pipeline_tag: sentence-similarity
language: ja
license: cc-by-sa-4.0
tags:
- transformers
- sentence-similarity
- feature-extraction
- sentence-transformers
widget:
- source_sentence: This widget can't work correctly now.
- sentences:
- Sorry :(
- Try this model in your local environment!
Japanese SimCSE (BERT-base)
summary
model name: pkshatech/simcse-ja-bert-base-clcmlp
This is a Japanese SimCSE model. You can easily extract sentence embedding representations from Japanese sentences. This model is based on cl-tohoku/bert-base-japanese-v2 and trained on JSNLI dataset, which is a Japanese natural language inference dataset.
Usage (Sentence-Transformers)
You can use this model easily with sentence-transformers.
You need fugashi and unidic-lite for tokenization.
Please install sentence-transformers, fugashi, and unidic-lite with pip as follows:
pip install -U fugashi[unidic-lite] sentence-transformers
You can load the model and convert sentences to dense vectors as follows:
from sentence_transformers import SentenceTransformer
sentences = [
"PKSHA Technologyは機械学習/深層学習技術に関わるアルゴリズムソリューションを展開している。",
"この深層学習モデルはPKSHA Technologyによって学習され、公開された。",
"広目天は、仏教における四天王の一尊であり、サンスクリット語の「種々の眼をした者」を名前の由来とする。",
]
model = SentenceTransformer('pkshatech/simcse-ja-bert-base-clcmlp')
embeddings = model.encode(sentences)
print(embeddings)
Since the loss function used during training is cosine similarity, we recommend using cosine similarity for downstream tasks.
Model Detail
Tokenization
We use the same tokenizer as tohoku/bert-base-japanese-v2. Please see the README of tohoku/bert-base-japanese-v2 for details.
Training
We set tohoku/bert-base-japanese-v2 as the initial value and trained it on the train set of JSNLI. We trained 20 epochs and published the checkpoint of the model with the highest Spearman's correlation coefficient on the validation set [^1] of the train set of JSTS
Training Parameters
| Parameter | Value |
|---|---|
| pooling_strategy | [CLS] -> single fully-connected layer |
| max_seq_length | 128 |
| with hard negative | true |
| temperature of contrastive loss | 0.05 |
| Batch size | 200 |
| Learning rate | 1e-5 |
| Weight decay | 0.01 |
| Max gradient norm | 1.0 |
| Warmup steps | 2012 |
| Scheduler | WarmupLinear |
| Epochs | 20 |
| Evaluation steps | 250 |
Licenses
This models are distributed under the terms of the Creative Creative Commons Attribution-ShareAlike 4.0.
[^1]: When we trained this model, the test data of JGLUE was not released, so we used the dev set of JGLUE as a private evaluation data. Therefore, we selected the checkpoint on the train set of JGLUE insted of its dev set.