zhongkaifu/CRFSharp
CRFSharp is Conditional Random Fields implemented by .NET(C#), a machine learning algorithm for learning from labeled sequences of examples.
This tool helps non-programmers accurately label sequences of text or data by training a custom model. You provide examples of correctly labeled data, and it learns to predict labels for new, unlabeled data, producing output in a similar structured format. It's ideal for domain experts, data analysts, or researchers who need to automate specialized text analysis or pattern recognition tasks.
123 stars. No commits in the last 6 months.
Use this if you need to extract specific entities, classify text segments, or break down sentences into meaningful parts in large volumes of text, based on patterns learned from your own examples.
Not ideal if you're looking for an off-the-shelf solution that doesn't require any training data or if your problem doesn't involve sequence labeling.
Stars
123
Forks
49
Language
C#
License
BSD-3-Clause
Category
Last pushed
Aug 04, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/zhongkaifu/CRFSharp"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
charles9n/bert-sklearn
a sklearn wrapper for Google's BERT model
jidasheng/bi-lstm-crf
A PyTorch implementation of the BI-LSTM-CRF model.
howl-anderson/seq2annotation
基于 TensorFlow & PaddlePaddle 的通用序列标注算法库(目前包含 BiLSTM+CRF, Stacked-BiLSTM+CRF 和...
guillaumegenthial/tf_ner
Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data
guillaumegenthial/sequence_tagging
Named Entity Recognition (LSTM + CRF) - Tensorflow