
## SPECTER

SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. 

If you're coming here because you want to embed papers, SPECTER has now been superceded by [SPECTER2](https://huggingface.co/allenai/specter2_proximity). Use that instead.

Paper: [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://arxiv.org/pdf/2004.07180.pdf)

Original Repo: [Github](https://github.com/allenai/specter)

Evaluation Benchmark: [SciDocs](https://github.com/allenai/scidocs)

Authors: *Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld*


{"language": "en", "license": "apache-2.0", "datasets": ["SciDocs"], "metrics": ["F1", "accuracy", "map", "ndcg"], "thumbnail": "https://camo.githubusercontent.com/7d080b7a769f7fdf64ac0ebeb47b039cb50be35287e3071f9d633f0fe33e7596/68747470733a2f2f692e6962622e636f2f33544331576d472f737065637465722d6c6f676f2d63726f707065642e706e67"}
transformers pytorch tf jax bert feature-extraction en dataset:SciDocs arxiv:2004.07180 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #en #dataset-SciDocs #arxiv-2004.07180 #license-apache-2.0 #endpoints_compatible #has_space #region-us 


## SPECTER

SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. 

If you're coming here because you want to embed papers, SPECTER has now been superceded by SPECTER2. Use that instead.

Paper: SPECTER: Document-level Representation Learning using Citation-informed Transformers

Original Repo: Github

Evaluation Benchmark: SciDocs

Authors: *Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld*


## SPECTER

SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. 

If you're coming here because you want to embed papers, SPECTER has now been superceded by SPECTER2. Use that instead.

Paper: SPECTER: Document-level Representation Learning using Citation-informed Transformers

Original Repo: Github

Evaluation Benchmark: SciDocs

Authors: *Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld*
TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #en #dataset-SciDocs #arxiv-2004.07180 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ## SPECTER

SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. 

If you're coming here because you want to embed papers, SPECTER has now been superceded by SPECTER2. Use that instead.

Paper: SPECTER: Document-level Representation Learning using Citation-informed Transformers

Original Repo: Github

Evaluation Benchmark: SciDocs

Authors: *Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld*
passage: TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #en #dataset-SciDocs #arxiv-2004.07180 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
## SPECTER

SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. 

If you're coming here because you want to embed papers, SPECTER has now been superceded by SPECTER2. Use that instead.

Paper: SPECTER: Document-level Representation Learning using Citation-informed Transformers

Original Repo: Github

Evaluation Benchmark: SciDocs

Authors: *Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld*