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:
- be42ccf98ca5396fe1d8f9d3ba7ef5b34f131800e4ed70e3aa7d27048fc47c01
- Size of remote file:
- 17.1 MB
- SHA256:
- f7bc482bbe4d17395039e8c52dfd60092475c46bdcd8cd16c7238b2c522cd2c8
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