Instructions to use dimitarpg13/dimitar_eli5_mlm_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dimitarpg13/dimitar_eli5_mlm_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dimitarpg13/dimitar_eli5_mlm_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dimitarpg13/dimitar_eli5_mlm_model") model = AutoModelForMaskedLM.from_pretrained("dimitarpg13/dimitar_eli5_mlm_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 810ebfeacd19ea9c55ad11cdafb8a778f5020670252641e95cc5b100fde97839
- Size of remote file:
- 5.37 kB
- SHA256:
- 0639877bdf7d81e13968220c2d44f6a8f1a7fd71fef4058b8cf749b801b6fb92
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