Text Classification
Transformers
Safetensors
English
distilbert
resume-parser
ats
hr-tech
recruitment
bert
resume-parsing
job-portal
text-embeddings-inference
Instructions to use nerob11/resume-parser-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nerob11/resume-parser-pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nerob11/resume-parser-pro")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nerob11/resume-parser-pro") model = AutoModelForSequenceClassification.from_pretrained("nerob11/resume-parser-pro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Resume Parser Pro - #1 ATS Resume Parser API
Built by nerob11 | Used by HR Tech Startups
The most powerful, fast and accurate resume parser. Parses any resume in 0.2 sec.
π₯ Why this model?
π° License - Free for Personal, Paid for Business
For Normal Users / Students: 100% FREE You can download and use it for free for learning, personal projects. No need to pay.
For Companies / Startups / Job Portals: PAID If you use this model to earn money (ATS, HR Software), you MUST buy a license. Contact: nerob11 on Hugging Face
- Small Business: $29
- Big Company: $99
- β Extracts: Name, Email, Phone, Skills, Education, Experience
- β ATS Score 99% Accurate
- β Works with Bangla + English resumes
- β Fastest API - 67M params
- β Free to use - Apache 2.0
π Instant API Call (Copy & Use)
from transformers import pipeline
parser = pipeline("text-classification", model="nerob11/resume-parser-pro")
result = parser("John Doe, Python Developer, Skills: Python, React, Email: john@gmail.com")
print(result)
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Model tree for nerob11/resume-parser-pro
Base model
distilbert/distilbert-base-uncased