baaraban/pytorch_ner
LSTM based model for Named Entity Recognition Task using pytorch and GloVe embeddings
This helps data scientists and NLP researchers automatically identify and categorize key information, like names of people, organizations, or locations, within unstructured text. You input raw text data, often in a CoNNL format, and it outputs predictions for named entities found in that text. This is designed for those working with natural language processing tasks who need a foundational model for entity extraction.
No commits in the last 6 months.
Use this if you need a basic, yet robust, named entity recognition (NER) solution built on PyTorch, particularly if you're working with text data that requires identifying specific entities.
Not ideal if you require an out-of-the-box solution without any programming, or if your primary need is for state-of-the-art accuracy using transformer-based models without custom implementation.
Stars
9
Forks
3
Language
HTML
License
—
Category
Last pushed
Mar 19, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/baaraban/pytorch_ner"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
codelion/adaptive-classifier
A flexible, adaptive classification system for dynamic text classification
jiegzhan/multi-class-text-classification-cnn-rnn
Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN...
jiegzhan/multi-class-text-classification-cnn
Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN...
cbaziotis/datastories-semeval2017-task4
Deep-learning model presented in "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention...
iamaziz/ar-embeddings
Sentiment Analysis for Arabic Text (tweets, reviews, and standard Arabic) using word2vec