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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 821aeee36ab83d4327bce358e45a606117341857ddb4d0b3cf3f4e877242c3bb
- Size of remote file:
- 3.45 kB
- SHA256:
- 4cbe798265365f742dc0072848958704d5df0c8bdda5b34d00a1835a1b4721e2
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