441 Open source projects that are alternatives of or similar to Torchnlp

🎨 🎨NLP 自然语言处理教程 🎨🎨 https://dataxujing.github.io/NLP-paper/

reference tensorflow code for named entity tagging

Transformers for Longer Sequences

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One-Stop Solution to encode sentence to fixed length vectors from various embedding techniques

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集成了Tensorflow 2版本的端到端语音识别模型，并且RTF(实时率)在0.1左右/Mandarin State-of-the-art Automatic Speech Recognition in Tensorflow 2

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遥感图像的语义分割，基于深度学习，在Tensorflow框架下，利用TF.Keras，运行环境TF2.0+

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datagrand 2019 information extraction competition rank9

pytorch implementation of Attention is all you need

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Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.

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The efficient and elegant JSON:API 1.1 server library for PHP

中文自然语言的实体抽取和意图识别（Natural Language Understanding），可选Bi-LSTM + CRF 或者 IDCNN + CRF

Transformer for Graph Classification (Pytorch and Tensorflow)

Fully featured implementation of Routing Transformer

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

Transformer implementation in PyTorch.

Transformer-based image captioning extension for pytorch/fairseq

This is a repository with the code for the ACL 2019 paper "Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned" and the paper "Analyzing Source and Target Contributions to NMT Predictions".

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Summarization, translation, sentiment-analysis, text-generation and more at blazing speed using a T5 version implemented in ONNX.

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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.

NLP for human. A fast and easy-to-use natural language processing (NLP) toolkit, satisfying your imagination about NLP.

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Transformer based on a variant of attention that is linear complexity in respect to sequence length

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Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.

Implementation of various self-attention mechanisms focused on computer vision. Ongoing repository.

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Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"

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