Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use danfeg/CAMeL_Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/CAMeL_Base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/CAMeL_Base") 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 danfeg/CAMeL_Base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danfeg/CAMeL_Base") model = AutoModel.from_pretrained("danfeg/CAMeL_Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d51364c8320b94de3352d24d3c24df0d7c0b4f3eb990f88e044c2ceddb27b82
- Size of remote file:
- 436 MB
- SHA256:
- a434f65b0675876067a877cdb7b28b2ec303b72fa23d29b61a03c9e9e64fe689
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