Instructions to use shafin/gbert-large-finetuned-cust18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shafin/gbert-large-finetuned-cust18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="shafin/gbert-large-finetuned-cust18")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("shafin/gbert-large-finetuned-cust18") model = AutoModelForMaskedLM.from_pretrained("shafin/gbert-large-finetuned-cust18", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shafin/gbert-large-finetuned-cust18: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/shafin/gbert-large-finetuned-cust18/resolve/main/training_args.bin
- Command line
-
hf download hf://shafin/gbert-large-finetuned-cust18/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shafin/gbert-large-finetuned-cust18/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 0c13b15a3b242ef1afb2beadc05b1e5d8281e6ac90e7d9ccf5d912cec301f9f2
- Size of remote file:
- 3.64 kB
- SHA256:
- 3fa0a42ef14d29e8f5bc45888c700797ed15970d065c2b8fb3a1222e93304635
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