Search Results for "dictionary"

ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams all over the world. It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments...

The mission of the FindSimilar project is to provide a powerful and versatile open source library that empowers developers to efficiently find similar objects and perform comparisons across a variety of data types. Whether dealing with texts, images, audio, or more, our project aims to simplify the process of identifying similarities and enhancing decision-making. https://github.com/findsimilar/find-similar - GitHub repo http://demo.findsimilar.org/ - Demo project and...

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. This document aims to track the progress in Natural Language Processing (NLP) and give an overview of the state-of-the-art (SOTA) across the most common NLP tasks and their corresponding datasets. It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging as well as more recent ones such...

...Pay attention to the public account of waiting for words and reply to kcws to get the corpus download address. Extract the corpus to a directory. Change to the code directory.After installing tensorflow, switch to the kcws code directory. Currently, the custom dictionary is supported in the decoding stage. Please refer to kcws/cc/test_seg.cc for specific usage. The dictionary is in text format.

...Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. Before you go any further, try running the code. Load up some data (either a DataFrame, or a list of dictionaries, where each dictionary is a row of data). Make a column_descriptions dictionary that tells us which attribute name in each row represents the value we’re trying to predict. Pass all that into auto_ml, and see what happens! You can pass in your own function to perform feature engineering on the data. This will be called as the first step in the pipeline that auto_ml builds out. ...

Drug name recognition and normalisation/grounding to DrugBank ids and standard names. Package provides 2 taggers: 1. DrugTagger - CRF-based with DrugBank presence feature (see feature set for details). 2. DrugnameGazetteer - gazetteer/dictionary-based. Dictionary created from DrugBank.ca database. Both taggers include grounding/normalisation to DrugBank ids and standard names. Feature set: Word, Word-1, Word+1, Word-1_Word, Word_Word+1, DrugBankPresence, POS DrugBankPresence feature indicates the presence of the drug name in the DrugBank. Using CONLL-Evaluation: processed 32065 tokens with 3656 phrases; found: 3251 phrases; correct: 2786. ...