https://github.com/nicolay-r/ARElight Skip to content Sign up * Product + Features + Mobile + Actions + Codespaces + Packages + Security + Code review + Issues + Integrations + GitHub Sponsors + Customer stories * Team * Enterprise * Explore + Explore GitHub + Learn and contribute + Topics + Collections + Trending + Skills + GitHub Sponsors + Open source guides + Connect with others + The ReadME Project + Events + Community forum + GitHub Education + GitHub Stars program * Marketplace * Pricing + Plans + Compare plans + Contact Sales + Education [ ] * # In this repository All GitHub | Jump to | * No suggested jump to results * # In this repository All GitHub | Jump to | * # In this user All GitHub | Jump to | * # In this repository All GitHub | Jump to | Sign in Sign up {{ message }} nicolay-r / ARElight Public * Notifications * Fork 0 * Star 25 Mass-media text processing application for your Relation Extraction task, powered by AREkit. nicolay-r.github.io/arelight-page/ License MIT license 25 stars 0 forks Star Notifications * Code * Issues 6 * Pull requests 0 * Actions * Projects 2 * Wiki * Security * Insights More * Code * Issues * Pull requests * Actions * Projects * Wiki * Security * Insights nicolay-r/ARElight This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main Switch branches/tags [ ] Branches Tags Could not load branches Nothing to show {{ refName }} default View all branches Could not load tags Nothing to show {{ refName }} default View all tags 3 branches 0 tags Code Latest commit @nicolay-r nicolay-r #39 fixed ... 0de8e6f Jun 3, 2022 #39 fixed 0de8e6f Git stats * 120 commits Files Permalink Failed to load latest commit information. Type Name Latest commit message Commit time arelight #39 fixed Jun 3, 2022 data Fixed: independing on the RuSentRel synonyms collection Apr 16, 2022 docker #21 Mystem3 fix May 1, 2022 docs Updated demo screenshot for pcnn. May 25, 2022 examples #35, #21 . added script-name parameter. Apr 19, 2022 test #39 fixed Jun 3, 2022 LICENSE Initial commit Mar 8, 2022 README.md Removing logos. Updated demo screenshots May 25, 2022 dependencies.txt #31 done Apr 13, 2022 download.py #21. Initial docker version. Apr 15, 2022 logo.png updated logo Apr 6, 2022 setup.py #31 done Apr 13, 2022 update_arekit.sh #30, #29 fixed. Large refactoring. (Providing missed parameters) Apr 10, 2022 View code [ ] ARElight 0.22.0 DEMO Dependencies Installation Docker verion (Quick) Full Inference Serialization Other Examples Papers Powered by README.md ARElight 0.22.0 DEMO This is a DEMO project of sentiment relations annotation, commonly powered by AREkit framework. [logo] Dependencies * arekit == 0.22.0 * gensim == 3.2.0 * deeppavlov == 0.11.0 * rusenttokenize * brat-v1.3 [github] * CUDA We adopt DeepPavlov for Named Entity Recognition in text sentences (BertOntoNotes model). Installation Docker verion (Quick) NOTE: Requires NVidia-docker * Download nicolay-r-arelight-0.1.1.tar * Import container and start Apache hosting: docker import nicolay-r-arelight-0.1.1.tar docker run --name arelight -itd --gpus all nicolay-r/arelight:0.1.1 docker attach arelight service apache2 start * Proceed with BERT demo: http://172.17.0.2/examples/demo/ wui_bert.py [demo] * PCNN example, finetuned on RuSentRel: http://172.17.0.2/examples/ demo/wui_nn.py [demo_pcnn] Full * ARElight: # Install the required dependencies pip install -r dependencies.txt # Donwload Required Resources python3.6 download.py * BRAT: Download and install library, and run standalone server as follows: ./install.sh -u python standalone.py Usage: proceed with the examples folder. Inference Infer sentiment attitudes from a mass-media document(s). Using the BERT fine-tuned model version: python3.6 infer_texts_bert.py --from-files data/texts-inosmi-rus/e1.txt \ --labels-count 3 \ --terms-per-context 50 \ --tokens-per-context 128 \ --text-b-type nli_m \ -o output/brat_inference_output [inference-] Using the pretrained PCNN model (including frames annotation): python3.6 infer_texts_nn.py --from-files data/texts-inosmi-rus/e1.txt \ --model-name pcnn \ --model-state-dir models/ \ --terms-per-context 50 \ --stemmer mystem \ --entities-parser bert-ontonotes \ --frames ruattitudes-20 \ --labels-count 3 \ --bags-per-minibatch 2 \ --model-input-type ctx \ --entity-fmt hidden-simple-eng \ --emb-filepath data/news_mystem_skipgram_1000_20_2015.bin.gz \ --synonyms-filepath data/synonyms.txt \ -o output/brat_inference_output [inference-] Serialization For the BERT model: python3.6 serialize_texts_bert.py --from-files data/texts-inosmi-rus/e1.txt --entities-parser bert-ontonotes \ --terms-per-context 50 [samples-be] For the other neural networks (including embedding and other features): python3.6 serialize_texts_nn.py --from-files data/texts-inosmi-rus/e1.txt \ --entities-parser bert-ontonotes \ --stemmer mystem \ --terms-per-context 50 \ --emb-filepath data/news_mystem_skipgram_1000_20_2015.bin.gz \ --synonyms-filepath data/synonyms.txt \ --frames ruattitudes-20 [samples-nn] Other Examples * Serialize RuSentRel collection for BERT [code] * Serialize RuSentRel collection for Neural Networks [code] * Finetune BERT on samples [code] * Finetune Neural Networks on RuSentRel [code] Papers * Nicolay Rusnachenko: Language Models Application in Sentiment Attitude Extraction Task (2021) Powered by * AREkit [github] [arekit_log] About Mass-media text processing application for your Relation Extraction task, powered by AREkit. nicolay-r.github.io/arelight-page/ Topics nlp machine-learning natural-language-processing deep-learning sentiment-analysis tensorflow named-entity-recognition bert brat relation-extraction bert-model deeppavlov deeppavlov-framework attitudes arekit Resources Readme License MIT license Stars 25 stars Watchers 2 watching Forks 0 forks Releases No releases published Packages 0 No packages published Languages * Python 93.8% * Dockerfile 2.3% * HTML 2.0% * Shell 1.9% * (c) 2022 GitHub, Inc. * Terms * Privacy * Security * Status * Docs * Contact GitHub * Pricing * API * Training * Blog * About You can't perform that action at this time. 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