o shj0@sddlZddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl m Z ddl m Z ddlmZddlmZddlmZmZmZmZmZmZmZmZmZmZmZddlZddl Z ddl!mZ"ddl#Z#ddl$m%Z%ddlm&Z&dd l m'Z'm(Z(m)Z)dd l*m+Z+dd l#m,Z,dd l-m.Z.m/Z/dd l0m1Z1ddl2m3Z3m4Z4ddl5m6Z6ddl7m8Z8ddl9m:Z:m;Z;mZ>ddl?m@Z@mAZAmBZBmCZCmDZDmEZEmFZFmGZGeHeIZJGddde)jKe8ZLdS)N) OrderedDict)contextmanager)Queue)Path) AnyCallableDictIterableIteratorListLiteralOptionalTupleUnionoverload)HfApi)ndarray)Tensordevicenn)trange)is_torch_npu_available) SentenceTransformerModelCardDatagenerate_model_card)SimilarityFunction)__MODEL_HUB_ORGANIZATION__ __version__)SentenceEvaluator)FitMixin) NormalizePooling Transformer)quantize_embeddings)batch_to_deviceget_device_nameimport_from_stringis_sentence_transformer_model load_dir_pathload_file_pathsave_to_hub_args_decoratortruncate_embeddingsc%seZdZdZ                 ddeedeeejdeedee eefdeed ee ee fd eed e d eed e dee e efdee e efdee dee eefdee eefdee eefdeeddf$fdd Z          dde eeefdeedeede d e d!eed"d#ed$d%e d&e ded'e de eeeeffd(d)Zedeefd*d+Zejd,e ee fddfd-d+Zed.ed/edefd0d1Zed.ed/edefd2d1Zedee eefe eefgeffd3d1Zed.ed/edefd4d5Zed.ed/edefd6d5Zedee eefe eefgeffd7d5Z dd8eede ed9effd:d;ZedZ      ddeede>jdddZ>ede fddZ?e?jdddZ?ede(j=fddZ@e@jddee e ee(j=fddfddZ@edeefddZAedeefddZBddddZCZDS)SentenceTransformeray Loads or creates a SentenceTransformer model that can be used to map sentences / text to embeddings. Args: model_name_or_path (str, optional): 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 (Iterable[nn.Module], optional): A list of torch Modules that should be called sequentially, can be used to create custom SentenceTransformer models from scratch. device (str, optional): 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], optional): 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, optional): The name of the prompt that should be used by default. If not set, no prompt will be applied. similarity_fn_name (str or SimilarityFunction, optional): 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, optional): Path to store models. Can also be set by the SENTENCE_TRANSFORMERS_HOME environment variable. trust_remote_code (bool, optional): 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, optional): 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, optional): Whether or not to only look at local files (i.e., do not try to download the model). token (bool or str, optional): Hugging Face authentication token to download private models. use_auth_token (bool or str, optional): Deprecated argument. Please use `token` instead. truncate_dim (int, optional): The dimension to truncate sentence embeddings to. `None` does no truncation. Truncation is only applicable during inference when :meth:`SentenceTransformer.encode` is called. model_kwargs (Dict[str, Any], optional): 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 `_), or `"flash_attention_2"` (using `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 `_ documentation for more details. tokenizer_kwargs (Dict[str, Any], optional): Additional tokenizer configuration parameters to be passed to the Huggingface Transformers tokenizer. See the `AutoTokenizer.from_pretrained `_ documentation for more details. config_kwargs (Dict[str, Any], optional): Additional model configuration parameters to be passed to the Huggingface Transformers config. See the `AutoConfig.from_pretrained `_ documentation for more details. model_card_data (:class:`~sentence_transformers.model_card.SentenceTransformerModelCardData`, optional): 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. Example: :: from sentence_transformers import SentenceTransformer # Load a pre-trained SentenceTransformer model model = SentenceTransformer('all-mpnet-base-v2') # Encode some texts sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium.", ] embeddings = model.encode(sentences) print(embeddings.shape) # (3, 768) # Get the similarity scores between all sentences similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.6817, 0.0492], # [0.6817, 1.0000, 0.0421], # [0.0492, 0.0421, 1.0000]]) NFmodel_name_or_pathmodulesrpromptsdefault_prompt_namesimilarity_fn_name cache_foldertrust_remote_coderevisionlocal_files_onlytokenuse_auth_token truncate_dim model_kwargstokenizer_kwargs config_kwargsmodel_card_datareturnc s|pi|_||_||_| |_|pt|_i|_d|_i|_| dur1t dt | dur/t d| } |dur:t d}|durIt}td||dkr^tjddur^ddlm}||dur|d krtd |gd }t j|sd |vs|d dkrt d|d |vr||vrtd |}t|| || | dr|j|| || || |||d }n|j|| || || |||d }|durt |t!st!ddt"|D}t#$||%|d|_&|jdur|j|jvrt d|jdt'|j(d|jrtt)|jdt'|j(|jr"t*d|jd|dvr.|j+ddn#|rQd |vrQd|,d dvrQt-dd|DrQt*d|j.|dS) Nz^The `use_auth_token` argument is deprecated and will be removed in v3 of SentenceTransformers.zV`token` and `use_auth_token` are both specified. Please set only the argument `token`.SENTENCE_TRANSFORMERS_HOMEzUse pytorch device_name: {}hpuoptimumr)adapt_transformers_to_gaudiz'Load pretrained SentenceTransformer: {})Dzalbert-base-v1zalbert-base-v2zalbert-large-v1zalbert-large-v2zalbert-xlarge-v1zalbert-xlarge-v2zalbert-xxlarge-v1zalbert-xxlarge-v2zbert-base-cased-finetuned-mrpczbert-base-casedzbert-base-chinesezbert-base-german-casedzbert-base-german-dbmdz-casedzbert-base-german-dbmdz-uncasedzbert-base-multilingual-casedzbert-base-multilingual-uncasedzbert-base-uncasedz3bert-large-cased-whole-word-masking-finetuned-squadz#bert-large-cased-whole-word-maskingzbert-large-casedz5bert-large-uncased-whole-word-masking-finetuned-squadz%bert-large-uncased-whole-word-maskingzbert-large-uncasedzcamembert-basectrlz%distilbert-base-cased-distilled-squadzdistilbert-base-casedzdistilbert-base-german-casedz"distilbert-base-multilingual-casedz'distilbert-base-uncased-distilled-squadz/distilbert-base-uncased-finetuned-sst-2-englishzdistilbert-base-uncased distilgpt2zdistilroberta-basez gpt2-largez gpt2-mediumzgpt2-xlgpt2z openai-gptzroberta-base-openai-detectorz roberta-basezroberta-large-mnlizroberta-large-openai-detectorz roberta-largezt5-11bzt5-3bzt5-basezt5-largezt5-smallztransfo-xl-wt103zxlm-clm-ende-1024zxlm-clm-enfr-1024zxlm-mlm-100-1280zxlm-mlm-17-1280zxlm-mlm-en-2048zxlm-mlm-ende-1024zxlm-mlm-enfr-1024zxlm-mlm-enro-1024zxlm-mlm-tlm-xnli15-1024zxlm-mlm-xnli15-1024zxlm-roberta-basez)xlm-roberta-large-finetuned-conll02-dutchz+xlm-roberta-large-finetuned-conll02-spanishz+xlm-roberta-large-finetuned-conll03-englishz*xlm-roberta-large-finetuned-conll03-germanzxlm-roberta-largezxlnet-base-casedzxlnet-large-cased\/rzPath {} not found)r2r4r5)r6r2r4r3r5r9r:r;cSsg|] \}}t||fqS)str).0idxmodulerHrHg/var/www/html/alpaca_bot/venv/lib/python3.10/site-packages/sentence_transformers/SentenceTransformer.py 8z0SentenceTransformer.__init__..FzDefault prompt name ';' not found in the configured prompts dictionary with keys .z$ prompts are loaded, with the keys: zDefault prompt name is set to 'z'. This prompt will be applied to all `encode()` calls, except if `encode()` is called with `prompt` or `prompt_name` parameters.)zhkunlp/instructor-basezhkunlp/instructor-largezhkunlp/instructor-xl)include_prompt instructorcSsg|] }t|tr|jqSrH isinstancer!rR)rJrLrHrHrMrNYrOzInstructor models require `include_prompt=False` in the pooling configuration. Either update the model configuration or call `model.set_pooling_include_prompt(False)` after loading the model.)/r/r0r1r8rr<_model_card_vars_model_card_text _model_configwarningswarn FutureWarning ValueErrorosgetenvr%loggerinfoformat importlibutil find_spec*optimum.habana.transformers.modeling_utilsrApathexistscountlowerrr'_load_sbert_model_load_auto_modelrUr enumeratesuper__init__tois_hpu_graph_enabledlistkeyslenwarningset_pooling_include_promptsplitanyregister_model)selfr-r.rr/r0r1r2r3r4r5r6r7r8r9r:r;r<rAbasic_transformer_models __class__rHrMrns     G       $   zSentenceTransformer.__init__ sentence_embeddingfloat32T sentences prompt_nameprompt batch_sizeshow_progress_bar output_value)r~token_embeddings precision)rint8uint8binaryubinaryconvert_to_numpyconvert_to_tensornormalize_embeddingsc  s.jjdkrjsddlm} | jjddd_|dur0t t j kp/t t j k}| r4d}|dkrkrut?d"dDnt?d#dDnt tj9rd$dD| rdS)%a2 Computes sentence embeddings. Args: sentences (Union[str, List[str]]): The sentences to embed. prompt_name (Optional[str], optional): The name of the prompt to use for encoding. Must be a key in the `prompts` dictionary, which is either set in the constructor or loaded from the model configuration. For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...}, then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because the sentence is appended to the prompt. If ``prompt`` is also set, this argument is ignored. Defaults to None. prompt (Optional[str], optional): The prompt to use for encoding. For example, if the prompt is "query: ", then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because the sentence is appended to the prompt. If ``prompt`` is set, ``prompt_name`` is ignored. Defaults to None. batch_size (int, optional): The batch size used for the computation. Defaults to 32. show_progress_bar (bool, optional): Whether to output a progress bar when encode sentences. Defaults to None. output_value (Optional[Literal["sentence_embedding", "token_embeddings"]], optional): 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". precision (Literal["float32", "int8", "uint8", "binary", "ubinary"], optional): The precision to use for the embeddings. Can be "float32", "int8", "uint8", "binary", or "ubinary". All non-float32 precisions are quantized embeddings. Quantized embeddings are smaller in size and faster to compute, but may have a lower accuracy. They are useful for reducing the size of the embeddings of a corpus for semantic search, among other tasks. Defaults to "float32". convert_to_numpy (bool, optional): Whether the output should be a list of numpy vectors. If False, it is a list of PyTorch tensors. Defaults to True. convert_to_tensor (bool, optional): Whether the output should be one large tensor. Overwrites `convert_to_numpy`. Defaults to False. device (str, optional): Which :class:`torch.device` to use for the computation. Defaults to None. normalize_embeddings (bool, optional): 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. Defaults to False. Returns: Union[List[Tensor], ndarray, Tensor]: By default, a 2d numpy array with shape [num_inputs, output_dimension] is returned. If only one string input is provided, then the output is a 1d array with shape [output_dimension]. If ``convert_to_tensor``, a torch Tensor is returned instead. If ``self.truncate_dim <= output_dimension`` then output_dimension is ``self.truncate_dim``. Example: :: from sentence_transformers import SentenceTransformer # Load a pre-trained SentenceTransformer model model = SentenceTransformer('all-mpnet-base-v2') # Encode some texts sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium.", ] embeddings = model.encode(sentences) print(embeddings.shape) # (3, 768) r?rNT)disable_tensor_cacheFr~__len__z Prompt name 'rPrQzzEncode with either a `prompt`, a `prompt_name`, or neither, but not both. Ignoring the `prompt_name` in favor of `prompt`.csg|]}|qSrHrH)rJsentence)rrHrMrNz.SentenceTransformer.encode.. input_idsr prompt_lengthcsg|]}| qSrH) _text_length)rJsenryrHrMrNcg|]}|qSrHrHrJrK)rrHrMrNrBatches)descdisable)dtypeattention_masktoken_type_idsrcsi|] }||qSrHrH)rJname) out_featuressent_idxrHrM sz.SentenceTransformer.encode..)pdimcrrHrHr)all_embeddingsrHrMrN&rr)rcSsg|]}|qSrH)floatnumpyrJembrHrHrMrN6rcSsg|]}|qSrH)rrrHrHrMrN8rcSsg|]}t|qSrH)torch from_numpy)rJ embeddingrHrHrMrN:)@rtyperphabana_frameworks.torchrr?wrap_in_hpu_graphevalr_getEffectiveLevelloggingINFODEBUGrUrIhasattrr/KeyErrorr\rqrrr0getrttokenizeshaperonpargsortrrsmathceillog2catonesrzerosr$updateno_gradforwardcopydeepcopyr+r8zipitemappendrangedetachr functional normalizecpuextendr#rrstackrrbfloat16asarray)ryrrrrrrrrrrrhtinput_was_stringextra_featurestokenized_promptlength_sorted_idxsentences_sorted start_indexsentences_batchfeaturescurr_tokenize_lenadditional_pad_len embeddings token_emb attention last_mask_idrowrH)rrrryrrrMencodebsC                     "     zSentenceTransformer.encodecCs|jS)a.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_fn_namerrHrHrMr1As z&SentenceTransformer.similarity_fn_namevaluecCs>t|tr|j}||_|durt||_t||_dSdSN)rUrrrto_similarity_fn _similarityto_similarity_pairwise_fn_similarity_pairwiseryrrHrHrMr1Ps   embeddings1 embeddings2cCdSrrHryrrrHrHrM similarityZzSentenceTransformer.similaritycCrrrHrrHrHrMr]rcC|jdur tj|_|jS)a 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]]) N)r1rCOSINErrrHrHrMr`s 'cCrrrHrrHrHrMsimilarity_pairwiserz'SentenceTransformer.similarity_pairwisecCrrrHrrHrHrMrrcCr)a 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]) N)r1rrrrrHrHrMrs target_devicesinputoutput processesc Cs|dur0tjrddttjD}ntr&ddttjD}n tddgd}td d t t || d|td }|}|}g}|D]}|jtj||||fd d }|||qX|||d S)a Starts a multi-process pool to process the encoding with several independent processes via :meth:`SentenceTransformer.encode_multi_process `. 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. Args: target_devices (List[str], optional): 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. Returns: Dict[str, Any]: A dictionary with the target processes, an input queue, and an output queue. NcSg|]}d|qS)zcuda:{}rarJirHrHrMrNrz@SentenceTransformer.start_multi_process_pool..cSr)znpu:{}rrrHrHrMrNrz1CUDA/NPU is not available. Starting 4 CPU workersrz'Start multi-process pool on devices: {}z, spawnT)targetargsdaemonr)rcuda is_availabler device_countrnpur_r`rajoinmaprIro share_memorymp get_contextrProcessr,_encode_multi_process_workerstartr)ryrctx input_queue output_queuer device_idrrHrHrMstart_multi_process_pools.        z,SentenceTransformer.start_multi_process_poolpoolcCsP|dD]}|q|dD] }||q|d|ddS)z 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 rrrN) terminater close)rrrHrHrMstop_multi_process_pools    z+SentenceTransformer.stop_multi_process_pool chunk_sizec  s|durttt|t|ddd}tdtt||d||d} d} g} |D]} | | t| |krQ| | || ||||g| d 7} g} q2t| dkrh| | || ||||g| d 7} |d tfd d t | Dd dd} t dd | D}|S)a Encodes a list of sentences using multiple processes and GPUs via :meth:`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. Args: sentences (List[str]): List of sentences to encode. pool (Dict[Literal["input", "output", "processes"], Any]): A pool of workers started with :meth:`SentenceTransformer.start_multi_process_pool `. prompt_name (Optional[str], optional): The name of the prompt to use for encoding. Must be a key in the `prompts` dictionary, which is either set in the constructor or loaded from the model configuration. For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", ...}, then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because the sentence is appended to the prompt. If ``prompt`` is also set, this argument is ignored. Defaults to None. prompt (Optional[str], optional): The prompt to use for encoding. For example, if the prompt is "query: ", then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" because the sentence is appended to the prompt. If ``prompt`` is set, ``prompt_name`` is ignored. Defaults to None. batch_size (int): Encode sentences with batch size. (default: 32) chunk_size (int): Sentences are chunked and sent to the individual processes. If None, it determines a sensible size. Defaults to None. precision (Literal["float32", "int8", "uint8", "binary", "ubinary"]): The precision to use for the embeddings. Can be "float32", "int8", "uint8", "binary", or "ubinary". All non-float32 precisions are quantized embeddings. Quantized embeddings are smaller in size and faster to compute, but may have lower accuracy. They are useful for reducing the size of the embeddings of a corpus for semantic search, among other tasks. Defaults to "float32". normalize_embeddings (bool): 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. Defaults to False. Returns: np.ndarray: A 2D numpy array with shape [num_inputs, output_dimension]. Example: :: from sentence_transformers import SentenceTransformer def main(): 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."] * 1000 pool = model.start_multi_process_pool() embeddings = model.encode_multi_process(sentences, pool) model.stop_multi_process_pool(pool) print(embeddings.shape) # => (4000, 768) if __name__ == "__main__": main() Nr izChunk data into z packages of size rrrrcsg|]}qSrH)r)rJ_rrHrMrNQrz.cSs|dS)NrrH)xrHrHrMQsz:SentenceTransformer.encode_multi_process..)keycSsg|]}|dqS)rrH)rJresultrHrHrMrNRr) minrrrsr_debugrputsortedrr concatenate)ryrrrrrrrrr last_chunk_idchunkr results_listrrHr rMencode_multi_processs,?$$   "z(SentenceTransformer.encode_multi_process target_devicemodelr results_queuec Cs` z"|\}}}}}} } |j||||d| d|| d } ||| gWn tjy.YdSwq)zU Internal working process to encode sentences in multi-process setup TF)rrrrrrrrN)rrr'queueEmpty) r.r/rr0chunk_idrrrrrrrrHrHrMrUs( z0SentenceTransformer._encode_multi_process_workerrRcCs$|D] }t|tr||_dSqdS)av 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 NrT)ryrRrLrHrHrMruqs  z.SentenceTransformer.set_pooling_include_promptcCst|dr |jSdS)z 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. max_seq_lengthN)r _first_moduler4rrHrHrMget_max_seq_lengths z&SentenceTransformer.get_max_seq_lengthtextscCs||S)aW Tokenizes the texts. Args: texts (Union[List[str], List[Dict], List[Tuple[str, str]]]): A list of texts to be tokenized. Returns: Dict[str, Tensor]: A dictionary of tensors with the tokenized texts. Common keys are "input_ids", "attention_mask", and "token_type_ids". )r5r)ryr7rHrHrMrs zSentenceTransformer.tokenizecGs|j|Sr)r5get_sentence_features)ryrrHrHrMr8sz)SentenceTransformer.get_sentence_featurescCsVd}t|jD]}t|dd}t|r|}nq |jdur)t|p%tj|jS|S)a Returns the number of dimensions in the output of :meth:`SentenceTransformer.encode `. Returns: Optional[int]: The number of dimensions in the output of `encode`. If it's not known, it's `None`. N get_sentence_embedding_dimension) reversed_modulesvaluesgetattrcallabler8r%rinf)ry output_dimmodsent_embedding_dim_methodrHrHrMr9s  z4SentenceTransformer.get_sentence_embedding_dimensionccs*|j}z ||_dVW||_dS||_w)aW In this context, :meth:`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 N)r8)ryr8original_output_dimrHrHrMtruncate_sentence_embeddingss z0SentenceTransformer.truncate_sentence_embeddingscC|jtt|jS)z4Returns the first module of this sequential embedder)r;nextiterrrHrHrMr5z!SentenceTransformer._first_modulecCrE)z3Returns the last module of this sequential embedder)r;rFr:rrHrHrM _last_modulerHz SentenceTransformer._last_modulerf model_namecreate_model_cardtrain_datasetssafe_serializationc Cs|durdStj|ddtd|g}ttjtjd|jd<t tj |dd$}|j }|j |d <|j|d <|j|d <tj||d d Wdn1sSwYt|jD]W\} } |j| } | dkrtt| trt|d} ntj |t| dt| j} tj| ddz | j| |dWnty| | Ynw|| | tj | t| jdq]t tj |dd}tj||d d Wdn1swY|r||||dSdS) 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. NTexist_okzSave model to {})sentence_transformers transformerspytorchr!config_sentence_transformers.jsonwr/r0r1r)indentrrGr)rM)rKrrfr modules.json)r]makedirsr_r`rarrRrrXopenrfr rr/r0r1jsondumprlr;rUr"rIr__name__save TypeErrorrbasename __module___create_model_card) ryrfrJrKrLrMmodules_configfOutconfigrKrrL model_pathrHrHrMr]sH         zSentenceTransformer.savecCs|j|||||ddS)rNrJrKrLrMN)r])ryrfrJrKrLrMrHrHrMsave_pretraineds z#SentenceTransformer.save_pretrained deprecatedcCs|rt|}|s|jjs||j_|jr/|jjdur/|j}|jjr.|dd|jjd}nzt|}WntyJt dt dYdSwt tj|ddd d }||WddS1shwYdS) a  Create an automatic model and stores it in the specified path. If no training was done and the loaded model was a Sentence Transformer model already, then its model card is reused. Args: path (str): The path where the model card will be stored. model_name (Optional[str], optional): The name of the model. Defaults to None. train_datasets (Optional[List[str]], optional): Deprecated argument. Defaults to "deprecated". Returns: None Nz.) rUdictrsrFrGr<rintsum)ryrrHrHrMrs  z SentenceTransformer._text_length evaluator output_pathcCs |dur tj|dd|||S)aC 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. NTrO)r]rX)ryrrrHrHrMevaluates  zSentenceTransformer.evaluatec Cstd|d||||d} |dur| ni| |}|dur"| ni| |}| dur.| ni| | } t||||| d} t| d} |jj||d| | gS)ad Creates a simple Transformer + Mean Pooling model and returns the modules Args: model_name_or_path (str): The name or path of the pre-trained model. token (Optional[Union[bool, str]]): The token to use for the model. cache_folder (Optional[str]): The folder to cache the model. revision (Optional[str], optional): The revision of the model. Defaults to None. trust_remote_code (bool, optional): Whether to trust remote code. Defaults to False. local_files_only (bool, optional): Whether to use only local files. Defaults to False. model_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the model. Defaults to None. tokenizer_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the tokenizer. Defaults to None. config_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the config. Defaults to None. Returns: List[nn.Module]: A list containing the transformer model and the pooling model. z/No sentence-transformers model found with name z'. Creating a new one with mean pooling.r6r3r4r5N) cache_dir model_argstokenizer_args config_argsmeanr4)r_rtr"r!get_word_embedding_dimensionr<set_base_model) ryr-r6r2r4r3r5r9r:r; shared_kwargstransformer_model pooling_modelrHrHrMrks* z$SentenceTransformer._load_auto_modelc  Cst|d||||d} | durnt| } t| |_Wdn1s#wYd|jvrKd|jdvrK|jddtkrKtd|jddt|j durX|j dd|_ |j sc|j di|_ |j sn|j d d|_ t|d ||||d} | durzt| d d  } | |_Wdn1swYWn tyYnwt|d ||||d} t|  } t| }Wdn1swYt}|D]}t|d}|tkr|ddkri}dD]f}t||||||d}|durIt|F} t| }d|vrd|dvr|ddd|vr$d|dvr$|ddd|vr7d|dvr7|ddWdn 1sBwYnq||||d}d|vrZi|d<d|vrci|d<d|vrli|d<|d||d||d||r|d||r|d|| r|d| t|fd|i|}n|tkrd}n t||d||||d}||}|||d<q|durt| }t|jdkrt| jd}t|dkr|}|jj||d|S)af Loads a full SentenceTransformer model using the modules.json file. Args: model_name_or_path (str): The name or path of the pre-trained model. token (Optional[Union[bool, str]]): The token to use for the model. cache_folder (Optional[str]): The folder to cache the model. revision (Optional[str], optional): The revision of the model. Defaults to None. trust_remote_code (bool, optional): Whether to trust remote code. Defaults to False. local_files_only (bool, optional): Whether to use only local files. Defaults to False. model_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the model. Defaults to None. tokenizer_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the tokenizer. Defaults to None. config_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the config. Defaults to None. Returns: OrderedDict[str, nn.Module]: An ordered dictionary containing the modules of the model. rT)r6r2r4r5NrrQzYou 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. r1r/r0rjrkrlrWrrfrB)zsentence_bert_config.jsonzsentence_roberta_config.jsonzsentence_distilbert_config.jsonzsentence_camembert_config.jsonzsentence_albert_config.jsonz sentence_xlm-roberta_config.jsonzsentence_xlnet_config.jsonrr3rrrrrr(r)r)rYrZloadrXrr_rtrar1rr/r0readrWrqrr&r"poprr r(rrspartsr<r)ryr-r6r2r4r3r5r9r:r;&config_sentence_transformers_json_pathfInmodel_card_pathmodules_json_pathrbr. module_config module_classkwargs config_name config_path hub_kwargsrL module_path path_partsrevision_path_partrHrHrMrj7s                    z%SentenceTransformer._load_sbert_modelcCst|Sr)r,) input_pathrHrHrMrszSentenceTransformer.loadc Cszt|jWStyBdtjdtttt ffdd}|j |d}z t|}|djWYStyAt dYYSww)z Get torch.device from module, assuming that the whole module has one device. In case there are no PyTorch parameters, fall back to CPU. rLr=cSsdd|jD}|S)NcSs"g|] \}}t|r||fqSrH)r is_tensor)rJkvrHrHrMrNs"zNSentenceTransformer.device..find_tensor_attributes..)__dict__items)rLtuplesrHrHrMfind_tensor_attributessz:SentenceTransformer.device..find_tensor_attributes)get_members_fnrr) rF parametersr StopIterationrModuler rrIr_named_membersr)ryrgen first_tuplerHrHrMrs    zSentenceTransformer.devicecC |jS)zJ Property to get the tokenizer that is used by this model r5 tokenizerrrHrHrMrs zSentenceTransformer.tokenizercC||_dS)zQ Property to set the tokenizer that should be used by this model NrrrHrHrMr cCr)a 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 r5r4rrHrHrMr4s z"SentenceTransformer.max_seq_lengthcCr)zs Property to set the maximal input sequence length for the model. Longer inputs will be truncated. NrrrHrHrMr4$rcCstd|jS)Nzj`SentenceTransformer._target_device` has been deprecated, please use `SentenceTransformer.device` instead.)r_rtrrrHrHrM_target_device+sz"SentenceTransformer._target_devicecCs||dSr)ro)ryrrHrHrMr2scC$z|jWStygYSwr)r5_no_split_modulesAttributeErrorrrHrHrMr6   z%SentenceTransformer._no_split_modulescCrr)r5_keys_to_ignore_on_saverrrHrHrMr=rz+SentenceTransformer._keys_to_ignore_on_savecCs(|D]}t|tr|j|SqdSr)rUr" auto_modelgradient_checkpointing_enable)rygradient_checkpointing_kwargsrLrHrHrMrDs  z1SentenceTransformer.gradient_checkpointing_enable)NNNNNNNFNFNNNNNNN) NNr}Nr~rTFNFr)NNr}NrF)NTNT)Nrh) NNNTrwNFFN)NNTrwNFFN)NFFNNN)r=r,)r=N)Er\r` __qualname____doc__r rIr rrrrrboolrrrrnr r rrrpropertyr1setterrrrrr staticmethodrrr-rrrur6rrrr8r9rr rDr5rIr]rgrar*rr~rrrrrkrjrrrr4rrrr __classcell__rHrHr{rMr,1s`      V      ` ,*,$ 1   Z 8    G    ,    C   &=(   ;   3   &r,)MrrbrZrrr]r1rrsrY collectionsr contextlibrmultiprocessingrpathlibrtypingrrrr r r r r rrrrrrtorch.multiprocessingrrRhuggingface_hubrrrrrtqdm.autonotebookrr sentence_transformers.model_cardrr*sentence_transformers.similarity_functionsrrBrr evaluationr fit_mixinrmodelsr r!r" quantizationr#rcr$r%r&r'r(r)r*r+ getLoggerr\r_ Sequentialr,rHrHrHrMsD    4         (