Deep Learning, NLP, NMT, AI, ML
Create a config.py with the above mandatory variables (Refer sample_config.py) and save it in the current working directory. git clone https://github.com/m4mallu/compress-pdf-tg-bot cd uniquify virtualenv -p python3 venv . ./venv/bin/activate pip3 install -r requirements.txt python3 bot.py
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python export detections_repeatability.py python compute_repeatability.py (NOTE: You have to edit *.yaml files to run corresponding tasks, especially for the following items model name: superpoint # magicpoint … data: name: coco #synthetic image_train_path: [‘./data/mp_coco_v2/images/train2017’,] #several data sets can be list here label_train_path: [‘./data/mp_coco_v2/labels/train2017/’,] image_test_path: ‘./data/mp_coco_v2/images/test2017/’ label_test_path: ‘./data/mp_coco_v2/labels/test2017/’
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A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI‘s GPT-2 text generation model (specifically the “small” 124M and “medium” 355M hyperparameter versions). Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to start with a given phrase. This package incorporates and makes minimal low-level changes to: Model management from OpenAI’s official GPT-2 repo (MIT License) Model finetuning from Neil Shepperd’s fork of […]
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This package provides spaCy components and architectures to use transformer models via Hugging Face’s transformers in spaCy. The result is convenient access to state-of-the-art transformer architectures, such as BERT, GPT-2, XLNet, etc. This release requires spaCy v3. For the previous version of this library, see the v0.6.x branch. Features Use pretrained transformer models like BERT, RoBERTa and XLNet to power your spaCy pipeline. Easy multi-task learning: backprop to one transformer model from several pipeline components. Train using spaCy v3’s powerful […]
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NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy’s NLP pipeline and extensible to new training datasets. For a brief introduction to coreference resolution and NeuralCoref, please refer to our blog post. NeuralCoref is written in Python/Cython and comes with a pre-trained statistical model for English only. NeuralCoref is accompanied by a visualization client NeuralCoref-Viz, a web interface powered by a REST server that can […]
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sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo. ?Version 2.0 (for spaCy v3) out now! Read the release notes here. ✨Features
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jsonfield is a reusable model field that allows you to store validated JSON, automatically handling serialization to and from the database. To use, add jsonfield.JSONField to one of your models. Note: django.contrib.postgres now supports PostgreSQL’s jsonb type, which includes extended querying capabilities. If you’re an end user of PostgreSQL and want full-featured JSON support, then it is recommended that you use the built-in JSONField. However, jsonfield is still useful when your app needs to be database-agnostic, or when the built-in […]
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Purpose and Motivation I come from a mainly C Programming Language background and have previous classroom experience with Java through OOP courses. I am interested in learning and gaining familiarity with the Python Programming Language now. This repository mainly serves as a place for me to store my projects as I explore the language 🙂 So far all of the mini projects I’ve completed come from the following site:https://www.upgrad.com/blog/python-projects-ideas-topics-beginners/ Running list of general concepts I’ve been able to try in […]
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This repository contains custom pipes and models related to using spaCy for scientific documents. In particular, there is a custom tokenizer that adds tokenization rules on top of spaCy’s rule-based tokenizer, a POS tagger and syntactic parser trained on biomedical data and an entity span detection model. Separately, there are also NER models for more specific tasks. Just looking to test out the models on your data? Check out our demo. Installation Installing scispacy requires two steps: installing the library […]
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