103 Open source projects that are alternatives of or similar to wrench

A PyTorch-based open-source framework that provides methods for improving the weakly annotated data and allows researchers to efficiently develop and compare their own methods.

Dataset for paper "Weak Supervision for Fake News Detection via Reinforcement Learning" published in AAAI'2020.

A curated list of programmatic weak supervision papers and resources

Weakly supervised medical named entity classification

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net/forum?id=SkeuexBtDr)

SPEAR: Programmatically label and build training data quickly.

Self-training with Weak Supervision (NAACL 2021)

A curated (most recent) list of resources for Learning with Noisy Labels

Weakly Supervised End-to-End Learning (NeurIPS 2021)

A (CNN+)RNN(LSTM/BiLSTM)+CRF model for sequence labelling.😏

Helsinki Prosody Corpus and A System for Predicting Prosodic Prominence from Text

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

Framework to learn Named Entity Recognition models without labelled data using weak supervision.

A very simple framework for state-of-the-art Natural Language Processing (NLP)

🔬 Empirical CLI

Inference with state-of-the-art models (pre-trained by LD-Net / AutoNER / VanillaNER / ...)

reference pytorch code for named entity tagging

This repository provides the codes and data used in our paper "Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art", where we implement and evaluate several state-of-the-art approaches, ranging from handcrafted-based methods to convolutional neural networks.

Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings

CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition

The standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.

The classical BiLSTM-CRF model implemented in Tensorflow, for sequence labeling tasks. In Vex version, everything is configurable.

No description or website provided.

A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)

Deep neural models for core NLP tasks (Pytorch version)

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

Ansible role for Red Hat 8 STIG Baseline

word2vec, sentence2vec, machine reading comprehension, dialog system, text classification, pretrained language model (i.e., XLNet, BERT, ELMo, GPT), sequence labeling, information retrieval, information extraction (i.e., entity, relation and event extraction), knowledge graph, text generation, network embedding

序列化标注工具，基于PyTorch实现BLSTM-CNN-CRF模型，CoNLL 2003 English NER测试集F1值为91.10%（word and char feature）。

Aspect extraction from product reviews - window-CNN+maxpool+CRF, BiLSTM+CRF, MLP+CRF

Cloud Rapid Experimentation and Analysis Toolkit

Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.

Ansible role for Ubuntu 2004 CIS Baseline

NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

RWP*Load Simulator - your tool for scripting, simulation and much more. Like having a bit of bash and SQL, a nip of C or Java, a dash of awk, a grain of sed plus drops of secret sauce in one single tool. See https://blogs.oracle.com/database/rwploadsim-oracle-db-performance-simluator for the announcement on the Oracle database blog.

中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）

PyTorch implementation of AAAI 2021 paper: A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization

Hierarchically-Refined Label Attention Network for Sequence Labeling

This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction.

Empower Sequence Labeling with Task-Aware Language Model

Continuous Perfomance Monitor (CPM) for C++ code

Code for ECCV 2020 paper (oral): Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation

A TensorFlow implementation of Recurrent Neural Networks for Sequence Classification and Sequence Labeling

A framework to benchmark different graph databases, based on generated data from customizable schema, distribution, and size.

LSTM-CRF in PyTorch

Learning Named Entity Tagger from Domain-Specific Dictionary

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF implement in pyotrch

slot filling, intent detection, joint training, ATIS & SNIPS datasets, the Facebook’s multilingual dataset, MIT corpus, E-commerce Shopping Assistant (ECSA) dataset, CoNLL2003 NER, ELMo, BERT, XLNet

A system for quickly generating training data with weak supervision

multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.

a Deep Learning Framework for Text

Official implementation of the paper “GECToR – Grammatical Error Correction: Tag, Not Rewrite” // Published on BEA15 Workshop (co-located with ACL 2020) https://www.aclweb.org/anthology/2020.bea-1.16.pdf

ACL 2018: Hybrid semi-Markov CRF for Neural Sequence Labeling (http://aclweb.org/anthology/P18-2038)

A TensorFlow implementation of Neural Sequence Labeling model, which is able to tackle sequence labeling tasks such as POS Tagging, Chunking, NER, Punctuation Restoration and etc.

Tensorflow implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)

Automatically labeling training data

🌈 Implementation of Neural Network based Named Entity Recognizer (Lample+, 2016) using Chainer.

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

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.