Search Results for "loading"

...The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. ...

The summarize-from-feedback repository implements the methods from the paper “Learning to Summarize from Human Feedback”. Its purpose is to train a summarization model that better aligns with human preferences by first collecting human feedback (comparisons between summaries) to train a reward model, and then fine-tuning a policy (summarizer) to maximize that learned reward. The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling...

...AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional allennlp subcommands. There is ecosystem of open source plugins, some of which are maintained by the AllenNLP team here at AI2, and some of which are maintained by the broader community. AllenNLP will automatically find any official AI2-maintained plugins that you have installed, but for AllenNLP to find personal or third-party plugins you've installed, you also have to create either a local plugins file named .allennlp_plugins in the directory where you run the allennlp command.

This is a program (written in Python, with graphics based on the Tk/Tcl framework) to test on vocabulary. It allows users to define their own languages and constructs per language. Afterwards, a dictionary of words can be created and the user can be tested on this dictionary. Currently, it is in v0.8 (Beta) of development. After a stable version is released (v1.0), a more detailed repository will be kept.

Ouroborus is an artificial life framework for mobile agents on a background of cellular automata. It can be used to teach and research topics such as population genetics, ecology and evolution. The demo includes a curses view and live Csound audio.

Moved to pyjflash.berlios.de. Cross-platform Japanese flashcard program, conversion and editor with popups, saving and loading of utf, euc, sjs, jfc, bfg, lvl and tsv, printing, print preview and saving to html.