Instructions to use diptanu/fBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diptanu/fBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="diptanu/fBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("diptanu/fBERT") model = AutoModelForMaskedLM.from_pretrained("diptanu/fBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 398f940e0a44d026a2a118dcc59635b1edb62c20e1711b6e3ccfb97bdc221ac1
- Size of remote file:
- 438 MB
- SHA256:
- cf82e59a100414c61bdbe5274cdc2fe36234a1f84cdd80d6c10143356d752191
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.