Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Nerdofdot/abc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Nerdofdot/abc with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nerdofdot/abc") 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 Nerdofdot/abc with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Nerdofdot/abc") model = AutoModel.from_pretrained("Nerdofdot/abc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c6ea87e3e52bb3f864db4c88be6cd2c0d66d17042d8cf403fb3b0642cf431f1
- Size of remote file:
- 14.8 MB
- SHA256:
- da145b5e7700ae40f16691ec32a0b1fdc1ee3298db22a31ea55f57a966c4a65d
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