readme: ## Introduction
  This is a zero-shot relation extractor based on the paper  [Exploring the zero-shot limit of FewRel](https://www.aclweb.org/anthology/2020.coling-main.124).
  
  ## Installation
  ```bash
  $ pip install zero-shot-re
  ```
  
  ## Run the Extractor
  ```python
  from transformers import AutoTokenizer
  from zero_shot_re import RelTaggerModel, RelationExtractor
  
  model = RelTaggerModel.from_pretrained("fractalego/fewrel-zero-shot")
  tokenizer = AutoTokenizer.from_pretrained("fractalego/fewrel-zero-shot")
  
  relations = ['noble title', 'founding date', 'occupation of a person']
  extractor = RelationExtractor(model, tokenizer, relations)
  ranked_rels = extractor.rank(text='John Smith received an OBE', head='John Smith', tail='OBE')
  print(ranked_rels)
  ```
  with results
  ```python3
  [('noble title', 0.9690611883997917),
   ('occupation of a person', 0.0012609362602233887),
   ('founding date', 0.00024014711380004883)]
  ```
  
  ## Accuracy
  The results as in the paper are
  
  | Model                  | 0-shot 5-ways | 0-shot 10-ways |
  |------------------------|--------------|----------------|
  |(1) Distillbert         |70.1±0.5      | 55.9±0.6       |
  |(2) Bert Large          |80.8±0.4      | 69.6±0.5       |
  |(3) Distillbert + SQUAD |81.3±0.4      | 70.0±0.2       |
  |(4) Bert Large + SQUAD  |86.0±0.6      | 76.2±0.4       |
  
  This version uses the (4) Bert Large + SQUAD model
  
  ## Cite as
  ```bibtex
  @inproceedings{cetoli-2020-exploring,
      title = "Exploring the zero-shot limit of {F}ew{R}el",
      author = "Cetoli, Alberto",
      booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
      month = dec,
      year = "2020",
      address = "Barcelona, Spain (Online)",
      publisher = "International Committee on Computational Linguistics",
      url = "https://www.aclweb.org/anthology/2020.coling-main.124",
      doi = "10.18653/v1/2020.coling-main.124",
      pages = "1447--1451",
      abstract = "This paper proposes a general purpose relation extractor that uses Wikidata descriptions to represent the relation{'}s surface form. The results are tested on the FewRel 1.0 dataset, which provides an excellent framework for training and evaluating the proposed zero-shot learning system in English. This relation extractor architecture exploits the implicit knowledge of a language model through a question-answering approach.",
  }
  ```
  
  