upskyy/gte-korean-base
This model is korsts and kornli finetuning model from 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
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
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
Usage (Sentence-Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("upskyy/gte-korean-base", trust_remote_code=True)
# 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]
print(similarities)
# tensor([[1.0000, 0.6274, 0.3788],
# [0.6274, 1.0000, 0.5978],
# [0.3788, 0.5978, 1.0000]])
Usage (HuggingFace Transformers)
Without sentence-transformers, 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.
from transformers import AutoTokenizer, AutoModel
import torch
# 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 = ["안녕하세요?", "한국어 문장 임베딩을 위한 버트 모델입니다."]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("upskyy/gte-korean-base")
model = AutoModel.from_pretrained("upskyy/gte-korean-base", trust_remote_code=True)
# 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)
Evaluation
Metrics
Semantic Similarity
- Dataset:
sts-dev
- Evaluated with
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 |
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
@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},
}
@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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Model tree for upskyy/gte-base-korean
Base model
Alibaba-NLP/gte-multilingual-baseEvaluation results
- Pearson Cosine on sts devself-reported0.868
- Spearman Cosine on sts devself-reported0.869
- Pearson Manhattan on sts devself-reported0.779
- Spearman Manhattan on sts devself-reported0.782
- Pearson Euclidean on sts devself-reported0.781
- Spearman Euclidean on sts devself-reported0.784
- Pearson Dot on sts devself-reported0.718
- Spearman Dot on sts devself-reported0.755
- Pearson Max on sts devself-reported0.868
- Spearman Max on sts devself-reported0.869