Instructions to use seldas/rxbert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seldas/rxbert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="seldas/rxbert-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("seldas/rxbert-v1") model = AutoModelForMaskedLM.from_pretrained("seldas/rxbert-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd0a10f28ff37148933bbb38099755d49a1c2d512eea7fd1776f78a521ec0785
- Size of remote file:
- 433 MB
- SHA256:
- 6e2a4875a241971eef63f14d6a03385c3a0d7056b964dd8126ddfcafb5916651
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