SentenceTransformer.py

import json
import logging
import os
import shutil
from collections import OrderedDict
import warnings
from typing import List, Dict, Tuple, Iterable, Type, Union, Callable, Optional, TYPE_CHECKING
import numpy as np
from numpy import ndarray
import transformers
from transformers import is_torch_npu_available
from huggingface_hub import HfApi
import torch
from torch import nn, Tensor, device
from torch.optim import Optimizer
from torch.utils.data import DataLoader
import torch.multiprocessing as mp
from tqdm.autonotebook import trange
import math
import queue
import tempfile

from . import __MODEL_HUB_ORGANIZATION__
from .evaluation import SentenceEvaluator
from .util import (
    import_from_string,
    batch_to_device,
    fullname,
    is_sentence_transformer_model,
    load_dir_path,
    load_file_path,
    save_to_hub_args_decorator,
    get_device_name,
)
from .models import Transformer, Pooling, Normalize
from .model_card_templates import ModelCardTemplate
from . import __version__

logger = logging.getLogger(__name__)


if TYPE_CHECKING:
    from sentence_transformers.readers import InputExample


class SentenceTransformer(nn.Sequential):
    """
    Loads or creates a SentenceTransformer model that can be used to map sentences / text to embeddings.

    :param model_name_or_path: If it is a filepath on disc, it loads the model from that path. If it is not a path,
        it first tries to download a pre-trained SentenceTransformer model. If that fails, tries to construct a model
        from the Hugging Face Hub with that name.
    :param modules: A list of torch Modules that should be called sequentially, can be used to create custom
        SentenceTransformer models from scratch.
    :param device: Device (like "cuda", "cpu", "mps", "npu") that should be used for computation. If None, checks if a GPU
        can be used.
    :param cache_folder: Path to store models. Can also be set by the SENTENCE_TRANSFORMERS_HOME environment variable.
    :param revision: The specific model version to use. It can be a branch name, a tag name, or a commit id,
        for a stored model on Hugging Face.
    :param trust_remote_code: Whether or not to allow for custom models defined on the Hub in their own modeling files.
        This option should only be set to True for repositories you trust and in which you have read the code, as it
        will execute code present on the Hub on your local machine.
    :param token: Hugging Face authentication token to download private models.
    """

    def __init__(
        self,
        model_name_or_path: Optional[str] = None,
        modules: Optional[Iterable[nn.Module]] = None,
        device: Optional[str] = None,
        cache_folder: Optional[str] = None,
        trust_remote_code: bool = False,
        revision: Optional[str] = None,
        token: Optional[Union[bool, str]] = None,
        use_auth_token: Optional[Union[bool, str]] = None,
    ):
        self._model_card_vars = {}
        self._model_card_text = None
        self._model_config = {}
        if use_auth_token is not None:
            warnings.warn(
                "The `use_auth_token` argument is deprecated and will be removed in v3 of SentenceTransformers.",
                FutureWarning,
            )
            if token is not None:
                raise ValueError(
                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
                )
            token = use_auth_token

        if cache_folder is None:
            cache_folder = os.getenv("SENTENCE_TRANSFORMERS_HOME")

        if model_name_or_path is not None and model_name_or_path != "":
            logger.info("Load pretrained SentenceTransformer: {}".format(model_name_or_path))

            # Old models that don't belong to any organization
            basic_transformer_models = [
                "albert-base-v1",
                "albert-base-v2",
                "albert-large-v1",
                "albert-large-v2",
                "albert-xlarge-v1",
                "albert-xlarge-v2",
                "albert-xxlarge-v1",
                "albert-xxlarge-v2",
                "bert-base-cased-finetuned-mrpc",
                "bert-base-cased",
                "bert-base-chinese",
                "bert-base-german-cased",
                "bert-base-german-dbmdz-cased",
                "bert-base-german-dbmdz-uncased",
                "bert-base-multilingual-cased",
                "bert-base-multilingual-uncased",
                "bert-base-uncased",
                "bert-large-cased-whole-word-masking-finetuned-squad",
                "bert-large-cased-whole-word-masking",
                "bert-large-cased",
                "bert-large-uncased-whole-word-masking-finetuned-squad",
                "bert-large-uncased-whole-word-masking",
                "bert-large-uncased",
                "camembert-base",
                "ctrl",
                "distilbert-base-cased-distilled-squad",
                "distilbert-base-cased",
                "distilbert-base-german-cased",
                "distilbert-base-multilingual-cased",
                "distilbert-base-uncased-distilled-squad",
                "distilbert-base-uncased-finetuned-sst-2-english",
                "distilbert-base-uncased",
                "distilgpt2",
                "distilroberta-base",
                "gpt2-large",
                "gpt2-medium",
                "gpt2-xl",
                "gpt2",
                "openai-gpt",
                "roberta-base-openai-detector",
                "roberta-base",
                "roberta-large-mnli",
                "roberta-large-openai-detector",
                "roberta-large",
                "t5-11b",
                "t5-3b",
                "t5-base",
                "t5-large",
                "t5-small",
                "transfo-xl-wt103",
                "xlm-clm-ende-1024",
                "xlm-clm-enfr-1024",
                "xlm-mlm-100-1280",
                "xlm-mlm-17-1280",
                "xlm-mlm-en-2048",
                "xlm-mlm-ende-1024",
                "xlm-mlm-enfr-1024",
                "xlm-mlm-enro-1024",
                "xlm-mlm-tlm-xnli15-1024",
                "xlm-mlm-xnli15-1024",
                "xlm-roberta-base",
                "xlm-roberta-large-finetuned-conll02-dutch",
                "xlm-roberta-large-finetuned-conll02-spanish",
                "xlm-roberta-large-finetuned-conll03-english",
                "xlm-roberta-large-finetuned-conll03-german",
                "xlm-roberta-large",
                "xlnet-base-cased",
                "xlnet-large-cased",
            ]

            if not os.path.exists(model_name_or_path):
                # Not a path, load from hub
                if "\\" in model_name_or_path or model_name_or_path.count("/") > 1:
                    raise ValueError("Path {} not found".format(model_name_or_path))

                if "/" not in model_name_or_path and model_name_or_path.lower() not in basic_transformer_models:
                    # A model from sentence-transformers
                    model_name_or_path = __MODEL_HUB_ORGANIZATION__ + "/" + model_name_or_path

            if is_sentence_transformer_model(model_name_or_path, token, cache_folder=cache_folder, revision=revision):
                modules = self._load_sbert_model(
                    model_name_or_path,
                    token=token,
                    cache_folder=cache_folder,
                    revision=revision,
                    trust_remote_code=trust_remote_code,
                )
            else:
                modules = self._load_auto_model(
                    model_name_or_path,
                    token=token,
                    cache_folder=cache_folder,
                    revision=revision,
                    trust_remote_code=trust_remote_code,
                )

        if modules is not None and not isinstance(modules, OrderedDict):
            modules = OrderedDict([(str(idx), module) for idx, module in enumerate(modules)])

        super().__init__(modules)
        if device is None:
            device = get_device_name()
            logger.info("Use pytorch device_name: {}".format(device))

        self.to(device)

    def encode(
        self,
        sentences: Union[str, List[str]],
        batch_size: int = 32,
        show_progress_bar: bool = None,
        output_value: str = "sentence_embedding",
        convert_to_numpy: bool = True,
        convert_to_tensor: bool = False,
        device: str = None,
        normalize_embeddings: bool = False,
    ) -> Union[List[Tensor], ndarray, Tensor]:
        """
        Computes sentence embeddings.

        :param sentences: the sentences to embed.
        :param batch_size: the batch size used for the computation.
        :param show_progress_bar: Whether to output a progress bar when encode sentences.
        :param output_value: The type of embeddings to return: "sentence_embedding" to get sentence embeddings,
            "token_embeddings" to get wordpiece token embeddings, and `None`, to get all output values. Defaults
            to "sentence_embedding".
        :param convert_to_numpy: Whether the output should be a list of numpy vectors. If False, it is a list of PyTorch tensors.
        :param convert_to_tensor: Whether the output should be one large tensor. Overwrites `convert_to_numpy`.
        :param device: Which `torch.device` to use for the computation.
        :param normalize_embeddings: Whether to normalize returned vectors to have length 1. In that case,
            the faster dot-product (util.dot_score) instead of cosine similarity can be used.

        :return: By default, a list of tensors is returned. If convert_to_tensor, a stacked tensor is returned.
            If convert_to_numpy, a numpy matrix is returned.
        """
        self.eval()
        if show_progress_bar is None:
            show_progress_bar = (
                logger.getEffectiveLevel() == logging.INFO or logger.getEffectiveLevel() == logging.DEBUG
            )

        if convert_to_tensor:
            convert_to_numpy = False

        if output_value != "sentence_embedding":
            convert_to_tensor = False
            convert_to_numpy = False

        input_was_string = False
        if isinstance(sentences, str) or not hasattr(
            sentences, "__len__"
        ):  # Cast an individual sentence to a list with length 1
            sentences = [sentences]
            input_was_string = True

        if device is None:
            device = self.device

        self.to(device)

        all_embeddings = []
        length_sorted_idx = np.argsort([-self._text_length(sen) for sen in sentences])
        sentences_sorted = [sentences[idx] for idx in length_sorted_idx]

        for start_index in trange(0, len(sentences), batch_size, desc="Batches", disable=not show_progress_bar):
            sentences_batch = sentences_sorted[start_index : start_index + batch_size]
            features = self.tokenize(sentences_batch)
            features = batch_to_device(features, device)

            with torch.no_grad():
                out_features = self.forward(features)

                if output_value == "token_embeddings":
                    embeddings = []
                    for token_emb, attention in zip(out_features[output_value], out_features["attention_mask"]):
                        last_mask_id = len(attention) - 1
                        while last_mask_id > 0 and attention[last_mask_id].item() == 0:
                            last_mask_id -= 1

                        embeddings.append(token_emb[0 : last_mask_id + 1])
                elif output_value is None:  # Return all outputs
                    embeddings = []
                    for sent_idx in range(len(out_features["sentence_embedding"])):
                        row = {name: out_features[name][sent_idx] for name in out_features}
                        embeddings.append(row)
                else:  # Sentence embeddings
                    embeddings = out_features[output_value]
                    embeddings = embeddings.detach()
                    if normalize_embeddings:
                        embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)

                    # fixes for #522 and #487 to avoid oom problems on gpu with large datasets
                    if convert_to_numpy:
                        embeddings = embeddings.cpu()

                all_embeddings.extend(embeddings)

        all_embeddings = [all_embeddings[idx] for idx in np.argsort(length_sorted_idx)]

        if convert_to_tensor:
            if len(all_embeddings):
                all_embeddings = torch.stack(all_embeddings)
            else:
                all_embeddings = torch.Tensor()
        elif convert_to_numpy:
            all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])

        if input_was_string:
            all_embeddings = all_embeddings[0]

        return all_embeddings

    def start_multi_process_pool(self, target_devices: List[str] = None):
        """
        Starts multi process to process the encoding with several, independent processes.
        This method is recommended if you want to encode on multiple GPUs or CPUs. It is advised
        to start only one process per GPU. This method works together with encode_multi_process
        and stop_multi_process_pool.

        :param target_devices: PyTorch target devices, e.g. ["cuda:0", "cuda:1", ...], ["npu:0", "npu:1", ...] or
            ["cpu", "cpu", "cpu", "cpu"]. If target_devices is None and CUDA/NPU is available, then all available
            CUDA/NPU devices will be used. If target_devices is None and CUDA/NPU is not available, then 4 CPU
            devices will be used.
        :return: Returns a dict with the target processes, an input queue and and output queue.
        """
        if target_devices is None:
            if torch.cuda.is_available():
                target_devices = ["cuda:{}".format(i) for i in range(torch.cuda.device_count())]
            elif is_torch_npu_available():
                target_devices = ["npu:{}".format(i) for i in range(torch.npu.device_count())]
            else:
                logger.info("CUDA/NPU is not available. Starting 4 CPU workers")
                target_devices = ["cpu"] * 4

        logger.info("Start multi-process pool on devices: {}".format(", ".join(map(str, target_devices))))

        self.to("cpu")
        self.share_memory()
        ctx = mp.get_context("spawn")
        input_queue = ctx.Queue()
        output_queue = ctx.Queue()
        processes = []

        for device_id in target_devices:
            p = ctx.Process(
                target=SentenceTransformer._encode_multi_process_worker,
                args=(device_id, self, input_queue, output_queue),
                daemon=True,
            )
            p.start()
            processes.append(p)

        return {"input": input_queue, "output": output_queue, "processes": processes}

    @staticmethod
    def stop_multi_process_pool(pool):
        """
        Stops all processes started with start_multi_process_pool
        """
        for p in pool["processes"]:
            p.terminate()

        for p in pool["processes"]:
            p.join()
            p.close()

        pool["input"].close()
        pool["output"].close()

    def encode_multi_process(
        self,
        sentences: List[str],
        pool: Dict[str, object],
        batch_size: int = 32,
        chunk_size: int = None,
        normalize_embeddings: bool = False,
    ):
        """
        This method allows to run encode() on multiple GPUs. The sentences are chunked into smaller packages
        and sent to individual processes, which encode these on the different GPUs. This method is only suitable
        for encoding large sets of sentences

        :param sentences: List of sentences
        :param pool: A pool of workers started with SentenceTransformer.start_multi_process_pool
        :param batch_size: Encode sentences with batch size
        :param chunk_size: Sentences are chunked and sent to the individual processes. If none, it determine a sensible size.
        :param normalize_embeddings: Whether to normalize returned vectors to have length 1. In that case,
            the faster dot-product (util.dot_score) instead of cosine similarity can be used.
        :return: Numpy matrix with all embeddings
        """

        if chunk_size is None:
            chunk_size = min(math.ceil(len(sentences) / len(pool["processes"]) / 10), 5000)

        logger.debug(f"Chunk data into {math.ceil(len(sentences) / chunk_size)} packages of size {chunk_size}")

        input_queue = pool["input"]
        last_chunk_id = 0
        chunk = []

        for sentence in sentences:
            chunk.append(sentence)
            if len(chunk) >= chunk_size:
                input_queue.put([last_chunk_id, batch_size, chunk, normalize_embeddings])
                last_chunk_id += 1
                chunk = []

        if len(chunk) > 0:
            input_queue.put([last_chunk_id, batch_size, chunk, normalize_embeddings])
            last_chunk_id += 1

        output_queue = pool["output"]
        results_list = sorted([output_queue.get() for _ in range(last_chunk_id)], key=lambda x: x[0])
        embeddings = np.concatenate([result[1] for result in results_list])
        return embeddings

    @staticmethod
    def _encode_multi_process_worker(target_device: str, model, input_queue, results_queue):
        """
        Internal working process to encode sentences in multi-process setup
        """
        while True:
            try:
                chunk_id, batch_size, sentences, normalize_embeddings = input_queue.get()
                embeddings = model.encode(
                    sentences,
                    device=target_device,
                    show_progress_bar=False,
                    convert_to_numpy=True,
                    batch_size=batch_size,
                    normalize_embeddings=normalize_embeddings,
                )

                results_queue.put([chunk_id, embeddings])
            except queue.Empty:
                break

    def get_max_seq_length(self):
        """
        Returns the maximal sequence length for input the model accepts. Longer inputs will be truncated
        """
        if hasattr(self._first_module(), "max_seq_length"):
            return self._first_module().max_seq_length

        return None

    def tokenize(self, texts: Union[List[str], List[Dict], List[Tuple[str, str]]]):
        """
        Tokenizes the texts
        """
        return self._first_module().tokenize(texts)

    def get_sentence_features(self, *features):
        return self._first_module().get_sentence_features(*features)

    def get_sentence_embedding_dimension(self):
        for mod in reversed(self._modules.values()):
            sent_embedding_dim_method = getattr(mod, "get_sentence_embedding_dimension", None)
            if callable(sent_embedding_dim_method):
                return sent_embedding_dim_method()
        return None

    def _first_module(self):
        """Returns the first module of this sequential embedder"""
        return self._modules[next(iter(self._modules))]

    def _last_module(self):
        """Returns the last module of this sequential embedder"""
        return self._modules[next(reversed(self._modules))]

    def save(
        self,
        path: str,
        model_name: Optional[str] = None,
        create_model_card: bool = True,
        train_datasets: Optional[List[str]] = None,
    ):
        """
        Saves all elements for this seq. sentence embedder into different sub-folders

        :param path: Path on disc
        :param model_name: Optional model name
        :param create_model_card: If True, create a README.md with basic information about this model
        :param train_datasets: Optional list with the names of the datasets used to to train the model
        """
        if path is None:
            return

        os.makedirs(path, exist_ok=True)

        logger.info("Save model to {}".format(path))
        modules_config = []

        # Save some model info
        if "__version__" not in self._model_config:
            self._model_config["__version__"] = {
                "sentence_transformers": __version__,
                "transformers": transformers.__version__,
                "pytorch": torch.__version__,
            }

        with open(os.path.join(path, "config_sentence_transformers.json"), "w") as fOut:
            json.dump(self._model_config, fOut, indent=2)

        # Save modules
        for idx, name in enumerate(self._modules):
            module = self._modules[name]
            if idx == 0 and isinstance(module, Transformer):  # Save transformer model in the main folder
                model_path = path + "/"
            else:
                model_path = os.path.join(path, str(idx) + "_" + type(module).__name__)

            os.makedirs(model_path, exist_ok=True)
            module.save(model_path)
            modules_config.append(
                {"idx": idx, "name": name, "path": os.path.basename(model_path), "type": type(module).__module__}
            )

        with open(os.path.join(path, "modules.json"), "w") as fOut:
            json.dump(modules_config, fOut, indent=2)

        # Create model card
        if create_model_card:
            self._create_model_card(path, model_name, train_datasets)

    def _create_model_card(
        self, path: str, model_name: Optional[str] = None, train_datasets: Optional[List[str]] = None
    ):
        """
        Create an automatic model and stores it in path
        """
        if self._model_card_text is not None and len(self._model_card_text) > 0:
            model_card = self._model_card_text
        else:
            tags = ModelCardTemplate.__TAGS__.copy()
            model_card = ModelCardTemplate.__MODEL_CARD__

            if (
                len(self._modules) == 2
                and isinstance(self._first_module(), Transformer)
                and isinstance(self._last_module(), Pooling)
                and self._last_module().get_pooling_mode_str() in ["cls", "max", "mean"]
            ):
                pooling_module = self._last_module()
                pooling_mode = pooling_module.get_pooling_mode_str()
                model_card = model_card.replace(
                    "{USAGE_TRANSFORMERS_SECTION}", ModelCardTemplate.__USAGE_TRANSFORMERS__
                )
                pooling_fct_name, pooling_fct = ModelCardTemplate.model_card_get_pooling_function(pooling_mode)
                model_card = (
                    model_card.replace("{POOLING_FUNCTION}", pooling_fct)
                    .replace("{POOLING_FUNCTION_NAME}", pooling_fct_name)
                    .replace("{POOLING_MODE}", pooling_mode)
                )
                tags.append("transformers")

            # Print full model
            model_card = model_card.replace("{FULL_MODEL_STR}", str(self))

            # Add tags
            model_card = model_card.replace("{TAGS}", "\n".join(["- " + t for t in tags]))

            datasets_str = ""
            if train_datasets is not None:
                datasets_str = "datasets:\n" + "\n".join(["- " + d for d in train_datasets])
            model_card = model_card.replace("{DATASETS}", datasets_str)

            # Add dim info
            self._model_card_vars["{NUM_DIMENSIONS}"] = self.get_sentence_embedding_dimension()

            # Replace vars we created while using the model
            for name, value in self._model_card_vars.items():
                model_card = model_card.replace(name, str(value))

            # Replace remaining vars with default values
            for name, value in ModelCardTemplate.__DEFAULT_VARS__.items():
                model_card = model_card.replace(name, str(value))

        if model_name is not None:
            model_card = model_card.replace("{MODEL_NAME}", model_name.strip())

        with open(os.path.join(path, "README.md"), "w", encoding="utf8") as fOut:
            fOut.write(model_card.strip())

    @save_to_hub_args_decorator
    def save_to_hub(
        self,
        repo_id: str,
        organization: Optional[str] = None,
        token: Optional[str] = None,
        private: Optional[bool] = None,
        commit_message: str = "Add new SentenceTransformer model.",
        local_model_path: Optional[str] = None,
        exist_ok: bool = False,
        replace_model_card: bool = False,
        train_datasets: Optional[List[str]] = None,
    ):
        """
        Uploads all elements of this Sentence Transformer to a new HuggingFace Hub repository.

        :param repo_id: Repository name for your model in the Hub, including the user or organization.
        :param token: An authentication token (See https://huggingface.co/settings/token)
        :param private: Set to true, for hosting a private model
        :param commit_message: Message to commit while pushing.
        :param local_model_path: Path of the model locally. If set, this file path will be uploaded. Otherwise, the current model will be uploaded
        :param exist_ok: If true, saving to an existing repository is OK. If false, saving only to a new repository is possible
        :param replace_model_card: If true, replace an existing model card in the hub with the automatically created model card
        :param train_datasets: Datasets used to train the model. If set, the datasets will be added to the model card in the Hub.
        :param organization: Deprecated. Organization in which you want to push your model or tokenizer (you must be a member of this organization).

        :return: The url of the commit of your model in the repository on the Hugging Face Hub.
        """
        if organization:
            if "/" not in repo_id:
                logger.warning(
                    f'Providing an `organization` to `save_to_hub` is deprecated, please use `repo_id="{organization}/{repo_id}"` instead.'
                )
                repo_id = f"{organization}/{repo_id}"
            elif repo_id.split("/")[0] != organization:
                raise ValueError(
                    "Providing an `organization` to `save_to_hub` is deprecated, please only use `repo_id`."
                )
            else:
                logger.warning(
                    f'Providing an `organization` to `save_to_hub` is deprecated, please only use `repo_id="{repo_id}"` instead.'
                )

        api = HfApi(token=token)
        repo_url = api.create_repo(
            repo_id=repo_id,
            private=private,
            repo_type=None,
            exist_ok=exist_ok,
        )
        if local_model_path:
            folder_url = api.upload_folder(
                repo_id=repo_id, folder_path=local_model_path, commit_message=commit_message
            )
        else:
            with tempfile.TemporaryDirectory() as tmp_dir:
                create_model_card = replace_model_card or not os.path.exists(os.path.join(tmp_dir, "README.md"))
                self.save(
                    tmp_dir,
                    model_name=repo_url.repo_id,
                    create_model_card=create_model_card,
                    train_datasets=train_datasets,
                )
                folder_url = api.upload_folder(repo_id=repo_id, folder_path=tmp_dir, commit_message=commit_message)

        refs = api.list_repo_refs(repo_id=repo_id)
        for branch in refs.branches:
            if branch.name == "main":
                return f"https://huggingface.co/{repo_id}/commit/{branch.target_commit}"
        # This isn't expected to ever be reached.
        return folder_url

    def smart_batching_collate(self, batch: List["InputExample"]) -> Tuple[List[Dict[str, Tensor]], Tensor]:
        """
        Transforms a batch from a SmartBatchingDataset to a batch of tensors for the model
        Here, batch is a list of InputExample instances: [InputExample(...), ...]

        :param batch:
            a batch from a SmartBatchingDataset
        :return:
            a batch of tensors for the model
        """
        texts = [example.texts for example in batch]
        sentence_features = [self.tokenize(sentence) for sentence in zip(*texts)]
        labels = torch.tensor([example.label for example in batch])
        return sentence_features, labels

    def _text_length(self, text: Union[List[int], List[List[int]]]):
        """
        Help function to get the length for the input text. Text can be either
        a list of ints (which means a single text as input), or a tuple of list of ints
        (representing several text inputs to the model).
        """

        if isinstance(text, dict):  # {key: value} case
            return len(next(iter(text.values())))
        elif not hasattr(text, "__len__"):  # Object has no len() method
            return 1
        elif len(text) == 0 or isinstance(text[0], int):  # Empty string or list of ints
            return len(text)
        else:
            return sum([len(t) for t in text])  # Sum of length of individual strings

    def fit(
        self,
        train_objectives: Iterable[Tuple[DataLoader, nn.Module]],
        evaluator: SentenceEvaluator = None,
        epochs: int = 1,
        steps_per_epoch=None,
        scheduler: str = "WarmupLinear",
        warmup_steps: int = 10000,
        optimizer_class: Type[Optimizer] = torch.optim.AdamW,
        optimizer_params: Dict[str, object] = {"lr": 2e-5},
        weight_decay: float = 0.01,
        evaluation_steps: int = 0,
        output_path: str = None,
        save_best_model: bool = True,
        max_grad_norm: float = 1,
        use_amp: bool = False,
        callback: Callable[[float, int, int], None] = None,
        show_progress_bar: bool = True,
        checkpoint_path: str = None,
        checkpoint_save_steps: int = 500,
        checkpoint_save_total_limit: int = 0,
    ):
        """
        Train the model with the given training objective
        Each training objective is sampled in turn for one batch.
        We sample only as many batches from each objective as there are in the smallest one
        to make sure of equal training with each dataset.

        :param train_objectives: Tuples of (DataLoader, LossFunction). Pass more than one for multi-task learning
        :param evaluator: An evaluator (sentence_transformers.evaluation) evaluates the model performance during training on held-out dev data. It is used to determine the best model that is saved to disc.
        :param epochs: Number of epochs for training
        :param steps_per_epoch: Number of training steps per epoch. If set to None (default), one epoch is equal the DataLoader size from train_objectives.
        :param scheduler: Learning rate scheduler. Available schedulers: constantlr, warmupconstant, warmuplinear, warmupcosine, warmupcosinewithhardrestarts
        :param warmup_steps: Behavior depends on the scheduler. For WarmupLinear (default), the learning rate is increased from o up to the maximal learning rate. After these many training steps, the learning rate is decreased linearly back to zero.
        :param optimizer_class: Optimizer
        :param optimizer_params: Optimizer parameters
        :param weight_decay: Weight decay for model parameters
        :param evaluation_steps: If > 0, evaluate the model using evaluator after each number of training steps
        :param output_path: Storage path for the model and evaluation files
        :param save_best_model: If true, the best model (according to evaluator) is stored at output_path
        :param max_grad_norm: Used for gradient normalization.
        :param use_amp: Use Automatic Mixed Precision (AMP). Only for Pytorch >= 1.6.0
        :param callback: Callback function that is invoked after each evaluation.
                It must accept the following three parameters in this order:
                `score`, `epoch`, `steps`
        :param show_progress_bar: If True, output a tqdm progress bar
        :param checkpoint_path: Folder to save checkpoints during training
        :param checkpoint_save_steps: Will save a checkpoint after so many steps
        :param checkpoint_save_total_limit: Total number of checkpoints to store
        """

        ##Add info to model card
        # info_loss_functions = "\n".join(["- {} with {} training examples".format(str(loss), len(dataloader)) for dataloader, loss in train_objectives])
        info_loss_functions = []
        for dataloader, loss in train_objectives:
            info_loss_functions.extend(ModelCardTemplate.get_train_objective_info(dataloader, loss))
        info_loss_functions = "\n\n".join([text for text in info_loss_functions])

        info_fit_parameters = json.dumps(
            {
                "evaluator": fullname(evaluator),
                "epochs": epochs,
                "steps_per_epoch": steps_per_epoch,
                "scheduler": scheduler,
                "warmup_steps": warmup_steps,
                "optimizer_class": str(optimizer_class),
                "optimizer_params": optimizer_params,
                "weight_decay": weight_decay,
                "evaluation_steps": evaluation_steps,
                "max_grad_norm": max_grad_norm,
            },
            indent=4,
            sort_keys=True,
        )
        self._model_card_text = None
        self._model_card_vars["{TRAINING_SECTION}"] = ModelCardTemplate.__TRAINING_SECTION__.replace(
            "{LOSS_FUNCTIONS}", info_loss_functions
        ).replace("{FIT_PARAMETERS}", info_fit_parameters)

        if use_amp:
            if is_torch_npu_available():
                scaler = torch.npu.amp.GradScaler()
            else:
                scaler = torch.cuda.amp.GradScaler()
        self.to(self.device)

        dataloaders = [dataloader for dataloader, _ in train_objectives]

        # Use smart batching
        for dataloader in dataloaders:
            dataloader.collate_fn = self.smart_batching_collate

        loss_models = [loss for _, loss in train_objectives]
        for loss_model in loss_models:
            loss_model.to(self.device)

        self.best_score = -9999999

        if steps_per_epoch is None or steps_per_epoch == 0:
            steps_per_epoch = min([len(dataloader) for dataloader in dataloaders])

        num_train_steps = int(steps_per_epoch * epochs)

        # Prepare optimizers
        optimizers = []
        schedulers = []
        for loss_model in loss_models:
            param_optimizer = list(loss_model.named_parameters())

            no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
            optimizer_grouped_parameters = [
                {
                    "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
                    "weight_decay": weight_decay,
                },
                {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
            ]

            optimizer = optimizer_class(optimizer_grouped_parameters, **optimizer_params)
            scheduler_obj = self._get_scheduler(
                optimizer, scheduler=scheduler, warmup_steps=warmup_steps, t_total=num_train_steps
            )

            optimizers.append(optimizer)
            schedulers.append(scheduler_obj)

        global_step = 0
        data_iterators = [iter(dataloader) for dataloader in dataloaders]

        num_train_objectives = len(train_objectives)

        skip_scheduler = False
        for epoch in trange(epochs, desc="Epoch", disable=not show_progress_bar):
            training_steps = 0

            for loss_model in loss_models:
                loss_model.zero_grad()
                loss_model.train()

            for _ in trange(steps_per_epoch, desc="Iteration", smoothing=0.05, disable=not show_progress_bar):
                for train_idx in range(num_train_objectives):
                    loss_model = loss_models[train_idx]
                    optimizer = optimizers[train_idx]
                    scheduler = schedulers[train_idx]
                    data_iterator = data_iterators[train_idx]

                    try:
                        data = next(data_iterator)
                    except StopIteration:
                        data_iterator = iter(dataloaders[train_idx])
                        data_iterators[train_idx] = data_iterator
                        data = next(data_iterator)

                    features, labels = data
                    labels = labels.to(self.device)
                    features = list(map(lambda batch: batch_to_device(batch, self.device), features))

                    if use_amp:
                        with torch.autocast(device_type=self.device.type):
                            loss_value = loss_model(features, labels)

                        scale_before_step = scaler.get_scale()
                        scaler.scale(loss_value).backward()
                        scaler.unscale_(optimizer)
                        torch.nn.utils.clip_grad_norm_(loss_model.parameters(), max_grad_norm)
                        scaler.step(optimizer)
                        scaler.update()

                        skip_scheduler = scaler.get_scale() != scale_before_step
                    else:
                        loss_value = loss_model(features, labels)
                        loss_value.backward()
                        torch.nn.utils.clip_grad_norm_(loss_model.parameters(), max_grad_norm)
                        optimizer.step()

                    optimizer.zero_grad()

                    if not skip_scheduler:
                        scheduler.step()

                training_steps += 1
                global_step += 1

                if evaluation_steps > 0 and training_steps % evaluation_steps == 0:
                    self._eval_during_training(
                        evaluator, output_path, save_best_model, epoch, training_steps, callback
                    )

                    for loss_model in loss_models:
                        loss_model.zero_grad()
                        loss_model.train()

                if (
                    checkpoint_path is not None
                    and checkpoint_save_steps is not None
                    and checkpoint_save_steps > 0
                    and global_step % checkpoint_save_steps == 0
                ):
                    self._save_checkpoint(checkpoint_path, checkpoint_save_total_limit, global_step)

            self._eval_during_training(evaluator, output_path, save_best_model, epoch, -1, callback)

        if evaluator is None and output_path is not None:  # No evaluator, but output path: save final model version
            self.save(output_path)

        if checkpoint_path is not None:
            self._save_checkpoint(checkpoint_path, checkpoint_save_total_limit, global_step)

    def evaluate(self, evaluator: SentenceEvaluator, output_path: str = None):
        """
        Evaluate the model

        :param evaluator:
            the evaluator
        :param output_path:
            the evaluator can write the results to this path
        """
        if output_path is not None:
            os.makedirs(output_path, exist_ok=True)
        return evaluator(self, output_path)

    def _eval_during_training(self, evaluator, output_path, save_best_model, epoch, steps, callback):
        """Runs evaluation during the training"""
        eval_path = output_path
        if output_path is not None:
            os.makedirs(output_path, exist_ok=True)
            eval_path = os.path.join(output_path, "eval")
            os.makedirs(eval_path, exist_ok=True)

        if evaluator is not None:
            score = evaluator(self, output_path=eval_path, epoch=epoch, steps=steps)
            if callback is not None:
                callback(score, epoch, steps)
            if score > self.best_score:
                self.best_score = score
                if save_best_model:
                    self.save(output_path)

    def _save_checkpoint(self, checkpoint_path, checkpoint_save_total_limit, step):
        # Store new checkpoint
        self.save(os.path.join(checkpoint_path, str(step)))

        # Delete old checkpoints
        if checkpoint_save_total_limit is not None and checkpoint_save_total_limit > 0:
            old_checkpoints = []
            for subdir in os.listdir(checkpoint_path):
                if subdir.isdigit():
                    old_checkpoints.append({"step": int(subdir), "path": os.path.join(checkpoint_path, subdir)})

            if len(old_checkpoints) > checkpoint_save_total_limit:
                old_checkpoints = sorted(old_checkpoints, key=lambda x: x["step"])
                shutil.rmtree(old_checkpoints[0]["path"])

    def _load_auto_model(
        self,
        model_name_or_path: str,
        token: Optional[Union[bool, str]],
        cache_folder: Optional[str],
        revision: Optional[str] = None,
        trust_remote_code: bool = False,
    ):
        """
        Creates a simple Transformer + Mean Pooling model and returns the modules
        """
        logger.warning(
            "No sentence-transformers model found with name {}. Creating a new one with MEAN pooling.".format(
                model_name_or_path
            )
        )
        transformer_model = Transformer(
            model_name_or_path,
            cache_dir=cache_folder,
            model_args={"token": token, "trust_remote_code": trust_remote_code, "revision": revision},
            tokenizer_args={"token": token, "trust_remote_code": trust_remote_code, "revision": revision},
        )
        pooling_model = Pooling(transformer_model.get_word_embedding_dimension(), "mean")
        return [transformer_model, pooling_model]

    def _load_sbert_model(
        self,
        model_name_or_path: str,
        token: Optional[Union[bool, str]],
        cache_folder: Optional[str],
        revision: Optional[str] = None,
        trust_remote_code: bool = False,
    ):
        """
        Loads a full sentence-transformers model
        """
        # Check if the config_sentence_transformers.json file exists (exists since v2 of the framework)
        config_sentence_transformers_json_path = load_file_path(
            model_name_or_path,
            "config_sentence_transformers.json",
            token=token,
            cache_folder=cache_folder,
            revision=revision,
        )
        if config_sentence_transformers_json_path is not None:
            with open(config_sentence_transformers_json_path) as fIn:
                self._model_config = json.load(fIn)

            if (
                "__version__" in self._model_config
                and "sentence_transformers" in self._model_config["__version__"]
                and self._model_config["__version__"]["sentence_transformers"] > __version__
            ):
                logger.warning(
                    "You try to use a model that was created with version {}, however, your version is {}. This might cause unexpected behavior or errors. In that case, try to update to the latest version.\n\n\n".format(
                        self._model_config["__version__"]["sentence_transformers"], __version__
                    )
                )

        # Check if a readme exists
        model_card_path = load_file_path(
            model_name_or_path, "README.md", token=token, cache_folder=cache_folder, revision=revision
        )
        if model_card_path is not None:
            try:
                with open(model_card_path, encoding="utf8") as fIn:
                    self._model_card_text = fIn.read()
            except Exception:
                pass

        # Load the modules of sentence transformer
        modules_json_path = load_file_path(
            model_name_or_path, "modules.json", token=token, cache_folder=cache_folder, revision=revision
        )
        with open(modules_json_path) as fIn:
            modules_config = json.load(fIn)

        modules = OrderedDict()
        for module_config in modules_config:
            module_class = import_from_string(module_config["type"])
            # For Transformer, don't load the full directory, rely on `transformers` instead
            # But, do load the config file first.
            if module_class == Transformer and module_config["path"] == "":
                kwargs = {}
                for config_name in [
                    "sentence_bert_config.json",
                    "sentence_roberta_config.json",
                    "sentence_distilbert_config.json",
                    "sentence_camembert_config.json",
                    "sentence_albert_config.json",
                    "sentence_xlm-roberta_config.json",
                    "sentence_xlnet_config.json",
                ]:
                    config_path = load_file_path(
                        model_name_or_path, config_name, token=token, cache_folder=cache_folder, revision=revision
                    )
                    if config_path is not None:
                        with open(config_path) as fIn:
                            kwargs = json.load(fIn)
                        break
                hub_kwargs = {"token": token, "trust_remote_code": trust_remote_code, "revision": revision}
                if "model_args" in kwargs:
                    kwargs["model_args"].update(hub_kwargs)
                else:
                    kwargs["model_args"] = hub_kwargs
                if "tokenizer_args" in kwargs:
                    kwargs["tokenizer_args"].update(hub_kwargs)
                else:
                    kwargs["tokenizer_args"] = hub_kwargs
                module = Transformer(model_name_or_path, cache_dir=cache_folder, **kwargs)
            else:
                # Normalize does not require any files to be loaded
                if module_class == Normalize:
                    module_path = None
                else:
                    module_path = load_dir_path(
                        model_name_or_path,
                        module_config["path"],
                        token=token,
                        cache_folder=cache_folder,
                        revision=revision,
                    )
                module = module_class.load(module_path)
            modules[module_config["name"]] = module

        return modules

    @staticmethod
    def load(input_path):
        return SentenceTransformer(input_path)

    @staticmethod
    def _get_scheduler(optimizer, scheduler: str, warmup_steps: int, t_total: int):
        """
        Returns the correct learning rate scheduler. Available scheduler: constantlr, warmupconstant, warmuplinear, warmupcosine, warmupcosinewithhardrestarts
        """
        scheduler = scheduler.lower()
        if scheduler == "constantlr":
            return transformers.get_constant_schedule(optimizer)
        elif scheduler == "warmupconstant":
            return transformers.get_constant_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps)
        elif scheduler == "warmuplinear":
            return transformers.get_linear_schedule_with_warmup(
                optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total
            )
        elif scheduler == "warmupcosine":
            return transformers.get_cosine_schedule_with_warmup(
                optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total
            )
        elif scheduler == "warmupcosinewithhardrestarts":
            return transformers.get_cosine_with_hard_restarts_schedule_with_warmup(
                optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total
            )
        else:
            raise ValueError("Unknown scheduler {}".format(scheduler))

    @property
    def device(self) -> device:
        """
        Get torch.device from module, assuming that the whole module has one device.
        """
        try:
            return next(self.parameters()).device
        except StopIteration:
            # For nn.DataParallel compatibility in PyTorch 1.5

            def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
                tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
                return tuples

            gen = self._named_members(get_members_fn=find_tensor_attributes)
            first_tuple = next(gen)
            return first_tuple[1].device

    @property
    def tokenizer(self):
        """
        Property to get the tokenizer that is used by this model
        """
        return self._first_module().tokenizer

    @tokenizer.setter
    def tokenizer(self, value):
        """
        Property to set the tokenizer that should be used by this model
        """
        self._first_module().tokenizer = value

    @property
    def max_seq_length(self):
        """
        Property to get the maximal input sequence length for the model. Longer inputs will be truncated.
        """
        return self._first_module().max_seq_length

    @max_seq_length.setter
    def max_seq_length(self, value):
        """
        Property to set the maximal input sequence length for the model. Longer inputs will be truncated.
        """
        self._first_module().max_seq_length = value

    @property
    def _target_device(self) -> torch.device:
        logger.warning(
            "`SentenceTransformer._target_device` has been removed, please use `SentenceTransformer.device` instead.",
        )
        return self.device

    @_target_device.setter
    def _target_device(self, device: Optional[Union[int, str, torch.device]] = None) -> None:
        self.to(device)