Sentence Similarity
sentence-transformers
PyTorch
mpnet
feature-extraction
text-embeddings-inference
Instructions to use mitra-mir/setfit_model_Ireland_binary_label2_epochs2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mitra-mir/setfit_model_Ireland_binary_label2_epochs2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mitra-mir/setfit_model_Ireland_binary_label2_epochs2") 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] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from mitra-mir/setfit_model_Ireland_binary_label2_epochs2: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/mitra-mir/setfit_model_Ireland_binary_label2_epochs2/resolve/main/tokenizer.json
- Command line
-
hf download hf://mitra-mir/setfit_model_Ireland_binary_label2_epochs2/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/mitra-mir/setfit_model_Ireland_binary_label2_epochs2/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.