metadata
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
ko-sbert-nli
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
λͺ¨λΈμ μ¬μ©νκΈ° μν΄μλ ko-sentence-transformers
λ₯Ό μ€μΉν΄μΌ ν©λλ€.
pip install -U ko-sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["μλ
νμΈμ?", "νκ΅μ΄ λ¬Έμ₯ μλ² λ©μ μν λ²νΈ λͺ¨λΈμ
λλ€."]
model = SentenceTransformer('ko-sbert-nli')
embeddings = model.encode(sentences)
print(embeddings)
Evaluation Results
KorNLI νμ΅ λ°μ΄ν°μ μΌλ‘ νμ΅ν ν KorSTS νκ° λ°μ΄ν°μ μΌλ‘ νκ°ν κ²°κ³Όμ λλ€.
λͺ¨λΈ | νμ΅ λ°μ΄ν° | Cosine Pearson | Cosine Spearman | Euclidean Pearson | Euclidean Spearman | Manhattan Pearson | Manhattan Spearman | Dot Pearson | Dot Spearman |
---|---|---|---|---|---|---|---|---|---|
SKT-KoBERT | NLI | 82.03 | 82.36 | 80.06 | 79.85 | 80.08 | 79.91 | 75.76 | 74.72 |
Training
The model was trained with the parameters:
DataLoader:
sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader
of length 8886 with parameters:
{'batch_size': 64}
Loss:
sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss
with parameters:
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
Parameters of the fit()-Method:
{
"epochs": 1,
"evaluation_steps": 888,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 889,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel
(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})
)