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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("diptanu/fBERT") model = AutoModelForMaskedLM.from_pretrained("diptanu/fBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_args.bin from diptanu/fBERT: direct link, hf CLI and curl.
- Browser
- Download file 2.29 kB
-
https://huggingface.co/diptanu/fBERT/resolve/main/training_args.bin
- Command line
-
hf download hf://diptanu/fBERT/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/diptanu/fBERT/resolve/main/training_args.bin
2.29 kB
- Xet hash:
- 5854177f297b4ea984bd9d57ebf9f4c2e625e128975f831f7be35418653ad6e3
- Size of remote file:
- 2.29 kB
- SHA256:
- f485f3b438868faaac8a0e5b54c2d26087a2ef507def94bd9a1815283397cf31
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