![]() * Port core files + ESM (because ESM code is odd) * Search-replace in modelling code * Fix up transfo_xl as well * Fix other core files + tests (still need to add correct import to tests) * Fix cookiecutter * make fixup, fix imports in some more core files * Auto-add imports to tests * Cleanup, add imports to sagemaker tests * Use correct exception for importing tf_keras * Fixes in modeling_tf_utils * make fixup * Correct version parsing code * Ensure the pipeline tests correctly revert to float32 after each test * Ensure the pipeline tests correctly revert to float32 after each test * More tf.keras -> keras * Add dtype cast * Better imports of tf_keras * Add a cast for tf.assign, just in case * Fix callback imports |
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.. | ||
benchmarking | ||
contrastive-image-text | ||
image-classification | ||
language-modeling | ||
language-modeling-tpu | ||
multiple-choice | ||
question-answering | ||
summarization | ||
text-classification | ||
token-classification | ||
translation | ||
_tests_requirements.txt | ||
README.md | ||
test_tensorflow_examples.py |
Examples
This folder contains actively maintained examples of the use of 🤗 Transformers organized into different ML tasks. All examples in this folder are TensorFlow examples and are written using native Keras. 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!