GitHub topics: ktrain

Repositories

bhattbhavesh91/ktrain-tutorial

Fine-tuning a BERT model using Ktrain | Transfer Learning NLP | Fine Tune Bert For Text Classification

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Vidhi1290/Text-Classification-with-Transformers-RoBERTa-and-XLNet-Model

This project explores advanced NLP techniques using RoBERTa and XLNet models to classify human emotions from textual data. By leveraging robust training and optimization methods, it aims to enhance emotion detection accuracy in applications like social media analysis and customer support.

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htaofeek01/Final-year-project

Web Application For Automatic Facial Age Estimation of Black Persons(Deep Learning Approach)

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czephyr/mylittlepony_nlp Fork of shihab1h/emotion_detection_text

🦄 NLP on My Little Pony episodes. Project for Text Mining and Sentiment Analysis exam.

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sudarshan-koirala/NLP-with-BERT-for-Sentiment-Analysis

Sentiment Analysis using BERT

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nakshatrasinghh/Yahoo-Answers-Text-Classification

Text Classification using ULMFiT and BERT. Challenge solved for ML Fellowship program @Fellowship.ai

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nakshatrasinghh/Deep-Learning

Neural Networks with TensorFlow 2 and Keras in Python (Jupyter notebooks included)

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miladnouriezade/Ktrain-BioBert_NER

This repository contains data and BioBert based NER model monologg/biobert_v1.1_pubmed from community-uploaded Hugging Face models for detecting entities such as chemical and disease.

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AnaMiguelRodrigues1/autolens

Automated & Augmented ML Toolbox for Image Classification

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mboukabous/Security-Intelligence-on-Exchanged-Multimedia-Messages-Based-on-Deep-Learning

Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representations of text, there are famous models like Word2vec, GloVe, and fastText. In fact, NLP took a big step forward when BERT and recently GTP-3 came out. Deep Learning algorithms are unable to deal with textual data in their natural language data form which is typically unstructured information; they require special representation of data as inputs instead. Usually, natural language text data needs to be converted into internal representations form that DL algorithms can read such as feature vectors, hence the necessity to use representation learning models. These models have shown a big leap during the last years. Their set ranges from the methods that embed words into distributed representations and use the language modeling objective to adjust them as model parameters (like Word2vec, fastText, and GloVe), to recently transfer learning models (like ELMo, BERT, ULMFiT, XLNet, ALBERT, RoBERTa, and GPT-2). These last use larger corpora, more parameters, more computing resources, and instead of assigning each word with a fixed vector, they use multilayer neural networks to calculate dynamic representations for the words according to their context, which is especially useful for the words with multiple meanings.

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SaifAlmaliki/Bert-NLP

BERT is a Transformer-based machine learning technique for natural language processing (NLP) pre-training developed by Google.

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ianuragbhatt/datascience-jupyter-notebooks

Data Science python notebooks (ktrain, AWS).

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Internship-BVoc/G1_TwitterSentimentAnalysis

Twitter Sentiment Analysis using various models

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r-sajal/DeepLearning-

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pradeepdev-1995/Question-answering-python

Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language.

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youssefkamil/Arabic-Dialect-Identification

Arabic Dialect Identification between 18 country-level Arabic dialects using QADI dataset and pretrained language model AraBERT

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dineshssdn-867/Sentiment_analysis_of_youtubers_webapp

Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. So, in this application, we are asking a YouTuber to enter the channel id and a particular timeline. By using the channel id and timeline we are performing sentiment analysis on his videos by fetching the subtitles of their videos in a particular timeline given by the YouTuber.Basically performing intent and emotion classification on his video subtitles. To know how the model was made please check the repository. Link: https://github.com/dineshssdn-867/Sentiment-analysis-of-youtubers. Don't Forget to star mark it.

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KelvinLam05/toxic_comment_classification

Build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate.

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KelvinLam05/employee_churn_prediction

Employee attrition prediction using ktrain.

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KelvinLam05/sms_spam_detection

Build a deep learning spam detection system for sms using ktrain.

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KelvinLam05/text-based_emotion_detection

Emotion analysis of English tweets using machine learning approach.

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KelvinLam05/price_prediction

Predicting Uber and Lyft fares using ktrain.

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ilos-vigil/ktrain-assessment-study

Part of data repository for thesis/skripsi titled: "Assessment Study on ktrain Library for Text Processing"

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lovelyoyrmia/age-predictor

All of you can use this a bit code that can help you build age predictor app. First thing first, you have to run the google colab so that you can get models machine learning of age prediction. I'm using deep learning algorithm to predict ages.

Language: Python - Size: 82.6 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 0 - Forks: 1

ilos-vigil/scl-2020-sentiment-analysis

12th place (top 4%) solution for Shopee Code League 2020 - Sentiment Analysis

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Tikquuss/eulascript

Machine learning (ML) solution that review end-user license agreements (EULA) for terms and conditions that are unacceptable to the government

Language: Python - Size: 10.4 MB - Last synced at: about 2 months ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 1

anikch/nlp-predict-tweets-about-real-disasters

Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies). But, it’s not always clear whether a person’s words are actually announcing a disaster. In this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t. You’ll have access to a dataset of 10,000 tweets that were hand classified.

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ktrain 32 nlp 14 bert 13 deep-learning 8 natural-language-processing 8 machine-learning 7 python 7 tensorflow 6 keras 5 huggingface 4 bert-model 4 xlnet 3 django 3 roberta 3 pandas 3 sentiment-analysis 3 albert 3 css 2 ulmfit 2 jupyter-notebook 2 nltk 2 word2vec 2 google-colab 2 text-classification 2 distilbert 2 bert-fine-tuning 2 transfer-learning 2 python3 2 spacy 2 ner 2 sklearn 2 named-entity-recognition 2 computer-vision 2 flask 2 fasttext 2 toxic-comments 1 javascript 1 imbalanced-classification 1 toxic-comment-classification 1 binary-classification 1 churn-prediction 1 intent-classification 1 multilabel-classification 1 html5 1 firebase-auth 1 emotion-recognition 1 chartjs 1 arabic-nlp 1 arabic-dialects 1 arabert 1 transformer 1 question-answering 1 natual-language-inference 1 haystack 1 farm 1 deeppavlov 1 cdqa-suit 1 cdqa 1 artificial-intelligence 1 mlp-classifier 1 twitter 1 allennlp 1 bert-embeddings 1 diff 1 legal-pegasus 1 p 1 tf-idf 1 html 1 docker 1 rasa 1 scikit-learn 1 fake-news 1 fakenewsdetection 1 bilstm-crf-model 1 keras-neural-networks 1 keras-tensorflow 1 lstm-neural-networks 1 huggingface-transformer 1 spam-detection 1 emotion-detection 1 multiclass-classification 1 tweets 1 price-prediction 1 regression-analysis 1 skripsi 1 thesis 1 deep-learning-algorithms 1 machine-learning-algorithms 1 streamlit 1 text-cleaning 1 eula 1 huggingface-transformers 1 kaggle-competition 1 natural-language-understanding 1 disease 1 bionlp 1 biomedical 1 biobert 1 universal-sentence-encoder 1 prediction-model 1

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