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Zero and Few shot named entity & relationships recognition

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Zshot is a highly customisable framework for performing Zero and Few shot named entity recognition.

Can be used to perform:

  • Mentions extraction: Identify globally relevant mentions or mentions relevant for a given domain
  • Wikification: The task of linking textual mentions to entities in Wikipedia
  • Zero and Few Shot named entity recognition: using language description perform NER to generalize to unseen domains (work in progress)
  • Zero and Few Shot named relationship recognition (work in progress)


  • Python 3.6+

  • spacy - Zshot rely on Spacy for pipelining and visualization

  • torch - PyTorch is required to run pytorch models.
  • transformers - Required for pre-trained language models.
  • evaluate - Required for evaluation.
  • datasets - Required to evaluate over datasets (e.g.: OntoNotes).

Optional Dependencies

  • flair - Required if you want to use Flair mentions extractor and for TARS linker.
  • blink - Required if you want to use Blink for linking to Wikipedia pages.


$ pip install zshot

---> 100%

Example: Zero-Shot Entity Recognition

How to use it

  • Create a file with:
import spacy
from zshot import PipelineConfig, displacy
from zshot.linker import LinkerRegen
from zshot.mentions_extractor import MentionsExtractorSpacy
from zshot.utils.data_models import Entity

nlp = spacy.load("en_core_web_sm")
nlp_config = PipelineConfig(
               description="Paris is located in northern central France, in a north-bending arc of the river Seine"),
               description="International Business Machines Corporation (IBM) is an American multinational technology corporation headquartered in Armonk, New York"),
        Entity(name="New York", description="New York is a city in U.S. state"),
        Entity(name="Florida", description="southeasternmost U.S. state"),
               description="American, something of, from, or related to the United States of America, commonly known as the United States or America"),
        Entity(name="Chemical formula",
               description="In chemistry, a chemical formula is a way of presenting information about the chemical proportions of atoms that constitute a particular chemical compound or molecule"),
               description="Acetamide (systematic name: ethanamide) is an organic compound with the formula CH3CONH2. It is the simplest amide derived from acetic acid. It finds some use as a plasticizer and as an industrial solvent."),
               description="Armonk is a hamlet and census-designated place (CDP) in the town of North Castle, located in Westchester County, New York, United States."),
        Entity(name="Acetic Acid",
               description="Acetic acid, systematically named ethanoic acid, is an acidic, colourless liquid and organic compound with the chemical formula CH3COOH"),
        Entity(name="Industrial solvent",
               description="Acetamide (systematic name: ethanamide) is an organic compound with the formula CH3CONH2. It is the simplest amide derived from acetic acid. It finds some use as a plasticizer and as an industrial solvent."),
nlp.add_pipe("zshot", config=nlp_config, last=True)

text = "International Business Machines Corporation (IBM) is an American multinational technology corporation" \
       " headquartered in Armonk, New York, with operations in over 171 countries."

doc = nlp(text)
displacy.serve(doc, style="ent")

Run it

Run with

$ python

Using the 'ent' visualizer
Serving on ...

The script will annotate the text using Zshot and use Displacy for visualising the annotations

Check it

Open your browser at .

You will see the annotated sentence:

How to create a custom component

If you want to implement your own mentions_extractor or linker and use it with ZShot you can do it. To make it easier for the user to implement a new component, some base classes are provided that you have to extend with your code.

It is as simple as create a new class extending the base class (MentionsExtractor or Linker). You will have to implement the predict method, which will receive the SpaCy Documents and will return a list of zshot.utils.data_models.Span for each document.

This is a simple mentions_extractor that will extract as mentions all words that contain the letter s:

from typing import Iterable
import spacy
from spacy.tokens import Doc
from zshot import PipelineConfig
from zshot.utils.data_models import Span
from zshot.mentions_extractor import MentionsExtractor

class SimpleMentionExtractor(MentionsExtractor):
    def predict(self, docs: Iterable[Doc], batch_size=None):
        spans = [[Span(tok.idx, tok.idx + len(tok)) for tok in doc if "s" in tok.text] for doc in docs]
        return spans

new_nlp = spacy.load("en_core_web_sm")

config = PipelineConfig(
new_nlp.add_pipe("zshot", config=config, last=True)
text_acetamide = "CH2O2 is a chemical compound similar to Acetamide used in International Business " \
        "Machines Corporation (IBM)."

doc = new_nlp(text_acetamide)

>>> [is, similar, used, Business, Machines, materials]

How to evaluate ZShot

Evaluation is an important process to keep improving the performance of the models, that's why ZShot allows to evaluate the component with two predefined datasets: OntoNotes and MedMentions, in a Zero-Shot version in which the entities of the test and validation splits don't appear in the train set.

The package evaluation contains all the functionalities to evaluate the ZShot components. The main function is zshot.evaluation.zshot_evaluate.evaluate, that will take as input the SpaCy nlp model and the dataset(s) and split(s) to evaluate. It will return a str containing a table with the results of the evaluation. For instance the evaluation of the ZShot custom component implemented above would be:

from zshot.evaluation.zshot_evaluate import evaluate
from datasets import Split

evaluation = evaluate(new_nlp, datasets="ontonotes",