Instructions to use vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman") model = AutoModelForMaskedLM.from_pretrained("vaibhav9/bert_uncased_L-4_H-256_A-4-finetuned-hangman", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 657bd3403d86e0382b62f2b13d02bb6d5c6caeb2bfcc016c86fd8ca3ab90bda7
- Size of remote file:
- 44.8 MB
- SHA256:
- 809f71a96aa2db410fcb492695539f020ecabfc9476ed6acb96cbea110767cd2
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