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ColBERT

Loads or creates a ColBERT model that can be used to map sentences / text to multi-vectors embeddings.

Parameters

  • model_name_or_path (str | None) – defaults to None

    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.

  • modules (Optional[Iterable[torch.nn.modules.module.Module]]) – defaults to None

    A list of torch Modules that should be called sequentially, can be used to create custom SentenceTransformer models from scratch.

  • device (str | None) – defaults to None

    Device (like "cuda", "cpu", "mps", "npu") that should be used for computation. If None, checks if a GPU can be used.

  • prompts (dict[str, str] | None) – defaults to None

    A dictionary with prompts for the model. The key is the prompt name, the value is the prompt text. The prompt text will be prepended before any text to encode. For example: {"query": "query: ", "passage": "passage: "} or {"clustering": "Identify the main category based on the titles in "}.

  • default_prompt_name (str | None) – defaults to None

    The name of the prompt that should be used by default. If not set, no prompt will be applied.

  • similarity_fn_name (Union[str, sentence_transformers.similarity_functions.SimilarityFunction, NoneType]) – defaults to None

    The name of the similarity function to use. Valid options are "cosine", "dot", "euclidean", and "manhattan". If not set, it is automatically set to "cosine" if similarity or similarity_pairwise are called while model.similarity_fn_name is still None.

  • cache_folder (str | None) – defaults to None

    Path to store models. Can also be set by the SENTENCE_TRANSFORMERS_HOME environment variable.

  • trust_remote_code (bool) – defaults to False

    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.

  • revision (str | None) – defaults to None

    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.

  • local_files_only (bool) – defaults to False

    Whether or not to only look at local files (i.e., do not try to download the model).

  • token (bool | str | None) – defaults to None

    Hugging Face authentication token to download private models.

  • use_auth_token (bool | str | None) – defaults to None

    Deprecated argument. Please use token instead.

  • truncate_dim (int | None) – defaults to None

    The dimension to truncate sentence embeddings to. None does no truncation. Truncation is only applicable during inference when :meth:SentenceTransformer.encode is called.

  • embedding_size (int | None) – defaults to None

    The output size of the projection layer. Default to 128.

  • bias (bool) – defaults to False

  • query_prefix (str | None) – defaults to [Q]

    Prefix to add to the queries.

  • document_prefix (str | None) – defaults to [D]

    Prefix to add to the documents.

  • add_special_tokens (bool) – defaults to True

    Add the prefix to the inputs.

  • truncation (bool) – defaults to True

    Truncate the inputs to the encoder max lengths or use sliding window encoding.

  • query_length (int | None) – defaults to None

    The length of the query to truncate/pad to with mask tokens. If set, will override the config value. Default to 32.

  • document_length (int | None) – defaults to None

    The max length of the document to truncate. If set, will override the config value. Default to 180.

  • attend_to_expansion_tokens (bool) – defaults to False

    Whether to attend to the expansion tokens in the attention layers model. If False, the original tokens will not only attend to the expansion tokens, only the expansion tokens will attend to the original tokens. Default is False (as in the original ColBERT codebase).

  • skiplist_words (list[str] | None) – defaults to None

    A list of words to skip from the documents scoring (note that these tokens are used for encoding and are only skipped during the scoring). Default is the list of string.punctuation.

  • model_kwargs (dict | None) – defaults to None

    Additional model configuration parameters to be passed to the Huggingface Transformers model. Particularly useful options are: - torch_dtype: Override the default torch.dtype and load the model under a specific dtype. The different options are: 1. torch.float16, torch.bfloat16 or torch.float: load in a specified dtype, ignoring the model's config.torch_dtype if one exists. If not specified - the model will get loaded in torch.float (fp32). 2. "auto" - A torch_dtype entry in the config.json file of the model will be attempted to be used. If this entry isn't found then next check the dtype of the first weight in the checkpoint that's of a floating point type and use that as dtype. This will load the model using the dtype it was saved in at the end of the training. It can't be used as an indicator of how the model was trained. Since it could be trained in one of half precision dtypes, but saved in fp32. - attn_implementation: The attention implementation to use in the model (if relevant). Can be any of "eager" (manual implementation of the attention), "sdpa" (using F.scaled_dot_product_attention <https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html>), or "flash_attention_2" (using Dao-AILab/flash-attention <https://github.com/Dao-AILab/flash-attention>). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual "eager" implementation. See the PreTrainedModel.from_pretrained <https://huggingface.co/docs/transformers/en/main_classes/model#transformers.PreTrainedModel.from_pretrained>_ documentation for more details.

  • tokenizer_kwargs (dict | None) – defaults to None

    Additional tokenizer configuration parameters to be passed to the Huggingface Transformers tokenizer. See the AutoTokenizer.from_pretrained <https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoTokenizer.from_pretrained>_ documentation for more details.

  • config_kwargs (dict | None) – defaults to None

    Additional model configuration parameters to be passed to the Huggingface Transformers config. See the AutoConfig.from_pretrained <https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoConfig.from_pretrained>_ documentation for more details.

  • model_card_data (Optional[sentence_transformers.model_card.SentenceTransformerModelCardData]) – defaults to None

    A model card data object that contains information about the model. This is used to generate a model card when saving the model. If not set, a default model card data object is created.

Attributes

  • device

    Get torch.device from module, assuming that the whole module has one device. In case there are no PyTorch parameters, fall back to CPU.

  • max_seq_length

    Returns the maximal input sequence length for the model. Longer inputs will be truncated. Returns: int: The maximal input sequence length. Example: :: from sentence_transformers import SentenceTransformer model = SentenceTransformer("all-mpnet-base-v2") print(model.max_seq_length) # => 384

  • similarity

    Compute the similarity between two collections of embeddings. The output will be a matrix with the similarity scores between all embeddings from the first parameter and all embeddings from the second parameter. This differs from similarity_pairwise which computes the similarity between each pair of embeddings. Args: embeddings1 (Union[Tensor, ndarray]): [num_embeddings_1, embedding_dim] or [embedding_dim]-shaped numpy array or torch tensor. embeddings2 (Union[Tensor, ndarray]): [num_embeddings_2, embedding_dim] or [embedding_dim]-shaped numpy array or torch tensor. Returns: Tensor: A [num_embeddings_1, num_embeddings_2]-shaped torch tensor with similarity scores. Example: :: >>> model = SentenceTransformer("all-mpnet-base-v2") >>> sentences = [ ... "The weather is so nice!", ... "It's so sunny outside.", ... "He's driving to the movie theater.", ... "She's going to the cinema.", ... ] >>> embeddings = model.encode(sentences, normalize_embeddings=True) >>> model.similarity(embeddings, embeddings) tensor([[1.0000, 0.7235, 0.0290, 0.1309], [0.7235, 1.0000, 0.0613, 0.1129], [0.0290, 0.0613, 1.0000, 0.5027], [0.1309, 0.1129, 0.5027, 1.0000]]) >>> model.similarity_fn_name "cosine" >>> model.similarity_fn_name = "euclidean" >>> model.similarity(embeddings, embeddings) tensor([[-0.0000, -0.7437, -1.3935, -1.3184], [-0.7437, -0.0000, -1.3702, -1.3320], [-1.3935, -1.3702, -0.0000, -0.9973], [-1.3184, -1.3320, -0.9973, -0.0000]])

  • similarity_fn_name

    Return the name of the similarity function used by :meth:SentenceTransformer.similarity and :meth:SentenceTransformer.similarity_pairwise. Returns: Optional[str]: The name of the similarity function. Can be None if not set, in which case any uses of :meth:SentenceTransformer.similarity and :meth:SentenceTransformer.similarity_pairwise default to "cosine". Example: >>> model = SentenceTransformer("multi-qa-mpnet-base-dot-v1") >>> model.similarity_fn_name 'dot'

  • similarity_pairwise

    Compute the similarity between two collections of embeddings. The output will be a vector with the similarity scores between each pair of embeddings. Args: embeddings1 (Union[Tensor, ndarray]): [num_embeddings, embedding_dim] or [embedding_dim]-shaped numpy array or torch tensor. embeddings2 (Union[Tensor, ndarray]): [num_embeddings, embedding_dim] or [embedding_dim]-shaped numpy array or torch tensor. Returns: Tensor: A [num_embeddings]-shaped torch tensor with pairwise similarity scores. Example: :: >>> model = SentenceTransformer("all-mpnet-base-v2") >>> sentences = [ ... "The weather is so nice!", ... "It's so sunny outside.", ... "He's driving to the movie theater.", ... "She's going to the cinema.", ... ] >>> embeddings = model.encode(sentences, normalize_embeddings=True) >>> model.similarity_pairwise(embeddings[::2], embeddings[1::2]) tensor([0.7235, 0.5027]) >>> model.similarity_fn_name "cosine" >>> model.similarity_fn_name = "euclidean" >>> model.similarity_pairwise(embeddings[::2], embeddings[1::2]) tensor([-0.7437, -0.9973])

  • tokenizer

    Property to get the tokenizer that is used by this model

Examples

>>> from pylate import models

>>> model = models.ColBERT(
...     model_name_or_path="sentence-transformers/all-MiniLM-L6-v2",
...     device="cpu",
... )

>>> embeddings = model.encode("Hello, how are you?")
>>> assert isinstance(embeddings, np.ndarray)

>>> embeddings = model.encode([
...     "Hello, how are you?",
...     "How is the weather today?"
... ])

>>> assert len(embeddings) == 2
>>> assert isinstance(embeddings[0], np.ndarray)
>>> assert isinstance(embeddings[1], np.ndarray)

>>> embeddings = model.encode([
...     [
...         "Hello, how are you?",
...         "How is the weather today?"
...     ],
...     [
...         "Hello, how are you?",
...         "How is the weather today?"
...     ],
... ])

>>> assert len(embeddings) == 2

>>> model.save_pretrained("test-model")

>>> model = models.ColBERT("test-model")

>>> embeddings = model.encode([
...     "Hello, how are you?",
...     "How is the weather today?"
... ])

>>> assert len(embeddings) == 2
>>> assert isinstance(embeddings[0], np.ndarray)
>>> assert isinstance(embeddings[1], np.ndarray)

Methods

call

Call self as a function.

Parameters

  • args
  • kwargs
add_module

Add a child module to the current module.

The module can be accessed as an attribute using the given name. Args: name (str): name of the child module. The child module can be accessed from this module using the given name module (Module): child module to be added to the module.

Parameters

  • name (str)
  • module (Optional[ForwardRef('Module')])
append

Append a given module to the end.

Args: module (nn.Module): module to append

Parameters

  • module (torch.nn.modules.module.Module)
apply

Apply fn recursively to every submodule (as returned by .children()) as well as self.

Typical use includes initializing the parameters of a model (see also :ref:nn-init-doc). Args: fn (:class:Module -> None): function to be applied to each submodule Returns: Module: self Example:: >>> @torch.no_grad() >>> def init_weights(m): >>> print(m) >>> if type(m) == nn.Linear: >>> m.weight.fill_(1.0) >>> print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=True) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )

Parameters

  • fn (Callable[[ForwardRef('Module')], NoneType])
bfloat16

Casts all floating point parameters and buffers to bfloat16 datatype.

.. note:: This method modifies the module in-place. Returns: Module: self

buffers

Return an iterator over module buffers.

Args: recurse (bool): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Yields: torch.Tensor: module buffer Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for buf in model.buffers(): >>> print(type(buf), buf.size()) (20L,) (20L, 1L, 5L, 5L)

Parameters

  • recurse (bool) – defaults to True
children

Return an iterator over immediate children modules.

Yields: Module: a child module

compile

Compile this Module's forward using :func:torch.compile.

This Module's __call__ method is compiled and all arguments are passed as-is to :func:torch.compile. See :func:torch.compile for details on the arguments for this function.

Parameters

  • args
  • kwargs
cpu

Move all model parameters and buffers to the CPU.

.. note:: This method modifies the module in-place. Returns: Module: self

cuda

Move all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized. .. note:: This method modifies the module in-place. Args: device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self

Parameters

  • device (Union[int, torch.device, NoneType]) – defaults to None
    Device (like "cuda", "cpu", "mps", "npu") that should be used for computation. If None, checks if a GPU can be used.
double

Casts all floating point parameters and buffers to double datatype.

.. note:: This method modifies the module in-place. Returns: Module: self

encode

Computes sentence embeddings.

Parameters

  • sentences (str | list[str])
  • prompt_name (str | None) – defaults to None
  • prompt (str | None) – defaults to None
  • batch_size (int) – defaults to 32
  • show_progress_bar (bool) – defaults to None
  • precision (Literal['float32', 'int8', 'uint8', 'binary', 'ubinary']) – defaults to float32
  • convert_to_numpy (bool) – defaults to True
  • convert_to_tensor (bool) – defaults to False
  • padding (bool) – defaults to False
  • device (str) – defaults to None
    Device (like "cuda", "cpu", "mps", "npu") that should be used for computation. If None, checks if a GPU can be used.
  • normalize_embeddings (bool) – defaults to True
  • is_query (bool) – defaults to True
  • pool_factor (int) – defaults to 1
  • protected_tokens (int) – defaults to 1
encode_multi_process

Encodes a list of sentences using multiple processes and GPUs via :meth:SentenceTransformer.encode <sentence_transformers.SentenceTransformer.encode>. The sentences are chunked into smaller packages and sent to individual processes, which encode them on different GPUs or CPUs. This method is only suitable for encoding large sets of sentences.

Parameters

  • sentences (list[str])
  • pool (dict[str, object])
  • prompt_name (str | None) – defaults to None
  • prompt (str | None) – defaults to None
  • batch_size (int) – defaults to 32
  • chunk_size (int) – defaults to None
  • precision (Literal['float32', 'int8', 'uint8', 'binary', 'ubinary']) – defaults to float32
  • normalize_embeddings (bool) – defaults to True
  • padding (bool) – defaults to False
  • is_query (bool) – defaults to True
  • pool_factor (int) – defaults to 1
  • protected_tokens (int) – defaults to 1
eval

Set the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:Dropout, :class:BatchNorm, etc. This is equivalent with :meth:self.train(False) <torch.nn.Module.train>. See :ref:locally-disable-grad-doc for a comparison between .eval() and several similar mechanisms that may be confused with it. Returns: Module: self

evaluate

Evaluate the model based on an evaluator

Args: evaluator (SentenceEvaluator): The evaluator used to evaluate the model. output_path (str, optional): The path where the evaluator can write the results. Defaults to None. Returns: The evaluation results.

Parameters

  • evaluator (sentence_transformers.evaluation.SentenceEvaluator.SentenceEvaluator)
  • output_path (str) – defaults to None
extend
extra_repr

Set the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

fit

Deprecated training method from before Sentence Transformers v3.0, it is recommended to use :class:~sentence_transformers.trainer.SentenceTransformerTrainer instead. This method uses :class:~sentence_transformers.trainer.SentenceTransformerTrainer behind the scenes, but does not provide as much flexibility as the Trainer itself.

This training approach uses a list of DataLoaders and Loss functions to train the model. Each DataLoader is sampled in turn for one batch. We sample only as many batches from each DataLoader as there are in the smallest one to make sure of equal training with each dataset, i.e. round robin sampling. This method should produce equivalent results in v3.0+ as before v3.0, but if you encounter any issues with your existing training scripts, then you may wish to use :meth:SentenceTransformer.old_fit <sentence_transformers.SentenceTransformer.old_fit> instead. That uses the old training method from before v3.0. Args: train_objectives: Tuples of (DataLoader, LossFunction). Pass more than one for multi-task learning 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. epochs: Number of epochs for training steps_per_epoch: Number of training steps per epoch. If set to None (default), one epoch is equal the DataLoader size from train_objectives. scheduler: Learning rate scheduler. Available schedulers: constantlr, warmupconstant, warmuplinear, warmupcosine, warmupcosinewithhardrestarts 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. optimizer_class: Optimizer optimizer_params: Optimizer parameters weight_decay: Weight decay for model parameters evaluation_steps: If > 0, evaluate the model using evaluator after each number of training steps output_path: Storage path for the model and evaluation files save_best_model: If true, the best model (according to evaluator) is stored at output_path max_grad_norm: Used for gradient normalization. use_amp: Use Automatic Mixed Precision (AMP). Only for Pytorch >= 1.6.0 callback: Callback function that is invoked after each evaluation. It must accept the following three parameters in this order: score, epoch, steps show_progress_bar: If True, output a tqdm progress bar checkpoint_path: Folder to save checkpoints during training checkpoint_save_steps: Will save a checkpoint after so many steps checkpoint_save_total_limit: Total number of checkpoints to store

Parameters

  • train_objectives (Iterable[Tuple[torch.utils.data.dataloader.DataLoader, torch.nn.modules.module.Module]])
  • evaluator (sentence_transformers.evaluation.SentenceEvaluator.SentenceEvaluator) – defaults to None
  • epochs (int) – defaults to 1
  • steps_per_epoch – defaults to None
  • scheduler (str) – defaults to WarmupLinear
  • warmup_steps (int) – defaults to 10000
  • optimizer_class (Type[torch.optim.optimizer.Optimizer]) – defaults to <class 'torch.optim.adamw.AdamW'>
  • optimizer_params (Dict[str, object]) – defaults to {'lr': 2e-05}
  • weight_decay (float) – defaults to 0.01
  • evaluation_steps (int) – defaults to 0
  • output_path (str) – defaults to None
  • save_best_model (bool) – defaults to True
  • max_grad_norm (float) – defaults to 1
  • use_amp (bool) – defaults to False
  • callback (Callable[[float, int, int], NoneType]) – defaults to None
  • show_progress_bar (bool) – defaults to True
  • checkpoint_path (str) – defaults to None
  • checkpoint_save_steps (int) – defaults to 500
  • checkpoint_save_total_limit (int) – defaults to 0
float

Casts all floating point parameters and buffers to float datatype.

.. note:: This method modifies the module in-place. Returns: Module: self

forward

Define the computation performed at every call.

Should be overridden by all subclasses. .. note:: Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters

  • input (Any)
get_buffer

Return the buffer given by target if it exists, otherwise throw an error.

See the docstring for get_submodule for a more detailed explanation of this method's functionality as well as how to correctly specify target. Args: target: The fully-qualified string name of the buffer to look for. (See get_submodule for how to specify a fully-qualified string.) Returns: torch.Tensor: The buffer referenced by target Raises: AttributeError: If the target string references an invalid path or resolves to something that is not a buffer

Parameters

  • target (str)
get_extra_state

Return any extra state to include in the module's state_dict.

Implement this and a corresponding :func:set_extra_state for your module if you need to store extra state. This function is called when building the module's state_dict(). Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes. Returns: object: Any extra state to store in the module's state_dict

get_max_seq_length

Returns the maximal sequence length that the model accepts. Longer inputs will be truncated.

Returns: Optional[int]: The maximal sequence length that the model accepts, or None if it is not defined.

get_parameter

Return the parameter given by target if it exists, otherwise throw an error.

See the docstring for get_submodule for a more detailed explanation of this method's functionality as well as how to correctly specify target. Args: target: The fully-qualified string name of the Parameter to look for. (See get_submodule for how to specify a fully-qualified string.) Returns: torch.nn.Parameter: The Parameter referenced by target Raises: AttributeError: If the target string references an invalid path or resolves to something that is not an nn.Parameter

Parameters

  • target (str)
get_sentence_embedding_dimension

Returns the number of dimensions in the output of :meth:SentenceTransformer.encode <sentence_transformers.SentenceTransformer.encode>.

Returns: Optional[int]: The number of dimensions in the output of encode. If it's not known, it's None.

get_sentence_features
get_submodule

Return the submodule given by target if it exists, otherwise throw an error.

For example, let's say you have an nn.Module A that looks like this: .. code-block:: text A( (net_b): Module( (net_c): Module( (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2)) ) (linear): Linear(in_features=100, out_features=200, bias=True) ) ) (The diagram shows an nn.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.) To check whether or not we have the linear submodule, we would call get_submodule("net_b.linear"). To check whether we have the conv submodule, we would call get_submodule("net_b.net_c.conv"). The runtime of get_submodule is bounded by the degree of module nesting in target. A query against named_modules achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, get_submodule should always be used. Args: target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) Returns: torch.nn.Module: The submodule referenced by target Raises: AttributeError: If the target string references an invalid path or resolves to something that is not an nn.Module

Parameters

  • target (str)
gradient_checkpointing_enable
half

Casts all floating point parameters and buffers to half datatype.

.. note:: This method modifies the module in-place. Returns: Module: self

insert
insert_prefix_token

Inserts a prefix token at the beginning of each sequence in the input tensor.

Parameters

  • input_ids (torch.Tensor)
  • prefix_id (int)
ipu

Move all model parameters and buffers to the IPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized. .. note:: This method modifies the module in-place. Arguments: device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self

Parameters

  • device (Union[int, torch.device, NoneType]) – defaults to None
    Device (like "cuda", "cpu", "mps", "npu") that should be used for computation. If None, checks if a GPU can be used.
load
load_state_dict

Copy parameters and buffers from :attr:state_dict into this module and its descendants.

If :attr:strict is True, then the keys of :attr:state_dict must exactly match the keys returned by this module's :meth:~torch.nn.Module.state_dict function. .. warning:: If :attr:assign is True the optimizer must be created after the call to :attr:load_state_dict unless :func:~torch.__future__.get_swap_module_params_on_conversion is True. Args: state_dict (dict): a dict containing parameters and persistent buffers. strict (bool, optional): whether to strictly enforce that the keys in :attr:state_dict match the keys returned by this module's :meth:~torch.nn.Module.state_dict function. Default: True assign (bool, optional): When False, the properties of the tensors in the current module are preserved while when True, the properties of the Tensors in the state dict are preserved. The only exception is the requires_grad field of :class:~torch.nn.Parameters for which the value from the module is preserved. Default: False Returns: NamedTuple with missing_keys and unexpected_keys fields: * missing_keys is a list of str containing any keys that are expected by this module but missing from the provided state_dict. * unexpected_keys is a list of str containing the keys that are not expected by this module but present in the provided state_dict. Note: If a parameter or buffer is registered as None and its corresponding key exists in :attr:state_dict, :meth:load_state_dict will raise a RuntimeError.

Parameters

  • state_dict (Mapping[str, Any])
  • strict (bool) – defaults to True
  • assign (bool) – defaults to False
modules

Return an iterator over all modules in the network.

Yields: Module: a module in the network Note: Duplicate modules are returned only once. In the following example, l will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.modules()): ... print(idx, '->', m) 0 -> Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) 1 -> Linear(in_features=2, out_features=2, bias=True)

named_buffers

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Args: prefix (str): prefix to prepend to all buffer names. recurse (bool, optional): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True. Yields: (str, torch.Tensor): Tuple containing the name and buffer Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, buf in self.named_buffers(): >>> if name in ['running_var']: >>> print(buf.size())

Parameters

  • prefix (str) – defaults to ``
  • recurse (bool) – defaults to True
  • remove_duplicate (bool) – defaults to True
named_children

Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields: (str, Module): Tuple containing a name and child module Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, module in model.named_children(): >>> if name in ['conv4', 'conv5']: >>> print(module)

named_modules

Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Args: memo: a memo to store the set of modules already added to the result prefix: a prefix that will be added to the name of the module remove_duplicate: whether to remove the duplicated module instances in the result or not Yields: (str, Module): Tuple of name and module Note: Duplicate modules are returned only once. In the following example, l will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.named_modules()): ... print(idx, '->', m) 0 -> ('', Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )) 1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

Parameters

  • memo (Optional[Set[ForwardRef('Module')]]) – defaults to None
  • prefix (str) – defaults to ``
  • remove_duplicate (bool) – defaults to True
named_parameters

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Args: prefix (str): prefix to prepend to all parameter names. recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. remove_duplicate (bool, optional): whether to remove the duplicated parameters in the result. Defaults to True. Yields: (str, Parameter): Tuple containing the name and parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size())

Parameters

  • prefix (str) – defaults to ``
  • recurse (bool) – defaults to True
  • remove_duplicate (bool) – defaults to True
old_fit

Deprecated training method from before Sentence Transformers v3.0, it is recommended to use :class:sentence_transformers.trainer.SentenceTransformerTrainer instead. This method should only be used if you encounter issues with your existing training scripts after upgrading to v3.0+.

This training approach uses a list of DataLoaders and Loss functions to train the model. Each DataLoader is sampled in turn for one batch. We sample only as many batches from each DataLoader as there are in the smallest one to make sure of equal training with each dataset, i.e. round robin sampling. Args: train_objectives: Tuples of (DataLoader, LossFunction). Pass more than one for multi-task learning 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. epochs: Number of epochs for training steps_per_epoch: Number of training steps per epoch. If set to None (default), one epoch is equal the DataLoader size from train_objectives. scheduler: Learning rate scheduler. Available schedulers: constantlr, warmupconstant, warmuplinear, warmupcosine, warmupcosinewithhardrestarts 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. optimizer_class: Optimizer optimizer_params: Optimizer parameters weight_decay: Weight decay for model parameters evaluation_steps: If > 0, evaluate the model using evaluator after each number of training steps output_path: Storage path for the model and evaluation files save_best_model: If true, the best model (according to evaluator) is stored at output_path max_grad_norm: Used for gradient normalization. use_amp: Use Automatic Mixed Precision (AMP). Only for Pytorch >= 1.6.0 callback: Callback function that is invoked after each evaluation. It must accept the following three parameters in this order: score, epoch, steps show_progress_bar: If True, output a tqdm progress bar checkpoint_path: Folder to save checkpoints during training checkpoint_save_steps: Will save a checkpoint after so many steps checkpoint_save_total_limit: Total number of checkpoints to store

Parameters

  • train_objectives (Iterable[Tuple[torch.utils.data.dataloader.DataLoader, torch.nn.modules.module.Module]])
  • evaluator (sentence_transformers.evaluation.SentenceEvaluator.SentenceEvaluator) – defaults to None
  • epochs (int) – defaults to 1
  • steps_per_epoch – defaults to None
  • scheduler (str) – defaults to WarmupLinear
  • warmup_steps (int) – defaults to 10000
  • optimizer_class (Type[torch.optim.optimizer.Optimizer]) – defaults to <class 'torch.optim.adamw.AdamW'>
  • optimizer_params (Dict[str, object]) – defaults to {'lr': 2e-05}
  • weight_decay (float) – defaults to 0.01
  • evaluation_steps (int) – defaults to 0
  • output_path (str) – defaults to None
  • save_best_model (bool) – defaults to True
  • max_grad_norm (float) – defaults to 1
  • use_amp (bool) – defaults to False
  • callback (Callable[[float, int, int], NoneType]) – defaults to None
  • show_progress_bar (bool) – defaults to True
  • checkpoint_path (str) – defaults to None
  • checkpoint_save_steps (int) – defaults to 500
  • checkpoint_save_total_limit (int) – defaults to 0
parameters

Return an iterator over module parameters.

This is typically passed to an optimizer. Args: recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. Yields: Parameter: module parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for param in model.parameters(): >>> print(type(param), param.size()) (20L,) (20L, 1L, 5L, 5L)

Parameters

  • recurse (bool) – defaults to True
pool_embeddings_hierarchical

Pools the embeddings hierarchically by clustering and averaging them.

Parameters

  • documents_embeddings (list[torch.Tensor])
  • pool_factor (int) – defaults to 1
  • protected_tokens (int) – defaults to 1

Returns

list[torch.Tensor]: A list of pooled embeddings for each document.

pop
push_to_hub

Uploads all elements of this Sentence Transformer to a new HuggingFace Hub repository.

Args: repo_id (str): Repository name for your model in the Hub, including the user or organization. token (str, optional): An authentication token (See https://huggingface.co/settings/token) private (bool, optional): Set to true, for hosting a private model safe_serialization (bool, optional): If true, save the model using safetensors. If false, save the model the traditional PyTorch way commit_message (str, optional): Message to commit while pushing. local_model_path (str, optional): Path of the model locally. If set, this file path will be uploaded. Otherwise, the current model will be uploaded exist_ok (bool, optional): If true, saving to an existing repository is OK. If false, saving only to a new repository is possible replace_model_card (bool, optional): If true, replace an existing model card in the hub with the automatically created model card train_datasets (List[str], optional): Datasets used to train the model. If set, the datasets will be added to the model card in the Hub. Returns: str: The url of the commit of your model in the repository on the Hugging Face Hub.

Parameters

  • repo_id (str)
  • token (Optional[str]) – defaults to None
    Hugging Face authentication token to download private models.
  • private (Optional[bool]) – defaults to None
  • safe_serialization (bool) – defaults to True
  • commit_message (str) – defaults to Add new SentenceTransformer model.
  • local_model_path (Optional[str]) – defaults to None
  • exist_ok (bool) – defaults to False
  • replace_model_card (bool) – defaults to False
  • train_datasets (Optional[List[str]]) – defaults to None
register_backward_hook

Register a backward hook on the module.

This function is deprecated in favor of :meth:~torch.nn.Module.register_full_backward_hook and the behavior of this function will change in future versions. Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

  • hook (Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]])
register_buffer

Add a buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm's running_mean is not a parameter, but is part of the module's state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting :attr:persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module's :attr:state_dict. Buffers can be accessed as attributes using given names. Args: name (str): name of the buffer. The buffer can be accessed from this module using the given name tensor (Tensor or None): buffer to be registered. If None, then operations that run on buffers, such as :attr:cuda, are ignored. If None, the buffer is not included in the module's :attr:state_dict. persistent (bool): whether the buffer is part of this module's :attr:state_dict. Example:: >>> # xdoctest: +SKIP("undefined vars") >>> self.register_buffer('running_mean', torch.zeros(num_features))

Parameters

  • name (str)
  • tensor (Optional[torch.Tensor])
  • persistent (bool) – defaults to True
register_forward_hook

Register a forward hook on the module.

The hook will be called every time after :func:forward has computed an output. If with_kwargs is False or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after :func:forward is called. The hook should have the following signature:: hook(module, args, output) -> None or modified output If with_kwargs is True, the forward hook will be passed the kwargs given to the forward function and be expected to return the output possibly modified. The hook should have the following signature:: hook(module, args, kwargs, output) -> None or modified output Args: hook (Callable): The user defined hook to be registered. prepend (bool): If True, the provided hook will be fired before all existing forward hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this :class:torch.nn.modules.Module. Note that global forward hooks registered with :func:register_module_forward_hook will fire before all hooks registered by this method. Default: False with_kwargs (bool): If True, the hook will be passed the kwargs given to the forward function. Default: False always_call (bool): If True the hook will be run regardless of whether an exception is raised while calling the Module. Default: False Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

  • hook (Union[Callable[[~T, Tuple[Any, ...], Any], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]]])
  • prepend (bool) – defaults to False
  • with_kwargs (bool) – defaults to False
  • always_call (bool) – defaults to False
register_forward_pre_hook

Register a forward pre-hook on the module.

The hook will be called every time before :func:forward is invoked. If with_kwargs is false or not specified, the input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the forward. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned (unless that value is already a tuple). The hook should have the following signature:: hook(module, args) -> None or modified input If with_kwargs is true, the forward pre-hook will be passed the kwargs given to the forward function. And if the hook modifies the input, both the args and kwargs should be returned. The hook should have the following signature:: hook(module, args, kwargs) -> None or a tuple of modified input and kwargs Args: hook (Callable): The user defined hook to be registered. prepend (bool): If true, the provided hook will be fired before all existing forward_pre hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this :class:torch.nn.modules.Module. Note that global forward_pre hooks registered with :func:register_module_forward_pre_hook will fire before all hooks registered by this method. Default: False with_kwargs (bool): If true, the hook will be passed the kwargs given to the forward function. Default: False Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

  • hook (Union[Callable[[~T, Tuple[Any, ...]], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]]])
  • prepend (bool) – defaults to False
  • with_kwargs (bool) – defaults to False
register_full_backward_hook

Register a backward hook on the module.

The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature:: hook(module, grad_input, grad_output) -> tuple(Tensor) or None The :attr:grad_input and :attr:grad_output are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the input that will be used in place of :attr:grad_input in subsequent computations. :attr:grad_input will only correspond to the inputs given as positional arguments and all kwarg arguments are ignored. Entries in :attr:grad_input and :attr:grad_output will be None for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error. Args: hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided hook will be fired before all existing backward hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this :class:torch.nn.modules.Module. Note that global backward hooks registered with :func:register_module_full_backward_hook will fire before all hooks registered by this method. Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

  • hook (Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]])
  • prepend (bool) – defaults to False
register_full_backward_pre_hook

Register a backward pre-hook on the module.

The hook will be called every time the gradients for the module are computed. The hook should have the following signature:: hook(module, grad_output) -> tuple[Tensor] or None The :attr:grad_output is a tuple. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the output that will be used in place of :attr:grad_output in subsequent computations. Entries in :attr:grad_output will be None for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error. Args: hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided hook will be fired before all existing backward_pre hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this :class:torch.nn.modules.Module. Note that global backward_pre hooks registered with :func:register_module_full_backward_pre_hook will fire before all hooks registered by this method. Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

  • hook (Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]])
  • prepend (bool) – defaults to False
register_load_state_dict_post_hook

Register a post hook to be run after module's load_state_dict is called.

It should have the following signature:: hook(module, incompatible_keys) -> None The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys. The given incompatible_keys can be modified inplace if needed. Note that the checks performed when calling :func:load_state_dict with strict=True are affected by modifications the hook makes to missing_keys or unexpected_keys, as expected. Additions to either set of keys will result in an error being thrown when strict=True, and clearing out both missing and unexpected keys will avoid an error. Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

  • hook
register_module

Alias for :func:add_module.

Parameters

  • name (str)
  • module (Optional[ForwardRef('Module')])
register_parameter

Add a parameter to the module.

The parameter can be accessed as an attribute using given name. Args: name (str): name of the parameter. The parameter can be accessed from this module using the given name param (Parameter or None): parameter to be added to the module. If None, then operations that run on parameters, such as :attr:cuda, are ignored. If None, the parameter is not included in the module's :attr:state_dict.

Parameters

  • name (str)
  • param (Optional[torch.nn.parameter.Parameter])
register_state_dict_pre_hook

Register a pre-hook for the :meth:~torch.nn.Module.state_dict method.

These hooks will be called with arguments: self, prefix, and keep_vars before calling state_dict on self. The registered hooks can be used to perform pre-processing before the state_dict call is made.

Parameters

  • hook
requires_grad_

Change if autograd should record operations on parameters in this module.

This method sets the parameters' :attr:requires_grad attributes in-place. This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training). See :ref:locally-disable-grad-doc for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it. Args: requires_grad (bool): whether autograd should record operations on parameters in this module. Default: True. Returns: Module: self

Parameters

  • requires_grad (bool) – defaults to True
save

Saves a model and its configuration files to a directory, so that it can be loaded with SentenceTransformer(path) again.

Args: path (str): Path on disc where the model will be saved. model_name (str, optional): Optional model name. create_model_card (bool, optional): If True, create a README.md with basic information about this model. train_datasets (list[str], optional): Optional list with the names of the datasets used to train the model. safe_serialization (bool, optional): If True, save the model using safetensors. If False, save the model the traditional (but unsafe) PyTorch way.

Parameters

  • path (str)
  • model_name (str | None) – defaults to None
  • create_model_card (bool) – defaults to True
  • train_datasets (list[str] | None) – defaults to None
  • safe_serialization (bool) – defaults to True
save_pretrained

Saves a model and its configuration files to a directory, so that it can be loaded with SentenceTransformer(path) again.

Args: path (str): Path on disc where the model will be saved. model_name (str, optional): Optional model name. create_model_card (bool, optional): If True, create a README.md with basic information about this model. train_datasets (List[str], optional): Optional list with the names of the datasets used to train the model. safe_serialization (bool, optional): If True, save the model using safetensors. If False, save the model the traditional (but unsafe) PyTorch way.

Parameters

  • path (str)
  • model_name (Optional[str]) – defaults to None
  • create_model_card (bool) – defaults to True
  • train_datasets (Optional[List[str]]) – defaults to None
  • safe_serialization (bool) – defaults to True
save_to_hub

DEPRECATED, use push_to_hub instead.

Uploads all elements of this Sentence Transformer to a new HuggingFace Hub repository. Args: repo_id (str): Repository name for your model in the Hub, including the user or organization. token (str, optional): An authentication token (See https://huggingface.co/settings/token) private (bool, optional): Set to true, for hosting a private model safe_serialization (bool, optional): If true, save the model using safetensors. If false, save the model the traditional PyTorch way commit_message (str, optional): Message to commit while pushing. local_model_path (str, optional): Path of the model locally. If set, this file path will be uploaded. Otherwise, the current model will be uploaded exist_ok (bool, optional): If true, saving to an existing repository is OK. If false, saving only to a new repository is possible replace_model_card (bool, optional): If true, replace an existing model card in the hub with the automatically created model card train_datasets (List[str], optional): Datasets used to train the model. If set, the datasets will be added to the model card in the Hub. Returns: str: The url of the commit of your model in the repository on the Hugging Face Hub.

Parameters

  • repo_id (str)
  • organization (Optional[str]) – defaults to None
  • token (Optional[str]) – defaults to None
    Hugging Face authentication token to download private models.
  • private (Optional[bool]) – defaults to None
  • safe_serialization (bool) – defaults to True
  • commit_message (str) – defaults to Add new SentenceTransformer model.
  • local_model_path (Optional[str]) – defaults to None
  • exist_ok (bool) – defaults to False
  • replace_model_card (bool) – defaults to False
  • train_datasets (Optional[List[str]]) – defaults to None
set_extra_state

Set extra state contained in the loaded state_dict.

This function is called from :func:load_state_dict to handle any extra state found within the state_dict. Implement this function and a corresponding :func:get_extra_state for your module if you need to store extra state within its state_dict. Args: state (dict): Extra state from the state_dict

Parameters

  • state (Any)
set_pooling_include_prompt

Sets the include_prompt attribute in the pooling layer in the model, if there is one.

This is useful for INSTRUCTOR models, as the prompt should be excluded from the pooling strategy for these models. Args: include_prompt (bool): Whether to include the prompt in the pooling layer. Returns: None

Parameters

  • include_prompt (bool)
share_memory

See :meth:torch.Tensor.share_memory_.

skiplist_mask

Create a mask for the set of input_ids that are in the skiplist.

Parameters

  • input_ids (torch.Tensor)
  • skiplist (list[int])
smart_batching_collate

Transforms a batch from a SmartBatchingDataset to a batch of tensors for the model Here, batch is a list of InputExample instances: [InputExample(...), ...]

Args: batch: a batch from a SmartBatchingDataset Returns: a batch of tensors for the model

Parameters

  • batch (List[ForwardRef('InputExample')])
start_multi_process_pool

Starts a multi-process pool 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.

Parameters

  • target_devices (list[str]) – defaults to None

Returns

dict: A dictionary with the target processes, an input queue, and an output queue.

state_dict

Return a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included. .. note:: The returned object is a shallow copy. It contains references to the module's parameters and buffers. .. warning:: Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases. .. warning:: Please avoid the use of argument destination as it is not designed for end-users. Args: destination (dict, optional): If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None. prefix (str, optional): a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''. keep_vars (bool, optional): by default the :class:~torch.Tensor s returned in the state dict are detached from autograd. If it's set to True, detaching will not be performed. Default: False. Returns: dict: a dictionary containing a whole state of the module Example:: >>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']

Parameters

  • args
  • destination – defaults to None
  • prefix – defaults to ``
  • keep_vars – defaults to False
stop_multi_process_pool

Stops all processes started with start_multi_process_pool.

Args: pool (Dict[str, object]): A dictionary containing the input queue, output queue, and process list. Returns: None

  • pool (Dict[Literal['input', 'output', 'processes'], Any])
to

Move and/or cast the parameters and buffers.

This can be called as .. function:: to(device=None, dtype=None, non_blocking=False) :noindex: .. function:: to(dtype, non_blocking=False) :noindex: .. function:: to(tensor, non_blocking=False) :noindex: .. function:: to(memory_format=torch.channels_last) :noindex: Its signature is similar to :meth:torch.Tensor.to, but only accepts floating point or complex :attr:dtype\ s. In addition, this method will only cast the floating point or complex parameters and buffers to :attr:dtype (if given). The integral parameters and buffers will be moved :attr:device, if that is given, but with dtypes unchanged. When :attr:non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices. See below for examples. .. note:: This method modifies the module in-place. Args: device (:class:torch.device): the desired device of the parameters and buffers in this module dtype (:class:torch.dtype): the desired floating point or complex dtype of the parameters and buffers in this module tensor (torch.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module memory_format (:class:torch.memory_format): the desired memory format for 4D parameters and buffers in this module (keyword only argument) Returns: Module: self Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> linear = nn.Linear(2, 2) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]]) >>> linear.to(torch.double) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]], dtype=torch.float64) >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1) >>> gpu1 = torch.device("cuda:1") >>> linear.to(gpu1, dtype=torch.half, non_blocking=True) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1') >>> cpu = torch.device("cpu") >>> linear.to(cpu) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16) >>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble) >>> linear.weight Parameter containing: tensor([[ 0.3741+0.j, 0.2382+0.j], [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128) >>> linear(torch.ones(3, 2, dtype=torch.cdouble)) tensor([[0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)

Parameters

  • args
  • kwargs
to_empty

Move the parameters and buffers to the specified device without copying storage.

Args: device (:class:torch.device): The desired device of the parameters and buffers in this module. recurse (bool): Whether parameters and buffers of submodules should be recursively moved to the specified device. Returns: Module: self

Parameters

  • device (Union[int, str, torch.device, NoneType])
    Device (like "cuda", "cpu", "mps", "npu") that should be used for computation. If None, checks if a GPU can be used.
  • recurse (bool) – defaults to True
tokenize

Tokenizes the input texts.

Args: texts (Union[list[str], list[dict], list[tuple[str, str]]]): A list of texts to be tokenized. is_query (bool): Flag to indicate if the texts are queries. Defaults to True. pad_document (bool): Flag to indicate if documents should be padded to max length. Defaults to False. Returns: dict[str, torch.Tensor]: A dictionary of tensors with the tokenized texts, including "input_ids", "attention_mask", and optionally "token_type_ids".

Parameters

  • texts (list[str] | list[dict] | list[tuple[str, str]])
  • is_query (bool) – defaults to True
  • pad_document (bool) – defaults to False
train

Set the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:Dropout, :class:BatchNorm, etc. Args: mode (bool): whether to set training mode (True) or evaluation mode (False). Default: True. Returns: Module: self

Parameters

  • mode (bool) – defaults to True
truncate_sentence_embeddings

In this context, :meth:SentenceTransformer.encode <sentence_transformers.SentenceTransformer.encode> outputs sentence embeddings truncated at dimension truncate_dim.

This may be useful when you are using the same model for different applications where different dimensions are needed. Args: truncate_dim (int, optional): The dimension to truncate sentence embeddings to. None does no truncation. Example: :: from sentence_transformers import SentenceTransformer model = SentenceTransformer("all-mpnet-base-v2") with model.truncate_sentence_embeddings(truncate_dim=16): embeddings_truncated = model.encode(["hello there", "hiya"]) assert embeddings_truncated.shape[-1] == 16

Parameters

  • truncate_dim (Optional[int])
    The dimension to truncate sentence embeddings to. None does no truncation. Truncation is only applicable during inference when :meth:SentenceTransformer.encode is called.
type

Casts all parameters and buffers to :attr:dst_type.

.. note:: This method modifies the module in-place. Args: dst_type (type or string): the desired type Returns: Module: self

Parameters

  • dst_type (Union[torch.dtype, str])
xpu

Move all model parameters and buffers to the XPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized. .. note:: This method modifies the module in-place. Arguments: device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self

Parameters

  • device (Union[int, torch.device, NoneType]) – defaults to None
    Device (like "cuda", "cpu", "mps", "npu") that should be used for computation. If None, checks if a GPU can be used.
zero_grad

Reset gradients of all model parameters.

See similar function under :class:torch.optim.Optimizer for more context. Args: set_to_none (bool): instead of setting to zero, set the grads to None. See :meth:torch.optim.Optimizer.zero_grad for details.

Parameters

  • set_to_none (bool) – defaults to True