Instructions to use khazen2/DistilBERT_Hemingway_SAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khazen2/DistilBERT_Hemingway_SAR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="khazen2/DistilBERT_Hemingway_SAR")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("khazen2/DistilBERT_Hemingway_SAR") model = AutoModelForMaskedLM.from_pretrained("khazen2/DistilBERT_Hemingway_SAR") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9add66aae8d20570ff04deb743d2618fdcf17a278cb90d3624d661bfd549a447
- Size of remote file:
- 268 MB
- SHA256:
- a2eac11eefa4358db1d999b9412ecb3d747c14c3c6c38e48a88b48854546e8cd
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