Recommendor-bert is a pre-trained language model to generate embeddings for research papers. It is pre-trained on a powerful signal of document-level relatedness: Arxiv tags, domains, citations, conferences, and co-authors. 
Recommendor-bert is built with the primary motivation of generating recommendations for research papers.

The model is finetuned using ```allenai/scibert_scivocab_uncased``` as the base model and a triplet loss function.

dataset: https://www.kaggle.com/datasets/Cornell-University/arxiv 

Github: https://github.com/adit-negi/GameOfPapers
{"language": ["en"], "license": "openrail", "pipeline_tag": "sentence-similarity"}
transformers pytorch bert feature-extraction sentence-similarity en license:openrail endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #feature-extraction #sentence-similarity #en #license-openrail #endpoints_compatible #region-us 

Recommendor-bert is a pre-trained language model to generate embeddings for research papers. It is pre-trained on a powerful signal of document-level relatedness: Arxiv tags, domains, citations, conferences, and co-authors. 
Recommendor-bert is built with the primary motivation of generating recommendations for research papers.

The model is finetuned using  as the base model and a triplet loss function.

dataset: URL 

Github: URL
TAGS
#transformers #pytorch #bert #feature-extraction #sentence-similarity #en #license-openrail #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #feature-extraction #sentence-similarity #en #license-openrail #endpoints_compatible #region-us 
