Sentence Similarity
sentence-transformers
ONNX
Safetensors
OpenVINO
bert
feature-extraction
Generated from Trainer
dataset_size:16199
loss:CustomBatchAllTripletLoss
Instructions to use Detomo/cl-nagoya-sup-simcse-ja-nss-v1_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Detomo/cl-nagoya-sup-simcse-ja-nss-v1_1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_1") sentences = [ "科目:コンクリート。名称:立上り壁コンクリート。", "科目:ユニット及びその他。名称:棚。", "科目:ユニット及びその他。名称:事務室スチールパーティション。", "科目:ユニット及びその他。名称:F-R#収納棚。" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit History
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Add new SentenceTransformer model with an onnx backend
Verified signatureHello! *This pull request has been automatically generated from the [`push_to_hub`](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.push_to_hub) method from the Sentence Transformers library.* ## Full Model Architecture: ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ORTModelForFeatureExtraction (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Tip: Consider testing this pull request before merging by loading the model from this PR with the `revision` argument: ```python from sentence_transformers import SentenceTransformer # TODO: Fill in the PR number pr_number = 2 model = Sentence…183f8bb -
Add new SentenceTransformer model
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