
# Aspect-based Document Similarity for Research Papers

A `scibert-scivocab-uncased` model fine-tuned on the CORD-19 corpus as in [Aspect-based Document Similarity for Research Papers](https://arxiv.org/abs/2010.06395).

<img src="https://raw.githubusercontent.com/malteos/aspect-document-similarity/master/docrel.png">

See GitHub for more details: https://github.com/malteos/aspect-document-similarity

## Demo

<a href="https://colab.research.google.com/github/malteos/aspect-document-similarity/blob/master/demo.ipynb"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Google Colab"></a>

You can try our trained models directly on Google Colab on all papers available on Semantic Scholar (via DOI, ArXiv ID, ACL ID, PubMed ID):

<a href="https://colab.research.google.com/github/malteos/aspect-document-similarity/blob/master/demo.ipynb"><img src="https://raw.githubusercontent.com/malteos/aspect-document-similarity/master/demo.gif" alt="Click here for demo"></a>


{"language": ["sci", "en"], "license": "mit", "tags": ["classification", "similarity"], "datasets": ["cord19"]}
transformers pytorch bert classification similarity sci en dataset:cord19 arxiv:2010.06395 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #classification #similarity #sci #en #dataset-cord19 #arxiv-2010.06395 #license-mit #endpoints_compatible #region-us 


# Aspect-based Document Similarity for Research Papers

A 'scibert-scivocab-uncased' model fine-tuned on the CORD-19 corpus as in Aspect-based Document Similarity for Research Papers.

<img src="URL

See GitHub for more details: URL

## Demo

<a href="URL src="URL alt="Google Colab"></a>

You can try our trained models directly on Google Colab on all papers available on Semantic Scholar (via DOI, ArXiv ID, ACL ID, PubMed ID):

<a href="URL src="URL alt="Click here for demo"></a>


# Aspect-based Document Similarity for Research Papers

A 'scibert-scivocab-uncased' model fine-tuned on the CORD-19 corpus as in Aspect-based Document Similarity for Research Papers.

<img src="URL

See GitHub for more details: URL ## Demo

<a href="URL src="URL alt="Google Colab"></a>

You can try our trained models directly on Google Colab on all papers available on Semantic Scholar (via DOI, ArXiv ID, ACL ID, PubMed ID):

<a href="URL src="URL alt="Click here for demo"></a>
TAGS
#transformers #pytorch #bert #classification #similarity #sci #en #dataset-cord19 #arxiv-2010.06395 #license-mit #endpoints_compatible #region-us 
 # Aspect-based Document Similarity for Research Papers

A 'scibert-scivocab-uncased' model fine-tuned on the CORD-19 corpus as in Aspect-based Document Similarity for Research Papers.

<img src="URL

See GitHub for more details: URL ## Demo

<a href="URL src="URL alt="Google Colab"></a>

You can try our trained models directly on Google Colab on all papers available on Semantic Scholar (via DOI, ArXiv ID, ACL ID, PubMed ID):

<a href="URL src="URL alt="Click here for demo"></a>
passage: TAGS
#transformers #pytorch #bert #classification #similarity #sci #en #dataset-cord19 #arxiv-2010.06395 #license-mit #endpoints_compatible #region-us 
# Aspect-based Document Similarity for Research Papers

A 'scibert-scivocab-uncased' model fine-tuned on the CORD-19 corpus as in Aspect-based Document Similarity for Research Papers.

<img src="URL

See GitHub for more details: URL## Demo

<a href="URL src="URL alt="Google Colab"></a>

You can try our trained models directly on Google Colab on all papers available on Semantic Scholar (via DOI, ArXiv ID, ACL ID, PubMed ID):

<a href="URL src="URL alt="Click here for demo"></a>