[PyPI] BERT Word Embeddings
-
Updated
Jan 11, 2022 - Python
[PyPI] BERT Word Embeddings
Create a knowledge graph based on Towards Data Science blogs.
Customer reviews analysis and segmentation. Create suggestions for app improvement based on the clustered reviews.
RAG Model Implementation
Full code and most data (in accordance with CrowdTangle’s Terms of Service) supporting an article on what would remain on French-language Facebook if news content was removed
Created BERT Named Entity Relation(NER) API
This project focuses the implementation on the Healthcare based chat-bot that answers the customer's queries as a therapist that is trained on the interaction between the patient and the therapist responses with the counsel chat dataset.
Space Model framework that allows for maintaining generalizability, and enhances the performance on the downstream task by utilizing task-specific context attribution. It is an external LLM layer, that improves accuracy in classification task for multiple datasets, such as HateXplain, IMDB movies reviews and more.
Music Genre Classification with Turkish Lyrics
This GitHub repository hosts my AI evaluation work, featuring a Kaggle dataset analysis, experiments with three ML algorithms (including hyperparameter tuning), and a detailed exploration of wine quality data through outlier detection, correlation, and normalization techniques.
Unsupervised clustering of 8 Indic languages using IndicBERT v1 and v3 embeddings, PCA, KMeans, DBSCAN, and GMM.
A production-ready, modular NLP pipeline fine-tuning BERT for text classification. Built with TensorFlow/Keras and HuggingFace Transformers.
Implementing Multilingual WSD using [Normal, Atten]BiLSTM, Seq2Seq[Atten], Multitask WSD
BERT-based senti- ment annotation is used to create unbiased datasets and then hybridize RNN with LSTM to find calculated ratings based on this unbiased reviews dataset.
CBERTdp is a strategy to speed up the clssification task by clustering BERT embeddings using different methods in order to use K-Means and the Dot-Product to obtaint the prediction results
This research proposes an "AI-Driven Cross-Cultural Commodity Expert" framework, which addresses three critical technical bottlenecks through synergistic innovations in multilingual sentiment analysis, cultural quantification engines, and dynamic knowledge graphs
Novel implementation for Topic Extraction
Sentiment Analysis performed on Amazon reviews - polarity
Node embedding technique based on Masked Language Model.
To associate your repository with the bert-embeddings topic, visit your repo's landing page and select "manage topics."