Instructions to use flax-community/multilingual-vqa-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flax-community/multilingual-vqa-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="flax-community/multilingual-vqa-ft")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("flax-community/multilingual-vqa-ft", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbeb0a0041c6c7889a7a0d88fa328e0f9b3f8a75ce651283b73809d70eb1d6cb
- Size of remote file:
- 1.03 GB
- SHA256:
- b48f39250228e1cb396b54ac66194fd31674b9d5f426a5e18108e2e562066107
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