Spaces:
Paused
Paused
File size: 39,232 Bytes
0fdb130 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 |
import json
import os
import tempfile
import warnings
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Set, Tuple, Union
# For Python 3.7 compatibility
try:
from typing import Literal
except ImportError:
from typing_extensions import Literal
import joblib
import numpy as np
import requests
import torch
from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
from huggingface_hub.utils import validate_hf_hub_args
from sentence_transformers import SentenceTransformer, models
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
from sklearn.multioutput import ClassifierChain, MultiOutputClassifier
from torch import nn
from torch.utils.data import DataLoader
from tqdm.auto import tqdm, trange
from transformers.utils import copy_func
from . import logging
from .data import SetFitDataset
from .model_card import SetFitModelCardData, generate_model_card
from .utils import set_docstring
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
MODEL_HEAD_NAME = "model_head.pkl"
CONFIG_NAME = "config_setfit.json"
class SetFitHead(models.Dense):
"""
A SetFit head that supports multi-class classification for end-to-end training.
Binary classification is treated as 2-class classification.
To be compatible with Sentence Transformers, we inherit `Dense` from:
https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/models/Dense.py
Args:
in_features (`int`, *optional*):
The embedding dimension from the output of the SetFit body. If `None`, defaults to `LazyLinear`.
out_features (`int`, defaults to `2`):
The number of targets. If set `out_features` to 1 for binary classification, it will be changed to 2 as 2-class classification.
temperature (`float`, defaults to `1.0`):
A logits' scaling factor. Higher values make the model less confident and lower values make
it more confident.
eps (`float`, defaults to `1e-5`):
A value for numerical stability when scaling logits.
bias (`bool`, *optional*, defaults to `True`):
Whether to add bias to the head.
device (`torch.device`, str, *optional*):
The device the model will be sent to. If `None`, will check whether GPU is available.
multitarget (`bool`, defaults to `False`):
Enable multi-target classification by making `out_features` binary predictions instead
of a single multinomial prediction.
"""
def __init__(
self,
in_features: Optional[int] = None,
out_features: int = 2,
temperature: float = 1.0,
eps: float = 1e-5,
bias: bool = True,
device: Optional[Union[torch.device, str]] = None,
multitarget: bool = False,
) -> None:
super(models.Dense, self).__init__() # init on models.Dense's parent: nn.Module
if out_features == 1:
logger.warning(
"Change `out_features` from 1 to 2 since we use `CrossEntropyLoss` for binary classification."
)
out_features = 2
if in_features is not None:
self.linear = nn.Linear(in_features, out_features, bias=bias)
else:
self.linear = nn.LazyLinear(out_features, bias=bias)
self.in_features = in_features
self.out_features = out_features
self.temperature = temperature
self.eps = eps
self.bias = bias
self._device = device or "cuda" if torch.cuda.is_available() else "cpu"
self.multitarget = multitarget
self.to(self._device)
self.apply(self._init_weight)
def forward(
self,
features: Union[Dict[str, torch.Tensor], torch.Tensor],
temperature: Optional[float] = None,
) -> Union[Dict[str, torch.Tensor], Tuple[torch.Tensor]]:
"""
SetFitHead can accept embeddings in:
1. Output format (`dict`) from Sentence-Transformers.
2. Pure `torch.Tensor`.
Args:
features (`Dict[str, torch.Tensor]` or `torch.Tensor):
The embeddings from the encoder. If using `dict` format,
make sure to store embeddings under the key: 'sentence_embedding'
and the outputs will be under the key: 'prediction'.
temperature (`float`, *optional*):
A logits' scaling factor. Higher values make the model less
confident and lower values make it more confident.
Will override the temperature given during initialization.
Returns:
[`Dict[str, torch.Tensor]` or `Tuple[torch.Tensor]`]
"""
temperature = temperature or self.temperature
is_features_dict = False # whether `features` is dict or not
if isinstance(features, dict):
assert "sentence_embedding" in features
is_features_dict = True
x = features["sentence_embedding"] if is_features_dict else features
logits = self.linear(x)
logits = logits / (temperature + self.eps)
if self.multitarget: # multiple targets per item
probs = torch.sigmoid(logits)
else: # one target per item
probs = nn.functional.softmax(logits, dim=-1)
if is_features_dict:
features.update(
{
"logits": logits,
"probs": probs,
}
)
return features
return logits, probs
def predict_proba(self, x_test: torch.Tensor) -> torch.Tensor:
self.eval()
return self(x_test)[1]
def predict(self, x_test: torch.Tensor) -> torch.Tensor:
probs = self.predict_proba(x_test)
if self.multitarget:
return torch.where(probs >= 0.5, 1, 0)
return torch.argmax(probs, dim=-1)
def get_loss_fn(self) -> nn.Module:
if self.multitarget: # if sigmoid output
return torch.nn.BCEWithLogitsLoss()
return torch.nn.CrossEntropyLoss()
@property
def device(self) -> torch.device:
"""
`torch.device`: The device on which the model is placed.
Reference from: https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/SentenceTransformer.py#L869
"""
return next(self.parameters()).device
def get_config_dict(self) -> Dict[str, Optional[Union[int, float, bool]]]:
return {
"in_features": self.in_features,
"out_features": self.out_features,
"temperature": self.temperature,
"bias": self.bias,
"device": self.device.type, # store the string of the device, instead of `torch.device`
}
@staticmethod
def _init_weight(module) -> None:
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 1e-2)
def __repr__(self) -> str:
return "SetFitHead({})".format(self.get_config_dict())
@dataclass
class SetFitModel(PyTorchModelHubMixin):
"""A SetFit model with integration to the [Hugging Face Hub](https://huggingface.co).
Example::
>>> from setfit import SetFitModel
>>> model = SetFitModel.from_pretrained("tomaarsen/setfit-bge-small-v1.5-sst2-8-shot")
>>> model.predict([
... "It's a charming and often affecting journey.",
... "It's slow -- very, very slow.",
... "A sometimes tedious film.",
... ])
['positive', 'negative', 'negative']
"""
model_body: Optional[SentenceTransformer] = None
model_head: Optional[Union[SetFitHead, LogisticRegression]] = None
multi_target_strategy: Optional[str] = None
normalize_embeddings: bool = False
labels: Optional[List[str]] = None
model_card_data: Optional[SetFitModelCardData] = field(default_factory=SetFitModelCardData)
attributes_to_save: Set[str] = field(
init=False, repr=False, default_factory=lambda: {"normalize_embeddings", "labels"}
)
def __post_init__(self):
self.model_card_data.register_model(self)
@property
def has_differentiable_head(self) -> bool:
# if False, sklearn is assumed to be used instead
return isinstance(self.model_head, nn.Module)
@property
def id2label(self) -> Dict[int, str]:
"""Return a mapping from integer IDs to string labels."""
if self.labels is None:
return {}
return dict(enumerate(self.labels))
@property
def label2id(self) -> Dict[str, int]:
"""Return a mapping from string labels to integer IDs."""
if self.labels is None:
return {}
return {label: idx for idx, label in enumerate(self.labels)}
def fit(
self,
x_train: List[str],
y_train: Union[List[int], List[List[int]]],
num_epochs: int,
batch_size: Optional[int] = None,
body_learning_rate: Optional[float] = None,
head_learning_rate: Optional[float] = None,
end_to_end: bool = False,
l2_weight: Optional[float] = None,
max_length: Optional[int] = None,
show_progress_bar: bool = True,
) -> None:
"""Train the classifier head, only used if a differentiable PyTorch head is used.
Args:
x_train (`List[str]`): A list of training sentences.
y_train (`Union[List[int], List[List[int]]]`): A list of labels corresponding to the training sentences.
num_epochs (`int`): The number of epochs to train for.
batch_size (`int`, *optional*): The batch size to use.
body_learning_rate (`float`, *optional*): The learning rate for the `SentenceTransformer` body
in the `AdamW` optimizer. Disregarded if `end_to_end=False`.
head_learning_rate (`float`, *optional*): The learning rate for the differentiable torch head
in the `AdamW` optimizer.
end_to_end (`bool`, defaults to `False`): If True, train the entire model end-to-end.
Otherwise, freeze the `SentenceTransformer` body and only train the head.
l2_weight (`float`, *optional*): The l2 weight for both the model body and head
in the `AdamW` optimizer.
max_length (`int`, *optional*): The maximum token length a tokenizer can generate. If not provided,
the maximum length for the `SentenceTransformer` body is used.
show_progress_bar (`bool`, defaults to `True`): Whether to display a progress bar for the training
epochs and iterations.
"""
if self.has_differentiable_head: # train with pyTorch
self.model_body.train()
self.model_head.train()
if not end_to_end:
self.freeze("body")
dataloader = self._prepare_dataloader(x_train, y_train, batch_size, max_length)
criterion = self.model_head.get_loss_fn()
optimizer = self._prepare_optimizer(head_learning_rate, body_learning_rate, l2_weight)
#
#
#
#
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.2)
# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=.25, patience=10, threshold=5 * 1e-5, min_lr=1e-7, verbose=True)
#
#
#
#
# Need to replace with ReduceOnPlateauLR()
#
#
#
#
for epoch_idx in trange(num_epochs, desc="Epoch", disable=not show_progress_bar):
total_loss = 0.
for batch in tqdm(dataloader, desc="Iteration", disable=not show_progress_bar, leave=False):
features, labels = batch
optimizer.zero_grad()
# to model's device
features = {k: v.to(self.device) for k, v in features.items()}
labels = labels.to(self.device)
outputs = self.model_body(features)
if self.normalize_embeddings:
outputs["sentence_embedding"] = nn.functional.normalize(
outputs["sentence_embedding"], p=2, dim=1
)
outputs = self.model_head(outputs)
logits = outputs["logits"]
loss: torch.Tensor = criterion(logits, labels)
total_loss += loss.item()
loss.backward()
optimizer.step()
if epoch_idx % 5 == 0:
print()
print(epoch_idx + 1, total_loss / len(dataloader))
print()
scheduler.step()
if not end_to_end:
self.unfreeze("body")
else: # train with sklearn
print()
print('I am using LogisticRegression!')
print()
embeddings = self.model_body.encode(x_train, normalize_embeddings=self.normalize_embeddings)
self.model_head.fit(embeddings, y_train)
def _prepare_dataloader(
self,
x_train: List[str],
y_train: Union[List[int], List[List[int]]],
batch_size: Optional[int] = None,
max_length: Optional[int] = None,
shuffle: bool = True,
) -> DataLoader:
max_acceptable_length = self.model_body.get_max_seq_length()
if max_length is None:
max_length = max_acceptable_length
logger.warning(
f"The `max_length` is `None`. Using the maximum acceptable length according to the current model body: {max_length}."
)
if max_length > max_acceptable_length:
logger.warning(
(
f"The specified `max_length`: {max_length} is greater than the maximum length of the current model body: {max_acceptable_length}. "
f"Using {max_acceptable_length} instead."
)
)
max_length = max_acceptable_length
dataset = SetFitDataset(
x_train,
y_train,
tokenizer=self.model_body.tokenizer,
max_length=max_length,
)
dataloader = DataLoader(
dataset,
batch_size=batch_size,
collate_fn=dataset.collate_fn,
shuffle=shuffle,
pin_memory=True,
#
#
#
#
#
drop_last=True
#
#
#
#
#
)
return dataloader
def _prepare_optimizer(
self,
head_learning_rate: float,
body_learning_rate: Optional[float],
l2_weight: float,
) -> torch.optim.Optimizer:
body_learning_rate = body_learning_rate or head_learning_rate
l2_weight = l2_weight or 1e-2
optimizer = torch.optim.Adam(
[
{
"params": self.model_body.parameters(),
"lr": body_learning_rate,
"weight_decay": l2_weight,
},
{"params": self.model_head.parameters(), "lr": head_learning_rate, "weight_decay": l2_weight},
],
)
return optimizer
def freeze(self, component: Optional[Literal["body", "head"]] = None) -> None:
"""Freeze the model body and/or the head, preventing further training on that component until unfrozen.
Args:
component (`Literal["body", "head"]`, *optional*): Either "body" or "head" to freeze that component.
If no component is provided, freeze both. Defaults to None.
"""
if component is None or component == "body":
self._freeze_or_not(self.model_body, to_freeze=True)
if (component is None or component == "head") and self.has_differentiable_head:
self._freeze_or_not(self.model_head, to_freeze=True)
def unfreeze(
self, component: Optional[Literal["body", "head"]] = None, keep_body_frozen: Optional[bool] = None
) -> None:
"""Unfreeze the model body and/or the head, allowing further training on that component.
Args:
component (`Literal["body", "head"]`, *optional*): Either "body" or "head" to unfreeze that component.
If no component is provided, unfreeze both. Defaults to None.
keep_body_frozen (`bool`, *optional*): Deprecated argument, use `component` instead.
"""
if keep_body_frozen is not None:
warnings.warn(
"`keep_body_frozen` is deprecated and will be removed in v2.0.0 of SetFit. "
'Please either pass "head", "body" or no arguments to unfreeze both.',
DeprecationWarning,
stacklevel=2,
)
# If the body must stay frozen, only unfreeze the head. Eventually, this entire if-branch
# can be removed.
if keep_body_frozen and not component:
component = "head"
if component is None or component == "body":
self._freeze_or_not(self.model_body, to_freeze=False)
if (component is None or component == "head") and self.has_differentiable_head:
self._freeze_or_not(self.model_head, to_freeze=False)
def _freeze_or_not(self, model: nn.Module, to_freeze: bool) -> None:
"""Set `requires_grad=not to_freeze` for all parameters in `model`"""
for param in model.parameters():
param.requires_grad = not to_freeze
def encode(
self, inputs: List[str], batch_size: int = 32, show_progress_bar: Optional[bool] = None
) -> Union[torch.Tensor, np.ndarray]:
"""Convert input sentences to embeddings using the `SentenceTransformer` body.
Args:
inputs (`List[str]`): The input sentences to embed.
batch_size (`int`, defaults to `32`): The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.
show_progress_bar (`Optional[bool]`, defaults to `None`): Whether to show a progress bar while encoding.
Returns:
Union[torch.Tensor, np.ndarray]: A matrix with shape [INPUT_LENGTH, EMBEDDING_SIZE], as a
torch Tensor if this model has a differentiable Torch head, or otherwise as a numpy array.
"""
return self.model_body.encode(
inputs,
batch_size=batch_size,
normalize_embeddings=self.normalize_embeddings,
convert_to_tensor=self.has_differentiable_head,
show_progress_bar=show_progress_bar,
)
def _output_type_conversion(
self, outputs: Union[torch.Tensor, np.ndarray], as_numpy: bool = False
) -> Union[torch.Tensor, np.ndarray]:
"""Return `outputs` in the desired type:
* Numpy array if no differentiable head is used.
* Torch tensor if a differentiable head is used.
Note:
If the model is trained with string labels, which is only possible with a non-differentiable head,
then we cannot output using torch Tensors, but only using a numpy array.
Returns:
Union[torch.Tensor, "ndarray"]: The input, correctly converted to the desired type.
"""
if as_numpy and self.has_differentiable_head:
outputs = outputs.detach().cpu().numpy()
elif not as_numpy and not self.has_differentiable_head and outputs.dtype.char != "U":
# Only output as tensor if the output isn't a string
outputs = torch.from_numpy(outputs)
return outputs
def predict_proba(
self,
inputs: Union[str, List[str]],
batch_size: int = 32,
as_numpy: bool = False,
show_progress_bar: Optional[bool] = None,
) -> Union[torch.Tensor, np.ndarray]:
"""Predict the probabilities of the various classes.
Args:
inputs (`Union[str, List[str]]`): The input sentences to predict class probabilities for.
batch_size (`int`, defaults to `32`): The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.
as_numpy (`bool`, defaults to `False`): Whether to output as numpy array instead.
show_progress_bar (`Optional[bool]`, defaults to `None`): Whether to show a progress bar while encoding.
Example::
>>> model = SetFitModel.from_pretrained(...)
>>> model.predict_proba(["What a boring display", "Exhilarating through and through", "I'm wowed!"])
tensor([[0.9367, 0.0633],
[0.0627, 0.9373],
[0.0890, 0.9110]], dtype=torch.float64)
>>> model.predict_proba("That was cool!")
tensor([0.8421, 0.1579], dtype=torch.float64)
Returns:
`Union[torch.Tensor, np.ndarray]`: A matrix with shape [INPUT_LENGTH, NUM_CLASSES] denoting
probabilities of predicting an input as a class. If the input is a string, then the output
is a vector with shape [NUM_CLASSES,].
"""
is_singular = isinstance(inputs, str)
if is_singular:
inputs = [inputs]
embeddings = self.encode(inputs, batch_size=batch_size, show_progress_bar=show_progress_bar)
probs = self.model_head.predict_proba(embeddings)
outputs = self._output_type_conversion(probs, as_numpy=as_numpy)
return outputs[0] if is_singular else outputs
def predict(
self,
inputs: Union[str, List[str]],
batch_size: int = 32,
as_numpy: bool = False,
use_labels: bool = True,
show_progress_bar: Optional[bool] = None,
) -> Union[torch.Tensor, np.ndarray, List[str], int, str]:
"""Predict the various classes.
Args:
inputs (`Union[str, List[str]]`): The input sentence or sentences to predict classes for.
batch_size (`int`, defaults to `32`): The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.
as_numpy (`bool`, defaults to `False`): Whether to output as numpy array instead.
use_labels (`bool`, defaults to `True`): Whether to try and return elements of `SetFitModel.labels`.
show_progress_bar (`Optional[bool]`, defaults to `None`): Whether to show a progress bar while encoding.
Example::
>>> model = SetFitModel.from_pretrained(...)
>>> model.predict(["What a boring display", "Exhilarating through and through", "I'm wowed!"])
["negative", "positive", "positive"]
>>> model.predict("That was cool!")
"positive"
Returns:
`Union[torch.Tensor, np.ndarray, List[str], int, str]`: A list of string labels with equal length to the
inputs if `use_labels` is `True` and `SetFitModel.labels` has been defined. Otherwise a vector with
equal length to the inputs, denoting to which class each input is predicted to belong. If the inputs
is a single string, then the output is a single label as well.
"""
is_singular = isinstance(inputs, str)
if is_singular:
inputs = [inputs]
embeddings = self.encode(inputs, batch_size=batch_size, show_progress_bar=show_progress_bar)
preds = self.model_head.predict(embeddings)
# If labels are defined, we don't have multilabels & the output is not already strings, then we convert to string labels
if (
use_labels
and self.labels
and preds.ndim == 1
and (self.has_differentiable_head or preds.dtype.char != "U")
):
outputs = [self.labels[int(pred)] for pred in preds]
else:
outputs = self._output_type_conversion(preds, as_numpy=as_numpy)
return outputs[0] if is_singular else outputs
def __call__(
self,
inputs: Union[str, List[str]],
batch_size: int = 32,
as_numpy: bool = False,
use_labels: bool = True,
show_progress_bar: Optional[bool] = None,
) -> Union[torch.Tensor, np.ndarray, List[str], int, str]:
"""Predict the various classes.
Args:
inputs (`Union[str, List[str]]`): The input sentence or sentences to predict classes for.
batch_size (`int`, defaults to `32`): The batch size to use in encoding the sentences to embeddings.
Higher often means faster processing but higher memory usage.
as_numpy (`bool`, defaults to `False`): Whether to output as numpy array instead.
use_labels (`bool`, defaults to `True`): Whether to try and return elements of `SetFitModel.labels`.
show_progress_bar (`Optional[bool]`, defaults to `None`): Whether to show a progress bar while encoding.
Example::
>>> model = SetFitModel.from_pretrained(...)
>>> model(["What a boring display", "Exhilarating through and through", "I'm wowed!"])
["negative", "positive", "positive"]
>>> model("That was cool!")
"positive"
Returns:
`Union[torch.Tensor, np.ndarray, List[str], int, str]`: A list of string labels with equal length to the
inputs if `use_labels` is `True` and `SetFitModel.labels` has been defined. Otherwise a vector with
equal length to the inputs, denoting to which class each input is predicted to belong. If the inputs
is a single string, then the output is a single label as well.
"""
return self.predict(
inputs,
batch_size=batch_size,
as_numpy=as_numpy,
use_labels=use_labels,
show_progress_bar=show_progress_bar,
)
@property
def device(self) -> torch.device:
"""Get the Torch device that this model is on.
Returns:
torch.device: The device that the model is on.
"""
return self.model_body._target_device
def to(self, device: Union[str, torch.device]) -> "SetFitModel":
"""Move this SetFitModel to `device`, and then return `self`. This method does not copy.
Args:
device (Union[str, torch.device]): The identifier of the device to move the model to.
Example::
>>> model = SetFitModel.from_pretrained(...)
>>> model.to("cpu")
>>> model(["cats are cute", "dogs are loyal"])
Returns:
SetFitModel: Returns the original model, but now on the desired device.
"""
# Note that we must also set _target_device, or any SentenceTransformer.fit() call will reset
# the body location
self.model_body._target_device = device if isinstance(device, torch.device) else torch.device(device)
self.model_body = self.model_body.to(device)
if self.has_differentiable_head:
self.model_head = self.model_head.to(device)
return self
def create_model_card(self, path: str, model_name: Optional[str] = "SetFit Model") -> None:
"""Creates and saves a model card for a SetFit model.
Args:
path (str): The path to save the model card to.
model_name (str, *optional*): The name of the model. Defaults to `SetFit Model`.
"""
if not os.path.exists(path):
os.makedirs(path)
# If the model_path is a folder that exists locally, i.e. when create_model_card is called
# via push_to_hub, and the path is in a temporary folder, then we only take the last two
# directories
model_path = Path(model_name)
if model_path.exists() and Path(tempfile.gettempdir()) in model_path.resolve().parents:
self.model_card_data.model_id = "/".join(model_path.parts[-2:])
with open(os.path.join(path, "README.md"), "w", encoding="utf-8") as f:
f.write(self.generate_model_card())
def generate_model_card(self) -> str:
"""Generate and return a model card string based on the model card data.
Returns:
str: The model card string.
"""
return generate_model_card(self)
def _save_pretrained(self, save_directory: Union[Path, str]) -> None:
save_directory = str(save_directory)
# Save the config
config_path = os.path.join(save_directory, CONFIG_NAME)
with open(config_path, "w") as f:
json.dump(
{
attr_name: getattr(self, attr_name)
for attr_name in self.attributes_to_save
if hasattr(self, attr_name)
},
f,
indent=2,
)
# Save the body
self.model_body.save(path=save_directory, create_model_card=False)
# Save the README
#
#
#
#
#
# self.create_model_card(path=save_directory, model_name=save_directory)
#
#
#
#
#
# Move the head to the CPU before saving
if self.has_differentiable_head:
self.model_head.to("cpu")
# Save the classification head
joblib.dump(self.model_head, str(Path(save_directory) / MODEL_HEAD_NAME))
if self.has_differentiable_head:
self.model_head.to(self.device)
@classmethod
@validate_hf_hub_args
def _from_pretrained(
cls,
model_id: str,
revision: Optional[str] = None,
cache_dir: Optional[str] = None,
force_download: Optional[bool] = None,
proxies: Optional[Dict] = None,
resume_download: Optional[bool] = None,
local_files_only: Optional[bool] = None,
token: Optional[Union[bool, str]] = None,
multi_target_strategy: Optional[str] = None,
use_differentiable_head: bool = False,
device: Optional[Union[torch.device, str]] = None,
**model_kwargs,
) -> "SetFitModel":
model_body = SentenceTransformer(model_id, cache_folder=cache_dir, use_auth_token=token, device=device)
device = model_body._target_device
model_body.to(device) # put `model_body` on the target device
# Try to load a SetFit config file
config_file: Optional[str] = None
if os.path.isdir(model_id):
if CONFIG_NAME in os.listdir(model_id):
config_file = os.path.join(model_id, CONFIG_NAME)
else:
try:
config_file = hf_hub_download(
repo_id=model_id,
filename=CONFIG_NAME,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
token=token,
local_files_only=local_files_only,
)
except requests.exceptions.RequestException:
pass
model_kwargs = {key: value for key, value in model_kwargs.items() if value is not None}
if config_file is not None:
with open(config_file, "r", encoding="utf-8") as f:
config = json.load(f)
# Update model_kwargs + warnings
for setting, value in config.items():
if setting in model_kwargs:
if model_kwargs[setting] != value:
logger.warning(
f"Overriding {setting} in model configuration from {value} to {model_kwargs[setting]}."
)
else:
model_kwargs[setting] = value
# Try to load a model head file
if os.path.isdir(model_id):
if MODEL_HEAD_NAME in os.listdir(model_id):
model_head_file = os.path.join(model_id, MODEL_HEAD_NAME)
else:
logger.info(
f"{MODEL_HEAD_NAME} not found in {Path(model_id).resolve()},"
" initialising classification head with random weights."
" You should TRAIN this model on a downstream task to use it for predictions and inference."
)
model_head_file = None
else:
try:
model_head_file = hf_hub_download(
repo_id=model_id,
filename=MODEL_HEAD_NAME,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
token=token,
local_files_only=local_files_only,
)
except requests.exceptions.RequestException:
logger.info(
f"{MODEL_HEAD_NAME} not found on HuggingFace Hub, initialising classification head with random weights."
" You should TRAIN this model on a downstream task to use it for predictions and inference."
)
model_head_file = None
model_card_data: SetFitModelCardData = model_kwargs.pop("model_card_data", SetFitModelCardData())
if model_head_file is not None:
model_head = joblib.load(model_head_file)
if isinstance(model_head, torch.nn.Module):
model_head.to(device)
model_card_data.infer_st_id(model_id)
else:
head_params = model_kwargs.pop("head_params", {})
if use_differentiable_head:
if multi_target_strategy is None:
use_multitarget = False
else:
if multi_target_strategy in ["one-vs-rest", "multi-output"]:
use_multitarget = True
else:
raise ValueError(
f"multi_target_strategy '{multi_target_strategy}' is not supported for differentiable head"
)
# Base `model_head` parameters
# - get the sentence embedding dimension from the `model_body`
# - follow the `model_body`, put `model_head` on the target device
base_head_params = {
"in_features": model_body.get_sentence_embedding_dimension(),
"device": device,
"multitarget": use_multitarget,
}
model_head = SetFitHead(**{**head_params, **base_head_params})
else:
clf = LogisticRegression(**head_params)
if multi_target_strategy is not None:
if multi_target_strategy == "one-vs-rest":
multilabel_classifier = OneVsRestClassifier(clf)
elif multi_target_strategy == "multi-output":
multilabel_classifier = MultiOutputClassifier(clf)
elif multi_target_strategy == "classifier-chain":
multilabel_classifier = ClassifierChain(clf)
else:
raise ValueError(f"multi_target_strategy {multi_target_strategy} is not supported.")
model_head = multilabel_classifier
else:
model_head = clf
model_card_data.set_st_id(model_id if "/" in model_id else f"sentence-transformers/{model_id}")
# Remove the `transformers` config
model_kwargs.pop("config", None)
return cls(
model_body=model_body,
model_head=model_head,
multi_target_strategy=multi_target_strategy,
model_card_data=model_card_data,
**model_kwargs,
)
docstring = SetFitModel.from_pretrained.__doc__
cut_index = docstring.find("model_kwargs")
if cut_index != -1:
docstring = (
docstring[:cut_index]
+ """labels (`List[str]`, *optional*):
If the labels are integers ranging from `0` to `num_classes-1`, then these labels indicate
the corresponding labels.
model_card_data (`SetFitModelCardData`, *optional*):
A `SetFitModelCardData` instance storing data such as model language, license, dataset name,
etc. to be used in the automatically generated model cards.
multi_target_strategy (`str`, *optional*):
The strategy to use with multi-label classification. One of "one-vs-rest", "multi-output",
or "classifier-chain".
use_differentiable_head (`bool`, *optional*):
Whether to load SetFit using a differentiable (i.e., Torch) head instead of Logistic Regression.
normalize_embeddings (`bool`, *optional*):
Whether to apply normalization on the embeddings produced by the Sentence Transformer body.
device (`Union[torch.device, str]`, *optional*):
The device on which to load the SetFit model, e.g. `"cuda:0"`, `"mps"` or `torch.device("cuda")`.
Example::
>>> from setfit import SetFitModel
>>> model = SetFitModel.from_pretrained(
... "sentence-transformers/paraphrase-mpnet-base-v2",
... labels=["positive", "negative"],
... )
"""
)
SetFitModel.from_pretrained = set_docstring(SetFitModel.from_pretrained, docstring)
SetFitModel.save_pretrained = copy_func(SetFitModel.save_pretrained)
SetFitModel.save_pretrained.__doc__ = SetFitModel.save_pretrained.__doc__.replace(
"~ModelHubMixin._from_pretrained", "SetFitModel.push_to_hub"
)
|