gte-base-korean / README.md

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	---
	tags:
	- korean
	- sentence-transformers
	- transformers
	- multilingual
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	license: apache-2.0
	language:
	- af
	- ar
	- az
	- be
	- bg
	- bn
	- ca
	- ceb
	- cs
	- cy
	- da
	- de
	- el
	- en
	- es
	- et
	- eu
	- fa
	- fi
	- fr
	- gl
	- gu
	- he
	- hi
	- hr
	- ht
	- hu
	- hy
	- id
	- is
	- it
	- ja
	- jv
	- ka
	- kk
	- km
	- kn
	- ko
	- ky
	- lo
	- lt
	- lv
	- mk
	- ml
	- mn
	- mr
	- ms
	- my
	- ne
	- nl
	- 'no'
	- pa
	- pl
	- pt
	- qu
	- ro
	- ru
	- si
	- sk
	- sl
	- so
	- sq
	- sr
	- sv
	- sw
	- ta
	- te
	- th
	- tl
	- tr
	- uk
	- ur
	- vi
	- yo
	- zh
	library_name: sentence-transformers
	base_model: Alibaba-NLP/gte-multilingual-base
	datasets: []
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: 이집트 군대가 형제애를 단속하다
	sentences:
	- 이집트의 군대가 무슬림 형제애를 단속하다
	- 아르헨티나의 기예르모 코리아와 네덜란드의 마틴 버커크의 또 다른 준결승전도 매력적이다.
	- 그것이 사실일 수도 있다고 생각하는 것은 재미있다.
	- source_sentence: 오, 그리고 다시 결혼은 근본적인 인권이라고 주장한다.
	sentences:
	- 특히 결혼은 근본적인 인권이라고 말한 후에.
	- 해변에 있는 흑인과 그의 개...
	- 이란은 핵 프로그램이 평화적인 목적을 위한 것이라고 주장한다
	- source_sentence: 두 남자가 난간에 상자를 올려놓고 있다.
	sentences:
	- 심장 박동이 빨라졌다.
	- 두 남자가 집에 있고, 깊이 잠들어 있다.
	- 두 남자가 난간에 상자를 놓고 있다.
	- source_sentence: 조지 샤힌은 안데르센 컨설팅 사업부에서 일했다.
	sentences:
	- 안데르센 컨설팅은 여전히 번창하는 사업이다.
	- 공개 전시 중에 총이 경례한다.
	- 이것은 내가 영국의 아서 안데르센 사업부의 파트너인 짐 와디아를 아서 안데르센 경영진이 선택한 것보다 래리 웨인바흐를 안데르센 월드와이드의
	경영 파트너로 승계하기 위해 안데르센 컨설팅 사업부(현재의 엑센츄어라고 알려져 있음)의 전 관리 파트너인 조지 샤힌에 대한 지지를 표명했을
	때 가장 명백했다.
	- source_sentence: 아이를 가진 엄마가 해변을 걷는다.
	sentences:
	- 국립공원에서 가장 큰 마을인 케스윅의 인구는 매년 여름 등산객, 뱃사람, 관광객이 도착함에 따라 증가한다.
	- 한 남자가 해변에서 개를 산책시킨다.
	- 두 사람이 해변을 걷는다.
	pipeline_tag: sentence-similarity
	model-index:
	- name: upskyy/gte-korean-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.8681402442523579
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8689161244129222
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7793706671294577
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7816816816264681
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7810210343196274
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7835693502057339
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.71802928588865
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7552957785734216
	name: Spearman Dot
	- type: pearson_max
	value: 0.8681402442523579
	name: Pearson Max
	- type: spearman_max
	value: 0.8689161244129222
	name: Spearman Max
	---

	# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 2098722cb5e9d7f96f46df0496f4d34b7338f79c -->
	- Maximum Sequence Length: 8192 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model
	(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

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

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("upskyy/gte-korean-base")

	# Run inference
	sentences = [
	'아이를 가진 엄마가 해변을 걷는다.',
	'두 사람이 해변을 걷는다.',
	'한 남자가 해변에서 개를 산책시킨다.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

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	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\| :----------------- \| :--------- \|
	\| pearson_cosine \| 0.8681 \|
	\| spearman_cosine \| 0.8689 \|
	\| pearson_manhattan \| 0.7794 \|
	\| spearman_manhattan \| 0.7817 \|
	\| pearson_euclidean \| 0.781 \|
	\| spearman_euclidean \| 0.7836 \|
	\| pearson_dot \| 0.718 \|
	\| spearman_dot \| 0.7553 \|
	\| pearson_max \| 0.8681 \|
	\| spearman_max \| 0.8689 \|

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	### Framework Versions
	- Python: 3.10.13
	- Sentence Transformers: 3.0.1
	- Transformers: 4.42.4
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.30.1
	- Datasets: 2.16.1
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	```bibtex
	@misc{zhang2024mgte,
	title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
	author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang},
	year={2024},
	eprint={2407.19669},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2407.19669},
	}
	```

	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

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