Transformers-Tutorials
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| Transformers-Tutorials | FLiPStackWeekly | |
|---|---|---|
| 7 | 79 | |
| 7,510 | 14 | |
| - | - | |
| 8.4 | 9.9 | |
| 16 days ago | 6 days ago | |
| Jupyter Notebook | ||
| MIT License | Apache License 2.0 |
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Transformers-Tutorials
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AI enthusiasm #6 - Finetune any LLM you want💡
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
- FLaNK Stack Weekly for 07August2023
- How to annotate compound words to build NER models?
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[discussion] Anybody Working with VITMAE?
I'm pretraining on 850K grayscale spectrograms of birdsongs. I'm on epoch 400 out of 800 and the loss has declined from about 1.2 to 0.7. I don't really have a sense of what is "good enough" and I guess the only way I can judge is by looking at the reconstruction. I'm doing that using this notebook as a guide and right now it's doing quite badly.
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[D] NLP has HuggingFace, what does Computer Vision have?
More tutorials can be found at https://github.com/NielsRogge/Transformers-Tutorials.
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[Discussion] Information Extraction with LayoutLMv2
Ive been looking for an off the shelf encoder-decoder document understanding model for key information extraction. I found a great Huggingface implementation with concise notebook examples. However, the token classification model outputs a list of token labels corresponding bounding boxes for the token, but, not the text contained within the labeled bounding boxes themselves. Am I missing something? LayoutLMv2 describes itself as being capable of information extraction but without extracting the text I feel like it's fallen short of that ambition.
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[Project] Deepmind's Perceiver IO available through Hugging Face
Example Notebooks
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