Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use codegood/KatzBot_MiniLLM_ST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codegood/KatzBot_MiniLLM_ST with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codegood/KatzBot_MiniLLM_ST") 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 codegood/KatzBot_MiniLLM_ST with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("codegood/KatzBot_MiniLLM_ST") model = AutoModel.from_pretrained("codegood/KatzBot_MiniLLM_ST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c67c28790e548a93d96a73956d9c17b1914eac7aca31fc49ed8708e9e0e3a144
- Size of remote file:
- 90.9 MB
- SHA256:
- e11df15f57619ee5c8bf01cb760b595ca3ddc5e630b3268e9765e8cf13b0580a
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