Instructions to use ManiKumarAdapala/distilbert-pii-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManiKumarAdapala/distilbert-pii-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ManiKumarAdapala/distilbert-pii-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ManiKumarAdapala/distilbert-pii-ner") model = AutoModelForTokenClassification.from_pretrained("ManiKumarAdapala/distilbert-pii-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signature# NLP Applications (S1-25_AIMLCZG519) ## Assignment 2 β Problem Statement β 25 ### Submitted by: Group 108 * **ADAPALA MANI KUMAR** * **BHAT MITALI MAHENDRA** * **CHELLAPPAN C** * **ELLURU SAI GAGAN** * **MD FAREED FAROOQUI** Finetuned Model & Project URL : https://drive.google.com/drive/folders/19ZP1RG_9Ms_kzsiLYBMrtrUbETej5dLq?usp=sharing # PII Detection and Masking System This project implements a PII (Personally Identifiable Information) detection and masking system using **DistilBERT** fine-tuned on the `ai4privacy/pii-masking-200k` dataset. The system exposes a Flask API for uploading text files and returning masked outputs. ## π Features * Transformer-based Named Entity Recognition (NER) * Detects multiple PII categories (Email, Phone, SSN, IP, etc.) * Batch processing (2 lines at a time) * REST API using Flask * Optimized with FP16 training * Entity-level F1 Score: **92.16%** ## π Project Structure ``` . . βββ app.py βββ design_document.docx βββ distilbert-ner ββ¦d855d16 -
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