Instructions to use pkshatech/simcse-ja-bert-base-clcmlp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pkshatech/simcse-ja-bert-base-clcmlp with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pkshatech/simcse-ja-bert-base-clcmlp") sentences = [ "This widget can't work correctly now.", "Sorry :(", "Try this model in your local environment!" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use pkshatech/simcse-ja-bert-base-clcmlp with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pkshatech/simcse-ja-bert-base-clcmlp") model = AutoModel.from_pretrained("pkshatech/simcse-ja-bert-base-clcmlp") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- 2_Dense/model.safetensors +3 -0
- model.safetensors +3 -0
2_Dense/model.safetensors
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oid sha256:16c60a72b388dfc8e645fefcbe39c661038553be65ccf866300fa9e956f97961
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size 2362560
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model.safetensors
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oid sha256:028fd63761c93a62d79cfc2a70b214e2f5fb2d4647025718351a6e66885d8471
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size 444855232
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