Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use dvilasuero/setfit-mini-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dvilasuero/setfit-mini-imdb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dvilasuero/setfit-mini-imdb") 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 dvilasuero/setfit-mini-imdb with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dvilasuero/setfit-mini-imdb") model = AutoModel.from_pretrained("dvilasuero/setfit-mini-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9487740595e230967495bab1b31703c5e6be8c5eebc552c5ae86f6d5d31a31d
- Size of remote file:
- 438 MB
- SHA256:
- 7f0638c729b0ed025ebf36c2be014f2bb95cafcd07cb6b205f49154b1aacd8a7
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