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	---
	base_model: BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- transformers
	license: apache-2.0
	widget:
	- source_sentence: How to discreetly optimize operating expenses?
	sentences:
	- Strategies for quietly reducing overhead costs
	- Subtle ways to cut down on operating expenses
	- >-
	Implementing technology to save on operating costs without broad
	announcements
	- Lowering daily business expenses through unnoticed efficiencies
	- >-
	Minimizing operational expenditures in small businesses without drawing
	attention
	datasets:
	- pszemraj/synthetic-text-similarity
	language:
	- en
	---
	# BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k


	This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

	- This model has been further trained from [BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka](https://hf.co/BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka) on `v3.0` of the `synthetic text similarity` dataset.
	- Intended for use in comparing the cosine similarity of longer document embeddings and/or clustering them.
	- Matryoshka dims: [768, 512, 256, 128, 64]

	An earlier version of this model (on `v1.0` of the dataset) can be found [here](https://huggingface.co/BEE-spoke-data/bert-plus-L8-v1.0-syntheticSTS-4k). TBD which performs better in practical tasks.

	## Usage (Sentence-Transformers)

	Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

	```
	pip install -U sentence-transformers
	```

	Then you can use the model like this:

	```python
	from sentence_transformers import SentenceTransformer
	sentences = ["This is an example sentence", "Each sentence is converted"]

	model = SentenceTransformer('BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k')
	embeddings = model.encode(sentences)
	print(embeddings)
	```



	## Usage (HuggingFace Transformers)

	Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

	```python
	import torch
	from transformers import AutoModel, AutoTokenizer


	# Mean Pooling - Take attention mask into account for correct averaging
	def mean_pooling(model_output, attention_mask):
	token_embeddings = model_output[
	0
	] # First element of model_output contains all token embeddings
	input_mask_expanded = (
	attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
	)
	return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
	input_mask_expanded.sum(1), min=1e-9
	)


	# Sentences we want sentence embeddings for
	sentences = ["This is an example sentence", "Each sentence is converted"]

	# Load model from HuggingFace Hub
	tokenizer = AutoTokenizer.from_pretrained(
	"BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k"
	)
	model = AutoModel.from_pretrained("BEE-spoke-data/bert-plus-L8-v1.0-synthSTSv3-4k")

	# Tokenize sentences
	encoded_input = tokenizer(
	sentences,
	padding=True,
	truncation=True,
	return_tensors="pt",
	)

	# Compute token embeddings
	with torch.no_grad():
	model_output = model(**encoded_input)

	# Perform pooling. In this case, mean pooling.
	sentence_embeddings = mean_pooling(
	model_output,
	encoded_input["attention_mask"],
	)

	print("Sentence embeddings:")
	print(sentence_embeddings)
	```

	## Training

	See training details below.

	Loss:

	`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
	```
	{'loss': 'CosineSimilarityLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1}
	```