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Commit 3e3a0d8f authored by Baber's avatar Baber
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Merge branch 'rm_multiple_target' into metrics 

# Conflicts:
#	lm_eval/api/filter.py
#	lm_eval/api/metrics.py
#	lm_eval/api/task.py
#	lm_eval/filters/extraction.py
parents 2b4cdd41 00a77ebd
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lm_eval/api/filter.py
View file @ 3e3a0d8f
from abc import ABC, abstractmethod
from collections.abc import Iterable
from dataclasses import dataclass
from typing import Iterable, List, Union
from lm_eval.api.instance import Instance
......@@ -20,7 +20,9 @@ class Filter(ABC):
"""
@abstractmethod
def apply(self, resps: Union[List, Iterable], docs: List[dict]) -> Iterable:
def apply(
self, resps: Iterable[list[str]], docs: Iterable[dict]
) -> Iterable[list[str]]:
"""
Defines the operation to perform on a list of the `inst.resps` properties of `Instance` objects.
Should return the list of (filtered) response lists *in the same order as they were input*, e.g.
......@@ -40,9 +42,9 @@ class FilterEnsemble:
"""
name: str
filters: List[type[Filter]]
filters: list[type[Filter]]
def apply(self, instances: List[Instance]) -> None:
def apply(self, instances: list[Instance]) -> None:
resps, docs = zip(*((inst.resps, inst.doc) for inst in instances))
resps, docs = list(resps), list(docs)
......
lm_eval/api/metrics.py
View file @ 3e3a0d8f
......@@ -207,13 +207,48 @@ def acc_mutual_info_fn(items): # This is a passthrough function
# See the License for the specific language governing permissions and
# limitations under the License.
def exact_match_hf_evaluate(
predictions: Iterable[str],
references: Iterable[str],
regexes_to_ignore=None,
ignore_case=False,
ignore_punctuation=False,
ignore_numbers=False,
predictions: Iterable[str] | str,
references: Iterable[str] | str,
regexes_to_ignore: list[str] | None = None,
ignore_case: bool = False,
ignore_punctuation: bool = False,
ignore_numbers: bool = False,
multi_target: bool = False,
):
"""
Compute exact match scores between predictions and references.
This function computes the exact match score by comparing predictions
and references. It supports optional preprocessing steps such as ignoring
case, punctuation, numbers, and specific regex patterns.
Note:
predictions and references can have different lengths.
numpy broadcasting rule applies
Args:
predictions (Iterable[str] | str): The predicted strings to evaluate.
references (Iterable[str] | str): The reference strings to compare against.
regexes_to_ignore (list[str], optional): A list of regex patterns to remove
from both predictions and references before comparison. Defaults to None.
ignore_case (bool, optional): If True, ignores case differences during comparison.
Defaults to False.
ignore_punctuation (bool, optional): If True, removes punctuation from strings
before comparison. Defaults to False.
ignore_numbers (bool, optional): If True, removes numeric characters from strings
before comparison. Defaults to False.
multi_target (bool, optional): If True, returns 1.0 if any prediction matches any
reference, otherwise 0.0. Defaults to False.
Returns:
dict: A dictionary containing the exact match score:
- "exact_match" (float): The mean exact match score or 1.0/0.0 if `multi_target` is True.
"""
predictions, references = list(predictions), list(references)
assert len(predictions) == len(references) if not multi_target else True, (
"predictions and references must have the same length unless `multi_target` is True"
)
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
predictions = np.array([re.sub(s, "", x) for x in predictions])
......@@ -238,7 +273,11 @@ def exact_match_hf_evaluate(
score_list = predictions == references
return {"exact_match": np.mean(score_list)}
return {
"exact_match": np.mean(score_list)
if not multi_target
else float(np.any(score_list))
}
###
......@@ -250,8 +289,8 @@ def exact_match_hf_evaluate(
output_type="generate_until",
aggregation="mean",
)
def exact_match_fn(**kwargs):
return exact_match_hf_evaluate(**kwargs)
def exact_match_fn(references: list[str], predictions: list[str], **kwargs):
return exact_match_hf_evaluate(predictions, references, **kwargs)
@register_metric(
......
lm_eval/api/task.py
View file @ 3e3a0d8f
......@@ -3,17 +3,14 @@ import ast
import logging
import random
import re
from collections.abc import Callable
from collections.abc import Callable, Iterable, Iterator, Mapping
from copy import deepcopy
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
Iterator,
List,
Literal,
Mapping,
Optional,
Tuple,
Union,
......@@ -530,8 +527,8 @@ class Task(abc.ABC):
# self.aggregation = lambda: {
# metric_name: get_metric_aggregation(metric_name)
# }
setattr(self._config, "metric_list", [MetricConfig(name=metric_name)])
setattr(self._config, "process_results", lambda *args: {"bypass": 0})
self._config.metric_list = [MetricConfig(name=metric_name)]
self._config.process_results = lambda *args: {"bypass": 0}
def set_fewshot_seed(self, seed: Optional[int] = None) -> None:
self.fewshot_rnd = random.Random(seed)
......@@ -788,7 +785,7 @@ class ConfigurableTask(Task):
return docs
# Fallback to parent implementation
if _num_fewshot := getattr(self.config, "num_fewshot"):
if _num_fewshot := self.config.num_fewshot:
if isinstance(_num_fewshot, int) and _num_fewshot > 0:
eval_logger.warning(
f"[Task: {self.config.task}] "
......@@ -1409,63 +1406,15 @@ class ConfigurableTask(Task):
# it assumes that doc_to_target returns a number.
choices = self.doc_to_choice(doc)
gold = choices[gold]
# we expect multiple_targets to be a list.
elif self.multiple_target:
gold = list(gold)
# TODO: handle this better
elif type(gold) is not type(result) and not (
"bypass" in use_metric or isinstance(result, list)
):
# cast gold to the same type as result
gold = type(result)(gold)
for metric in self.config._metric_list:
if self.multiple_target:
# in the case where we have multiple targets,
# return true if any are true
# TODO: this may break for multipLe_target, non zero-or-1 metrics
scores = []
if not isinstance(gold, list):
# sometimes, a multiple_target dataset has exceptions where one doc has only one string answer
# print(gold)
gold = [gold]
if metric.name == "exact_match":
result = [result for _ in range(len(gold))]
scores = metric.fn(
references=gold,
predictions=result,
**metric.kwargs,
)[metric]
result_score = 1.0 if scores > 0.0 else 0.0
else:
for gold_option in gold:
try:
result_score = metric.fn(
references=[gold_option],
predictions=[result],
**metric.kwargs,
)
except (
TypeError
): # TODO: this is hacky and I don't want to do it
result_score = metric.fn([gold_option, result])
if isinstance(result_score, dict):
# TODO: this handles the case where HF evaluate returns a dict.
result_score = result_score[metric]
scores.append(result_score)
if any(scores):
result_score = 1.0
else:
result_score = 0.0
else:
try:
result_score = metric.fn(
references=[gold],
predictions=[result],
**metric.kwargs,
)
except TypeError: # needed for now in order to use a different interface between our own metrics and HF Evaluate metrics
result_score = metric.fn([gold, result])
for metric in self._metric_fn_list.keys():
try:
result_score = self._metric_fn_list[metric](
references=[gold] if not isinstance(gold, list) else gold,
predictions=[result],
**self._metric_fn_kwargs[metric],
)
except TypeError: # needed for now in order to use a different interface between our own metrics and HF Evaluate metrics
result_score = self._metric_fn_list[metric]([gold, result])
if isinstance(result_score, dict):
# TODO: this handles the case where HF evaluate returns a dict.
# This allows for multiple metrics to be returned from the same function
......@@ -1515,7 +1464,7 @@ class MultipleChoiceTask(Task):
Instance(
request_type="loglikelihood",
doc=doc,
arguments=(ctx, " {}".format(choice)),
arguments=(ctx, f" {choice}"),
idx=i,
**kwargs,
)
......
lm_eval/filters/extraction.py
View file @ 3e3a0d8f
import re
import sys
import unicodedata
from collections.abc import Iterable
from lm_eval.api.filter import Filter
from lm_eval.api.registry import register_filter
......@@ -32,7 +33,9 @@ class RegexFilter(Filter):
self.group_select = group_select
self.fallback = fallback
def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
def apply(
self, resps: Iterable[list[str]], docs: Iterable[dict]
) -> Iterable[list[str]]:
# here, we assume we have a list, in which each element is
# a list of model responses for some particular input/target pair.
# so we process each of these (same input/target response sets)
......@@ -59,59 +62,13 @@ class RegexFilter(Filter):
return filtered_resps
@register_filter("regex_pos")
class POSFilter(Filter):
""" """
def __init__(
self,
regex_pattern: str = r"\['(.*?)'\]",
group_select=0,
fallback=None,
**kwargs,
) -> None:
"""
pass a string `regex` to run `re.compile(r"regex")` on.
`fallback` defines the output returned if no matches for the regex are located.
"""
super().__init__(**kwargs)
if fallback is None:
fallback = ["invalid"]
self.regex_pattern = regex_pattern
self.regex = re.compile(regex_pattern)
self.group_select = group_select
self.fallback = fallback
def apply(self, resps, docs):
def extract_tagged_tokens(text):
# Extract tagged tokens list from text input using regex
tokens = re.findall(r"\('([^']*)', '([^']*)'\)", text)
return [(token, pos) for token, pos in tokens]
def extract_pos_tags(result):
pos_tags = []
if isinstance(result, str):
result = extract_tagged_tokens(result)
pos_tags.extend(pos for _, pos in result)
return pos_tags if pos_tags else self.fallback
def filter_set(inst):
filtered = []
for resp in inst:
match = extract_pos_tags(resp)
filtered.append(match)
return filtered
filtered_resps = map(lambda x: filter_set(x), resps)
return filtered_resps
@register_filter("remove_whitespace")
class WhitespaceFilter(Filter):
"""Filters out leading whitespace from responses."""
def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
def apply(
self, resps: Iterable[list[str]], docs: Iterable[dict]
) -> Iterable[list[str]]:
def filter_set(inst):
filtered_resp = []
for resp in inst:
......@@ -156,7 +113,9 @@ class MultiChoiceRegexFilter(RegexFilter):
self.ignore_punctuation = ignore_punctuation
self.regexes_to_ignore = regexes_to_ignore
def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
def apply(
self, resps: Iterable[list[str]], docs: Iterable[dict]
) -> Iterable[list[str]]:
# here, we assume we have a list, in which each element is
# a list of model responses for some particular input/target pair.
# so we process each of these (same input/target response sets)
......
lm_eval/tasks/README.md
View file @ 3e3a0d8f
......@@ -5,170 +5,171 @@
For more information, including a full list of task names and their precise meanings or sources, follow the links provided to the individual README.md files for each subfolder.
| Task Family | Description | Language(s) |
|--------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
| [aclue](aclue/README.md) | Tasks focusing on ancient Chinese language understanding and cultural aspects. | Ancient Chinese |
| [acp_bench](acpbench/README.md) | Tasks evaluating the reasoning ability about Action, Change, and Planning | English |
| [acp_bench_hard](acpbench/README.md) | Tasks evaluating the reasoning ability about Action, Change, and Planning | English |
| [aexams](aexams/README.md) | Tasks in Arabic related to various academic exams covering a range of subjects. | Arabic |
| [agieval](agieval/README.md) | Tasks involving historical data or questions related to history and historical texts. | English, Chinese |
| [anli](anli/README.md) | Adversarial natural language inference tasks designed to test model robustness. | English |
| [arabic_leaderboard_complete](arabic_leaderboard_complete/README.md) | A full version of the tasks in the Open Arabic LLM Leaderboard, focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabic_leaderboard_light](arabic_leaderboard_light/README.md) | A light version of the tasks in the Open Arabic LLM Leaderboard (i.e., 10% samples of the test set in the original benchmarks), focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabicmmlu](arabicmmlu/README.md) | Localized Arabic version of MMLU with multiple-choice questions from 40 subjects. | Arabic |
| [ArabCulture](arab_culture/README.md) | Benchmark for evaluating modeles' commonsense cultural knowledge across different 13 different Arab Countries. | Arabic |
[AraDICE](aradice/README.md) | A collection of multiple tasks carefully designed to evaluate dialectal and cultural capabilities in large language models (LLMs). | Arabic |
| [arc](arc/README.md) | Tasks involving complex reasoning over a diverse set of questions. | English |
| [arithmetic](arithmetic/README.md) | Tasks involving numerical computations and arithmetic reasoning. | English |
| [asdiv](asdiv/README.md) | Tasks involving arithmetic and mathematical reasoning challenges. | English |
| [babi](babi/README.md) | Tasks designed as question and answering challenges based on simulated stories. | English |
| [basque_bench](basque_bench/README.md) | Collection of tasks in Basque encompassing various evaluation areas. | Basque |
| [basqueglue](basqueglue/README.md) | Tasks designed to evaluate language understanding in Basque language. | Basque |
| [bbh](bbh/README.md) | Tasks focused on deep semantic understanding through hypothesization and reasoning. | English, German |
| [bbq](bbq/README.md) | A question-answering benchmark designed to measure social biases in language models across various demographic categories and contexts. | English |
| [belebele](belebele/README.md) | Language understanding tasks in a variety of languages and scripts. | Multiple (122 languages) |
| benchmarks | General benchmarking tasks that test a wide range of language understanding capabilities. | |
| [bertaqa](bertaqa/README.md) | Local Basque cultural trivia QA tests in English and Basque languages. | English, Basque, Basque (MT) |
| [bigbench](bigbench/README.md) | Broad tasks from the BIG-bench benchmark designed to push the boundaries of large models. | Multiple |
| [blimp](blimp/README.md) | Tasks testing grammatical phenomena to evaluate language model's linguistic capabilities. | English |
| [c4](c4/README.md) | Tasks based on a colossal, cleaned version of Common Crawl's web crawl corpus to assess models' language modeling capabilities. | English |
| [careqa](careqa/README.md) | Multiple choice and open-ended medical question answering based on the Spanish Specialised Healthcare Training (MIR) exams. | English, Spanish |
| [catalan_bench](catalan_bench/README.md) | Collection of tasks in Catalan encompassing various evaluation areas. | Catalan |
| [ceval](ceval/README.md) | Tasks that evaluate language understanding and reasoning in an educational context. | Chinese |
| [cmmlu](cmmlu/README.md) | Multi-subject multiple choice question tasks for comprehensive academic assessment. | Chinese |
| code_x_glue | Tasks that involve understanding and generating code across multiple programming languages. | Go, Java, JS, PHP, Python, Ruby |
| [commonsense_qa](commonsense_qa/README.md) | CommonsenseQA, a multiple-choice QA dataset for measuring commonsense knowledge. | English |
| [copal_id](copal_id/README.md) United States | Indonesian causal commonsense reasoning dataset that captures local nuances. | Indonesian |
| [coqa](coqa/README.md) | Conversational question answering tasks to test dialog understanding. | English |
| [crows_pairs](crows_pairs/README.md) | Tasks designed to test model biases in various sociodemographic groups. | English, French |
| csatqa | Tasks related to SAT and other standardized testing questions for academic assessment. | Korean |
| [darija_bench](darija_bench/README.md) | Traditional NLP tasks (Translation, Summariation, etc..) for Moroccan Darija | Moroccan Darija (some MT) |
| [darijahellaswag](darijahellaswag/README.md) | Moroccan Darija version of HellaSwag. | Moroccan Darija (MT) |
| [darijammlu](darijammlu/README.md)| Multiple-choice QA in Moroccan Darija (an Arabic dialect). | Moroccan Darija (MT) |
| [drop](drop/README.md) | Tasks requiring numerical reasoning, reading comprehension, and question answering. | English |
| [egyhellaswag](egyhellaswag/README.md) | Egyptian Arabic (Masri) version of HellaSwag. | Egyptian Arabic (MT) |
| [egymmlu](egymmlu/README.md) | Multiple-choice QA in Egyptian Arabic. | Egyptian Arabic (MT) |
| [eq_bench](eq_bench/README.md) | Tasks focused on equality and ethics in question answering and decision-making. | English |
| [eus_exams](eus_exams/README.md) | Tasks based on various professional and academic exams in the Basque language. | Basque |
| [eus_proficiency](eus_proficiency/README.md) | Tasks designed to test proficiency in the Basque language across various topics. | Basque |
| [eus_reading](eus_reading/README.md) | Reading comprehension tasks specifically designed for the Basque language. | Basque |
| [eus_trivia](eus_trivia/README.md) | Trivia and knowledge testing tasks in the Basque language. | Basque |
| [evalita_LLM](evalita_llm/README.md) | A native Italian benchmark with diverse tasks formats and multiple prompts. | Italian |
| [fda](fda/README.md) | Tasks for extracting key-value pairs from FDA documents to test information extraction. | English |
| [fld](fld/README.md) | Tasks involving free-form and directed dialogue understanding. | English |
| [french_bench](french_bench/README.md) | Set of tasks designed to assess language model performance in French. | French |
| [galician_bench](galician_bench/README.md) | Collection of tasks in Galician encompassing various evaluation areas. | Galician |
| [global_mmlu](global_mmlu/README.md) | Collection of culturally sensitive and culturally agnostic MMLU tasks in 15 languages with human translations or post-edits. | Multiple (15 languages) |
| [glue](glue/README.md) | General Language Understanding Evaluation benchmark to test broad language abilities. | English |
| [gpqa](gpqa/README.md) | Tasks designed for general public question answering and knowledge verification. | English |
| [gsm8k](gsm8k/README.md) | A benchmark of grade school math problems aimed at evaluating reasoning capabilities. | English |
| [groundcocoa](groundcocoa/README.md) | A benchmark evaluating the conditional and compositional reasoning of language models using a grounding task. | English |
| [haerae](haerae/README.md) | Tasks focused on assessing detailed factual and historical knowledge. | Korean |
| [headqa](headqa/README.md) | A high-level education-based question answering dataset to test specialized knowledge. | Spanish, English |
| [hellaswag](hellaswag/README.md) | Tasks to predict the ending of stories or scenarios, testing comprehension and creativity. | English |
| [hendrycks_ethics](hendrycks_ethics/README.md) | Tasks designed to evaluate the ethical reasoning capabilities of models. | English |
| [hendrycks_math](hendrycks_math/README.md) | Mathematical problem-solving tasks to test numerical reasoning and problem-solving. | English |
| [histoires_morales](histoires_morales/README.md) | A dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | French (Some MT) |
| [hrm8k](hrm8k/README.md) | A challenging bilingual math reasoning benchmark for Korean and English. | Korean (Some MT), English (Some MT) |
| [humaneval](humaneval/README.md) | Code generation task that measure functional correctness for synthesizing programs from docstrings. | Python |
| [ifeval](ifeval/README.md) | Interactive fiction evaluation tasks for narrative understanding and reasoning. | English |
| [inverse_scaling](inverse_scaling/README.md) | Multiple-choice tasks from the Inverse Scaling Prize, designed to find settings where larger language models perform worse. | English |
| [japanese_leaderboard](japanese_leaderboard/README.md) | Japanese language understanding tasks to benchmark model performance on various linguistic aspects. | Japanese |
| [jsonschema_bench](jsonschema_bench/README.md) | Evaluate the ability of LLMs to generate JSON objects that conform to a given JSON schema, including API, configuration files, and other structured data formats. | JSON |
| [kbl](kbl/README.md) | Korean Benchmark for Legal Language Understanding. | Korean |
| [kmmlu](kmmlu/README.md) | Knowledge-based multi-subject multiple choice questions for academic evaluation. | Korean |
| [kobest](kobest/README.md) | A collection of tasks designed to evaluate understanding in Korean language. | Korean |
| [kormedmcqa](kormedmcqa/README.md) | Medical question answering tasks in Korean to test specialized domain knowledge. | Korean |
| [lambada](lambada/README.md) | Tasks designed to predict the endings of text passages, testing language prediction skills. | English |
| [lambada_cloze](lambada_cloze/README.md) | Cloze-style LAMBADA dataset. | English |
| [lambada_multilingual](lambada_multilingual/README.md) | Multilingual LAMBADA dataset. This is a legacy version of the multilingual dataset, and users should instead use `lambada_multilingual_stablelm`. | German, English, Spanish, French, Italian |
| [lambada_multilingual_stablelm](lambada_multilingual_stablelm/README.md) | Multilingual LAMBADA dataset. Users should prefer evaluating on this version of the multilingual dataset instead of on `lambada_multilingual`. | German, English, Spanish, French, Italian, Dutch, Portuguese |
| [leaderboard](leaderboard/README.md) | Task group used by Hugging Face's [Open LLM Leaderboard v2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard). Those tasks are static and will not change through time | English |
| [lingoly](lingoly/README.md) | Challenging logical reasoning benchmark in low-resource languages with controls for memorization | English, Multilingual |
| [logiqa](logiqa/README.md) | Logical reasoning tasks requiring advanced inference and deduction. | English, Chinese |
| [logiqa2](logiqa2/README.md) | Large-scale logical reasoning dataset adapted from the Chinese Civil Service Examination. | English, Chinese |
| [mastermind](mastermind/README.md) | Reasoning benchmark based on the board game of Mastermind. | English |
| [mathqa](mathqa/README.md) | Question answering tasks involving mathematical reasoning and problem-solving. | English |
| [mbpp](mbpp/README.md) | A benchmark designed to measure the ability to synthesize short Python programs from natural language descriptions. | Python |
| [meddialog](meddialog/README.md) | Medical open-ended QA and Question Entailment stemming from the MedDialog dataset. | English |
| [medtext](medtext/README.md) | Medical open-ended QA from the MedText Clinical Notes dataset. | English |
| [mimic_repsum](mimic_repsum/README.md) | Medical report summarization from the MIMIC-III dataset. | English |
| [mc_taco](mc_taco/README.md) | Question-answer pairs that require temporal commonsense comprehension. | English |
| [med_concepts_qa](med_concepts_qa/README.md) | Benchmark for evaluating LLMs on their abilities to interpret medical codes and distinguish between medical concept. | English |
| [metabench](metabench/README.md) | Distilled versions of six popular benchmarks which are highly predictive of overall benchmark performance and of a single general ability latent trait. | English |
| [mediqa_qa2019](mediqa_qa2019/README.md) | Open-ended healthcare question answering benchmark from the MEDIQA 2019 challenge. | English |
| medmcqa | Medical multiple choice questions assessing detailed medical knowledge. | English |
| medqa | Multiple choice question answering based on the United States Medical License Exams. | |
| [meqsum](meqsum/README.md) | Healtcare Question Entailment benchmark from the MeqSum dataset. | |
| [mgsm](mgsm/README.md) | Benchmark of multilingual grade-school math problems. | Spanish, French, German, Russian, Chinese, Japanese, Thai, Swahili, Bengali, Telugu |
| [minerva_math](minerva_math/README.md) | Mathematics-focused tasks requiring numerical reasoning and problem-solving skills. | English |
| [mlqa](mlqa/README.md) | MultiLingual Question Answering benchmark dataset for evaluating cross-lingual question answering performance. | English, Arabic, German, Spanish, Hindi, Vietnamese, Simplified Chinese |
| [mmlu](mmlu/README.md) | Massive Multitask Language Understanding benchmark for broad domain language evaluation. Several variants are supported. | English |
| [mmlu_pro](mmlu_pro/README.md) | A refined set of MMLU, integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. | English |
| [mmlu-pro-plus](mmlu-pro-plus/README.md) | A new test set for evaluating shortcut learning and higher-order reasoning of LLMs. | English |
| [mmlu_prox](mmlu_prox/README.md) | A multilingual benchmark that extends MMLU-Pro to multiple typologically diverse languages with human validation. | English, Japanese, Chinese, Korean, French, German, Spanish, Portuguese, Swahili, Thai, Arabic, Hindi, Bengali |
| [mmlusr](mmlusr/README.md) | Variation of MMLU designed to be more rigorous. | English |
| model_written_evals | Evaluation tasks auto-generated for evaluating a collection of AI Safety concerns. | |
| [moral_stories](moral_stories/README.md) | A crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | English |
| [mts_dialog](mts_dialog/README.md) | Open-ended healthcare QA from the MTS-Dialog dataset. | English |
| [mutual](mutual/README.md) | A retrieval-based dataset for multi-turn dialogue reasoning. | English |
| [noreval](noreval/README.md) | A human-created Norwegian language understanding and generation benchmark. | Norwegian (Bokmål and Nynorsk) |
| [nq_open](nq_open/README.md) | Open domain question answering tasks based on the Natural Questions dataset. | English |
| [okapi/arc_multilingual](okapi/arc_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [okapi/hellaswag_multilingual](okapi/hellaswag_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (30 languages) **Machine Translated.** |
| okapi/mmlu_multilingual | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (34 languages) **Machine Translated.** |
| [okapi/truthfulqa_multilingual](okapi/truthfulqa_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [olaph](olaph/README.md) | Open-ended medical factuality Question Answering from the OLAPH dataset. | English |
| [openbookqa](openbookqa/README.md) | Open-book question answering tasks that require external knowledge and reasoning. | English |
| [paloma](paloma/README.md) | Paloma is a comprehensive benchmark designed to evaluate open language models across a wide range of domains, ranging from niche artist communities to mental health forums on Reddit. | English |
| [paws-x](paws-x/README.md) | Paraphrase Adversaries from Word Scrambling, focusing on cross-lingual capabilities. | English, French, Spanish, German, Chinese, Japanese, Korean |
| [pile](pile/README.md) | Open source language modelling data set that consists of 22 smaller, high-quality datasets. | English |
| [pile_10k](pile_10k/README.md) | The first 10K elements of The Pile, useful for debugging models trained on it. | English |
| [piqa](piqa/README.md) | Physical Interaction Question Answering tasks to test physical commonsense reasoning. | English |
| [polemo2](polemo2/README.md) | Sentiment analysis and emotion detection tasks based on Polish language data. | Polish |
| [portuguese_bench](portuguese_bench/README.md) | Collection of tasks in European Portuguese encompassing various evaluation areas. | Portuguese |
| [prost](prost/README.md) | Tasks requiring understanding of professional standards and ethics in various domains. | English |
| [pubmedqa](pubmedqa/README.md) | Question answering tasks based on PubMed research articles for biomedical understanding. | English |
| [qa4mre](qa4mre/README.md) | Question Answering for Machine Reading Evaluation, assessing comprehension and reasoning. | English |
| [qasper](qasper/README.md) | Question Answering dataset based on academic papers, testing in-depth scientific knowledge. | English |
| [race](race/README.md) | Reading comprehension assessment tasks based on English exams in China. | English |
| realtoxicityprompts | Tasks to evaluate language models for generating text with potential toxicity. | |
| [ruler](ruler/README.md) | RULER is a benchmark for testing how well language models handle long pieces of text. Requires custom arg (see readme) | English |
| [sciq](sciq/README.md) | Science Question Answering tasks to assess understanding of scientific concepts. | English |
| [score](score/README.md) | Systematic consistency and robustness evaluation for LLMs on 3 datasets(MMLU-Pro, Agi Eval and MATH) | English |
| [scrolls](scrolls/README.md) | Tasks that involve long-form reading comprehension across various domains. | English |
| [simple_cooccurrence_bias](simple_cooccurrence_bias/README.md) | A metric that evaluates language models for biases based on stereotypical word associations and co-occurrences in text. | English |
| [siqa](siqa/README.md) | Social Interaction Question Answering to evaluate common sense and social reasoning. | English |
| [spanish_bench](spanish_bench/README.md) | Collection of tasks in Spanish encompassing various evaluation areas. | Spanish |
| [squad_completion](squad_completion/README.md) | A variant of the SQuAD question answering task designed for zero-shot evaluation of small LMs. | English |
| [squadv2](squadv2/README.md) | Stanford Question Answering Dataset version 2, a reading comprehension benchmark. | English |
| [storycloze](storycloze/README.md) | Tasks to predict story endings, focusing on narrative logic and coherence. | English |
| [super_glue](super_glue/README.md) | A suite of challenging tasks designed to test a range of language understanding skills. | English |
| [swag](swag/README.md) | Situations With Adversarial Generations, predicting the next event in videos. | English |
| [swde](swde/README.md) | Information extraction tasks from semi-structured web pages. | English |
| [tinyBenchmarks](tinyBenchmarks/README.md) | Evaluation of large language models with fewer examples using tiny versions of popular benchmarks. | English |
| [tmmluplus](tmmluplus/README.md) | An extended set of tasks under the TMMLU framework for broader academic assessments. | Traditional Chinese |
| [toxigen](toxigen/README.md) | Tasks designed to evaluate language models on their propensity to generate toxic content. | English |
| [translation](translation/README.md) | Tasks focused on evaluating the language translation capabilities of models. | Arabic, English, Spanish, Basque, Hindi, Indonesian, Burmese, Russian, Swahili, Telugu, Chinese |
| [triviaqa](triviaqa/README.md) | A large-scale dataset for trivia question answering to test general knowledge. | English |
| [truthfulqa](truthfulqa/README.md) | A QA task aimed at evaluating the truthfulness and factual accuracy of model responses. | English |
| [truthfulqa-multi](truthfulqa-multi/README.md) | Is a multilingual version of TruthfulQA, a QA task aimed at evaluating the truthfulness and factual accuracy of model responses. | English, Spanish, Catalan, Basque, Galician |
| [turkishmmlu](turkishmmlu/README.md) | A multiple-choice QA test modeled after MMLU, written in Turkish based on Turkish high-school level exams. | Turkish |
| [unitxt](unitxt/README.md) | A number of tasks implemented using the unitxt library for flexible, shareable, and reusable data preparation and evaluation for generative AI. | English |
| [unscramble](unscramble/README.md) | Tasks involving the rearrangement of scrambled sentences to test syntactic understanding. | English |
| [webqs](webqs/README.md) | Web-based question answering tasks designed to evaluate internet search and retrieval. | English |
| [wikitext](wikitext/README.md) | Tasks based on text from Wikipedia articles to assess language modeling and generation. | English |
| [winogender](winogender/README.md) | A diagnostic dataset that tests for gender bias in coreference resolution by measuring how models associate pronouns with different occupations. | English |
| [winogrande](winogrande/README.md) | A large-scale dataset for coreference resolution, inspired by the Winograd Schema Challenge. | English |
| [wmdp](wmdp/README.md) | A benchmark with the objective of minimizing performance, based on potentially-sensitive multiple-choice knowledge questions. | English |
| [wmt2016](wmt2016/README.md) | Tasks from the WMT 2016 shared task, focusing on translation between multiple languages. | English, Czech, German, Finnish, Russian, Romanian, Turkish |
| [wsc273](wsc273/README.md) | The Winograd Schema Challenge, a test of commonsense reasoning and coreference resolution. | English |
| [xcopa](xcopa/README.md) | Cross-lingual Choice of Plausible Alternatives, testing reasoning in multiple languages. | Estonian, Haitian, Indonesian, Italian, Quechua, Swahili, Tamil, Thai, Turkish, Vietnamese, Chinese |
| Task Family | Description | Language(s) |
|--------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|
| [aclue](aclue/README.md) | Tasks focusing on ancient Chinese language understanding and cultural aspects. | Ancient Chinese |
| [acp_bench](acpbench/README.md) | Tasks evaluating the reasoning ability about Action, Change, and Planning | English |
| [acp_bench_hard](acpbench/README.md) | Tasks evaluating the reasoning ability about Action, Change, and Planning | English |
| [aexams](aexams/README.md) | Tasks in Arabic related to various academic exams covering a range of subjects. | Arabic |
| [agieval](agieval/README.md) | Tasks involving historical data or questions related to history and historical texts. | English, Chinese |
| [anli](anli/README.md) | Adversarial natural language inference tasks designed to test model robustness. | English |
| [arabic_leaderboard_complete](arabic_leaderboard_complete/README.md) | A full version of the tasks in the Open Arabic LLM Leaderboard, focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabic_leaderboard_light](arabic_leaderboard_light/README.md) | A light version of the tasks in the Open Arabic LLM Leaderboard (i.e., 10% samples of the test set in the original benchmarks), focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabicmmlu](arabicmmlu/README.md) | Localized Arabic version of MMLU with multiple-choice questions from 40 subjects. | Arabic |
| [ArabCulture](arab_culture/README.md) | Benchmark for evaluating modeles' commonsense cultural knowledge across different 13 different Arab Countries. | Arabic |
| [AraDICE](aradice/README.md) | A collection of multiple tasks carefully designed to evaluate dialectal and cultural capabilities in large language models (LLMs). | Arabic |
| [arc](arc/README.md) | Tasks involving complex reasoning over a diverse set of questions. | English |
| [arithmetic](arithmetic/README.md) | Tasks involving numerical computations and arithmetic reasoning. | English |
| [asdiv](asdiv/README.md) | Tasks involving arithmetic and mathematical reasoning challenges. | English |
| [babi](babi/README.md) | Tasks designed as question and answering challenges based on simulated stories. | English |
| [basque_bench](basque_bench/README.md) | Collection of tasks in Basque encompassing various evaluation areas. | Basque |
| [basqueglue](basqueglue/README.md) | Tasks designed to evaluate language understanding in Basque language. | Basque |
| [bbh](bbh/README.md) | Tasks focused on deep semantic understanding through hypothesization and reasoning. | English, German |
| [bbq](bbq/README.md) | A question-answering benchmark designed to measure social biases in language models across various demographic categories and contexts. | English |
| [belebele](belebele/README.md) | Language understanding tasks in a variety of languages and scripts. | Multiple (122 languages) |
| benchmarks | General benchmarking tasks that test a wide range of language understanding capabilities. | |
| [bertaqa](bertaqa/README.md) | Local Basque cultural trivia QA tests in English and Basque languages. | English, Basque, Basque (MT) |
| [bigbench](bigbench/README.md) | Broad tasks from the BIG-bench benchmark designed to push the boundaries of large models. | Multiple |
| [blimp](blimp/README.md) | Tasks testing grammatical phenomena to evaluate language model's linguistic capabilities. | English |
| [c4](c4/README.md) | Tasks based on a colossal, cleaned version of Common Crawl's web crawl corpus to assess models' language modeling capabilities. | English |
| [careqa](careqa/README.md) | Multiple choice and open-ended medical question answering based on the Spanish Specialised Healthcare Training (MIR) exams. | English, Spanish |
| [catalan_bench](catalan_bench/README.md) | Collection of tasks in Catalan encompassing various evaluation areas. | Catalan |
| [ceval](ceval/README.md) | Tasks that evaluate language understanding and reasoning in an educational context. | Chinese |
| [cmmlu](cmmlu/README.md) | Multi-subject multiple choice question tasks for comprehensive academic assessment. | Chinese |
| code_x_glue | Tasks that involve understanding and generating code across multiple programming languages. | Go, Java, JS, PHP, Python, Ruby |
| [commonsense_qa](commonsense_qa/README.md) | CommonsenseQA, a multiple-choice QA dataset for measuring commonsense knowledge. | English |
| [copal_id](copal_id/README.md) United States | Indonesian causal commonsense reasoning dataset that captures local nuances. | Indonesian |
| [coqa](coqa/README.md) | Conversational question answering tasks to test dialog understanding. | English |
| [crows_pairs](crows_pairs/README.md) | Tasks designed to test model biases in various sociodemographic groups. | English, French |
| csatqa | Tasks related to SAT and other standardized testing questions for academic assessment. | Korean |
| [darija_bench](darija_bench/README.md) | Traditional NLP tasks (Translation, Summariation, etc..) for Moroccan Darija | Moroccan Darija (some MT) |
| [darijahellaswag](darijahellaswag/README.md) | Moroccan Darija version of HellaSwag. | Moroccan Darija (MT) |
| [darijammlu](darijammlu/README.md) | Multiple-choice QA in Moroccan Darija (an Arabic dialect). | Moroccan Darija (MT) |
| [drop](drop/README.md) | Tasks requiring numerical reasoning, reading comprehension, and question answering. | English |
| [egyhellaswag](egyhellaswag/README.md) | Egyptian Arabic (Masri) version of HellaSwag. | Egyptian Arabic (MT) |
| [egymmlu](egymmlu/README.md) | Multiple-choice QA in Egyptian Arabic. | Egyptian Arabic (MT) |
| [eq_bench](eq_bench/README.md) | Tasks focused on equality and ethics in question answering and decision-making. | English |
| [eus_exams](eus_exams/README.md) | Tasks based on various professional and academic exams in the Basque language. | Basque |
| [eus_proficiency](eus_proficiency/README.md) | Tasks designed to test proficiency in the Basque language across various topics. | Basque |
| [eus_reading](eus_reading/README.md) | Reading comprehension tasks specifically designed for the Basque language. | Basque |
| [eus_trivia](eus_trivia/README.md) | Trivia and knowledge testing tasks in the Basque language. | Basque |
| [evalita_LLM](evalita_llm/README.md) | A native Italian benchmark with diverse tasks formats and multiple prompts. | Italian |
| [fda](fda/README.md) | Tasks for extracting key-value pairs from FDA documents to test information extraction. | English |
| [fld](fld/README.md) | Tasks involving free-form and directed dialogue understanding. | English |
| [french_bench](french_bench/README.md) | Set of tasks designed to assess language model performance in French. | French |
| [galician_bench](galician_bench/README.md) | Collection of tasks in Galician encompassing various evaluation areas. | Galician |
| [global_mmlu](global_mmlu/README.md) | Collection of culturally sensitive and culturally agnostic MMLU tasks in 15 languages with human translations or post-edits. | Multiple (15 languages) |
| [glue](glue/README.md) | General Language Understanding Evaluation benchmark to test broad language abilities. | English |
| [gpqa](gpqa/README.md) | Tasks designed for general public question answering and knowledge verification. | English |
| [gsm8k](gsm8k/README.md) | A benchmark of grade school math problems aimed at evaluating reasoning capabilities. | English |
| [groundcocoa](groundcocoa/README.md) | A benchmark evaluating the conditional and compositional reasoning of language models using a grounding task. | English |
| [haerae](haerae/README.md) | Tasks focused on assessing detailed factual and historical knowledge. | Korean |
| [headqa](headqa/README.md) | A high-level education-based question answering dataset to test specialized knowledge. | Spanish, English |
| [hellaswag](hellaswag/README.md) | Tasks to predict the ending of stories or scenarios, testing comprehension and creativity. | English |
| [hendrycks_ethics](hendrycks_ethics/README.md) | Tasks designed to evaluate the ethical reasoning capabilities of models. | English |
| [hendrycks_math](hendrycks_math/README.md) | Mathematical problem-solving tasks to test numerical reasoning and problem-solving. | English |
| [histoires_morales](histoires_morales/README.md) | A dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | French (Some MT) |
| [hrm8k](hrm8k/README.md) | A challenging bilingual math reasoning benchmark for Korean and English. | Korean (Some MT), English (Some MT) |
| [humaneval](humaneval/README.md) | Code generation task that measure functional correctness for synthesizing programs from docstrings. | Python |
| [ifeval](ifeval/README.md) | Interactive fiction evaluation tasks for narrative understanding and reasoning. | English |
| [inverse_scaling](inverse_scaling/README.md) | Multiple-choice tasks from the Inverse Scaling Prize, designed to find settings where larger language models perform worse. | English |
| [japanese_leaderboard](japanese_leaderboard/README.md) | Japanese language understanding tasks to benchmark model performance on various linguistic aspects. | Japanese |
| [jsonschema_bench](jsonschema_bench/README.md) | Evaluate the ability of LLMs to generate JSON objects that conform to a given JSON schema, including API, configuration files, and other structured data formats. | JSON |
| [kbl](kbl/README.md) | Korean Benchmark for Legal Language Understanding. | Korean |
| [kmmlu](kmmlu/README.md) | Knowledge-based multi-subject multiple choice questions for academic evaluation. | Korean |
| [kobest](kobest/README.md) | A collection of tasks designed to evaluate understanding in Korean language. | Korean |
| [kormedmcqa](kormedmcqa/README.md) | Medical question answering tasks in Korean to test specialized domain knowledge. | Korean |
| [lambada](lambada/README.md) | Tasks designed to predict the endings of text passages, testing language prediction skills. | English |
| [lambada_cloze](lambada_cloze/README.md) | Cloze-style LAMBADA dataset. | English |
| [lambada_multilingual](lambada_multilingual/README.md) | Multilingual LAMBADA dataset. This is a legacy version of the multilingual dataset, and users should instead use `lambada_multilingual_stablelm`. | German, English, Spanish, French, Italian |
| [lambada_multilingual_stablelm](lambada_multilingual_stablelm/README.md) | Multilingual LAMBADA dataset. Users should prefer evaluating on this version of the multilingual dataset instead of on `lambada_multilingual`. | German, English, Spanish, French, Italian, Dutch, Portuguese |
| [leaderboard](leaderboard/README.md) | Task group used by Hugging Face's [Open LLM Leaderboard v2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard). Those tasks are static and will not change through time | English |
| [lingoly](lingoly/README.md) | Challenging logical reasoning benchmark in low-resource languages with controls for memorization | English, Multilingual |
| [logiqa](logiqa/README.md) | Logical reasoning tasks requiring advanced inference and deduction. | English, Chinese |
| [logiqa2](logiqa2/README.md) | Large-scale logical reasoning dataset adapted from the Chinese Civil Service Examination. | English, Chinese |
| [mastermind](mastermind/README.md) | Reasoning benchmark based on the board game of Mastermind. | English |
| [mathqa](mathqa/README.md) | Question answering tasks involving mathematical reasoning and problem-solving. | English |
| [mbpp](mbpp/README.md) | A benchmark designed to measure the ability to synthesize short Python programs from natural language descriptions. | Python |
| [meddialog](meddialog/README.md) | Medical open-ended QA and Question Entailment stemming from the MedDialog dataset. | English |
| [medtext](medtext/README.md) | Medical open-ended QA from the MedText Clinical Notes dataset. | English |
| [mimic_repsum](mimic_repsum/README.md) | Medical report summarization from the MIMIC-III dataset. | English |
| [mc_taco](mc_taco/README.md) | Question-answer pairs that require temporal commonsense comprehension. | English |
| [med_concepts_qa](med_concepts_qa/README.md) | Benchmark for evaluating LLMs on their abilities to interpret medical codes and distinguish between medical concept. | English |
| [metabench](metabench/README.md) | Distilled versions of six popular benchmarks which are highly predictive of overall benchmark performance and of a single general ability latent trait. | English |
| [mediqa_qa2019](mediqa_qa2019/README.md) | Open-ended healthcare question answering benchmark from the MEDIQA 2019 challenge. | English |
| medmcqa | Medical multiple choice questions assessing detailed medical knowledge. | English |
| medqa | Multiple choice question answering based on the United States Medical License Exams. | |
| [meqsum](meqsum/README.md) | Healtcare Question Entailment benchmark from the MeqSum dataset. | |
| [mgsm](mgsm/README.md) | Benchmark of multilingual grade-school math problems. | Spanish, French, German, Russian, Chinese, Japanese, Thai, Swahili, Bengali, Telugu |
| [minerva_math](minerva_math/README.md) | Mathematics-focused tasks requiring numerical reasoning and problem-solving skills. | English |
| [mlqa](mlqa/README.md) | MultiLingual Question Answering benchmark dataset for evaluating cross-lingual question answering performance. | English, Arabic, German, Spanish, Hindi, Vietnamese, Simplified Chinese |
| [mmlu](mmlu/README.md) | Massive Multitask Language Understanding benchmark for broad domain language evaluation. Several variants are supported. | English |
| [mmlu_pro](mmlu_pro/README.md) | A refined set of MMLU, integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. | English |
| [mmlu-pro-plus](mmlu-pro-plus/README.md) | A new test set for evaluating shortcut learning and higher-order reasoning of LLMs. | English |
| [mmlu_prox](mmlu_prox/README.md) | A multilingual benchmark that extends MMLU-Pro to multiple typologically diverse languages with human validation. | English, Japanese, Chinese, Korean, French, German, Spanish, Portuguese, Swahili, Thai, Arabic, Hindi, Bengali |
| [mmlusr](mmlusr/README.md) | Variation of MMLU designed to be more rigorous. | English |
| model_written_evals | Evaluation tasks auto-generated for evaluating a collection of AI Safety concerns. | |
| [moral_stories](moral_stories/README.md) | A crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | English |
| [mts_dialog](mts_dialog/README.md) | Open-ended healthcare QA from the MTS-Dialog dataset. | English |
| [multiblimp](multiblimp/README.md) | MultiBLiMP is a (synthetic) multilingual benchmark testing models on linguistic minimal pairs to judge grammatical acceptability | Multiple (101 languages) - Synthetic |
| [mutual](mutual/README.md) | A retrieval-based dataset for multi-turn dialogue reasoning. | English |
| [noreval](noreval/README.md) | A human-created Norwegian language understanding and generation benchmark. | Norwegian (Bokmål and Nynorsk) |
| [nq_open](nq_open/README.md) | Open domain question answering tasks based on the Natural Questions dataset. | English |
| [okapi/arc_multilingual](okapi/arc_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [okapi/hellaswag_multilingual](okapi/hellaswag_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (30 languages) **Machine Translated.** |
| okapi/mmlu_multilingual | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (34 languages) **Machine Translated.** |
| [okapi/truthfulqa_multilingual](okapi/truthfulqa_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [olaph](olaph/README.md) | Open-ended medical factuality Question Answering from the OLAPH dataset. | English |
| [openbookqa](openbookqa/README.md) | Open-book question answering tasks that require external knowledge and reasoning. | English |
| [paloma](paloma/README.md) | Paloma is a comprehensive benchmark designed to evaluate open language models across a wide range of domains, ranging from niche artist communities to mental health forums on Reddit. | English |
| [paws-x](paws-x/README.md) | Paraphrase Adversaries from Word Scrambling, focusing on cross-lingual capabilities. | English, French, Spanish, German, Chinese, Japanese, Korean |
| [pile](pile/README.md) | Open source language modelling data set that consists of 22 smaller, high-quality datasets. | English |
| [pile_10k](pile_10k/README.md) | The first 10K elements of The Pile, useful for debugging models trained on it. | English |
| [piqa](piqa/README.md) | Physical Interaction Question Answering tasks to test physical commonsense reasoning. | English |
| [polemo2](polemo2/README.md) | Sentiment analysis and emotion detection tasks based on Polish language data. | Polish |
| [portuguese_bench](portuguese_bench/README.md) | Collection of tasks in European Portuguese encompassing various evaluation areas. | Portuguese |
| [prost](prost/README.md) | Tasks requiring understanding of professional standards and ethics in various domains. | English |
| [pubmedqa](pubmedqa/README.md) | Question answering tasks based on PubMed research articles for biomedical understanding. | English |
| [qa4mre](qa4mre/README.md) | Question Answering for Machine Reading Evaluation, assessing comprehension and reasoning. | English |
| [qasper](qasper/README.md) | Question Answering dataset based on academic papers, testing in-depth scientific knowledge. | English |
| [race](race/README.md) | Reading comprehension assessment tasks based on English exams in China. | English |
| realtoxicityprompts | Tasks to evaluate language models for generating text with potential toxicity. | |
| [ruler](ruler/README.md) | RULER is a benchmark for testing how well language models handle long pieces of text. Requires custom arg (see readme) | English |
| [sciq](sciq/README.md) | Science Question Answering tasks to assess understanding of scientific concepts. | English |
| [score](score/README.md) | Systematic consistency and robustness evaluation for LLMs on 3 datasets(MMLU-Pro, Agi Eval and MATH) | English |
| [scrolls](scrolls/README.md) | Tasks that involve long-form reading comprehension across various domains. | English |
| [simple_cooccurrence_bias](simple_cooccurrence_bias/README.md) | A metric that evaluates language models for biases based on stereotypical word associations and co-occurrences in text. | English |
| [siqa](siqa/README.md) | Social Interaction Question Answering to evaluate common sense and social reasoning. | English |
| [spanish_bench](spanish_bench/README.md) | Collection of tasks in Spanish encompassing various evaluation areas. | Spanish |
| [squad_completion](squad_completion/README.md) | A variant of the SQuAD question answering task designed for zero-shot evaluation of small LMs. | English |
| [squadv2](squadv2/README.md) | Stanford Question Answering Dataset version 2, a reading comprehension benchmark. | English |
| [storycloze](storycloze/README.md) | Tasks to predict story endings, focusing on narrative logic and coherence. | English |
| [super_glue](super_glue/README.md) | A suite of challenging tasks designed to test a range of language understanding skills. | English |
| [swag](swag/README.md) | Situations With Adversarial Generations, predicting the next event in videos. | English |
| [swde](swde/README.md) | Information extraction tasks from semi-structured web pages. | English |
| [tinyBenchmarks](tinyBenchmarks/README.md) | Evaluation of large language models with fewer examples using tiny versions of popular benchmarks. | English |
| [tmmluplus](tmmluplus/README.md) | An extended set of tasks under the TMMLU framework for broader academic assessments. | Traditional Chinese |
| [toxigen](toxigen/README.md) | Tasks designed to evaluate language models on their propensity to generate toxic content. | English |
| [translation](translation/README.md) | Tasks focused on evaluating the language translation capabilities of models. | Arabic, English, Spanish, Basque, Hindi, Indonesian, Burmese, Russian, Swahili, Telugu, Chinese |
| [triviaqa](triviaqa/README.md) | A large-scale dataset for trivia question answering to test general knowledge. | English |
| [truthfulqa](truthfulqa/README.md) | A QA task aimed at evaluating the truthfulness and factual accuracy of model responses. | English |
| [truthfulqa-multi](truthfulqa-multi/README.md) | Is a multilingual version of TruthfulQA, a QA task aimed at evaluating the truthfulness and factual accuracy of model responses. | English, Spanish, Catalan, Basque, Galician |
| [turkishmmlu](turkishmmlu/README.md) | A multiple-choice QA test modeled after MMLU, written in Turkish based on Turkish high-school level exams. | Turkish |
| [unitxt](unitxt/README.md) | A number of tasks implemented using the unitxt library for flexible, shareable, and reusable data preparation and evaluation for generative AI. | English |
| [unscramble](unscramble/README.md) | Tasks involving the rearrangement of scrambled sentences to test syntactic understanding. | English |
| [webqs](webqs/README.md) | Web-based question answering tasks designed to evaluate internet search and retrieval. | English |
| [wikitext](wikitext/README.md) | Tasks based on text from Wikipedia articles to assess language modeling and generation. | English |
| [winogender](winogender/README.md) | A diagnostic dataset that tests for gender bias in coreference resolution by measuring how models associate pronouns with different occupations. | English |
| [winogrande](winogrande/README.md) | A large-scale dataset for coreference resolution, inspired by the Winograd Schema Challenge. | English |
| [wmdp](wmdp/README.md) | A benchmark with the objective of minimizing performance, based on potentially-sensitive multiple-choice knowledge questions. | English |
| [wmt2016](wmt2016/README.md) | Tasks from the WMT 2016 shared task, focusing on translation between multiple languages. | English, Czech, German, Finnish, Russian, Romanian, Turkish |
| [wsc273](wsc273/README.md) | The Winograd Schema Challenge, a test of commonsense reasoning and coreference resolution. | English |
| [xcopa](xcopa/README.md) | Cross-lingual Choice of Plausible Alternatives, testing reasoning in multiple languages. | Estonian, Haitian, Indonesian, Italian, Quechua, Swahili, Tamil, Thai, Turkish, Vietnamese, Chinese |
| [xnli](xnli/README.md) | Cross-Lingual Natural Language Inference to test understanding across different languages. | Arabic, Bulgarian, German, Greek, English, Spanish, French, Hindi, Russian, Swahili, Thai, Turkish, Urdu, Vietnamese, Chinese |
| [xnli_eu](xnli_eu/README.md) | Cross-lingual Natural Language Inference tasks in Basque. | Basque |
| [xquad](xquad/README.md) | Cross-lingual Question Answering Dataset in multiple languages. | Arabic, German, Greek, English, Spanish, Hindi, Romanian, Russian, Thai, Turkish, Vietnamese, Chinese |
| [xstorycloze](xstorycloze/README.md) | Cross-lingual narrative understanding tasks to predict story endings in multiple languages. | Russian, Simplified Chinese, Spanish, Arabic, Hindi, Indonesian, Telugu, Swahili, Basque, Burmese |
| [xwinograd](xwinograd/README.md) | Cross-lingual Winograd schema tasks for coreference resolution in multiple languages. | English, French, Japanese, Portuguese, Russian, Chinese |
| [xnli_eu](xnli_eu/README.md) | Cross-lingual Natural Language Inference tasks in Basque. | Basque |
| [xquad](xquad/README.md) | Cross-lingual Question Answering Dataset in multiple languages. | Arabic, German, Greek, English, Spanish, Hindi, Romanian, Russian, Thai, Turkish, Vietnamese, Chinese |
| [xstorycloze](xstorycloze/README.md) | Cross-lingual narrative understanding tasks to predict story endings in multiple languages. | Russian, Simplified Chinese, Spanish, Arabic, Hindi, Indonesian, Telugu, Swahili, Basque, Burmese |
| [xwinograd](xwinograd/README.md) | Cross-lingual Winograd schema tasks for coreference resolution in multiple languages. | English, French, Japanese, Portuguese, Russian, Chinese |
## Multimodal Tasks
| Task Family | Description | Modality |
......
lm_eval/tasks/afrobench/masakhapos/README.md
View file @ 3e3a0d8f
......@@ -73,3 +73,5 @@ HomePage: https://github.com/masakhane-io/masakhane-pos
abstract = "In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages."
}
```
## Changelog
- 2025-07-21: Refactored. Scores should not be affected.
lm_eval/tasks/afrobench/masakhapos/prompt_1/masakhapos_yaml
View file @ 3e3a0d8f
......@@ -14,19 +14,18 @@ validation_split: validation
test_split: test
fewshot_split: train
doc_to_target: !function utils.doc_to_target
process_results: !function utils.process_results
should_decontaminate: true
doc_to_decontamination_query: "Sentence: {{token}}\nOutput:"
filter_list:
- filter:
- function: regex_pos
- function: "custom"
filter_fn: !function utils.extract_pos
- function: "take_first"
name: flexible-extract
metric_list:
- metric: acc
aggregation: !function utils.acc_score
aggregation: mean
higher_is_better: true
ignore_case: true
ignore_punctuation: true
regexes_to_ignore:
- ","
metadata:
version: 1.0
lm_eval/tasks/afrobench/masakhapos/prompt_1/utils.py
View file @ 3e3a0d8f
from itertools import chain
import re
from collections.abc import Iterable
from typing import Any
from sklearn.metrics import accuracy_score
from lm_eval.utils import weighted_f1_score
def doc_to_target(doc):
pos_tag_map = {
......@@ -29,27 +29,40 @@ def doc_to_target(doc):
return [pos_tag_map[tag] for tag in doc["upos"]]
def acc_score(items):
unzipped_list = list(zip(*items))
def extract_pos(resps: Iterable[list[str]], *args) -> Iterable[list[str]]:
def extract_tagged_tokens(text: str) -> list[tuple[str, str]]:
# Extract tagged tokens list from text input using regex
tokens = re.findall(
r"\('([^']*)', '([^']*)'\)",
"Here are some tuples: ('apple', 'red'), ('banana', 'yellow'), ('grape', 'purple')",
)
return [(token, pos) for token, pos in tokens]
def extract_pos_tags(result: str):
pos_tags = []
if isinstance(result, str):
result_ = extract_tagged_tokens(result)
pos_tags.extend(pos for _, pos in result_)
return pos_tags if pos_tags else ["invalid"]
def filter_set(inst: list[str]) -> list[str]:
filtered = []
for resp in inst:
match = extract_pos_tags(resp)
filtered.append(match)
return filtered
golds, preds = unzipped_list[0], unzipped_list[1]
filtered_resps = map(lambda x: filter_set(x), resps)
# Flatten preds' inner lists
flattened_preds = [list(chain.from_iterable(p)) for p in preds]
return filtered_resps
# Calculate the accuracy for each gold-pred pair
accuracy_scores = []
for gold, pred in zip(golds, flattened_preds):
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(gold), len(pred))
gold = gold[:min_length]
pred = pred[:min_length]
# Calculate accuracy for the current pair and add to the list
accuracy = accuracy_score(gold, pred)
accuracy_scores.append(accuracy)
def process_results(doc: dict[str, Any], results: list[list[str]]):
golds, preds = doc_to_target(doc), results[0]
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(golds), len(preds))
gold = golds[:min_length]
pred = preds[:min_length]
accuracy = accuracy_score(gold, pred)
mean_accuracy = (
sum(accuracy_scores) / len(accuracy_scores) if accuracy_scores else 0
)
return mean_accuracy
return {"acc": accuracy}
lm_eval/tasks/afrobench/masakhapos/prompt_2/masakhapos_yaml
View file @ 3e3a0d8f
......@@ -16,17 +16,16 @@ fewshot_split: train
doc_to_target: !function utils.doc_to_target
should_decontaminate: true
doc_to_decontamination_query: "Sentence: {{token}}\nOutput:"
process_results: !function utils.process_results
filter_list:
- filter:
- function: regex_pos
- function: "custom"
filter_fn: !function utils.extract_pos
- function: "take_first"
name: flexible-extract
metric_list:
- metric: acc
aggregation: !function utils.acc_score
aggregation: mean
higher_is_better: true
ignore_case: true
ignore_punctuation: true
regexes_to_ignore:
- ","
metadata:
version: 1.0
lm_eval/tasks/afrobench/masakhapos/prompt_2/utils.py
View file @ 3e3a0d8f
from itertools import chain
import re
from collections.abc import Iterable
from typing import Any
from sklearn.metrics import accuracy_score
from lm_eval.utils import weighted_f1_score
def doc_to_target(doc):
pos_tag_map = {
......@@ -29,27 +29,40 @@ def doc_to_target(doc):
return [pos_tag_map[tag] for tag in doc["upos"]]
def acc_score(items):
unzipped_list = list(zip(*items))
def extract_pos(resps: Iterable[list[str]], *args) -> Iterable[list[str]]:
def extract_tagged_tokens(text: str) -> list[tuple[str, str]]:
# Extract tagged tokens list from text input using regex
tokens = re.findall(
r"\('([^']*)', '([^']*)'\)",
"Here are some tuples: ('apple', 'red'), ('banana', 'yellow'), ('grape', 'purple')",
)
return [(token, pos) for token, pos in tokens]
def extract_pos_tags(result: str):
pos_tags = []
if isinstance(result, str):
result_ = extract_tagged_tokens(result)
pos_tags.extend(pos for _, pos in result_)
return pos_tags if pos_tags else ["invalid"]
def filter_set(inst: list[str]) -> list[str]:
filtered = []
for resp in inst:
match = extract_pos_tags(resp)
filtered.append(match)
return filtered
golds, preds = unzipped_list[0], unzipped_list[1]
filtered_resps = map(lambda x: filter_set(x), resps)
# Flatten preds' inner lists
flattened_preds = [list(chain.from_iterable(p)) for p in preds]
return filtered_resps
# Calculate the accuracy for each gold-pred pair
accuracy_scores = []
for gold, pred in zip(golds, flattened_preds):
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(gold), len(pred))
gold = gold[:min_length]
pred = pred[:min_length]
# Calculate accuracy for the current pair and add to the list
accuracy = accuracy_score(gold, pred)
accuracy_scores.append(accuracy)
def process_results(doc: dict[str, Any], results: list[list[str]]):
golds, preds = doc_to_target(doc), results[0]
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(golds), len(preds))
gold = golds[:min_length]
pred = preds[:min_length]
accuracy = accuracy_score(gold, pred)
mean_accuracy = (
sum(accuracy_scores) / len(accuracy_scores) if accuracy_scores else 0
)
return mean_accuracy
return {"acc": accuracy}
lm_eval/tasks/afrobench/masakhapos/prompt_3/masakhapos_yaml
View file @ 3e3a0d8f
......@@ -16,17 +16,16 @@ fewshot_split: train
doc_to_target: !function utils.doc_to_target
should_decontaminate: true
doc_to_decontamination_query: "Sentence: {{token}}\nOutput:"
process_results: !function utils.process_results
filter_list:
- filter:
- function: regex_pos
- function: "custom"
filter_fn: !function utils.extract_pos
- function: "take_first"
name: flexible-extract
metric_list:
- metric: acc
aggregation: !function utils.acc_score
aggregation: mean
higher_is_better: true
ignore_case: true
ignore_punctuation: true
regexes_to_ignore:
- ","
metadata:
version: 1.0
lm_eval/tasks/afrobench/masakhapos/prompt_3/utils.py
View file @ 3e3a0d8f
from itertools import chain
import re
from collections.abc import Iterable
from typing import Any
from sklearn.metrics import accuracy_score
from lm_eval.utils import weighted_f1_score
def doc_to_target(doc):
pos_tag_map = {
......@@ -29,27 +29,40 @@ def doc_to_target(doc):
return [pos_tag_map[tag] for tag in doc["upos"]]
def acc_score(items):
unzipped_list = list(zip(*items))
def extract_pos(resps: Iterable[list[str]], *args) -> Iterable[list[str]]:
def extract_tagged_tokens(text: str) -> list[tuple[str, str]]:
# Extract tagged tokens list from text input using regex
tokens = re.findall(
r"\('([^']*)', '([^']*)'\)",
"Here are some tuples: ('apple', 'red'), ('banana', 'yellow'), ('grape', 'purple')",
)
return [(token, pos) for token, pos in tokens]
def extract_pos_tags(result: str):
pos_tags = []
if isinstance(result, str):
result_ = extract_tagged_tokens(result)
pos_tags.extend(pos for _, pos in result_)
return pos_tags if pos_tags else ["invalid"]
def filter_set(inst: list[str]) -> list[str]:
filtered = []
for resp in inst:
match = extract_pos_tags(resp)
filtered.append(match)
return filtered
golds, preds = unzipped_list[0], unzipped_list[1]
filtered_resps = map(lambda x: filter_set(x), resps)
# Flatten preds' inner lists
flattened_preds = [list(chain.from_iterable(p)) for p in preds]
return filtered_resps
# Calculate the accuracy for each gold-pred pair
accuracy_scores = []
for gold, pred in zip(golds, flattened_preds):
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(gold), len(pred))
gold = gold[:min_length]
pred = pred[:min_length]
# Calculate accuracy for the current pair and add to the list
accuracy = accuracy_score(gold, pred)
accuracy_scores.append(accuracy)
def process_results(doc: dict[str, Any], results: list[list[str]]):
golds, preds = doc_to_target(doc), results[0]
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(golds), len(preds))
gold = golds[:min_length]
pred = preds[:min_length]
accuracy = accuracy_score(gold, pred)
mean_accuracy = (
sum(accuracy_scores) / len(accuracy_scores) if accuracy_scores else 0
)
return mean_accuracy
return {"acc": accuracy}
lm_eval/tasks/afrobench/masakhapos/prompt_4/masakhapos_yaml
View file @ 3e3a0d8f
......@@ -16,17 +16,16 @@ fewshot_split: train
doc_to_target: !function utils.doc_to_target
should_decontaminate: true
doc_to_decontamination_query: "Sentence: {{token}}\nOutput:"
process_results: !function utils.process_results
filter_list:
- filter:
- function: regex_pos
- function: "custom"
filter_fn: !function utils.extract_pos
- function: "take_first"
name: flexible-extract
metric_list:
- metric: acc
aggregation: !function utils.acc_score
aggregation: mean
higher_is_better: true
ignore_case: true
ignore_punctuation: true
regexes_to_ignore:
- ","
metadata:
version: 1.0
lm_eval/tasks/afrobench/masakhapos/prompt_4/utils.py
View file @ 3e3a0d8f
from itertools import chain
import re
from collections.abc import Iterable
from typing import Any
from sklearn.metrics import accuracy_score
from lm_eval.utils import weighted_f1_score
def doc_to_target(doc):
pos_tag_map = {
......@@ -29,27 +29,40 @@ def doc_to_target(doc):
return [pos_tag_map[tag] for tag in doc["upos"]]
def acc_score(items):
unzipped_list = list(zip(*items))
def extract_pos(resps: Iterable[list[str]], *args) -> Iterable[list[str]]:
def extract_tagged_tokens(text: str) -> list[tuple[str, str]]:
# Extract tagged tokens list from text input using regex
tokens = re.findall(
r"\('([^']*)', '([^']*)'\)",
"Here are some tuples: ('apple', 'red'), ('banana', 'yellow'), ('grape', 'purple')",
)
return [(token, pos) for token, pos in tokens]
def extract_pos_tags(result: str):
pos_tags = []
if isinstance(result, str):
result_ = extract_tagged_tokens(result)
pos_tags.extend(pos for _, pos in result_)
return pos_tags if pos_tags else ["invalid"]
def filter_set(inst: list[str]) -> list[str]:
filtered = []
for resp in inst:
match = extract_pos_tags(resp)
filtered.append(match)
return filtered
golds, preds = unzipped_list[0], unzipped_list[1]
filtered_resps = map(lambda x: filter_set(x), resps)
# Flatten preds' inner lists
flattened_preds = [list(chain.from_iterable(p)) for p in preds]
return filtered_resps
# Calculate the accuracy for each gold-pred pair
accuracy_scores = []
for gold, pred in zip(golds, flattened_preds):
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(gold), len(pred))
gold = gold[:min_length]
pred = pred[:min_length]
# Calculate accuracy for the current pair and add to the list
accuracy = accuracy_score(gold, pred)
accuracy_scores.append(accuracy)
def process_results(doc: dict[str, Any], results: list[list[str]]):
golds, preds = doc_to_target(doc), results[0]
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(golds), len(preds))
gold = golds[:min_length]
pred = preds[:min_length]
accuracy = accuracy_score(gold, pred)
mean_accuracy = (
sum(accuracy_scores) / len(accuracy_scores) if accuracy_scores else 0
)
return mean_accuracy
return {"acc": accuracy}
lm_eval/tasks/afrobench/masakhapos/prompt_5/masakhapos_yaml
View file @ 3e3a0d8f
......@@ -16,17 +16,16 @@ fewshot_split: train
doc_to_target: !function utils.doc_to_target
should_decontaminate: true
doc_to_decontamination_query: "Sentence: {{token}}\nOutput:"
process_results: !function utils.process_results
filter_list:
- filter:
- function: regex_pos
- function: "custom"
filter_fn: !function utils.extract_pos
- function: "take_first"
name: flexible-extract
metric_list:
- metric: acc
aggregation: !function utils.acc_score
aggregation: mean
higher_is_better: true
ignore_case: true
ignore_punctuation: true
regexes_to_ignore:
- ","
metadata:
version: 1.0
lm_eval/tasks/afrobench/masakhapos/prompt_5/utils.py
View file @ 3e3a0d8f
from itertools import chain
import re
from collections.abc import Iterable
from typing import Any
from sklearn.metrics import accuracy_score
from lm_eval.utils import weighted_f1_score
def doc_to_target(doc):
pos_tag_map = {
......@@ -29,27 +29,40 @@ def doc_to_target(doc):
return [pos_tag_map[tag] for tag in doc["upos"]]
def acc_score(items):
unzipped_list = list(zip(*items))
def extract_pos(resps: Iterable[list[str]], *args) -> Iterable[list[str]]:
def extract_tagged_tokens(text: str) -> list[tuple[str, str]]:
# Extract tagged tokens list from text input using regex
tokens = re.findall(
r"\('([^']*)', '([^']*)'\)",
"Here are some tuples: ('apple', 'red'), ('banana', 'yellow'), ('grape', 'purple')",
)
return [(token, pos) for token, pos in tokens]
def extract_pos_tags(result: str):
pos_tags = []
if isinstance(result, str):
result_ = extract_tagged_tokens(result)
pos_tags.extend(pos for _, pos in result_)
return pos_tags if pos_tags else ["invalid"]
def filter_set(inst: list[str]) -> list[str]:
filtered = []
for resp in inst:
match = extract_pos_tags(resp)
filtered.append(match)
return filtered
golds, preds = unzipped_list[0], unzipped_list[1]
filtered_resps = map(lambda x: filter_set(x), resps)
# Flatten preds' inner lists
flattened_preds = [list(chain.from_iterable(p)) for p in preds]
return filtered_resps
# Calculate the accuracy for each gold-pred pair
accuracy_scores = []
for gold, pred in zip(golds, flattened_preds):
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(gold), len(pred))
gold = gold[:min_length]
pred = pred[:min_length]
# Calculate accuracy for the current pair and add to the list
accuracy = accuracy_score(gold, pred)
accuracy_scores.append(accuracy)
def process_results(doc: dict[str, Any], results: list[list[str]]):
golds, preds = doc_to_target(doc), results[0]
# Ensure both lists are of the same length, otherwise truncate to match
min_length = min(len(golds), len(preds))
gold = golds[:min_length]
pred = preds[:min_length]
accuracy = accuracy_score(gold, pred)
mean_accuracy = (
sum(accuracy_scores) / len(accuracy_scores) if accuracy_scores else 0
)
return mean_accuracy
return {"acc": accuracy}
lm_eval/tasks/afrobench/masakhapos/utils.py
View file @ 3e3a0d8f
from lm_eval.utils import weighted_f1_score
def doc_to_text(doc):
output = """Please provide the POS tags for each word in the input sentence. The input will be a list of words in
the sentence. The output format should be a list of tuples, where each tuple consists of a word from the input text
......
lm_eval/tasks/glue/cola/default.yaml
View file @ 3e3a0d8f
tag: glue
task: cola
dataset_path: glue
dataset_path: nyu-mll/glue
dataset_name: cola
output_type: multiple_choice
training_split: train
......
lm_eval/tasks/glue/mnli/default.yaml
View file @ 3e3a0d8f
tag: glue
task: mnli
dataset_path: glue
dataset_path: nyu-mll/glue
dataset_name: mnli
output_type: multiple_choice
training_split: train
......
lm_eval/tasks/glue/mrpc/default.yaml
View file @ 3e3a0d8f
tag: glue
task: mrpc
dataset_path: glue
dataset_path: nyu-mll/glue
dataset_name: mrpc
output_type: multiple_choice
training_split: train
......
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