Multilingual Sentence & Image Embeddings with BERT

This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity. We provide an increasing number of state-of-the-art pretrained models for more than 100 languages, fine-tuned for various use-cases. Further, this framework allows an easy fine-tuning of custom embeddings models, to achieve maximal performance on your specific task. For the full documentation, see www.SBERT.net. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (EMNLP 2019) Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation (EMNLP 2020) Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks (NAACL 2021) The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes (arXiv 2020) TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning (arXiv 2021) BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models (arXiv 2021) 

A text analytics library with support for codemixed data

Lingualytics is a Python library for dealing with indic text. Lingualytics is powered by powerful libraries like Pytorch, Transformers, Texthero, NLTK and Scikit-learn. Preprocessing  Remove stopwords Remove punctuations, with an option to add punctuations of your own language Remove words less than a character limit Representation  Find n-grams from given text NLP  Classification using PyTorch  Train a classifier on your data to perform tasks like Sentiment Analysis Evaluate the classifier with metrics like accuracy, f1 score, precision and recall Use the trained tokenizer to tokenize text 