job-tagging-summary

Model Card for Model ID

This model is a multilabel classifier that takes an input string containing position_title and summary of responsibilities, and returns job tags.

Model Details

Fine tuned bert-base-uncased for custom job tagging.

Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [Usman Naveed]
• Model type: [Encoder|Text Classifier]
• Language(s) (NLP): [English]
• Finetuned from model [optional]: [bert-base-uncased]

Model Sources [optional]

<!-- Provide the basic links for the model. -->

• Repository: [https://huggingface.co/google-bert/bert-base-uncased]

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

This model is only intended to classify jobs into job tags.

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The dataset trained on was imbalanced, and contained more Engineering and Finance jobs than any other tag. This may cause bias towards Engineering and Finance tags.

[More Information Needed]

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

Dataset used to train this model: https://huggingface.co/datasets/usman-yello/job-tagging-with-summary

Preprocessing [optional]

• Remove HTML and markdown from the responsibilties
• Create a new text column which is the concatenation of the position_title and the responsibilities, use this column

Testing Data, Factors & Metrics

Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

Results

[More Information Needed]

Summary