Deep_ner
Named entity recognizer based on ELMo or BERT as feature extractor and CRF as final classifier
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Fastnlp | 2,940 |  | 2 | a year ago | 24 | October 31, 2022 | 62 | apache-2.0 | Python | |
| fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation. | ||||||||||
| Grobid | 2,749 | 9 | 2 | 5 months ago | 1 | August 05, 2015 | 384 | apache-2.0 | Java | |
| A machine learning software for extracting information from scholarly documents | ||||||||||
| Kcws | 2,044 | 6 years ago | 44 | C++ | ||||||
| Deep Learning Chinese Word Segment | ||||||||||
| Nlp Journey | 1,563 | 7 months ago | 3 | April 29, 2020 | apache-2.0 | Python | ||||
| Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc. All codes are implemented intensorflow 2.0. | ||||||||||
| Anago | 1,428 |  | 5 | 1 | 2 years ago | 14 | July 17, 2018 | 43 | mit | Python |
| Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on. | ||||||||||
| Ncrf | 731 | 2 years ago | 4 | apache-2.0 | Python | |||||
| Cancer metastasis detection with neural conditional random field (NCRF) | ||||||||||
| Lidar Bonnetal | 443 | 3 years ago | 9 | mit | Python | |||||
| Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving | ||||||||||
| Ner Lstm Crf | 325 | 6 years ago | 6 | Python | ||||||
| An easy-to-use named entity recognition (NER) toolkit, implemented the Bi-LSTM+CRF model in tensorflow. | ||||||||||
| Rnnsharp | 272 | 4 years ago | 18 | bsd-3-clause | C# | |||||
| RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others. | ||||||||||
| Open Sesame | 219 | 2 years ago | 15 | apache-2.0 | Python | |||||
| A frame-semantic parsing system based on a softmax-margin SegRNN. | ||||||||||
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Python
Deep Learning
Ner
Crf