sup-simcse-ja-base
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U fugashi[unidic-lite] sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["こんにちは、世界！", "文埋め込み最高！文埋め込み最高と叫びなさい", "極度乾燥しなさい"]
model = SentenceTransformer("cl-nagoya/sup-simcse-ja-base")
embeddings = model.encode(sentences)
print(embeddings)
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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("cl-nagoya/sup-simcse-ja-base")
model = AutoModel.from_pretrained("cl-nagoya/sup-simcse-ja-base")
# 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, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
Model Summary
- Fine-tuning method: Supervised SimCSE
- Base model: cl-tohoku/bert-base-japanese-v3
- Training dataset: JSNLI
- Pooling strategy: cls (with an extra MLP layer only during training)
- Hidden size: 768
- Learning rate: 5e-5
- Batch size: 512
- Temperature: 0.05
- Max sequence length: 64
- Number of training examples: 2^20
- Validation interval (steps): 2^6
- Warmup ratio: 0.1
- Dtype: BFloat16
See the GitHub repository for a detailed experimental setup.
Citing & Authors
@misc{
hayato-tsukagoshi-2023-simple-simcse-ja,
author = {Hayato Tsukagoshi},
title = {Japanese Simple-SimCSE},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/hppRC/simple-simcse-ja}}
}