Instructions to use lukecarlate/BERT_P_SM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/BERT_P_SM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/BERT_P_SM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/BERT_P_SM") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/BERT_P_SM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2731e9d6840163731b461b806b0b569a783f0196fa135119b1bfe61768911334
- Size of remote file:
- 438 MB
- SHA256:
- 3e786e86b9f168b3c88904d8a08228d6521c22784d27d3730720fd85127638d1
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