89 lines
3.4 KiB
Markdown
89 lines
3.4 KiB
Markdown
<p align="center"> <img src="http://sayef.tech:8082/uploads/FSNER-LOGO-2.png" alt="FSNER LOGO"> </p>
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<p align="center">
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Implemented by <a href="https://huggingface.co/sayef"> sayef </a>.
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</p>
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## Overview
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The FSNER model was proposed in [Example-Based Named Entity Recognition](https://arxiv.org/abs/2008.10570) 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.
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## Abstract
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----
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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.
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## Model Training Details
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-----
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| identifier | epochs | datasets |
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| ---------- |:----------:| :-----:|
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| [sayef/fsner-bert-base-uncased](https://huggingface.co/sayef/fsner-bert-base-uncased) | 10 | ontonotes5, conll2003, wnut2017, and fin (Alvarado et al.). |
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## Installation and Example Usage
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------
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You can use the FSNER model in 3 ways:
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1. Install directly from PyPI: `pip install fsner` and import the model as shown in the code example below
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or
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2. Install from source: `python setup.py install` and import the model as shown in the code example below
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or
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3. Clone repo and change directory to `src` and import the model as shown in the code example below
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```python
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from fsner import FSNERModel, FSNERTokenizerUtils
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model = FSNERModel("sayef/fsner-bert-base-uncased")
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tokenizer = FSNERTokenizerUtils("sayef/fsner-bert-base-uncased")
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# 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).
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query = [
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'KWE 4000 can reach with a maximum speed from up to 450 P/min an accuracy from 50 mg',
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'I would like to order a computer from eBay.',
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]
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# each list in supports are the examples of one entity type
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# wrap entities around with [E] and [/E] in the examples
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supports = [
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[
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'Horizontal flow wrapper [E] Pack 403 [/E] features the new retrofit-kit „paper-ON-form“',
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'[E] Paloma Pick-and-Place-Roboter [/E] arranges the bakery products for the downstream tray-forming equipment',
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'Finally, the new [E] Kliklok ACE [/E] carton former forms cartons and trays without the use of glue',
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'We set up our pilot plant with the right [E] FibreForm® [/E] configuration to make prototypes for your marketing tests and package validation',
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'The [E] CAR-T5 [/E] is a reliable, purely mechanically driven cartoning machine for versatile application fields'
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],
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[
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"[E] Walmart [/E] is a leading e-commerce company",
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"I recently ordered a book from [E] Amazon [/E]",
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"I ordered this from [E] ShopClues [/E]",
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"[E] Flipkart [/E] started it's journey from zero"
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]
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]
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device = 'cpu'
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W_query = tokenizer.tokenize(query).to(device)
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W_supports = tokenizer.tokenize(supports).to(device)
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start_prob, end_prob = model(W_query, W_supports)
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output = tokenizer.extract_entity_from_scores(query, W_query, start_prob, end_prob, thresh=0.50)
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print(output)
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```
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