{"id":13935281,"url":"https://github.com/balavenkatesh3322/NLP-pretrained-model","last_synced_at":"2025-07-19T20:31:45.997Z","repository":{"id":118938135,"uuid":"280349319","full_name":"balavenkatesh3322/NLP-pretrained-model","owner":"balavenkatesh3322","description":"A collection of Natural language processing pre-trained models.","archived":false,"fork":false,"pushed_at":"2020-07-22T09:07:29.000Z","size":897,"stargazers_count":170,"open_issues_count":0,"forks_count":29,"subscribers_count":8,"default_branch":"master","last_synced_at":"2024-11-10T00:16:34.208Z","etag":null,"topics":["deep-learning","deep-neural-networks","keras","machine-learning","model","mxnet","natural-language-processing","neural-networks","nlp","nlp-machine-learning","python","python3","pytorch","tensorflow","text","text-classification","text-to-number","text-to-speech"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/balavenkatesh3322.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2020-07-17T06:50:03.000Z","updated_at":"2024-08-10T00:04:56.000Z","dependencies_parsed_at":null,"dependency_job_id":"eda21227-76c3-4446-aaf5-dfc42d1728d8","html_url":"https://github.com/balavenkatesh3322/NLP-pretrained-model","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/balavenkatesh3322%2FNLP-pretrained-model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/balavenkatesh3322%2FNLP-pretrained-model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/balavenkatesh3322%2FNLP-pretrained-model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/balavenkatesh3322%2FNLP-pretrained-model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/balavenkatesh3322","download_url":"https://codeload.github.com/balavenkatesh3322/NLP-pretrained-model/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":226666654,"owners_count":17665059,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","deep-neural-networks","keras","machine-learning","model","mxnet","natural-language-processing","neural-networks","nlp","nlp-machine-learning","python","python3","pytorch","tensorflow","text","text-classification","text-to-number","text-to-speech"],"created_at":"2024-08-07T23:01:32.786Z","updated_at":"2025-07-19T20:31:45.982Z","avatar_url":"https://github.com/balavenkatesh3322.png","language":null,"funding_links":[],"categories":["Others","Other Pre-trained Models"],"sub_categories":[],"readme":"\n\n![Maintenance](https://img.shields.io/badge/Maintained%3F-YES-green.svg)\n![GitHub](https://img.shields.io/badge/Release-PROD-yellow.svg)\n![GitHub](https://img.shields.io/badge/Languages-MULTI-blue.svg)\n![GitHub](https://img.shields.io/badge/License-MIT-lightgrey.svg)\n\n# NLP-pretrained-model\n\n![NLP logo](https://github.com/balavenkatesh3322/NLP-pretrained-model/blob/master/logo.jpg)\n\n## What is pre-trained Model?\nA pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application.\n\n## Other Pre-trained Models\n* [Computer Vision Pre-trained Models](https://github.com/balavenkatesh3322/CV-pretrained-model)\n* [Audio and Speech Pre-trained Models](https://github.com/balavenkatesh3322/audio-pretrained-model)\n\n## Model Deployment library\n* [Model Serving](https://github.com/balavenkatesh3322/model_deployment)\n\n### Framework\n\n* [Tensorflow](#tensorflow)\n* [Keras](#keras)\n* [PyTorch](#pytorch)\n* [MXNet](#mxnet)\n* [Caffe](#caffe)\n\n\n### Model visualization\nYou can see visualizations of each model's network architecture by using [Netron](https://github.com/lutzroeder/Netron).\n\n![NLP logo](https://github.com/balavenkatesh3322/NLP-pretrained-model/blob/master/netron.png)\n\n### Tensorflow \u003ca name=\"tensorflow\"/\u003e\n\n| Model Name | Description | Framework |\n|   :---:      |     :---:      |     :---:     |\n| [Chatbot]( https://github.com/Conchylicultor/DeepQA)  | This work tries to reproduce the results of A Neural Conversational Model (aka the Google chatbot). It uses a RNN (seq2seq model) for sentence prediction     | `Tensorflow`\n| [Show, Attend and Tell]( https://github.com/yunjey/show-attend-and-tell)  | Attention Based Image Caption Generator.     | `Tensorflow`\n| [Seq2seq-Chatbot]( https://github.com/tensorlayer/seq2seq-chatbot)  | Chatbot in 200 lines of code.     | `Tensorflow`\n| [Neural Caption Generator]( https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow)  | Implementation of \"Show and Tell\".     | `Tensorflow`\n| [TensorFlow White Paper Notes]( https://github.com/samjabrahams/tensorflow-white-paper-notes)  | Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation.     | `Tensorflow`\n| [Neural machine translation between the writings of Shakespeare and modern English using TensorFlow]( https://github.com/tokestermw/tensorflow-shakespeare)  | This performs a monolingual translation, going from modern English to Shakespeare and vice-versa.     | `Tensorflow`\n| [Mnemonic Descent Method](https://github.com/trigeorgis/mdm)  | Tensorflow implementation of \"Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment\"     | `Tensorflow`\n| [Improved CycleGAN]( https://github.com/luoxier/CycleGAN_Tensorlayer)  | Unpaired Image to Image Translation.     | `Tensorflow`\n| [im2im]( https://github.com/zsdonghao/Unsup-Im2Im)  | Unsupervised Image to Image Translation with Generative Adversarial Networks.     | `Tensorflow`\n| [DeepSpeech]( https://github.com/tensorflow/models/tree/master/research/deep_speech)  | Automatic speech recognition.     | `Tensorflow`\n| [Im2txt]( https://github.com/tensorflow/models/tree/master/research/im2txt)  | Image-to-text neural network for image captioning.     | `Tensorflow`\n\n\u003cdiv align=\"right\"\u003e\n    \u003cb\u003e\u003ca href=\"#framework\"\u003e↥ Back To Top\u003c/a\u003e\u003c/b\u003e\n\u003c/div\u003e\n\n***\n\n### Keras \u003ca name=\"keras\"/\u003e\n\n| Model Name | Description | Framework |\n|   :---:      |     :---:      |     :---:     |\n| [Monolingual and Multilingual Image Captioning]( https://github.com/elliottd/GroundedTranslation)  | This is the source code that accompanies Multilingual Image Description with Neural Sequence Models.     | `Keras`\n| [pix2pix]( https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/pix2pix)  | Keras implementation of Image-to-Image Translation with Conditional Adversarial Networks.     | `Keras`\n| [DualGAN]( https://github.com/eriklindernoren/Keras-GAN/blob/master/dualgan/dualgan.py)  | Implementation of _DualGAN: Unsupervised Dual Learning for Image-to-Image Translation_.     | `Keras`\n| [CycleGAN]( https://github.com/eriklindernoren/Keras-GAN/blob/master/cyclegan/cyclegan.py)  | Implementation of _Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks_.     | `Keras`\n\n\u003cdiv align=\"right\"\u003e\n    \u003cb\u003e\u003ca href=\"#framework\"\u003e↥ Back To Top\u003c/a\u003e\u003c/b\u003e\n\u003c/div\u003e\n\n***\n\n### PyTorch \u003ca name=\"pytorch\"/\u003e\n\n| Model Name | Description | Framework |\n|   :---:      |     :---:      |     :---:     |\n| [pytorch-CycleGAN-and-pix2pix]( https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)  | PyTorch implementation for both unpaired and paired image-to-image translation.     | `PyTorch`\n| [vid2vid]( https://github.com/NVIDIA/vid2vid)  | Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.     | `PyTorch`\n| [Neural Machine Translation (NMT) System]( https://github.com/OpenNMT/OpenNMT-py)  | This is a Pytorch port of OpenNMT, an open-source (MIT) neural machine translation system. It is designed to be research friendly to try out new ideas in translation, summary, image-to-text, morphology, and many other domains.     | `PyTorch`\n| [UNIT]( https://github.com/mingyuliutw/UNIT)  | PyTorch Implementation of our Coupled VAE-GAN algorithm for Unsupervised Image-to-Image Translation.     | `PyTorch`\n| [espnet]( https://github.com/espnet/espnet)  | End-to-End Speech Processing Toolkit.  | `PyTorch`\n| [TTS]( https://github.com/mozilla/TTS)  | Deep learning for Text2Speech.     | `PyTorch`\n| [Neural Sequence labeling model]( https://github.com/jiesutd/NCRFpp)  | Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation.    | `PyTorch`\n| [UnsupervisedMT]( https://github.com/facebookresearch/UnsupervisedMT)  | Phrase-Based \u0026 Neural Unsupervised Machine Translation.     | `PyTorch`\n| [waveglow]( https://github.com/NVIDIA/waveglow)  | A Flow-based Generative Network for Speech Synthesis.     | `PyTorch`\n| [deepvoice3_pytorch]( https://github.com/r9y9/deepvoice3_pytorch)  | PyTorch implementation of convolutional networks-based text-to-speech synthesis models.     | `PyTorch`\n| [deepspeech2]( https://github.com/SeanNaren/deepspeech.pytorch)  | Implementation of DeepSpeech2 using Baidu Warp-CTC. Creates a network based on the DeepSpeech2 architecture, trained with the CTC activation function.    | `PyTorch`\n| [pytorch-seq2seq]( https://github.com/IBM/pytorch-seq2seq)  | A framework for sequence-to-sequence (seq2seq) models implemented in PyTorch.     | `PyTorch`\n| [loop]( https://github.com/facebookarchive/loop)  | A method to generate speech across multiple speakers.     | `PyTorch`\n| [neuraltalk2-pytorch]( https://github.com/ruotianluo/ImageCaptioning.pytorch)  | Image captioning model in pytorch (finetunable cnn in branch with_finetune)     | `PyTorch`\n| [seq2seq]( https://github.com/MaximumEntropy/Seq2Seq-PyTorch)  | This repository contains implementations of Sequence to Sequence (Seq2Seq) models in PyTorch.     | `PyTorch`\n| [seq2seq.pytorch]( https://github.com/eladhoffer/seq2seq.pytorch)  | Sequence-to-Sequence learning using PyTorch.     | `PyTorch`\n| [self-critical.pytorch]( https://github.com/ruotianluo/self-critical.pytorch)  | Self-critical Sequence Training for Image Captioning.     | `PyTorch`\n| [Hierarchical Attention Networks for Document Classification]( https://github.com/EdGENetworks/attention-networks-for-classification)  | We know that documents have a hierarchical structure, words combine to form sentences and sentences combine to form documents.     | `PyTorch`\n| [nmtpytorch]( https://github.com/lium-lst/nmtpytorch)  | Neural Machine Translation Framework in PyTorch.     | `PyTorch`\n| [pix2pix-pytorch]( https://github.com/mrzhu-cool/pix2pix-pytorch)  | PyTorch implementation of \"Image-to-Image Translation Using Conditional Adversarial Networks\".     | `PyTorch`\n| [torch_waveglow]( https://github.com/npuichigo/waveglow)  | A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis.     | `PyTorch`\n| [Open Source Chatbot with PyTorch]( https://github.com/jinfagang/pytorch_chatbot)  | Aim to build a Marvelous ChatBot.     | `PyTorch`\n| [nonauto-nmt]( https://github.com/salesforce/nonauto-nmt)  | PyTorch Implementation of \"Non-Autoregressive Neural Machine Translation\".     | `PyTorch`\n| [tacotron_pytorch]( https://github.com/r9y9/tacotron_pytorch)  | PyTorch implementation of Tacotron speech synthesis model.     | `PyTorch`\n| [pytorch-seq2seq-intent-parsing]( https://github.com/spro/pytorch-seq2seq-intent-parsing)  | Intent parsing and slot filling in PyTorch with seq2seq + attention.    | `PyTorch`\n| [captionGen]( https://github.com/eladhoffer/captionGen)  | Generate captions for an image using PyTorch.     | `PyTorch`\n| [bandit-nmt]( https://github.com/khanhptnk/bandit-nmt)  | This is code repo for our EMNLP 2017 paper \"Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback\".     | `PyTorch`\n| [Pytorch Poetry Generation]( https://github.com/jhave/pytorch-poetry-generation)  | is a repurposing of http://pytorch.org/: an early release beta software (developed by a consortium led by Facebook and NVIDIA), a deep learning software that puts Python first.     | `PyTorch`\n| [translagent]( https://github.com/facebookresearch/translagent)  | Code for Emergent Translation in Multi-Agent Communication.     | `PyTorch`\n\n\u003cdiv align=\"right\"\u003e\n    \u003cb\u003e\u003ca href=\"#framework\"\u003e↥ Back To Top\u003c/a\u003e\u003c/b\u003e\n\u003c/div\u003e\n\n***\n\n\n### MXNet \u003ca name=\"mxnet\"/\u003e\n\n| Model Name | Description | Framework |\n|   :---:      |     :---:      |     :---:     |\n| [MXNMT]( https://github.com/magic282/MXNMT)  | This is an implementation of seq2seq with attention for neural machine translation with MXNet.     | `MXNet`\n| [deepspeech]( https://github.com/samsungsds-rnd/deepspeech.mxnet)  | This example based on DeepSpeech2 of Baidu helps you to build Speech-To-Text (STT) models at scale using.     | `MXNet`\n| [mxnet-seq2seq]( https://github.com/yoosan/mxnet-seq2seq)  | This project implements the sequence to sequence learning with mxnet for open-domain chatbot.     | `MXNet`\n\n\u003cdiv align=\"right\"\u003e\n    \u003cb\u003e\u003ca href=\"#framework\"\u003e↥ Back To Top\u003c/a\u003e\u003c/b\u003e\n\u003c/div\u003e\n\n***\n\n### Caffe \u003ca name=\"caffe\"/\u003e\n\n| Model Name | Description | Framework |\n|   :---:      |     :---:      |     :---:     |\n| [Speech Recognition](https://github.com/pannous/caffe-speech-recognition)  | Speech Recognition with the caffe deep learning framework.     | `Caffe`\n\n\u003cdiv align=\"right\"\u003e\n    \u003cb\u003e\u003ca href=\"#framework\"\u003e↥ Back To Top\u003c/a\u003e\u003c/b\u003e\n\u003c/div\u003e\n\n***\n\n## Contributions\nYour contributions are always welcome!!\nPlease have a look at contributing.md\n\n## License\n\n[MIT License](LICENSE)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbalavenkatesh3322%2FNLP-pretrained-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbalavenkatesh3322%2FNLP-pretrained-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbalavenkatesh3322%2FNLP-pretrained-model/lists"}