File size: 28,027 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 |
# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from collections.abc import Sequence
from enum import unique
from typing import TYPE_CHECKING, List, Optional, Union
import torch
from torch import Tensor
from torch.nn import functional as F # noqa: N812
from torch.utils.data import DataLoader
from typing_extensions import Literal
from torchmetrics.functional.text.helper_embedding_metric import (
TokenizedDataset,
_get_progress_bar,
_input_data_collator,
_load_tokenizer_and_model,
)
from torchmetrics.utilities.enums import EnumStr
from torchmetrics.utilities.imports import _TRANSFORMERS_GREATER_EQUAL_4_4
if TYPE_CHECKING and _TRANSFORMERS_GREATER_EQUAL_4_4:
from transformers import PreTrainedModel, PreTrainedTokenizerBase
if not _TRANSFORMERS_GREATER_EQUAL_4_4:
__doctest_skip__ = ["infolm"]
_ALLOWED_INFORMATION_MEASURE_LITERAL = Literal[
"kl_divergence",
"alpha_divergence",
"beta_divergence",
"ab_divergence",
"renyi_divergence",
"l1_distance",
"l2_distance",
"l_infinity_distance",
"fisher_rao_distance",
]
@unique
class _IMEnum(EnumStr):
"""A helper Enum class for storing the information measure."""
@staticmethod
def _name() -> str:
return "Information measure"
KL_DIVERGENCE = "kl_divergence"
ALPHA_DIVERGENCE = "alpha_divergence"
BETA_DIVERGENCE = "beta_divergence"
AB_DIVERGENCE = "ab_divergence"
RENYI_DIVERGENCE = "renyi_divergence"
L1_DISTANCE = "l1_distance"
L2_DISTANCE = "l2_distance"
L_INFINITY_DISTANCE = "l_infinity_distance"
FISHER_RAO_DISTANCE = "fisher_rao_distance"
class _InformationMeasure:
"""A wrapper class used for the calculation of different information measures.
This metric can be used to measure the information between the discrete reference distributions of predicted and
reference sentences. The class also handles input validation for `alpha` and `beta` parameters.
Args:
information_measure:
A name of information measure to be used. Please use one of: ['kl_divergence', 'alpha_divergence',
'beta_divergence', 'ab_divergence', 'renyi_divergence', 'l1_distance', 'l2_distance', 'l_infinity_distance',
'fisher_rao_distance']
alpha:
Alpha parameter of the divergence used for alpha, AB and Rényi divergence measures.
beta:
Beta parameter of the divergence used for beta and AB divergence measures.
Raises:
ValueError:
If information measure is one from alpha, AB or Rényi divergence and parameter `alpha` is `None`.
ValueError:
If information measure is one from beta or divergence and parameter `beta` is `None`.
ValueError:
If information measure is alpha divergence and parameter `alpha` equals 0 or 1.
ValueError:
If information measure is beta divergence and parameter `beta` equals 0 or -1.
ValueError:
If information measure is AB divergence and parameter `alpha`, `beta` or `alpha + beta` equal 0.
ValueError:
If information measure is Rényi divergence and parameter `alpha` equals 1.
"""
def __init__(
self,
information_measure: _ALLOWED_INFORMATION_MEASURE_LITERAL,
alpha: Optional[float] = None,
beta: Optional[float] = None,
) -> None:
self.information_measure = _IMEnum.from_str(information_measure)
_bad_measures = (_IMEnum.ALPHA_DIVERGENCE, _IMEnum.AB_DIVERGENCE, _IMEnum.RENYI_DIVERGENCE)
if self.information_measure in _bad_measures and not isinstance(alpha, float):
raise ValueError(f"Parameter `alpha` is expected to be defined for {information_measure}.")
if self.information_measure in [_IMEnum.BETA_DIVERGENCE, _IMEnum.AB_DIVERGENCE] and not isinstance(beta, float):
raise ValueError(f"Parameter `beta` is expected to be defined for {information_measure}.")
if self.information_measure == _IMEnum.ALPHA_DIVERGENCE and (not isinstance(alpha, float) or alpha in [0, 1]):
raise ValueError(
f"Parameter `alpha` is expected to be float differened from 0 and 1 for {information_measure}."
)
if self.information_measure == _IMEnum.BETA_DIVERGENCE and (not isinstance(beta, float) or beta in [0, -1]):
raise ValueError(
f"Parameter `beta` is expected to be float differened from 0 and -1 for {information_measure}."
)
if self.information_measure == _IMEnum.AB_DIVERGENCE and (
alpha is None
or beta is None
or (any(not isinstance(p, float) for p in [alpha, beta]) or 0 in [alpha, beta, alpha + beta])
):
raise ValueError(
"Parameters `alpha`, `beta` and their sum are expected to be differened from 0 for "
f"{information_measure}."
)
if self.information_measure == _IMEnum.RENYI_DIVERGENCE and (not isinstance(alpha, float) or alpha == 1):
raise ValueError(f"Parameter `alpha` is expected to be float differened from 1 for {information_measure}.")
# We ensure self.alpha and self.beta to be different from None to ensure mypy compliance
self.alpha = alpha or 0
self.beta = beta or 0
def __call__(self, preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
information_measure_function = getattr(self, f"_calculate_{self.information_measure.value}")
return torch.nan_to_num(information_measure_function(preds_distribution, target_distribution))
@staticmethod
def _calculate_kl_divergence(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
"""Calculate Kullback-Leibler divergence between discrete distributions of predicted and reference sentences.
Args:
preds_distribution:
Discrete reference distribution of predicted sentences over the vocabulary.
target_distribution:
Discrete reference distribution of reference sentences over the vocabulary.
Return:
Kullback-Leibler divergence between discrete distributions of predicted and reference sentences.
"""
return torch.sum(target_distribution * torch.log(preds_distribution / target_distribution), dim=-1)
def _calculate_alpha_divergence(self, preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
"""Calculate alpha divergence between discrete distributions of predicted and reference sentences.
Args:
preds_distribution:
Discrete reference distribution of predicted sentences over the vocabulary.
target_distribution:
Discrete reference distribution of reference sentences over the vocabulary.
Return:
Alpha divergence between discrete distributions of predicted and reference sentences.
"""
_alpha_denom = self.alpha * (self.alpha - 1)
return (
1 - torch.sum(target_distribution**self.alpha * preds_distribution ** (1 - self.alpha), dim=-1)
) / _alpha_denom
def _calculate_ab_divergence(self, preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
"""Calculate AB divergence between discrete distributions of predicted and reference sentences.
Args:
preds_distribution:
Discrete reference distribution of predicted sentences over the vocabulary.
target_distribution:
Discrete reference distribution of reference sentences over the vocabulary.
Return:
AB divergence between discrete distributions of predicted and reference sentences.
"""
a = torch.log(torch.sum(target_distribution ** (self.beta + self.alpha), dim=-1))
a /= self.beta * (self.beta + self.alpha)
b = torch.log(torch.sum(preds_distribution ** (self.beta + self.alpha), dim=-1))
b /= self.alpha * (self.beta + self.alpha)
c = torch.log(torch.sum(target_distribution**self.alpha * preds_distribution**self.beta, dim=-1))
c /= self.alpha * self.beta
return a + b - c
def _calculate_beta_divergence(self, preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
"""Calculate beta divergence between discrete distributions of predicted and reference sentences.
Args:
preds_distribution:
Discrete reference distribution of predicted sentences over the vocabulary.
target_distribution:
Discrete reference distribution of reference sentences over the vocabulary.
Return:
Beta divergence between discrete distributions of predicted and reference sentences.
"""
self.alpha = 1.0
return self._calculate_ab_divergence(preds_distribution, target_distribution)
def _calculate_renyi_divergence(self, preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
"""Calculate Rényi divergence between discrete distributions of predicted and reference sentences.
Args:
preds_distribution:
Discrete reference distribution of predicted sentences over the vocabulary.
target_distribution:
Discrete reference distribution of reference sentences over the vocabulary.
Return:
Rényi divergence between discrete distributions of predicted and reference sentences.
"""
return (
torch.log(torch.sum(target_distribution**self.alpha * preds_distribution ** (1 - self.alpha), dim=-1))
) / (self.alpha - 1)
@staticmethod
def _calculate_l1_distance(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
"""Calculate L1 distance between discrete distributions of predicted and reference sentences.
Args:
preds_distribution:
Discrete reference distribution of predicted sentences over the vocabulary.
target_distribution:
Discrete reference distribution of reference sentences over the vocabulary.
Return:
L1 distance between discrete distributions of predicted and reference sentences.
"""
return torch.norm(target_distribution - preds_distribution, p=1, dim=-1)
@staticmethod
def _calculate_l2_distance(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
"""Calculate L2 distance between discrete distributions of predicted and reference sentences.
Args:
preds_distribution:
Discrete reference distribution of predicted sentences over the vocabulary.
target_distribution:
Discrete reference distribution of reference sentences over the vocabulary.
Return:
L2 distance between discrete distributions of predicted and reference sentences.
"""
return torch.norm(target_distribution - preds_distribution, p=2, dim=-1)
@staticmethod
def _calculate_l_infinity_distance(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
"""Calculate L-infinity distance between discrete distributions of predicted and reference sentences.
Args:
preds_distribution:
Discrete reference distribution of predicted sentences over the vocabulary.
target_distribution:
Discrete reference distribution of reference sentences over the vocabulary.
Return:
L-infinity distance between discrete distributions of predicted and reference sentences.
"""
return torch.norm(target_distribution - preds_distribution, p=float("inf"), dim=-1)
@staticmethod
def _calculate_fisher_rao_distance(preds_distribution: Tensor, target_distribution: Tensor) -> Tensor:
"""Calculate Fisher-Rao distance between discrete distributions of predicted and reference sentences.
Args:
preds_distribution:
Discrete reference distribution of predicted sentences over the vocabulary.
target_distribution:
Discrete reference distribution of reference sentences over the vocabulary.
Return:
Fisher-Rao distance between discrete distributions of predicted and reference sentences.
"""
return 2 * torch.acos(torch.clamp(torch.sqrt(preds_distribution * target_distribution).sum(-1), 0, 1))
def _get_dataloader(
input_ids: Tensor, attention_mask: Tensor, idf: bool, batch_size: int, num_workers: int
) -> DataLoader:
"""Prepare dataloader.
Args:
input_ids:
Indices of input sequence tokens in the vocabulary.
attention_mask:
Mask to avoid performing attention on padding token indices.
idf:
A bool indicating whether normalization using inverse document frequencies should be used.
batch_size:
A batch size used for model processing.
num_workers:
A number of workers to use for a dataloader.
Return:
An instance of ``torch.utils.data.DataLoader`` used for iterating over examples.
"""
dataset = TokenizedDataset(input_ids, attention_mask, idf)
return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
def _get_special_tokens_map(tokenizer: "PreTrainedTokenizerBase") -> dict[str, int]:
"""Build a dictionary of model/tokenizer special tokens.
Args:
tokenizer:
Initialized tokenizer from HuggingFace's `transformers package.
Return:
A dictionary containing: mask_token_id, pad_token_id, sep_token_id and cls_token_id.
"""
return {
"mask_token_id": tokenizer.mask_token_id,
"pad_token_id": tokenizer.pad_token_id,
"sep_token_id": tokenizer.sep_token_id,
"cls_token_id": tokenizer.cls_token_id,
}
def _get_token_mask(input_ids: Tensor, pad_token_id: int, sep_token_id: int, cls_token_id: int) -> Tensor:
"""Generate a token mask for differentiating all special tokens in the input batch.
There are 0s for special tokens and 1s otherwise.
Args:
input_ids:
Indices of input sequence tokens in the vocabulary.
pad_token_id:
An id of ``<PAD>`` tokens that are used to make arrays of tokens the same size for batching purpose
cls_token_id:
An id of ``<CLS>`` token that represents the class of the input. (It might be ``<BOS>`` token for some
models.)
sep_token_id:
An id of ``<SEP>`` token that separates two different sentences in the same input. (It might be ``<EOS>``
token for some models.)
Return:
Tensor mask of 0s and 1s that masks all special tokens in the ``input_ids`` tensor.
"""
token_mask = input_ids.eq(pad_token_id) | input_ids.eq(sep_token_id) | input_ids.eq(cls_token_id)
return ~token_mask
def _get_batch_distribution(
model: "PreTrainedModel",
batch: dict[str, Tensor],
temperature: float,
idf: bool,
special_tokens_map: dict[str, int],
) -> Tensor:
"""Calculate a discrete probability distribution for a batch of examples. See `InfoLM`_ for details.
Args:
model:
Initialized model from HuggingFace's `transformers package.
batch:
An input batch dictionary containing ``input_ids`` and ``attention_mask``.
temperature:
A temperature for calibrating language modelling. For more information, please reference `InfoLM`_ paper.
max_length:
A maximum length of input sequences. Sequences longer than `max_length` are to be trimmed.
idf:
An indication of whether normalization using inverse document frequencies should be used.
special_tokens_map:
A dictionary mapping tokenizer special tokens into the corresponding integer values.
Return:
A discrete probability distribution.
"""
seq_len = batch["input_ids"].shape[1]
prob_distribution_batch_list: List[Tensor] = []
token_mask = _get_token_mask(
batch["input_ids"],
special_tokens_map["pad_token_id"],
special_tokens_map["sep_token_id"],
special_tokens_map["cls_token_id"],
)
for mask_idx in range(seq_len):
input_ids = batch["input_ids"].clone()
input_ids[:, mask_idx] = special_tokens_map["mask_token_id"]
logits_distribution = model(input_ids, batch["attention_mask"]).logits
# [batch_size, seq_len, vocab_size] -> [batch_size, vocab_size]
logits_distribution = logits_distribution[:, mask_idx, :]
prob_distribution = F.softmax(logits_distribution / temperature, dim=-1)
if idf:
prob_distribution *= batch["input_ids_idf"][:, mask_idx].unsqueeze(1).to(prob_distribution.device)
prob_distribution_batch_list.append(prob_distribution.unsqueeze(1).cpu()) # [batch_size, 1, vocab_size]
# Clean from memory
del input_ids, logits_distribution, prob_distribution
prob_distribution_batch = torch.cat(prob_distribution_batch_list, dim=1) # [batch_size, seq_len, vocab_size]
prob_distribution_batch = torch.einsum("bsv, bs -> bsv", prob_distribution_batch.to(token_mask.device), token_mask)
if idf:
masked_input_ids_idf = token_mask * batch["input_ids_idf"].to(token_mask.device)
return prob_distribution_batch.sum(dim=1) / masked_input_ids_idf.sum(dim=1).unsqueeze(1)
return prob_distribution_batch.sum(dim=1) / token_mask.sum(dim=1).unsqueeze(1)
@torch.no_grad()
def _get_data_distribution(
model: "PreTrainedModel",
dataloader: DataLoader,
temperature: float,
idf: bool,
special_tokens_map: dict[str, int],
verbose: bool,
) -> Tensor:
"""Calculate a discrete probability distribution according to the methodology described in `InfoLM`_.
Args:
model:
Initialized model from HuggingFace's `transformers package.
dataloader:
An instance of `torch.utils.data.DataLoader` used for iterating over examples.
temperature:
A temperature for calibrating language modelling. For more information, please reference `InfoLM`_ paper.
max_length:
A maximum length of input sequences. Sequences longer than `max_length` are to be trimmed.
idf:
An indication of whether normalization using inverse document frequencies should be used.
special_tokens_map:
A dictionary mapping tokenizer special tokens into the corresponding integer values.
verbose:
An indication of whether a progress bar to be displayed during the embeddings calculation.
Return:
A discrete probability distribution.
"""
device = model.device
prob_distribution: List[Tensor] = []
for batch in _get_progress_bar(dataloader, verbose):
batch = _input_data_collator(batch, device)
prob_distribution.append(_get_batch_distribution(model, batch, temperature, idf, special_tokens_map))
return torch.cat(prob_distribution, dim=0)
def _infolm_update(
preds: Union[str, Sequence[str]],
target: Union[str, Sequence[str]],
tokenizer: "PreTrainedTokenizerBase",
max_length: int,
) -> tuple[Tensor, Tensor, Tensor, Tensor]:
"""Update the metric state by a tokenization of ``preds`` and ``target`` sentencens.
Args:
preds:
An iterable of hypothesis corpus.
target:
An iterable of reference corpus.
tokenizer:
Initialized tokenizer from HuggingFace's `transformers package.
max_length:
A maximum length of input sequences. Sequences longer than `max_length` are to be trimmed.
Return:
Tokenizerd ``preds`` and ``target`` sentences represented with ``input_ids`` and ``attention_mask`` tensors.
"""
# HuggingFace tokenizer expects an input to be of a type str or List[str]
if not isinstance(preds, (str, list)):
preds = list(preds)
if not isinstance(target, (str, list)):
target = list(target)
preds_input = tokenizer(preds, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt")
target_input = tokenizer(target, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt")
return preds_input.input_ids, preds_input.attention_mask, target_input.input_ids, target_input.attention_mask
def _infolm_compute(
model: "PreTrainedModel",
preds_dataloader: DataLoader,
target_dataloader: DataLoader,
temperature: float,
idf: bool,
information_measure_cls: _InformationMeasure,
special_tokens_map: dict[str, int],
verbose: bool = True,
) -> Tensor:
"""Calculate selected information measure using the pre-trained language model.
Args:
model:
Initialized model from HuggingFace's `transformers package.
preds_dataloader:
Loader iterating over tokenizer predicted sentences.
target_dataloader:
Loader iterating over tokenizer reference sentences.
temperature:
A temperature for calibrating language modelling. For more information, please reference `InfoLM`_ paper.
idf:
An indication of whether normalization using inverse document frequencies should be used.
information_measure_cls:
Information measure class containing all parameters necessary for calculating information measure values
using ``preds_distribution`` and ``target_distribution``.
special_tokens_map:
A dictionary mapping tokenizer special tokens into the corresponding integer values.
verbose:
An indication of whether a progress bar to be displayed during the embeddings calculation.
Return:
A corpus-level InfoLM score.
"""
preds_distribution = _get_data_distribution(model, preds_dataloader, temperature, idf, special_tokens_map, verbose)
target_distribution = _get_data_distribution(
model, target_dataloader, temperature, idf, special_tokens_map, verbose
)
# Sort preds and target sentences
preds_distribution = preds_distribution[preds_dataloader.dataset.sorting_indices]
target_distribution = target_distribution[target_dataloader.dataset.sorting_indices]
# Calculate information measure
return information_measure_cls(preds_distribution, target_distribution)
def infolm(
preds: Union[str, Sequence[str]],
target: Union[str, Sequence[str]],
model_name_or_path: Union[str, os.PathLike] = "bert-base-uncased",
temperature: float = 0.25,
information_measure: _ALLOWED_INFORMATION_MEASURE_LITERAL = "kl_divergence",
idf: bool = True,
alpha: Optional[float] = None,
beta: Optional[float] = None,
device: Optional[Union[str, torch.device]] = None,
max_length: Optional[int] = None,
batch_size: int = 64,
num_threads: int = 0,
verbose: bool = True,
return_sentence_level_score: bool = False,
) -> Union[Tensor, tuple[Tensor, Tensor]]:
"""Calculate `InfoLM`_ [1].
InfoML corresponds to distance/divergence between predicted and reference sentence discrete distribution using
one of the following information measures:
- `KL divergence`_
- `alpha divergence`_
- `beta divergence`_
- `AB divergence`_
- `Rényi divergence`_
- L1 distance
- L2 distance
- L-infinity distance
- `Fisher-Rao distance`_
`InfoLM`_ is a family of untrained embedding-based metrics which addresses some famous flaws of standard
string-based metrics thanks to the usage of pre-trained masked language models. This family of metrics is mainly
designed for summarization and data-to-text tasks.
If you want to use IDF scaling over the whole dataset, please use the class metric.
The implementation of this metric is fully based HuggingFace `transformers`' package.
Args:
preds:
An iterable of hypothesis corpus.
target:
An iterable of reference corpus.
model_name_or_path:
A name or a model path used to load `transformers` pretrained model.
temperature:
A temperature for calibrating language modelling. For more information, please reference `InfoLM`_ paper.
information_measure:
A name of information measure to be used. Please use one of: ['kl_divergence', 'alpha_divergence',
'beta_divergence', 'ab_divergence', 'renyi_divergence', 'l1_distance', 'l2_distance', 'l_infinity_distance',
'fisher_rao_distance']
idf:
An indication of whether normalization using inverse document frequencies should be used.
alpha:
Alpha parameter of the divergence used for alpha, AB and Rényi divergence measures.
beta:
Beta parameter of the divergence used for beta and AB divergence measures.
device:
A device to be used for calculation.
max_length:
A maximum length of input sequences. Sequences longer than `max_length` are to be trimmed.
batch_size:
A batch size used for model processing.
num_threads:
A number of threads to use for a dataloader.
verbose:
An indication of whether a progress bar to be displayed during the embeddings calculation.
return_sentence_level_score:
An indication whether a sentence-level InfoLM score to be returned.
Returns:
A corpus-level InfoLM score.
(Optionally) A list of sentence-level InfoLM scores if `return_sentence_level_score=True`.
Example:
>>> from torchmetrics.functional.text.infolm import infolm
>>> preds = ['he read the book because he was interested in world history']
>>> target = ['he was interested in world history because he read the book']
>>> infolm(preds, target, model_name_or_path='google/bert_uncased_L-2_H-128_A-2', idf=False)
tensor(-0.1784)
References:
[1] InfoLM: A New Metric to Evaluate Summarization & Data2Text Generation by Pierre Colombo, Chloé Clavel and
Pablo Piantanida `InfoLM`_
"""
tokenizer, model = _load_tokenizer_and_model(model_name_or_path, device)
information_measure_cls = _InformationMeasure(information_measure, alpha, beta)
max_length = max_length or model.config.max_length
special_tokens_map = _get_special_tokens_map(tokenizer)
preds_input_ids, preds_attention_mask, target_input_ids, target_attention_mask = _infolm_update(
preds, target, tokenizer, max_length
)
preds_dataloader = _get_dataloader(preds_input_ids, preds_attention_mask, idf, batch_size, num_threads)
target_dataloader = _get_dataloader(target_input_ids, target_attention_mask, idf, batch_size, num_threads)
info_lm_score = _infolm_compute(
model,
preds_dataloader,
target_dataloader,
temperature,
idf,
information_measure_cls,
special_tokens_map,
verbose,
)
if return_sentence_level_score:
return info_lm_score.mean(), info_lm_score
return info_lm_score.mean()
|