Instructions to use tzmtwtr/albert-embedding-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tzmtwtr/albert-embedding-ja with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tzmtwtr/albert-embedding-ja") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use tzmtwtr/albert-embedding-ja with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tzmtwtr/albert-embedding-ja") model = AutoModel.from_pretrained("tzmtwtr/albert-embedding-ja") - Notebooks
- Google Colab
- Kaggle
日本語のSentence Embedding用モデル
以下のモデルから転移学習を実施。
https://huggingface.co/ken11/albert-base-japanese-v1-with-japanese-tokenizer
学習データには以下を使用。
https://huggingface.co/datasets/tzmtwtr/tw-posts-ja
モチベーション
ベクトル検索のために小規模言語モデルが必要になった。
AWS Lambdaで動かせるようにしたい。
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