Instructions to use prodm93/bert-rp-1-sentchunks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prodm93/bert-rp-1-sentchunks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="prodm93/bert-rp-1-sentchunks")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("prodm93/bert-rp-1-sentchunks") model = AutoModelForMaskedLM.from_pretrained("prodm93/bert-rp-1-sentchunks") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 365f1da5afb1f98c8ecf93c379cccc54997bde274394cf81dccfa3d4f7089844
- Size of remote file:
- 453 MB
- SHA256:
- fce93467b234b101ed48f3127c40b1ec12e68e97605c8fd07474f521cf612dc5
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