metadata

license: apache-2.0
language:
  - en
widget:
  - text: >-
      We work with political parties, investors, media organisations, think
      tanks, NGOs and companies of all sizes around the world to help them
      understand public opinion, how it affects them, and what they should do in
      response to it. 
  - text: >-
      5+ years of experience in data analysis, data science, decision science,
      or similar quantitative fields, applying experimentation methods to test
      various hypotheses for customer segmentation, consumer sentiment or
      perception, and outbound online marketing campaign evaluation
  - text: >-
      We offer a competitive salaries based on candidate's qualifications. We
      also offers three weeks paid vacation per year, paid holidays, a 401(k)
      plan with employee matching funds, a discretionary bonus and an overall
      comprehensive benefits package.
pipeline_tag: text-classification

JoBert

JoBert is a text classifier designed to analyze job offer paragraph texts and categorize each one into predefined 5 classes.

Please refer to this repository when using the model.

Developed by: AhmedBou
License: apache-2.0

Classes:

About the Company
Job Description
Job Requirements
Responsibilities
Benefits
Other

About the Company:
Details about the hiring company, including its values, mission, and culture.
Job Description:
General information about the role, the tasks involved, and the purpose of the job.
Job Requirements:
Skills, qualifications, and experience needed for the job.
Responsibilities:
Specific tasks and duties associated with the role.
Benefits:
Information about the perks, benefits, and compensation offered.
Other:
Additional information that doesn't fit into the above categories.

Load the Model for Inference:

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("AhmedBou/JoBert")
model = AutoModelForSequenceClassification.from_pretrained("AhmedBou/JoBert")


label_names = ['About the Company', 'Job Description', 'Job Requirements', 'Responsibilities', 'Benefits', 'Other']
inference_model = model

text_snippet = "you must know how to use Python, Java, and SQL, and you should have 3 years of experience"
inference_inputs = tokenizer(text_snippet, return_tensors='pt')
inference_inputs = {key: val for key, val in inference_inputs.items()}
inference_outputs = inference_model(**inference_inputs)
inference_logits = inference_outputs.logits
inference_prediction = torch.argmax(inference_logits).item()
inference_label_name = label_names[inference_prediction]

print(f"Inference Result: Predicted Label - {inference_label_name}")