Feature Extraction
PEFT
Safetensors
sentence-transformers
lora
scibert
embeddings
retrieval
scientific-papers
arxiv
research-library
Instructions to use PeytonT/1m-paper-embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use PeytonT/1m-paper-embedding-model with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("allenai/scibert_scivocab_uncased") model = PeftModel.from_pretrained(base_model, "PeytonT/1m-paper-embedding-model") - sentence-transformers
How to use PeytonT/1m-paper-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PeytonT/1m-paper-embedding-model") 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] - Notebooks
- Google Colab
- Kaggle
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