Instructions to use dheerajpai/PATENT_BERT_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dheerajpai/PATENT_BERT_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dheerajpai/PATENT_BERT_2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dheerajpai/PATENT_BERT_2") model = AutoModelForMaskedLM.from_pretrained("dheerajpai/PATENT_BERT_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fa930c832c3289c38cc258386f9823b2e9a48d8645954180d1bf23383cc5182
- Size of remote file:
- 4.92 kB
- SHA256:
- 06ebd46d76700bcfa920047dfb7fa841c9eb93836dc2f3b5151fbe67b563979e
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