69e16abf98
* remove sum for list flattening * change to chain(*) * make chain object a list * delete empty lines per sgugger's suggestions Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Nicholas Broad <nicholas@nmbroad.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> |
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| .. | ||
| benchmarking | ||
| language-modeling | ||
| multiple-choice | ||
| question-answering | ||
| summarization | ||
| text-classification | ||
| token-classification | ||
| translation | ||
| README.md | ||
Examples
This folder contains actively maintained examples of use of 🤗 Transformers organized into different NLP tasks. All examples in this folder are TensorFlow examples, and are written using native Keras rather than classes like TFTrainer, which we now consider deprecated. If you've previously only used 🤗 Transformers via TFTrainer, we highly recommend taking a look at the new style - we think it's a big improvement!
In addition, all scripts here now support the 🤗 Datasets library - you can grab entire datasets just by changing one command-line argument!
A note on code folding
Most of these examples have been formatted with #region blocks. In IDEs such as PyCharm and VSCode, these blocks mark named regions of code that can be folded for easier viewing. If you find any of these scripts overwhelming or difficult to follow, we highly recommend beginning with all regions folded and then examining regions one at a time!
The Big Table of Tasks
Here is the list of all our examples:
| Task | Example datasets |
|---|---|
language-modeling |
WikiText-2 |
multiple-choice |
SWAG |
question-answering |
SQuAD |
summarization |
XSum |
text-classification |
GLUE |
token-classification |
CoNLL NER |
translation |
WMT |
Coming soon
- Colab notebooks to easily run through these scripts!