Alternatives and similar repositories for Hugging-Captions

Users that are interested in Hugging-Captions are comparing it to the libraries listed below

• Santosh-Gupta / NaturalLanguageRecommendations
  Getting recommendations from natural language
  ☆123Updated 5 years ago
• Georgetown-IR-Lab / ExtendedSumm
  On Generating Extended Summaries of Long Documents
  ☆78Updated 4 years ago
• CurationCorp / curation-corpus
  Code for obtaining the Curation Corpus abstractive text summarisation dataset
  ☆126Updated 4 years ago
• renatoviolin / Semantic-Search
  Semantic search using Transformers and others
  ☆110Updated 4 years ago
• shevisj / gpt-2_bot
  This is a reddit bot based on OpenAi's GPT-2 117M model
  ☆102Updated 5 years ago
• AliOsm / simplerepresentations
  Easy-to-use text representations extraction library based on the Transformers library.
  ☆32Updated 2 years ago
• wangcongcong123 / ttt
  A package for fine-tuning Transformers with TPUs, written in Tensorflow2.0+
  ☆38Updated 4 years ago
• rajesh-bhat / dhs_summit_2019_image_captioning
  Image captioning using attention models
  ☆39Updated 4 years ago
• bhavsarpratik / semantic-search
  [WIP] Behold, semantic-search, built over sentence-transformers to make it easy for search engineers to evaluate, optimise and deploy mod…
  ☆15Updated 2 years ago
• maxent-ai / zeroshot_topics
  Topic Inference with Zeroshot models
  ☆61Updated 2 years ago
• jph00 / hello_nbdev
  A tutorial example for nbdev
  ☆15Updated 3 years ago
• RasaHQ / breakoutbot
  A text based adventure game built with Rasa.
  ☆41Updated 4 years ago
• victordibia / neuralqa
  NeuralQA: A Usable Library for Question Answering on Large Datasets with BERT
  ☆231Updated 2 years ago
• ramsrigouthamg / Paraphrase-any-question-with-T5-Text-To-Text-Transfer-Transformer-
  Paraphrase any question with T5 (Text-To-Text Transfer Transformer) - Pretrained model and training script provided
  ☆186Updated 2 years ago
• will-thompson-k / deeplearning-nlp-models
  A small, interpretable codebase containing the re-implementation of a few "deep" NLP models in PyTorch. Colab notebooks to run with GPUs.…
  ☆75Updated 4 years ago
• tomasrasymas / quote-me-ai
  Quotes generating bot
  ☆35Updated 2 years ago
• searchableai / KitanaQA
  KitanaQA: Adversarial training and data augmentation for neural question-answering models
  ☆57Updated last year
• vumaasha / Atlas
  Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization
  ☆75Updated 2 years ago
• kaustubhdhole / natural-dont-know
  Code for the paper: Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries
  ☆19Updated 3 years ago
• shabeelkandi / Handling-Out-of-Vocabulary-Words-in-Natural-Language-Processing-using-Language-Modelling
  ☆69Updated 5 years ago
• cyrilou242 / learning-lightnr
  Generate multiple choice fill-in-the-blank questions from any article.
  ☆13Updated 2 years ago
• karndeb / NLP-Service
  ☆13Updated 3 years ago
• renatoviolin / T5-paraphrase-generation
  ☆66Updated 4 years ago
• Santosh-Gupta / ScientificSummarizationDataSets
  Datasets I have created for scientific summarization, and a trained BertSum model
  ☆115Updated 5 years ago
• hate-alert / DE-LIMIT
  DeEpLearning models for MultIlingual haTespeech (DELIMIT): Benchmarking multilingual models across 9 languages and 16 datasets.
  ☆109Updated 2 years ago
• gsarti / lambda-bert
  A 🤗-style implementation of BERT using lambda layers instead of self-attention
  ☆69Updated 4 years ago
• spring-media / headliner
  🏖 Easy training and deployment of seq2seq models.
  ☆228Updated 4 years ago
• masesk / process-google-dataset
  Process Google Dataset is a tool to download and process images for neural networks from a Google Image Search using a Chrome extension a…
  ☆33Updated 3 years ago
• ofirpress / shortformer
  Code for the Shortformer model, from the ACL 2021 paper by Ofir Press, Noah A. Smith and Mike Lewis.
  ☆147Updated 3 years ago
• prakhar21 / Writing-with-BERT
  Using BERT for doing the task of Conditional Natural Language Generation by fine-tuning pre-trained BERT on custom dataset.
  ☆41Updated 5 years ago