def generate(
generate_task_examples,
train_data: List[Dict[str, Any]],
test_data: List[Dict[str, Any]],
validation_size: float = 0.1,
validation_data: Optional[List[Dict[str, Any]]] = None,
) -> List[ProbingTaskExample]:
if validation_data is None:
train_data, validation_data = train_val_split(train_data, validation_size)
logger.info(f"Num train examples: {len(train_data)}")
logger.info(f"Num validation examples: {len(validation_data)}")
logger.info(f"Num test examples: {len(test_data)}")
task_examples = []
train_task_examples = generate_task_examples(train_data, split="tr")
task_examples.extend(train_task_examples)
validation_task_examples = generate_task_examples(validation_data, split="va")
task_examples.extend(validation_task_examples)
test_task_examples = generate_task_examples(test_data, split="te")
task_examples.extend(test_task_examples)
logger.info(f"Num train task examples: {len(train_task_examples)}")
logger.info(f"Num validation task examples: {len(validation_task_examples)}")
logger.info(f"Num test task examples: {len(test_task_examples)}")
return task_examples
def generate(
train_data: List[Dict[str, Any]],
test_data: List[Dict[str, Any]],
validation_size: float = 0.1,
validation_data: Optional[List[Dict[str, Any]]] = None,
argument: str = "head",
roles: Optional[List[str]] = None,
) -> List[ProbingTaskExample]:
logger.info("Generating dataset for probing task: ArgumentGrammaticalRole")
if argument not in {"head", "tail"}:
raise (f"Invalid argument [{argument}]")
if roles is None:
roles = DEFAULT_ROLES
if validation_data is None:
train_data, validation_data = train_val_split(train_data,
validation_size)
logger.info(f"Using argument: {argument}")
logger.info(f"Num train examples: {len(train_data)}")
logger.info(f"Num validation examples: {len(validation_data)}")
logger.info(f"Num test examples: {len(test_data)}")
task_examples = []
train_task_examples = generate_task_examples(train_data,
argument,
roles,
split="tr")
task_examples.extend(train_task_examples)
validation_task_examples = generate_task_examples(validation_data,
argument,
roles,
split="va")
task_examples.extend(validation_task_examples)
test_task_examples = generate_task_examples(test_data,
argument,
roles,
split="te")
task_examples.extend(test_task_examples)
logger.info(f"Num train task examples: {len(train_task_examples)}")
logger.info(
f"Num validation task examples: {len(validation_task_examples)}")
logger.info(f"Num test task examples: {len(test_task_examples)}")
return task_examples
def generate(
train_data: List[Dict[str, Any]],
test_data: List[Dict[str, Any]],
validation_size: float = 0.1,
validation_data: Optional[List[Dict[str, Any]]] = None,
buckets: Optional[List[Tuple[int, int]]] = None,
) -> List[ProbingTaskExample]:
logger.info("Generating dataset for probing task: SentLength")
if buckets is None:
buckets = DEFAULT_BUCKETS
logger.info(f"Buckets: {buckets}")
if validation_data is None:
train_data, validation_data = train_val_split(train_data,
validation_size)
logger.info(f"Num train examples: {len(train_data)}")
logger.info(f"Num validation examples: {len(validation_data)}")
logger.info(f"Num test examples: {len(test_data)}")
task_examples = []
train_task_examples = generate_task_examples(train_data,
buckets,
split="tr")
task_examples.extend(train_task_examples)
validation_task_examples = generate_task_examples(validation_data,
buckets,
split="va")
task_examples.extend(validation_task_examples)
test_task_examples = generate_task_examples(test_data, buckets, split="te")
task_examples.extend(test_task_examples)
logger.info(f"Num train task examples: {len(train_task_examples)}")
logger.info(
f"Num validation task examples: {len(validation_task_examples)}")
logger.info(f"Num test task examples: {len(test_task_examples)}")
return task_examples
def generate(
train_data: List[Dict[str, Any]],
test_data: List[Dict[str, Any]],
validation_size: float = 0.1,
validation_data: Optional[List[Dict[str, Any]]] = None,
ner_count_tag: str = "ORGANIZATION",
) -> List[ProbingTaskExample]:
logger.info("Generating dataset for probing task: EntCountBetween")
if validation_data is None:
train_data, validation_data = train_val_split(train_data,
validation_size)
logger.info(f"Using NER tag: {ner_count_tag}")
logger.info(f"Num train examples: {len(train_data)}")
logger.info(f"Num validation examples: {len(validation_data)}")
logger.info(f"Num test examples: {len(test_data)}")
task_examples = []
train_task_examples = generate_task_examples(train_data,
ner_count_tag,
split="tr")
task_examples.extend(train_task_examples)
validation_task_examples = generate_task_examples(validation_data,
ner_count_tag,
split="va")
task_examples.extend(validation_task_examples)
test_task_examples = generate_task_examples(test_data,
ner_count_tag,
split="te")
task_examples.extend(test_task_examples)
logger.info(f"Num train task examples: {len(train_task_examples)}")
logger.info(
f"Num validation task examples: {len(validation_task_examples)}")
logger.info(f"Num test task examples: {len(test_task_examples)}")
return task_examples
def generate(
train_data: List[Dict[str, Any]],
test_data: List[Dict[str, Any]],
argument: str,
validation_size: float = 0.1,
validation_data: Optional[List[Dict[str, Any]]] = None,
keep_types: Optional[List[str]] = None,
) -> List[ProbingTaskExample]:
logger.info(
f"Generating dataset for probing task: ArgumentType{argument.capitalize()}"
)
if argument not in ["head", "tail"]:
raise ValueError(f"'{argument}' is not a valid argument.")
if validation_data is None:
train_data, validation_data = train_val_split(train_data,
validation_size)
logger.info(f"Argument: {argument}")
logger.info(f"Num train examples: {len(train_data)}")
logger.info(f"Num validation examples: {len(validation_data)}")
logger.info(f"Num test examples: {len(test_data)}")
all_arg_types = Counter()
for data in [train_data, validation_data, test_data]:
for example in data:
field = f"{argument}_type"
all_arg_types.update([example[field]])
logger.info(f"Label distribution: {all_arg_types.most_common()}")
type2idx = {arg_type: i for i, arg_type in enumerate(list(all_arg_types))}
task_examples = []
train_task_examples = generate_task_examples(train_data,
argument,
type2idx,
keep_types,
split="tr")
task_examples.extend(train_task_examples)
idx2type = {v: k for k, v in type2idx.items()}
class_distribution = Counter(
[idx2type[example.label] for example in train_task_examples])
logger.info(f"CT: {class_distribution}")
validation_task_examples = generate_task_examples(validation_data,
argument,
type2idx,
keep_types,
split="va")
task_examples.extend(validation_task_examples)
test_task_examples = generate_task_examples(test_data,
argument,
type2idx,
keep_types,
split="te")
task_examples.extend(test_task_examples)
logger.info(f"Num train task examples: {len(train_task_examples)}")
logger.info(
f"Num validation task examples: {len(validation_task_examples)}")
logger.info(f"Num test task examples: {len(test_task_examples)}")
return task_examples
def generate(
train_data: List[Dict[str, Any]],
test_data: List[Dict[str, Any]],
argument: str,
position: str,
validation_size: float = 0.1,
validation_data: Optional[List[Dict[str, Any]]] = None,
keep_tags: Optional[List[str]] = None,
) -> List[ProbingTaskExample]:
logger.info("Generating dataset for probing task: " +
f"PosTag{argument.capitalize()}{position.capitalize()}")
if argument not in ["head", "tail"]:
raise ValueError(f"'{argument}' is not a valid argument.")
if position not in ["left", "right"]:
raise ValueError(f"'{position}' is not a valid position.")
if validation_data is None:
train_data, validation_data = train_val_split(train_data,
validation_size)
logger.info(f"Argument: {argument}")
logger.info(f"Position: {position}")
logger.info(f"Num train examples: {len(train_data)}")
logger.info(f"Num validation examples: {len(validation_data)}")
logger.info(f"Num test examples: {len(test_data)}")
all_pos_tags = Counter()
for data in [train_data, validation_data, test_data]:
for example in data:
all_pos_tags.update(example["pos"])
logger.info(f"Label distribution: {all_pos_tags}")
pos2idx = {pos_tag: i for i, pos_tag in enumerate(list(all_pos_tags))}
task_examples = []
train_task_examples = generate_task_examples(train_data,
argument,
position,
pos2idx,
keep_tags,
split="tr")
task_examples.extend(train_task_examples)
idx2pos = {v: k for k, v in pos2idx.items()}
class_distribution = Counter(
[idx2pos[example.label] for example in train_task_examples])
logger.info(f"CT: {class_distribution}")
validation_task_examples = generate_task_examples(validation_data,
argument,
position,
pos2idx,
keep_tags,
split="va")
task_examples.extend(validation_task_examples)
test_task_examples = generate_task_examples(test_data,
argument,
position,
pos2idx,
keep_tags,
split="te")
task_examples.extend(test_task_examples)
logger.info(f"Num train task examples: {len(train_task_examples)}")
logger.info(
f"Num validation task examples: {len(validation_task_examples)}")
logger.info(f"Num test task examples: {len(test_task_examples)}")
return task_examples
