Create README.md

README.md

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+# FSNER
+
+Implemented by [sayef](https://huggingface.co/sayef).
+
+## Overview
+
+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.
+
+
+
+## Abstract
+----
+> 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
+-----
+
+| identifier        | epochs           | datasets  |
+| ---------- |:----------:| :-----:|
+| [sayef/fsner-bert-base-uncased](https://huggingface.co/sayef/fsner-bert-base-uncased)      | 10 | ontonotes5, conll2003, wnut2017, and fin (Alvarado et al.). |
+
+
+## Installation and Example Usage
+------
+You need to clone `transformers` repository and go to this directory: `transformers/examples/research_projects/fsner`. Then, you will be able to use the FSNER model in two ways:
+
+1. Install as a package: `python setup.py install` and import the model as shown in the code example below
+
+    or
+
+2. Change directory to `src` and import the model as shown in the code example below
+
+
+
+```python
+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]",
+            "Fridge can be ordered in [E] Amazon [/E]",
+            "[E] Flipkart [/E] started it's journey from zero"
+        ]
+   ]
+
+device = 'cpu'
+
+W_query = tokenizer.tokenize(query).to(device)
+W_supports = tokenizer.tokenize([s for support in supports for s in support]).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)
+```