How to use HamzaFarhan/PDFSegs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HamzaFarhan/PDFSegs") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3]
How to use HamzaFarhan/PDFSegs with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("HamzaFarhan/PDFSegs") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds)
https://huggingface.co/HamzaFarhan/PDFSegs/resolve/refs%2Fpr%2F1/tokenizer.json
hf download hf://HamzaFarhan/PDFSegs@refs/pr/1/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/HamzaFarhan/PDFSegs/resolve/refs%2Fpr%2F1/tokenizer.json
