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 pytorch_model.bin from shafin/gbert-large-finetuned-cust18: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/shafin/gbert-large-finetuned-cust18/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shafin/gbert-large-finetuned-cust18/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shafin/gbert-large-finetuned-cust18/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- c53ed3bb90e5a6880dbc3c500d270c58972e5b2903c50b44fb6e8129f66c176a
- Size of remote file:
- 1.34 GB
- SHA256:
- 0ded680e4dc62d46dac0ffe448cf0e846f6cb80727ca9127b0a818e3c2dd8bd1
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