Instructions to use CLASS-MATE/BERT-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLASS-MATE/BERT-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CLASS-MATE/BERT-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CLASS-MATE/BERT-MLM") model = AutoModelForMaskedLM.from_pretrained("CLASS-MATE/BERT-MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0070707532cc4ebcdc767f0229f7b098777acead48a66b2bff636112e2b8262
- Size of remote file:
- 4.79 kB
- SHA256:
- 126327bbd43737948cb3e9c1c05ae62aed137a50d1f673f3fcde64c5b0fc7318
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