NLP 领域常见任务的实现,包括新词发现、以及基于pytorch的词向量、中文文本分类、实体识别、摘要文本生成、句子相似度判断、三元组抽取、预训练模型等。
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Updated
May 20, 2023 - Python
NLP 领域常见任务的实现,包括新词发现、以及基于pytorch的词向量、中文文本分类、实体识别、摘要文本生成、句子相似度判断、三元组抽取、预训练模型等。
Text to abstract art generation for the holidays!
A monolingual and cross-lingual meta-embedding generation and evaluation framework
PyTorch repository for text categorization and NER experiments in Turkish and English.
An evaluation of word-embeddings for classification
Improving Word Translation via Two-Stage Contrastive Learning (ACL 2022). Keywords: Bilingual Lexicon Induction, Word Translation, Cross-Lingual Word Embeddings.
Ensemble PhoBERT with FastText Embedding to improve performance on Vietnamese Sentiment Analysis tasks.
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking (Findings of EMNLP 2022). Keywords: Bilingual Lexicon Induction, Word Translation, Cross-Lingual Word Embeddings.
This project contains the code to use custom fasttext embeddings with flair framework.
🔍 A simple topic detector.
✔머신러닝 기반 온라인 기사 분석 서비스✔
Implementation of Meta-Word-Embeddings, a combination of word2vec, GloVe, and fassttext word embeddings using various types of autoencoders.
The study of negative online behavior, like toxic comments i.e. comments that are rude and disrespectful or otherwise are likely to make someone leave a conversation.
Flask-based REST API for Dashwork and ML pipelines for the training of NLP.
API server for word embeddings for Russian language
A app to intelligently search through COVID-19 Open Research Dataset (CORD-19) and find similar papers
Modified SVM algorithm called Pegasos implemented with Python
Word embedding using fastText algorithm with EuroSense dataset
Automatic paper clustering and search tool by fastext from Facebook Research
Ce fut mon prémier projet NLP où j'ai réalisé la détection de spam en utilisant les algorithmes d'embedding pour encorder mes textes. J'ai utilisé Random Forest et Milti-Layres Perceptrons pour la phase de classification. Ce qui a pemit l'obtension des précisions respective de 97% et 98%. J'ai aussi appris à documenter mes codes via sphinx
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