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 tokenizer_config.json from tzmtwtr/albert-embedding-ja: direct link, hf CLI and curl.
- Browser
- Download file 525 Bytes
-
https://huggingface.co/tzmtwtr/albert-embedding-ja/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://tzmtwtr/albert-embedding-ja/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/tzmtwtr/albert-embedding-ja/resolve/main/tokenizer_config.json
525 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "do_subword_tokenize": true, | |
| "do_word_tokenize": true, | |
| "jumanpp_kwargs": null, | |
| "mask_token": "[MASK]", | |
| "mecab_kwargs": null, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "subword_tokenizer_type": "wordpiece", | |
| "sudachi_kwargs": null, | |
| "tokenizer_class": "BertJapaneseTokenizer", | |
| "unk_token": "[UNK]", | |
| "word_tokenizer_type": "mecab" | |
| } | |