Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use futuredatascience/from-classifier-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use futuredatascience/from-classifier-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("futuredatascience/from-classifier-v2") 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 futuredatascience/from-classifier-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("futuredatascience/from-classifier-v2") model = AutoModel.from_pretrained("futuredatascience/from-classifier-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 968b3edce21160fb348dd8c686081337522201d2deb0ae2f2c1fb8438eff9e0d
- Size of remote file:
- 438 MB
- SHA256:
- 462daf3d69c4b58cacb303ca566ff7128de1571628a8ce01ed90dd9f8d5e354b
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