Instructions to use gszabo/test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gszabo/test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gszabo/test1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gszabo/test1") model = AutoModelForMaskedLM.from_pretrained("gszabo/test1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a85cc352b2eb4fe8e55151083fa680c0fb3de408e533d5b7f614ec04c6f7699
- Size of remote file:
- 626 MB
- SHA256:
- 733175b176590d37ea5c931b8da8cbc34c2ac6e170116fd2934b4077c06828c3
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