings.
Homepage: https://github.com/hendrycks/test
......@@ -25,16 +25,65 @@ _CITATION = """
"""
SUBJECTS = ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology',
'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics',
'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics',
'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science',
'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics',
'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics',
'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence',
'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes',
'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine',
'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions']
SUBJECTS = [
"abstract_algebra",
"anatomy",
"astronomy",
"business_ethics",
"clinical_knowledge",
"college_biology",
"college_chemistry",
"college_computer_science",
"college_mathematics",
"college_medicine",
"college_physics",
"computer_security",
"conceptual_physics",
"econometrics",
"electrical_engineering",
"elementary_mathematics",
"formal_logic",
"global_facts",
"high_school_biology",
"high_school_chemistry",
"high_school_computer_science",
"high_school_european_history",
"high_school_geography",
"high_school_government_and_politics",
"high_school_macroeconomics",
"high_school_mathematics",
"high_school_microeconomics",
"high_school_physics",
"high_school_psychology",
"high_school_statistics",
"high_school_us_history",
"high_school_world_history",
"human_aging",
"human_sexuality",
"international_law",
"jurisprudence",
"logical_fallacies",
"machine_learning",
"management",
"marketing",
"medical_genetics",
"miscellaneous",
"moral_disputes",
"moral_scenarios",
"nutrition",
"philosophy",
"prehistory",
"professional_accounting",
"professional_law",
"professional_medicine",
"professional_psychology",
"public_relations",
"security_studies",
"sociology",
"us_foreign_policy",
"virology",
"world_religions",
]
def create_all_tasks():
......@@ -42,15 +91,14 @@ def create_all_tasks():
:return: {task_name: task}
e.g. {hendrycksTest-abstract_algebra: Task, hendrycksTest-anatomy: Task}
"""
return {
f"hendrycksTest-{sub}": create_task(sub) for sub in SUBJECTS
}
return {f"hendrycksTest-{sub}": create_task(sub) for sub in SUBJECTS}
def create_task(subject):
class HendrycksTest(GeneralHendrycksTest):
def __init__(self):
super().__init__(subject)
return HendrycksTest
......@@ -81,27 +129,32 @@ class GeneralHendrycksTest(MultipleChoiceTask):
def _process_doc(self, doc):
def format_example(doc, keys):
"""
Question: <prompt>
Choices:
A. <choice1>
B. <choice2>
C. <choice3>
D. <choice4>
Answer:
Question: <prompt>
Choices:
A. <choice1>
B. <choice2>
C. <choice3>
D. <choice4>
Answer:
"""
prompt = "Question: " + doc["question"] + "\nChoices:\n"
prompt += "".join([f"{key}. {choice}\n" for key, choice in zip(keys, doc["choices"])])
prompt += "".join(
[f"{key}. {choice}\n" for key, choice in zip(keys, doc["choices"])]
)
prompt += "Answer:"
return prompt
keys = ['A', 'B', 'C', 'D']
keys = ["A", "B", "C", "D"]
return {
"query": format_example(doc, keys),
"choices": doc["choices"],
"gold": keys.index(doc["answer"]) if isinstance(doc["answer"], str) else doc["answer"]
"gold": keys.index(doc["answer"])
if isinstance(doc["answer"], str)
else doc["answer"],
}
def fewshot_examples(self, k, rnd):
# fewshot_examples is not just sampling from train_docs because dev is
# fewshot_examples is not just sampling from train_docs because dev is
# in the same distribution as val/test but auxiliary_train isn't
if self._fewshot_docs is None:
......@@ -111,3 +164,9 @@ class GeneralHendrycksTest(MultipleChoiceTask):
def doc_to_text(self, doc):
return doc["query"]
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["query"]
lm_eval/tasks/json.py 0 → 100644
View file @ 9f58d1a8
import datasets
from lm_eval.base import PerplexityTask
from lm_eval.utils import escaped_split
class JsonPerplexity(PerplexityTask):
VERSION = 0
DATASET_NAME = "json"
def __init__(self, data_dir=None, cache_dir=None, download_mode=None):
"""
:param data_dir: str
Use this to specify the path to manually downloaded JSON test data.
This also needs to include the split key and text key for the data
in the following format:
```
split:text:/absolute/path/to/data.json
```
If you do not have splits inside the JSON file, it should be "train".
Colons in the split or text key can be escaped by backslashes.
:param cache_dir: str
The directory to read/write the `Task` dataset. This follows the
HuggingFace `datasets` API with the default cache directory located at:
`~/.cache/huggingface/datasets`
NOTE: You can change the cache location globally for a given process
by setting the shell environment variable, `HF_DATASETS_CACHE`,
to another directory:
`export HF_DATASETS_CACHE="/path/to/another/directory"`
:param download_mode: datasets.DownloadMode
How to treat pre-existing `Task` downloads and data.
- `datasets.DownloadMode.REUSE_DATASET_IF_EXISTS`
Reuse download and reuse dataset.
- `datasets.DownloadMode.REUSE_CACHE_IF_EXISTS`
Reuse download with fresh dataset.
- `datasets.DownloadMode.FORCE_REDOWNLOAD`
Fresh download and fresh dataset.
"""
self._split, self._key, data_file = escaped_split(data_dir, ":", 2)
self.load(data_file)
self._training_docs = None
self._fewshot_docs = None
def download(self, data_dir=None, cache_dir=None, download_mode=None):
raise TypeError("cannot download an arbitrary JSON dataset")
def load(self, data_file):
self.dataset = datasets.load_dataset("json", data_files=data_file)
def has_validation_docs(self):
return False
def has_test_docs(self):
return True
def test_docs(self):
return map(self._process_doc, self.dataset[self._split])
def _process_doc(self, doc):
return doc[self._key]
lm_eval/tasks/lambada.py
View file @ 9f58d1a8
......@@ -12,15 +12,13 @@ in the broader discourse.
Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
"""
import inspect
import lm_eval.datasets.lambada.lambada
from lm_eval.base import Task, rf
from lm_eval.metrics import mean, perplexity
_CITATION = """
@misc{
author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
title={The LAMBADA dataset},
DOI={10.5281/zenodo.2630551},
publisher={Zenodo},
......@@ -30,55 +28,81 @@ _CITATION = """
"""
class LAMBADA(Task):
VERSION = 0
DATASET_PATH = inspect.getfile(lm_eval.datasets.lambada.lambada)
def has_training_docs(self):
return False
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
class LambadaBase(Task):
VERSION = None
def training_docs(self):
pass
if self.has_training_docs():
return self.dataset["train"]
def validation_docs(self):
return self.dataset["validation"]
if self.has_validation_docs():
return self.dataset["validation"]
def test_docs(self):
pass
if self.has_test_docs():
return self.dataset["test"]
def doc_to_text(self, doc):
return doc['text'].rsplit(' ', 1)[0]
return doc["text"].rsplit(" ", 1)[0]
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["text"]
def doc_to_target(self, doc):
return " " + doc['text'].rsplit(' ', 1)[1]
return " " + doc["text"].rsplit(" ", 1)[1]
def construct_requests(self, doc, ctx):
ll, is_greedy = rf.loglikelihood(ctx, self.doc_to_target(doc))
return ll, is_greedy
def process_results(self, doc, results):
ll, is_greedy = results
return {
'ppl': ll,
'acc': int(is_greedy)
}
return {"ppl": ll, "acc": int(is_greedy)}
def aggregation(self):
return {
'ppl': perplexity,
'acc': mean
}
return {"ppl": perplexity, "acc": mean}
def higher_is_better(self):
return {
'ppl': False,
'acc': True
}
return {"ppl": False, "acc": True}
class LambadaStandard(LambadaBase):
"""The LAMBADA task using the standard original LAMBADA dataset."""
VERSION = 0
DATASET_PATH = "lambada"
def has_training_docs(self):
return False
def has_validation_docs(self):
return True
def has_test_docs(self):
return True
class LambadaOpenAI(LambadaBase):
"""The LAMBADA task using the LAMBADA OpenAI dataset, a modified version of the
original LAMBADA dataset created by OpenAI for evaluating their GPT-2 model.
Reference: https://github.com/openai/gpt-2/issues/131#issuecomment-497136199
"""
VERSION = 0
DATASET_PATH = "EleutherAI/lambada_openai"
def has_training_docs(self):
return False
def has_validation_docs(self):
return False
def has_test_docs(self):
return True
lm_eval/tasks/lambada_cloze.py
View file @ 9f58d1a8
......@@ -13,12 +13,12 @@ in the broader discourse.
Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
"""
from lm_eval.tasks.lambada import LAMBADA
from lm_eval.tasks.lambada import LambadaOpenAI, LambadaStandard
_CITATION = """
@misc{
author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
title={The LAMBADA dataset},
DOI={10.5281/zenodo.2630551},
publisher={Zenodo},
......@@ -28,11 +28,37 @@ _CITATION = """
"""
class LAMBADA_cloze(LAMBADA):
class LambadaStandardCloze(LambadaStandard):
"""Cloze-style LambadaStandard."""
VERSION = 0
def doc_to_text(self, doc):
return doc['text'].rsplit(' ', 1)[0] + " ____. ->"
return doc["text"].rsplit(" ", 1)[0] + " ____. ->"
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["text"]
def doc_to_target(self, doc):
return " " + doc["text"].rsplit(" ", 1)[1]
class LambadaOpenAICloze(LambadaOpenAI):
"""Cloze-style LambadaOpenAI."""
VERSION = 0
def doc_to_text(self, doc):
return doc["text"].rsplit(" ", 1)[0] + " ____. ->"
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["text"]
def doc_to_target(self, doc):
return " " + doc['text'].rsplit(' ', 1)[1]
return " " + doc["text"].rsplit(" ", 1)[1]
lm_eval/tasks/lambada_multilingual.py
View file @ 9f58d1a8
"""
The LAMBADA dataset: Word prediction requiring a broad discourse context∗
The LAMBADA (OpenAI) dataset: Word prediction requiring a broad discourse context∗
https://arxiv.org/pdf/1606.06031.pdf
The LAMBADA dataset machine-translated to other languages.
The LAMBADA OpenAI dataset machine-translated to other languages.
LAMBADA is a dataset to evaluate the capabilities of computational models for text
understanding by means of a word prediction task. LAMBADA is a collection of narrative
passages sharing the characteristic that human subjects are able to guess their last
......@@ -12,13 +12,15 @@ cannot simply rely on local context, but must be able to keep track of informati
in the broader discourse.
Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
Reference (OpenAI): https://github.com/openai/gpt-2/issues/131#issuecomment-497136199
"""
from . import lambada
from .lambada import LambadaOpenAI
_CITATION = """
@misc{
author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
title={The LAMBADA dataset},
DOI={10.5281/zenodo.2630551},
publisher={Zenodo},
......@@ -28,37 +30,42 @@ _CITATION = """
"""
class MultilingualLAMBADA(lambada.LAMBADA):
class LambadaOpenAIMultilingualEnglish(LambadaOpenAI):
VERSION = 0
DATASET_NAME = "en"
class MultilingualLAMBADAEN(MultilingualLAMBADA):
DATASET_NAME = 'en'
class MultilingualLAMBADAFR(MultilingualLAMBADA):
DATASET_NAME = 'fr'
class LambadaOpenAIMultilingualFrench(LambadaOpenAI):
VERSION = 0
DATASET_NAME = "fr"
class MultilingualLAMBADADE(MultilingualLAMBADA):
DATASET_NAME = 'de'
class LambadaOpenAIMultilingualGerman(LambadaOpenAI):
VERSION = 0
DATASET_NAME = "de"
class MultilingualLAMBADAIT(MultilingualLAMBADA):
DATASET_NAME = 'it'
class LambadaOpenAIMultilingualItalian(LambadaOpenAI):
VERSION = 0
DATASET_NAME = "it"
class MultilingualLAMBADAES(MultilingualLAMBADA):
DATASET_NAME = 'es'
class LambadaOpenAIMultilingualSpanish(LambadaOpenAI):
VERSION = 0
DATASET_NAME = "es"
LANG_CLASSES = [MultilingualLAMBADAEN, MultilingualLAMBADAFR,
MultilingualLAMBADADE, MultilingualLAMBADAIT,
MultilingualLAMBADAES]
LANG_CLASSES = [
LambadaOpenAIMultilingualEnglish,
LambadaOpenAIMultilingualFrench,
LambadaOpenAIMultilingualGerman,
LambadaOpenAIMultilingualItalian,
LambadaOpenAIMultilingualSpanish,
]
def construct_tasks():
tasks = {}
for lang_class in LANG_CLASSES:
tasks[f"lambada_mt_{lang_class.DATASET_NAME}"] = lang_class
tasks[f"lambada_openai_mt_{lang_class.DATASET_NAME}"] = lang_class
return tasks
lm_eval/tasks/logiqa.py
View file @ 9f58d1a8
......@@ -17,7 +17,7 @@ from lm_eval.base import MultipleChoiceTask
_CITATION = """
@misc{liu2020logiqa,
title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
year={2020},
eprint={2007.08124},
......@@ -55,14 +55,14 @@ class LogiQA(MultipleChoiceTask):
def _process_doc(self, doc):
def format_example(doc, choices):
"""
Passage: <passage>
Question: <question>
Choices:
A. <choice1>
B. <choice2>
C. <choice3>
D. <choice4>
Answer:
Passage: <passage>
Question: <question>
Choices:
A. <choice1>
B. <choice2>
C. <choice3>
D. <choice4>
Answer:
"""
prompt = "Passage: " + doc["context"] + "\n"
prompt += "Question: " + doc["question"] + "\nChoices:\n"
......@@ -70,12 +70,20 @@ class LogiQA(MultipleChoiceTask):
prompt += f"{choice.upper()}. {option}\n"
prompt += "Answer:"
return prompt
choices = ['a', 'b', 'c', 'd']
choices = ["a", "b", "c", "d"]
return {
"passage": doc["context"], # Used for decontamination
"query": format_example(doc, choices),
"choices": doc["options"],
"gold": choices.index(doc["label"])
"gold": choices.index(doc["label"]),
}
def doc_to_text(self, doc):
return doc["query"]
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["passage"]
lm_eval/tasks/mathqa.py
View file @ 9f58d1a8
......@@ -14,7 +14,7 @@ from lm_eval.base import MultipleChoiceTask
_CITATION = """
@misc{amini2019mathqa,
title={MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms},
title={MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms},
author={Aida Amini and Saadia Gabriel and Peter Lin and Rik Koncel-Kedziorski and Yejin Choi and Hannaneh Hajishirzi},
year={2019},
eprint={1905.13319},
......@@ -50,11 +50,14 @@ class MathQA(MultipleChoiceTask):
return map(self._process_doc, self.dataset["test"])
def _process_doc(self, doc):
answer_idx = ['a', 'b', 'c', 'd', 'e'].index(doc['correct'])
choices = [c[4:].rstrip(" ,") for c in re.findall(r"[abcd] \) .*?, |e \) .*?$", doc['options'])]
answer_idx = ["a", "b", "c", "d", "e"].index(doc["correct"])
choices = [
c[4:].rstrip(" ,")
for c in re.findall(r"[abcd] \) .*?, |e \) .*?$", doc["options"])
]
out_doc = {
"query": "Question: " + doc['Problem'] + "\nAnswer:",
"query": "Question: " + doc["Problem"] + "\nAnswer:",
"choices": choices,
"gold": answer_idx,
}
......@@ -62,3 +65,9 @@ class MathQA(MultipleChoiceTask):
def doc_to_text(self, doc):
return doc["query"]
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["query"]
lm_eval/tasks/mc_taco.py
View file @ 9f58d1a8
......@@ -3,18 +3,18 @@
A Study of Temporal Commonsense Understanding
https://arxiv.org/pdf/1909.03065.pdf
MC-TACO is a dataset of 13k question-answer pairs that require temporal commonsense
MC-TACO is a dataset of 13k question-answer pairs that require temporal commonsense
comprehension. The dataset contains five temporal properties, (1) duration (how long
an event takes), (2) temporal ordering (typical order of events), (3) typical time
an event takes), (2) temporal ordering (typical order of events), (3) typical time
(when an event occurs), (4) frequency (how often an event occurs), and (5) stationarity
(whether a state is maintained for a very long time or indefinitely).
WARNING: Running this task with a `--limit` arg will give misleading results! The
WARNING: Running this task with a `--limit` arg will give misleading results! The
corresponding dataset is structured such that each multiple-choice-question gathered
by the authors is split into question-option pairs, where each such pair gets
by the authors is split into question-option pairs, where each such pair gets
siloed into an individual document for plausibility testing. Because the harness
shuffles these documents, setting `--limit` will likely "cut off" certain candidate
answers. This is a problem because the task's metrics require an exhaustive evaluation
answers. This is a problem because the task's metrics require an exhaustive evaluation
of a question's options. See section 4 of the paper for details.
Homepage: https://leaderboard.allenai.org/mctaco/submissions/public
......@@ -55,14 +55,22 @@ class MCTACO(Task):
return self.dataset["test"]
def doc_to_text(self, doc):
return f"{doc['sentence']}\nQuestion: {doc['question']}\n"\
return (
f"{doc['sentence']}\nQuestion: {doc['question']}\n"
f"Answer: {doc['answer']}\nPlausible:"
)
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc["question"] + " " + doc["sentence"]
def doc_to_target(self, doc):
return " " + ["no", "yes"][doc['label']]
return " " + ["no", "yes"][doc["label"]]
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
......@@ -87,18 +95,15 @@ class MCTACO(Task):
The results of the requests created in construct_requests.
"""
ll_no, ll_yes = results
gold = doc['label']
gold = doc["label"]
pred = int(ll_yes > ll_no)
question_id = self._question2id(doc)
items = (gold, pred, question_id)
return {
"em": items,
"f1": items
}
return {"em": items, "f1": items}
def _question2id(self, doc):
""" Returns an identifier for the question in the given document. """
return " ".join([doc['sentence'], doc['question']])
"""Returns an identifier for the question in the given document."""
return " ".join([doc["sentence"], doc["question"]])
def aggregation(self):
return {
......@@ -126,7 +131,7 @@ def exact_match(items):
def f1(items):
""" See section 4 "Evaluation Metrics" in the paper about the F1 metric used. """
"""See section 4 "Evaluation Metrics" in the paper about the F1 metric used."""
results = list(zip(*items))
# Group the positive ("yes" = 1) golds and predictions by question.
gold_positives, pred_positives = defaultdict(list), defaultdict(list)
......@@ -140,5 +145,5 @@ def f1(items):
p = tp / pp if pp > 0.0 else 1.0
r = tp / gp if gp > 0.0 else 1.0
if p + r > 0.0:
f1.append(2. * (p * r) / (p + r))
f1.append(2.0 * (p * r) / (p + r))
return np.mean(f1)
lm_eval/tasks/mgsm.py 0 → 100644
View file @ 9f58d1a8
"""
Language Models are Multilingual Chain-of-Thought Reasoners
https://arxiv.org/abs/2210.03057
Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2210.03057).
The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are:
- Spanish
- French
- German
- Russian
- Chinese
- Japanese
- Thai
- Swahili
- Bengali
- Telugu
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
You can find the input and targets for each of the ten languages (and English) as `.tsv` files.
We also include few-shot exemplars that are also manually translated from each language in `exemplars.py`.
Homepage: https://github.com/google-research/url-nlp/tree/main/mgsm
"""
import re
from lm_eval.base import Task, rf
from lm_eval.metrics import mean
_CITATION = """
@misc{cobbe2021training,
title={Training Verifiers to Solve Math Word Problems},
author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},
year={2021},
eprint={2110.14168},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@misc{shi2022language,
title={Language Models are Multilingual Chain-of-Thought Reasoners},
author={Freda Shi and Mirac Suzgun and Markus Freitag and Xuezhi Wang and Suraj Srivats and Soroush Vosoughi and Hyung Won Chung and Yi Tay and Sebastian Ruder and Denny Zhou and Dipanjan Das and Jason Wei},
year={2022},
eprint={2210.03057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
ANS_RE = re.compile(r"(\-?\d+)")
INVALID_ANS = "[invalid]"
class MGSM(Task):
VERSION = 0
DATASET_PATH = "juletxara/mgsm"
DATASET_NAME = None
QUESTION = "Question:"
ANSWER = "Step-by-Step Answer:"
def has_training_docs(self):
return True
def has_validation_docs(self):
return False
def has_test_docs(self):
return True
def training_docs(self):
return self.dataset["train"]
def validation_docs(self):
raise NotImplementedError
def test_docs(self):
return self.dataset["test"]
def doc_to_text(self, doc):
if doc["answer"] is not None:
return doc["question"] + "\n" + self.ANSWER
else:
return self.QUESTION + " " + doc["question"] + "\n" + self.ANSWER
def doc_to_target(self, doc):
if doc["answer"] is not None:
return " " + doc["answer"][len(self.ANSWER) + 1 :]
else:
return " " + str(doc["answer_number"])
def construct_requests(self, doc, ctx):
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
completion = rf.greedy_until(ctx, {"until": ["\n", ":", self.QUESTION]})
return completion
def _extract_answer(self, completion):
match = re.findall(ANS_RE, completion)
if match:
return int(match[-1])
else:
return INVALID_ANS
def _is_correct(self, completion, answer):
gold = answer
assert gold != INVALID_ANS, "No ground truth answer found in the document."
return self._extract_answer(completion) == gold
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param results:
The results of the requests created in construct_requests.
"""
completion = results[0]
answer = doc["answer_number"]
return {"acc": self._is_correct(completion, answer)}
def aggregation(self):
"""
:returns: {str: [float] -> float}
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
return {"acc": mean}
def higher_is_better(self):
"""
:returns: {str: bool}
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {"acc": True}
class MGSM_English(MGSM):
DATASET_NAME = "en"
QUESTION = "Question:"
ANSWER = "Step-by-Step Answer:"
class MGSM_Spanish(MGSM):
DATASET_NAME = "es"
QUESTION = "Pregunta:"
ANSWER = "Respuesta paso a paso:"
class MGSM_French(MGSM):
DATASET_NAME = "fr"
QUESTION = "Question :"
ANSWER = "R\u00e9ponse \u00e9tape par \u00e9tape :"
class MGSM_German(MGSM):
DATASET_NAME = "de"
QUESTION = "Frage:"
ANSWER = "Schritt-f\u00fcr-Schritt-Antwort:"
class MGSM_Russian(MGSM):
DATASET_NAME = "ru"
QUESTION = "\u0417\u0430\u0434\u0430\u0447\u0430:"
ANSWER = "\u041f\u043e\u0448\u0430\u0433\u043e\u0432\u043e\u0435\u0440\u0435\u0448\u0435\u043d\u0438\u0435:"
class MGSM_Chinese(MGSM):
DATASET_NAME = "zh"
QUESTION = "\u95ee\u9898:"
ANSWER = "\u9010\u6b65\u89e3\u7b54:"
class MGSM_Japanese(MGSM):
DATASET_NAME = "ja"
QUESTION = "\u554f\u984c:"
ANSWER = "\u30b9\u30c6\u30c3\u30d7\u3054\u3068\u306e\u7b54\u3048:"
class MGSM_Thai(MGSM):
DATASET_NAME = "th"
QUESTION = "\u0e42\u0e08\u0e17\u0e22\u0e4c:"
ANSWER = "\u0e04\u0e33\u0e15\u0e2d\u0e1a\u0e17\u0e35\u0e25\u0e30\u0e02\u0e31\u0e49\u0e19\u0e15\u0e2d\u0e19:"
class MGSM_Swahili(MGSM):
DATASET_NAME = "sw"
QUESTION = "Swali:"
ANSWER = "Jibu la Hatua kwa Hatua:"
class MGSM_Bengali(MGSM):
DATASET_NAME = "bn"
QUESTION = "\u09aa\u09cd\u09b0\u09b6\u09cd\u09a8:"
ANSWER = "\u09a7\u09be\u09aa\u09c7 \u09a7\u09be\u09aa\u09c7 \u0989\u09a4\u09cd\u09a4\u09b0:"
class MGSM_Telugu(MGSM):
DATASET_NAME = "te"
QUESTION = "\u0c2a\u0c4d\u0c30\u0c36\u0c4d\u0c28:"
ANSWER = "\u0c26\u0c36\u0c32\u0c35\u0c3e\u0c30\u0c40\u0c17\u0c3e \u0c38\u0c2e\u0c3e\u0c27\u0c3e\u0c28\u0c02:"
LANGS = ["en", "es", "fr", "de", "ru", "zh", "ja", "th", "sw", "bn", "te"]
LANG_CLASSES = [
MGSM_English,
MGSM_Spanish,
MGSM_French,
MGSM_German,
MGSM_Russian,
MGSM_Chinese,
MGSM_Japanese,
MGSM_Thai,
MGSM_Swahili,
MGSM_Bengali,
MGSM_Telugu,
]
def construct_tasks():
tasks = {}
for lang, lang_class in zip(LANGS, LANG_CLASSES):
tasks[f"mgsm_{lang}"] = lang_class
return tasks
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