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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| } | |
| ] |