Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use futuredatascience/to-classifier-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use futuredatascience/to-classifier-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("futuredatascience/to-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/to-classifier-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("futuredatascience/to-classifier-v2") model = AutoModel.from_pretrained("futuredatascience/to-classifier-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca2d9d6e8014a81014f091d6a2d0f20d895b05c9e935b74633e3d33ab865f3f5
- Size of remote file:
- 438 MB
- SHA256:
- bdae5e6b8d9b10dd1c69eee9ec2b3c509137a1304779dfe10a5ed1820712d387
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