Instructions to use vaibhav9/hangman-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhav9/hangman-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vaibhav9/hangman-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vaibhav9/hangman-bert-base") model = AutoModelForMaskedLM.from_pretrained("vaibhav9/hangman-bert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5a6be80d7e8a5dee38acea4c0ccdb27722ab925b75c2b13da19852fa9ab9015a
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size 438081024
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