Instructions to use abhi1nandy2/EManuals_BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhi1nandy2/EManuals_BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="abhi1nandy2/EManuals_BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("abhi1nandy2/EManuals_BERT") model = AutoModelForMaskedLM.from_pretrained("abhi1nandy2/EManuals_BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b26920fec46b8818c4eed1f052764b0eb0ece8fac8868a557b4d0bd3cccb25a7
- Size of remote file:
- 438 MB
- SHA256:
- 9607fdabc9953a8c7697230db6ed1a79310b0c15cebcb86b9d51511c379f924c
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