Sentence Similarity
sentence-transformers
Safetensors
English
mteb
Sentence Transformers
Eval Results (legacy)
Instructions to use cjell/text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cjell/text with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cjell/text") 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] - Notebooks
- Google Colab
- Kaggle
fixing
Browse files- {model/1_Pooling → 1_Pooling}/config.json +0 -0
- model/README.md → README.md +0 -0
- model/config.json → config.json +0 -0
- model/config_sentence_transformers.json → config_sentence_transformers.json +0 -0
- model/model.safetensors → model.safetensors +0 -0
- model/modules.json → modules.json +0 -0
- model/sentence_bert_config.json → sentence_bert_config.json +0 -0
- model/special_tokens_map.json → special_tokens_map.json +0 -0
- model/tokenizer.json → tokenizer.json +0 -0
- model/tokenizer_config.json → tokenizer_config.json +0 -0
- model/vocab.txt → vocab.txt +0 -0
{model/1_Pooling → 1_Pooling}/config.json
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