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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tzmtwtr/albert-embedding-ja") model = AutoModel.from_pretrained("tzmtwtr/albert-embedding-ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tzmtwtr/albert-embedding-ja: direct link, hf CLI and curl.
- Browser
- Download file 47.8 MB
-
https://huggingface.co/tzmtwtr/albert-embedding-ja/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tzmtwtr/albert-embedding-ja/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tzmtwtr/albert-embedding-ja/resolve/main/pytorch_model.bin
47.8 MB
- Xet hash:
- 7adb20e41ef5e51cf487df4259c484b9809868c66e560e90b5923b690e16eb1c
- Size of remote file:
- 47.8 MB
- SHA256:
- 1f3684b0b1047c1b63d7920599ba3e3b34f5bb51cc3513d687c25931ec53e329
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