Topic: ulmfit Goto Github
Some thing interesting about ulmfit
Some thing interesting about ulmfit
ulmfit,Method Development for Predicting Protein Subcellular Localization Based on Deep Learning
User: 1073521013
ulmfit,Quickly fine-tune language models for your downstream NLP tasks.
User: aayux
ulmfit, Language Modeling and Text Classification in Malayalam Language using ULMFiT
User: adamshamsudeen
ulmfit,Stack Overflow question tagging. Find the tutorial on Medium
User: aditya10
ulmfit,🤖 Deep Catalan: Bring closer the Catalan Language to Deep Learning using ULMFit.
User: adriacabeza
ulmfit,One-Stop Solution to encode sentence to fixed length vectors from various embedding techniques
User: amansrivastava17
ulmfit,Applying a semi-supervised ULMFiT model to Twitter US Airlines Sentiment.
User: anmolpant
Home Page: https://www.kaggle.com/crowdflower/twitter-airline-sentiment
ulmfit,ULMFiT model for Twitter US Airlines Sentiment
User: apchavan
ulmfit,Deploy FastAI Trained PyTorch Model in TorchServe and Host in GCP's AI-Platform Prediciton.
Organization: artefactory
ulmfit,Sentiment-Analysis using RNN LSTM ULMFiT
User: avinashhsinghh
ulmfit,Analyzing Jigsaw's toxic comments Kaggle challenge using fastai + pytorch
User: ck37
Home Page: https://www.floydhub.com/cekaos/projects/toxic-comments-fastai-pytorch/
ulmfit,Sentiment classification for restaurant reviews using Bag of Words and Ulmfit in fastai
User: collinjia
ulmfit,Pre-trained AWD-LSTM language model trained on Filipino text corpus using fastai v2. Instructions included.
User: danjohnvelasco
ulmfit,sequence tagging for NER for ULMFiT
User: emrys-hong
ulmfit,ULMFit model for the Italian language / creation of a parallel corpus
User: ikros98
ulmfit,INSPIRE text classification microservice
Organization: inspirehep
ulmfit,Review materials for the TWiML Study Group. Contains annotated versions of the original Jupyter noteboooks (look for names like *_jcat.ipynb ), slide decks from weekly Zoom meetups, etc.
User: jcatanza
Home Page: https://github.com/jcatanza/Fastai-Deep-Learning-From-the-Foundations-TWiML-Study-Group.git
ulmfit,AWD-LSTM and ULMFiT reproduction from scratch
User: jcblaisecruz02
ulmfit,Filipino pretrained BERT & ULMFiT models, plus large unlabeled text corpora
User: jcblaisecruz02
Home Page: https://arxiv.org/abs/1907.00409
ulmfit,👩🏫 Pre-trained German Language Model with sub-word tokenization for ULMFIT
User: jfilter
ulmfit,A Supervised ULMFiT model on Twitter US Airlines Sentiment Challenge.
User: karthikayan4u
ulmfit,Flair predictor for r/india subreddit using NLP models: https://flairr.herokuapp.com/
User: kartikaggarwal98
ulmfit,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.
User: mboukabous
ulmfit,Project aimed at differentiating between positive and negative reviews using the fastai's ULMFiT implementation method.
User: merrillm1
ulmfit,Master's Thesis: “Stance Detection in Scientific Reviews"
User: mihayy
ulmfit,Text Classification using ULMFiT and BERT. Challenge solved for ML Fellowship program @Fellowship.ai
User: nakshatrasinghh
ulmfit,ULMFiT, BERT-QA
User: parvathysarat
ulmfit,中文ULMFiT 情感分析 文本分类
User: practicingman
ulmfit,Predicting which Tweets are about real disasters and which one’s aren’t.
User: prashantkh19
ulmfit,Applying NLP transfer learning techniques to predict Tweet stance toward a topic
User: prrao87
ulmfit,This is a repository for Dravidian-Code-mix-FIRE 2021 competition for our team. Task of this project is a message-level polarity classification task. Given a comment, systems have to classify it into positive, negative, neutral, mixed emotions, or not in the intended languages.
User: sanjeepan23
Home Page: https://sanjeepan23.github.io/Dravidian-CodeMix-FIRE-2021/
ulmfit,ULMFiT language model for Czech language
User: simecek
ulmfit,ULMFiT-based toxicity detection model for BPE tokenized russian texts.
User: snail-fuji
ulmfit,Flask based application using to detect depression for transcripts of interviews from patients
User: srijha09
ulmfit,The News Headline Classifier can Predict the Category of the news headline
User: srj00
ulmfit,Supplementary scripts and data for my thesis
User: tblock
ulmfit,IEEE BigMM Grand Challenge
User: vntkumar8
ulmfit,Multi-Label Text Classification with Transfer Learning
User: vondersam
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