Custom Named Entity Recognizer (NER)

Typically the pretrained natural language processing (NLP) models are trained to recognise limited entities.
This notebook provides a short and crisp way to recognise user set of entities to cater to a specific task, where an entity can sequence of tokens (word, character etc.).

The process of annotating data follow the BILUO convention, where the scheme is defined as follows:

TAG	DESCRIPTION
B EGIN	The first token of a multi-token entity.
I N	An inner token of a multi-token entity.
L AST	The final token of a multi-token entity.
U NIT	A single-token entity.
O UT	A non-entity token.

One can read this paper by Akbik et al., this should help in understanding the algorithm behind the sequence labelling (i.e. multiple-word entities).
FAQ: Please do read faq for more clarification.

Installation Process with Error Resolution

Using spaCy: Industrial-Strength Natural Language Processing
Installation
- pip install spacy
Download a pretrained model
- python -m spacy download en - This downloads only simples and light weight model
- To download other pretrained model python -m spacy download en_core_web_md (source) this is also small and only has tagger, parser, ner. There are other and more options are available at the source along with for different languages.
- To load the model run following:
```
  import spacy
  nlp = spacy.load('en') # en_core_web_md
  # If this line outputs a error (lint or something) please see below to resolve them
```

Not required for this notebook:

Pretrained Transformer model
- The prerequisite is to install spacy transformer pip install spacy-transformers
- To install one of the transformer model: python -m spacy download --always-link en_trf_bertbaseuncased_lg --timeout=10000. Timeout arg is for to avoid error because of timeout. Might need to install timeout package
- To load the model run following:
```
import spacy
nlp = spacy.load('en_trf_bertbaseuncased_lg')
```
- The above line might cause an error something along [E050] Can't find model 'en_trf_bertbaseuncased_lg'. It doesn't seem to be a shortcut link, a Python package or a valid path to a data directory. The solution is as follows:
```
from spacy.cli import link
from spacy.util import get_package_path

model_name = "en_trf_bertbaseuncased_lg" # model_name = 'en_core_web_md'
model_path = get_package_path(model_name)
link(model_name, model_name, force=True, model_path=model_path)
```
Note:
- Follow the above steps if there is error loading this, en_core_web_md, or any other model from here.
- If there is error something like [E048] Can't import language trf from spacy.lang: No module named 'spacy.lang.trf' then make sure you have already installed spacy-transformers using pip install spacy-transformers. Look if it is installed using pip list. If not then install it else restart the project of ipython console again should work.

FAQ

Q: Will the training for new types of entities can harm the previously learned entities?

ANS Yes, most likely if the older type is not included in current training data.

# Note: If you're using an existing model, make sure to mix in examples of
# other entity types that spaCy correctly recognized before. Otherwise, your
# model might learn the new type, but "forget" what it previously knew.
# https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting

Q: How to reproduce results in spcCy?

Ans: Fix the random seed for both spacy and numpy if only running on CPU:

import spacy
import numpy as np

s = 999
np.random.seed(s)
spacy.util.fix_random_seed(s)

if running on GPU then also need to fix the random seed for cupy

cupy.random.seed(s)

oluwayetty / new-entity-labelling

Custom Named Entity Recognizer (NER)

Installation Process with Error Resolution

Not required for this notebook:

FAQ

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