Your ML model largely depends on the dataset that you are training on and training on less accurate dataset results in poor outcomes in the real world. Whereas accurately labelled data can help you achieve not only good results but also stands out in real-world scenarios as well.
Our dataset can adapt to all kinds of requirements including changes in labels, number of data points, changes in the input language. Predictly provides a fully customizable service to ensure we meet all requirements of our clients.
Enhanced User Experience
A well-curated annotated dataset help in achieving good accuracy of the output and as in such type of models use at the end-user the end-user gets a smoother and seamless experience. Such as in NLP, text annotation provides such models providing a more enhanced and user-friendly experience to different people globally through multiple devices.
In every organization, the Human Resource (HR) team spends more time while doing resume screening. For a long time, recruiters have been screening hundreds of resumes manually. In this process, they go through every candidate’s resume and evaluate based on the candidate’s skill set, education details, work experience, etc. To evaluate and select desired candidates based on their company requirements, recruiters would take a long time.
So to reduce the time for this process, recruiters follow two kinds of ways
- They take the top few resumes from the whole set of resumes and select desired candidates from that.
- Another way is reviewing all resumes but spend less time and focus on limited sections of every resume.
In both cases, recruiters don’t get the desired candidate’s resume effectively. Because in the first case, they may lose skilled persons in the remaining set of resumes. In the second case, recruiters may not focus on all essential fields in every resume.
To avoid this issue in the HR field, we need to automate this process to focus on more important sections in less time. How can we automate the resume screening process? By using Natural Language Processing techniques, we can create a model/system to automate the process. If we want to build an accurate and effective model, we need a proper Resume Named Entity Recognition annotated dataset. You can get such a proper dataset at Predictly.
- Cloud Storage
- Cloud Bucket
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Data Update Frequency
Dataset Export Formats
- EXCEL Sheet
By using the above dataset we can build a model which can identify the entities with ease. And how we can build an automated and efficient resume screener is we will first identify the resume based on different entities and create an index out of it. When HR wants to filter any candidate based on skills, experience then it will simply search through the resume indexes powered by Elasticsearch and it allows to fetch the exact matching of resumes. With this kind of tool,HR can easily filter out unnecessary candidates.