Check whether a text value is present in another text value. Convert a pivot table style to a basictabler style. Perform basic checks on a function argument. Find the first value in an array that is larger than the specified value.
Get the large plain theme for styling a pivot table. R6 class that defines a collection of styles. Get the compact theme for styling a pivot table. Standard function for Shiny scaffolding.
Get pivot table style declarations from a pivot table style. Handle identifiers that may be illegal e. R6 class that represents a pivot table. Get the a theme for styling to a pivot table that looks more like a standard table i. Handle an identifier that may be illegal e. Should the current value be skipped during export? Quickly get a Latex representation of a basic pivot table.
Get the default theme for styling a pivot table. Determine if a value range expression is a single value. Convert a CSS colour into a hex based colour code. Replace the current value with a placeholder during export. Scale a number from a range into a colour gradient. Convert CSS border values to those used by the openxlsx package.
Get a built-in theme for styling a pivot table. Read the value from a single-valued value range expression. Convert a simple range expression to a standard R logical expression. Check whether a numeric value is present. Get an empty theme for applying no styling to a table. Check whether a text value is present. Render a pivot table as a HTML widget. Output a table into a package vignette.
Test if two numeric values are equal within tolerance. Rescale a number from one range into another range. Quickly render a basic pivot table in HTML. Test whether a value matches a value range expression.
Determine if a value range expression is a simple range expression. Jul 6, Jul 3, Jun 29, Jun 2, May 22, May 14, May 7, Mar 30, Mar 24, Feb 19, Jan 31, Dec 20, Dec 13, Dec 3, Nov 26, Oct 11, Oct 9, Sep 26, Aug 17, Download the file for your platform.
If you're not sure which to choose, learn more about installing packages. Warning Some features may not work without JavaScript. Please try enabling it if you encounter problems. Search PyPI Search. Latest version Released: Nov 17, Navigation Project description Release history Download files. Project links Homepage. Maintainers lysandre patrickvonplaten sgugger Thomwolf.
Online demos You can test most of our models directly on their pages from the model hub. If you are looking for custom support from the Hugging Face team Quick tour To immediately use a model on a given text, we provide the pipeline API.
Why should I use transformers? Low barrier to entry for educators and practitioners. Few user-facing abstractions with just three classes to learn. A unified API for using all our pretrained models. Lower compute costs, smaller carbon footprint: Researchers can share trained models instead of always retraining. Practitioners can reduce compute time and production costs. Dozens of architectures with over 2, pretrained models, some in more than languages.
Choose the right framework for every part of a model's lifetime: Train state-of-the-art models in 3 lines of code. Move a single model between TF2. Seamlessly pick the right framework for training, evaluation and production.
Easily customize a model or an example to your needs: We provide examples for each architecture to reproduce the results published by its original authors. Model internals are exposed as consistently as possible. Model files can be used independently of the library for quick experiments.
Why shouldn't I use transformers? This library is not a modular toolbox of building blocks for neural nets. The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library. While we strive to present as many use cases as possible, the scripts in our examples folder are just that: examples.
It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. Installation With pip This repository is tested on Python 3. With conda Since Transformers version v4.
Tixier, Michalis Vazirgiannis. Varshney, Caiming Xiong and Richard Socher. Le, Christopher D. Mahoney, Kurt Keutzer. Peters, Arman Cohan. Johnson, Sebastian Ruder. Alvarez, Ping Luo.
Weinberger, Yoav Artzi. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Le, Ruslan Salakhutdinov. Wav2Vec2 from Facebook AI released with the paper wav2vec 2. Want to contribute a new model? We have added a detailed guide and templates to guide you in the process of adding a new model. You can find them in the templates folder of the repository. Be sure to check the contributing guidelines and contact the maintainers or open an issue to collect feedbacks before starting your PR. Project details Project links Homepage.
Download files Download the file for your platform. Files for transformers, version 4. Close Hashes for transformers Preprocessing tutorial. Training and fine-tuning. Model sharing and uploading.
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