Entity Recognition Extract information from Job posting¶
Soumil Nitin Shah¶
Bachelor in Electronic Engineering | Masters in Electrical Engineering | Master in Computer Engineering |
- Website : https://soumilshah.herokuapp.com
- Github: https://github.com/soumilshah1995
- Linkedin: https://www.linkedin.com/in/shah-soumil/
- Blog: https://soumilshah1995.blogspot.com/
- Youtube : https://www.youtube.com/channel/UC_eOodxvwS_H7x2uLQa-svw?view_as=subscriber
- Facebook Page : https://www.facebook.com/soumilshah1995/
- Email : shahsoumil519@gmail.com
- projects : https://soumilshah.herokuapp.com/project
Excellent experience of building scalable and high-performance Software Applications combining distinctive skill sets in Internet of Things (IoT), Machine Learning and Full Stack Web Development in Python.
Dataset download link https://www.kaggle.com/madhab/jobposts¶
Step 1:¶
- define imports
In [228]:
import pandas as pd
import os
import sys
import re
import ast
from ast import literal_eval
import plac
import random
from pathlib import Path
import spacy
import re
from spacy.util import minibatch, compounding
import seaborn as sns
In [192]:
df = pd.read_csv("data job posts.csv")
In [217]:
sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis')
Out[217]:
In [220]:
df.head(1)
Out[220]:
In [223]:
df["Title"].nunique()
Out[223]:
In [224]:
df.describe()
Out[224]:
- there are 8636 uniqe job title in dataset
Step 2:¶
- Data Exploration
In [193]:
df.tail(2)
Out[193]:
Training Data¶
In [194]:
class TrainDataGenerator(object):
def __init__(self, text):
self.text = text
self.entities = []
def add_entity(self, searchTerm = '', entity_name=''):
try:
response = re.search(searchTerm, self.text)
data_entity = (response.start(), response.end(), entity_name)
self.entities.append(data_entity)
except Exception as e:pass
def complete_entity(self):
entity_tem = {"entities":self.entities}
data = (self.text, entity_tem)
entites = entity_tem.get("entities")
# check if entity first index is overlapping with another one
for i in range(0, len(entites)):
for j in range(i+1, len(entites)):
StartIndex1 = entites[i][0]
endIndex1 = entites[i][1]
StartIndex2 = entites[j][0]
endIndex2 = entites[j][1]
if StartIndex1 in range(StartIndex2, endIndex2):
return False
if endIndex2 in range(StartIndex2, endIndex2):
return False
return data
In [195]:
TRAIN_DATA = []
In [196]:
df.columns
Out[196]:
In [197]:
for x in df[["jobpost", "Title", "Company", "Salary", "Eligibility", "Duration"]].iterrows():
text = x[1].jobpost
_helper = TrainDataGenerator(text=text)
# Title Training Data
_helper.add_entity(entity_name='Title', searchTerm= x[1].Title)
# Company Training data
_helper.add_entity(entity_name='Company', searchTerm=x[1].Company)
# Salary Training data
_helper.add_entity(entity_name='Salary', searchTerm=x[1].Salary)
# Eligibility Training data
_helper.add_entity(entity_name='Eligibility', searchTerm=x[1].Eligibility)
# Duration Training data
_helper.add_entity(entity_name='Eligibility', searchTerm=x[1].Duration)
response = _helper.complete_entity()
if response == False:
pass
else:
TRAIN_DATA.append(response)
In [198]:
len(TRAIN_DATA)
Out[198]:
In [199]:
TRAIN_DATA = TRAIN_DATA[::100]
In [200]:
len(TRAIN_DATA)
Out[200]:
Model¶
In [201]:
class Model(object):
def __init__(self, modelName = "testmodel"):
self.nlp = spacy.blank("en")
self.modelName = modelName
def train(self, output_dir=None, n_iter=80):
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if "ner" not in self.nlp.pipe_names:
ner = self.nlp.create_pipe("ner")
self.nlp.add_pipe(ner, last=True)
# otherwise, get it so we can add labels
else:
ner = self.nlp.get_pipe("ner")
# add labels
for _, annotations in TRAIN_DATA:
for ent in annotations.get("entities"):
ner.add_label(ent[2])
# get names of other pipes to disable them during training
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe
for pipe in self.nlp.pipe_names if pipe not in pipe_exceptions]
with self.nlp.disable_pipes(*other_pipes): # only train NER
# reset and initialize the weights randomly – but only if we're
# training a new model
self.nlp.begin_training()
for itn in range(n_iter):
print("Iteration : {} ".format(itn))
random.shuffle(TRAIN_DATA)
losses = {}
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
self.nlp.update(
texts, # batch of texts
annotations, # batch of annotations
drop=0.5, # dropout - make it harder to memorise data
losses=losses,
)
print("Losses", losses)
self.nlp.to_disk(self.modelName)
print('Model has been trained and saved on your disk ')
print("use nlp = spacy.load(NAME) ")
print("\n")
In [202]:
def main():
train = Model(modelName='jobposting')
response = train.train()
In [203]:
main()
Test¶
In [205]:
nlp = spacy.load("jobposting")
In [213]:
text = """
'Aldo\r\nTITLE: Retail Merchandiser\r\nSTART DATE/ TIME: Immediate employment\r\nDURATION: Long-term\r\nLOCATION: Yerevan, Armenia\r\nJOB DESCRIPTION: Aldo is seeking a Retail Merchandiser to drive maximum\r\nprofitability through planning stock intake to meet budgeted sales, build\r\nrelationships and work effectively with the host brand teams.\r\nJOB RESPONSIBILITIES:\r\n- Maximize and achieve revenue and profitability targets through\r\neffective merchandise planning and selection, product strategy and\r\nplanning, pricing, promotions, inventory control and vendor management;\r\n- Manage the buying budget and process, product pricing and margin\r\nmanagement;\r\n- Control the stock management and flow planning of all incoming product\r\nlines against monthly and annual budgets that includes for building and\r\nsupporting commercial strategies and trading plans;\r\n- Create seasonal sales and buying plans in order to maximize commercial\r\nopportunity and which meets profit objectives;\r\n- Work closely with marketing and operations teams to develop\r\nadvertisement, and sales promotions as well as arrangement of product\r\ncategories to adjust store inventory levels;\r\n- Supervise instore Visual Presentation, re-layout and re-merchandising;\r\n- Responsible for in-store visual merchandisers development and talent\r\nmanagement in order to achieve merchandising business objectives.\r\nREQUIRED QUALIFICATIONS:\r\n- University Degree in Business Administration, Finance or Marketing; \r\n- At least 2 years of work experience in financial analytics,\r\nmerchandising or product management;\r\n- Relevant work experience in retail organization or environment would be\r\nan added advantage;\r\n- Excellent verbal and written communication skills in English and\r\nArmenian languages; \r\n- Proven ability to motivate others; \r\n- Excellent analytical and numerical skill;\r\n- Strong entrepreneurial spirit with a passionate commitment to the\r\ncustomer and product quality;\r\n- Strong team player with good people management and strong leadership\r\nqualities with the ability to work with people of all levels;\r\n- Willingness to travel occasionally;\r\n- PC literacy.\r\nREMUNERATION/ SALARY: Highly competitive\r\nAPPLICATION PROCEDURES: Interested candidates are encouraged to submit a\r\nCV to: hr.franchise@... with a note of " Retail Merchandiser " in\r\nthe subject line or call 52 57 22 for inquiries. The Group thanks all who\r\nexpress interest in this opportunity; however only those selected for an\r\ninterview will be contacted. Applications privacy and confidentiality are\r\nguaranteed.\r\nPlease clearly mention in your application letter that you learned of\r\nthis job opportunity through Career Center and mention the URL of its\r\nwebsite - www.careercenter.am, Thanks.\r\nOPENING DATE: 30 July 2012\r\nAPPLICATION DEADLINE: 29 August 2012\r\n----------------------------------\r\nTo place a free posting for job or other career-related opportunities\r\navailable in your organization, just go to the www.careercenter.am\r\nwebsite and follow the "Post an Announcement" link.'"""
In [226]:
doc = nlp(text)
data = [{ent.label_: ent.text} for ent in doc.ents]
In [227]:
data
Out[227]:
---------------------------------------------------------------------------
ReplyDeleteValueError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_20000/1817008154.py in
3 response = train.train()
4
----> 5 main()
~\AppData\Local\Temp/ipykernel_20000/1817008154.py in main()
1 def main():
2 train = Model(modelName='jobposting')
----> 3 response = train.train()
4
5 main()
~\AppData\Local\Temp/ipykernel_20000/4253876312.py in train(self, output_dir, n_iter)
11 if "ner" not in self.nlp.pipe_names:
12 ner = self.nlp.create_pipe("ner")
---> 13 self.nlp.add_pipe(ner, last=True)
14
15
C:\Python38\lib\site-packages\spacy\language.py in add_pipe(self, factory_name, name, before, after, first, last, source, config, raw_config, validate)
753 bad_val = repr(factory_name)
754 err = Errors.E966.format(component=bad_val, name=name)
--> 755 raise ValueError(err)
756 name = name if name is not None else factory_name
757 if name in self.component_names:
ValueError: [E966] `nlp.add_pipe` now takes the string name of the registered component factory, not a callable component. Expected string, but got (name: 'None').
- If you created your component with `nlp.create_pipe('name')`: remove nlp.create_pipe and call `nlp.add_pipe('name')` instead.
- If you passed in a component like `TextCategorizer()`: call `nlp.add_pipe` with the string name instead, e.g. `nlp.add_pipe('textcat')`.
- If you're using a custom component: Add the decorator `@Language.component` (for function components) or `@Language.factory` (for class components / factories) to your custom component and assign it a name, e.g. `@Language.component('your_name')`. You can then run `nlp.add_pipe('your_name')` to add it to the pipeline.
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