Instructions to use emanjavacas/GysBERT-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emanjavacas/GysBERT-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="emanjavacas/GysBERT-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("emanjavacas/GysBERT-v2") model = AutoModelForMaskedLM.from_pretrained("emanjavacas/GysBERT-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c81e3efb8dd71790d27e3602ea3d003767a07c9d34e1f65f36df477ce9e9428f
- Size of remote file:
- 440 MB
- SHA256:
- f895df52eab3c147b346832deaef90dc379612607d08e24f024a14b9d4a55291
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