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161 | """All benchmarks tasks used in EuroEval."""
from . import metrics as m
from .constants import NUM_GENERATION_TOKENS_FOR_CLASSIFICATION
from .data_models import Task
from .enums import GenerativeType, ModelType, TaskGroup
from .prompt_templates import (
LA_TEMPLATES,
MULTIPLE_CHOICE_TEMPLATES,
NER_TEMPLATES,
RC_TEMPLATES,
SENT_TEMPLATES,
SUMM_TEMPLATES,
)
def get_all_tasks() -> dict[str, Task]:
"""Get a list of all the dataset tasks.
Returns:
A mapping between names of dataset tasks and their configurations.
"""
return {cfg.name: cfg for cfg in globals().values() if isinstance(cfg, Task)}
LA = Task(
name="linguistic-acceptability",
task_group=TaskGroup.SEQUENCE_CLASSIFICATION,
template_dict=LA_TEMPLATES,
metrics=[m.mcc_metric, m.macro_f1_metric],
default_num_few_shot_examples=12,
default_max_generated_tokens=NUM_GENERATION_TOKENS_FOR_CLASSIFICATION,
default_labels=["correct", "incorrect"],
uses_logprobs=True,
)
NER = Task(
name="named-entity-recognition",
task_group=TaskGroup.TOKEN_CLASSIFICATION,
template_dict=NER_TEMPLATES,
metrics=[m.micro_f1_no_misc_metric, m.micro_f1_metric],
default_num_few_shot_examples=8,
default_max_generated_tokens=128,
default_labels=[
"o",
"b-loc",
"i-loc",
"b-org",
"i-org",
"b-per",
"i-per",
"b-misc",
"i-misc",
],
uses_structured_output=True,
)
RC = Task(
name="reading-comprehension",
task_group=TaskGroup.QUESTION_ANSWERING,
template_dict=RC_TEMPLATES,
metrics=[m.f1_metric, m.em_metric],
default_num_few_shot_examples=4,
default_max_generated_tokens=32,
default_labels=["start_positions", "end_positions"],
)
SENT = Task(
name="sentiment-classification",
task_group=TaskGroup.SEQUENCE_CLASSIFICATION,
template_dict=SENT_TEMPLATES,
metrics=[m.mcc_metric, m.macro_f1_metric],
default_num_few_shot_examples=12,
default_max_generated_tokens=NUM_GENERATION_TOKENS_FOR_CLASSIFICATION,
default_labels=["positive", "neutral", "negative"],
uses_logprobs=True,
)
SUMM = Task(
name="summarization",
task_group=TaskGroup.TEXT_TO_TEXT,
template_dict=SUMM_TEMPLATES,
metrics=[m.bert_score_metric, m.rouge_l_metric],
default_num_few_shot_examples=1,
default_max_generated_tokens=256,
default_labels=[],
default_allowed_model_types=[ModelType.GENERATIVE],
)
KNOW = Task(
name="knowledge",
task_group=TaskGroup.MULTIPLE_CHOICE_CLASSIFICATION,
template_dict=MULTIPLE_CHOICE_TEMPLATES,
metrics=[m.mcc_metric, m.accuracy_metric],
default_num_few_shot_examples=5,
default_max_generated_tokens=NUM_GENERATION_TOKENS_FOR_CLASSIFICATION,
default_labels=["a", "b", "c", "d"],
default_allowed_model_types=[ModelType.GENERATIVE],
uses_logprobs=True,
)
MCRC = Task(
name="multiple-choice-reading-comprehension",
task_group=TaskGroup.MULTIPLE_CHOICE_CLASSIFICATION,
template_dict=MULTIPLE_CHOICE_TEMPLATES,
metrics=[m.mcc_metric, m.accuracy_metric],
default_num_few_shot_examples=5,
default_max_generated_tokens=NUM_GENERATION_TOKENS_FOR_CLASSIFICATION,
default_labels=["a", "b", "c", "d"],
default_allowed_model_types=[ModelType.GENERATIVE],
uses_logprobs=True,
)
COMMON_SENSE = Task(
name="common-sense-reasoning",
task_group=TaskGroup.MULTIPLE_CHOICE_CLASSIFICATION,
template_dict=MULTIPLE_CHOICE_TEMPLATES,
metrics=[m.mcc_metric, m.accuracy_metric],
default_num_few_shot_examples=5,
default_max_generated_tokens=NUM_GENERATION_TOKENS_FOR_CLASSIFICATION,
default_labels=["a", "b", "c", "d"],
default_allowed_model_types=[ModelType.GENERATIVE],
uses_logprobs=True,
)
EUROPEAN_VALUES = Task(
name="european-values",
task_group=TaskGroup.MULTIPLE_CHOICE_CLASSIFICATION,
template_dict=MULTIPLE_CHOICE_TEMPLATES,
metrics=[m.european_values_metric],
default_num_few_shot_examples=0,
default_max_generated_tokens=NUM_GENERATION_TOKENS_FOR_CLASSIFICATION,
default_labels=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"],
default_allowed_model_types=[ModelType.GENERATIVE],
default_allowed_generative_types=[
GenerativeType.INSTRUCTION_TUNED,
GenerativeType.REASONING,
],
requires_zero_shot=True,
uses_logprobs=True,
default_allow_invalid_model_outputs=False,
)
SPEED = Task(
name="speed",
task_group=TaskGroup.SPEED,
template_dict={},
metrics=[m.speed_metric, m.speed_short_metric],
default_num_few_shot_examples=0,
default_max_generated_tokens=5,
default_labels=[],
)
|