BM-K / KoSimCSE-roberta

huggingface.co
Total runs: 6.0K
24-hour runs: 1.2K
7-day runs: 2.7K
30-day runs: -347
Model's Last Updated: Bước đều 24 2023
feature-extraction

Introduction of KoSimCSE-roberta

Model Details of KoSimCSE-roberta

https://github.com/BM-K/Sentence-Embedding-is-all-you-need

Korean-Sentence-Embedding

🍭 Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.

Quick tour
import torch
from transformers import AutoModel, AutoTokenizer

def cal_score(a, b):
    if len(a.shape) == 1: a = a.unsqueeze(0)
    if len(b.shape) == 1: b = b.unsqueeze(0)

    a_norm = a / a.norm(dim=1)[:, None]
    b_norm = b / b.norm(dim=1)[:, None]
    return torch.mm(a_norm, b_norm.transpose(0, 1)) * 100

model = AutoModel.from_pretrained('BM-K/KoSimCSE-roberta')
tokenizer = AutoTokenizer.from_pretrained('BM-K/KoSimCSE-roberta')

sentences = ['치타가 들판을 가로 질러 먹이를 쫓는다.',
             '치타 한 마리가 먹이 뒤에서 달리고 있다.',
             '원숭이 한 마리가 드럼을 연주한다.']

inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
embeddings, _ = model(**inputs, return_dict=False)

score01 = cal_score(embeddings[0][0], embeddings[1][0])
score02 = cal_score(embeddings[0][0], embeddings[2][0])
Performance
  • Semantic Textual Similarity test set results
Model AVG Cosine Pearson Cosine Spearman Euclidean Pearson Euclidean Spearman Manhattan Pearson Manhattan Spearman Dot Pearson Dot Spearman
KoSBERT SKT 77.40 78.81 78.47 77.68 77.78 77.71 77.83 75.75 75.22
KoSBERT 80.39 82.13 82.25 80.67 80.75 80.69 80.78 77.96 77.90
KoSRoBERTa 81.64 81.20 82.20 81.79 82.34 81.59 82.20 80.62 81.25
KoSentenceBART 77.14 79.71 78.74 78.42 78.02 78.40 78.00 74.24 72.15
KoSentenceT5 77.83 80.87 79.74 80.24 79.36 80.19 79.27 72.81 70.17
KoSimCSE-BERT SKT 81.32 82.12 82.56 81.84 81.63 81.99 81.74 79.55 79.19
KoSimCSE-BERT 83.37 83.22 83.58 83.24 83.60 83.15 83.54 83.13 83.49
KoSimCSE-RoBERTa 83.65 83.60 83.77 83.54 83.76 83.55 83.77 83.55 83.64
KoSimCSE-BERT-multitask 85.71 85.29 86.02 85.63 86.01 85.57 85.97 85.26 85.93
KoSimCSE-RoBERTa-multitask 85.77 85.08 86.12 85.84 86.12 85.83 86.12 85.03 85.99

Runs of BM-K KoSimCSE-roberta on huggingface.co

6.0K
Total runs
1.2K
24-hour runs
1.2K
3-day runs
2.7K
7-day runs
-347
30-day runs

More Information About KoSimCSE-roberta huggingface.co Model

KoSimCSE-roberta huggingface.co

KoSimCSE-roberta huggingface.co is an AI model on huggingface.co that provides KoSimCSE-roberta's model effect (), which can be used instantly with this BM-K KoSimCSE-roberta model. huggingface.co supports a free trial of the KoSimCSE-roberta model, and also provides paid use of the KoSimCSE-roberta. Support call KoSimCSE-roberta model through api, including Node.js, Python, http.

KoSimCSE-roberta huggingface.co Url

https://huggingface.co/BM-K/KoSimCSE-roberta

BM-K KoSimCSE-roberta online free

KoSimCSE-roberta huggingface.co is an online trial and call api platform, which integrates KoSimCSE-roberta's modeling effects, including api services, and provides a free online trial of KoSimCSE-roberta, you can try KoSimCSE-roberta online for free by clicking the link below.

BM-K KoSimCSE-roberta online free url in huggingface.co:

https://huggingface.co/BM-K/KoSimCSE-roberta

KoSimCSE-roberta install

KoSimCSE-roberta is an open source model from GitHub that offers a free installation service, and any user can find KoSimCSE-roberta on GitHub to install. At the same time, huggingface.co provides the effect of KoSimCSE-roberta install, users can directly use KoSimCSE-roberta installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

KoSimCSE-roberta install url in huggingface.co:

https://huggingface.co/BM-K/KoSimCSE-roberta

Url of KoSimCSE-roberta

KoSimCSE-roberta huggingface.co Url

Provider of KoSimCSE-roberta huggingface.co

BM-K
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