rishabbh-sahu / intent_and_slot_classification

One of the main NLU tasks is to understand the intents (sequence classification) and slots (entities within the sequence). This repo help classify both together using Joint Model (multitask model). BERT_SMALL is used which can be changed to any other BERT variant.

Hi 👋, I'm Rishabbh

Data scientist and consultant, having experience across multiple industries like retail, manufacturing, FMCG, banking(fintech), insurance, consumer business etc.

I'm currently:

• 🔭 Working on deep learning projects such as transfer learning, pre-trained models (transformers), incremental models, layer pruning, quantization & distillation, language models to solve NLP downstream tasks like document parsing pipeline (banking sector), summarization, Q&A, NLU/NLG, context based auto-completion, RestAPI's flask-endpoint, deployment & productionization, docker images & containers, kubernetes, GCR-GCP, CI/CD pipeline, gitHub hooks/actions(pre/post commit, workflows), DVC-pipeline, mongoDB, google-OCR, label studio, Transformer's model interpretability etc.

• 🌱 Learning autoencoders, self-supervised learning, optimization, time series analysis using deep learning (deepstates model-gluonTS), linear programming (optimization), anomaly detection, feature learning, data comprssion techniques(SVD, matrix factorization), MLOps (ML pipeline), model interpretability (Explicable-AI), ablation study, TextRank - grpah representation of text with PageRank

• 👀 Interested in doing research work/consulting assignments by sharing, learning and exploring to/from open source communities. Motivated to create numerous projects in the field of AI/ML/DL with the focus to deploy into production.

• 👯 Looking forward to collaborate on ML/DL projects and Kaggle competitions

About my work:

• 👨‍💻 All of my projects are available at https://github.com/Rishabbh-Sahu

• 💬 Ask me about NLU/NLP, intent/sequence/text/email classification, NER (named entity recognition), sentence/document/semantic similarity, information retrieval using tfidf/context-based, time series analysis (forecasting), deep learning, data augmentation, dialog system (voice models), PLM's (pre-trained language models), model ensembling/stacking, feature selection methods, segmentation, tokenization (text), optimization, recommendation engine, customer-360 analysis, statistics, retail analytics, supply chain analytics, dimensionality reduction, regularization techniques, bias & varaince, DOE-design of experiments (ANOVA,T/F/Chi^2/Z-test), KS-test, sampling methods, A/B testing, crowd-sourcing (Toloka, Mtark etc.), Big-query, SQL

• 📫 You can reach me on www.linkedin.com/in/rishabbh-sahu-pmp

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intent_and_slot_classification's Issues

Preparation of the data

Can you suggest, how to prepare the dataset.

TypeError: 'BertTokenizerFast' object is not callable

Hi,
i get the following error when running flask_api.py: TypeError: 'BertTokenizerFast' object is not callable.
I'm not sure if this has to do with the version of transformers i am using.
What version did you use for this project ?