Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use nayan06/binary-classifier-conversion-intent-1.1-mpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nayan06/binary-classifier-conversion-intent-1.1-mpnet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nayan06/binary-classifier-conversion-intent-1.1-mpnet") 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 nayan06/binary-classifier-conversion-intent-1.1-mpnet with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nayan06/binary-classifier-conversion-intent-1.1-mpnet") model = AutoModel.from_pretrained("nayan06/binary-classifier-conversion-intent-1.1-mpnet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02c2009ece5b0ff30608a7b698c6276389e6463c0b20eca1a56ad80410ddbfe8
- Size of remote file:
- 438 MB
- SHA256:
- 78206941e4c0bcdc4deaaa076050cf7d07bcc45fc42a1db86abf43887d715111
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