Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use textgain/TopicAwareSTBertBaseNYT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use textgain/TopicAwareSTBertBaseNYT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("textgain/TopicAwareSTBertBaseNYT") 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 textgain/TopicAwareSTBertBaseNYT with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("textgain/TopicAwareSTBertBaseNYT") model = AutoModel.from_pretrained("textgain/TopicAwareSTBertBaseNYT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32c6597b4afbc7ce084549370f2ede812e44347befe2a21f9038c7823d68f809
- Size of remote file:
- 438 MB
- SHA256:
- 0298c98a510f47b6e3e96b94545d5c0281910b5f2fb762ffe17261a97ed8ebd9
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