KoSimCSE-roberta-multitask huggingface.co api & BM-K KoSimCSE-roberta-multitask github AI Model

Introduction of KoSimCSE-roberta-multitask

Model Details of KoSimCSE-roberta-multitask

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-multitask') 
AutoTokenizer.from_pretrained('BM-K/KoSimCSE-roberta-multitask')

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-multitask on huggingface.co

31.6K

Total runs

-7.3K

24-hour runs

-8.8K

3-day runs

-9.6K

7-day runs

-10.2K

30-day runs

More Information About KoSimCSE-roberta-multitask huggingface.co Model

KoSimCSE-roberta-multitask huggingface.co

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

KoSimCSE-roberta-multitask huggingface.co Url

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

BM-K KoSimCSE-roberta-multitask online free

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

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

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

KoSimCSE-roberta-multitask install

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