transformers/examples/research_projects/fsner/README.md

Implemented by sayef .

Overview

The FSNER model was proposed in Example-Based Named Entity Recognition by Morteza Ziyadi, Yuting Sun, Abhishek Goswami, Jade Huang, Weizhu Chen. To identify entity spans in a new domain, it uses a train-free few-shot learning approach inspired by question-answering.

Abstract

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We present a novel approach to named entity recognition (NER) in the presence of scarce data that we call example-based NER. Our train-free few-shot learning approach takes inspiration from question-answering to identify entity spans in a new and unseen domain. In comparison with the current state-of-the-art, the proposed method performs significantly better, especially when using a low number of support examples.

Model Training Details

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identifier	epochs	datasets
sayef/fsner-bert-base-uncased	10	ontonotes5, conll2003, wnut2017, and fin (Alvarado et al.).

Installation and Example Usage

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You can use the FSNER model in 3 ways:

1. Install directly from PyPI: pip install fsner and import the model as shown in the code example below

   or

2. Install from source: python setup.py install and import the model as shown in the code example below

   or

3. Clone repo and change directory to src and import the model as shown in the code example below

from fsner import FSNERModel, FSNERTokenizerUtils

model = FSNERModel("sayef/fsner-bert-base-uncased")

tokenizer = FSNERTokenizerUtils("sayef/fsner-bert-base-uncased")

# size of query and supports must be the same. If you want to find all the entitites in one particular query, just repeat the same query n times where n is equal to the number of supports (or entities).


query = [
    'KWE 4000 can reach with a maximum speed from up to 450 P/min an accuracy from 50 mg',
    'I would like to order a computer from eBay.',
]

# each list in supports are the examples of one entity type
# wrap entities around with [E] and [/E] in the examples

supports = [
        [
           'Horizontal flow wrapper [E] Pack 403 [/E] features the new retrofit-kit „paper-ON-form“',
           '[E] Paloma Pick-and-Place-Roboter [/E] arranges the bakery products for the downstream tray-forming equipment',
           'Finally, the new [E] Kliklok ACE [/E] carton former forms cartons and trays without the use of glue',
           'We set up our pilot plant with the right [E] FibreForm® [/E] configuration to make prototypes for your marketing tests and package validation',
           'The [E] CAR-T5 [/E] is a reliable, purely mechanically driven cartoning machine for versatile application fields'
        ],
        [
            "[E] Walmart [/E] is a leading e-commerce company",
            "I recently ordered a book from [E] Amazon [/E]",
            "I ordered this from [E] ShopClues [/E]",
            "[E] Flipkart [/E] started it's journey from zero"
        ]
   ]

device = 'cpu'

W_query = tokenizer.tokenize(query).to(device)
W_supports = tokenizer.tokenize(supports).to(device)

start_prob, end_prob = model(W_query, W_supports)

output = tokenizer.extract_entity_from_scores(query, W_query, start_prob, end_prob, thresh=0.50)

print(output)