Instructions to use AidenGO/KDXF_Bert4MaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AidenGO/KDXF_Bert4MaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AidenGO/KDXF_Bert4MaskedLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AidenGO/KDXF_Bert4MaskedLM") model = AutoModelForMaskedLM.from_pretrained("AidenGO/KDXF_Bert4MaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AidenGO/KDXF_Bert4MaskedLM: direct link, hf CLI and curl.
- Browser
- Download file 421 MB
-
https://huggingface.co/AidenGO/KDXF_Bert4MaskedLM/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AidenGO/KDXF_Bert4MaskedLM/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AidenGO/KDXF_Bert4MaskedLM/resolve/main/pytorch_model.bin
421 MB
- Xet hash:
- 4d82e2dfdde0d96fdbb301cd2213e913eaad98b0fa87bfdc9dc1627c963cccde
- Size of remote file:
- 421 MB
- SHA256:
- e1c39d1db901cc5e7a6a935c580315c3d4a1708d93bf09694a7fb0d4536adaa7
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