Instructions to use jinbbong/kbert_base_esg_e10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinbbong/kbert_base_esg_e10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jinbbong/kbert_base_esg_e10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jinbbong/kbert_base_esg_e10") model = AutoModelForMaskedLM.from_pretrained("jinbbong/kbert_base_esg_e10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jinbbong/kbert_base_esg_e10: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/jinbbong/kbert_base_esg_e10/resolve/main/training_args.bin
- Command line
-
hf download hf://jinbbong/kbert_base_esg_e10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jinbbong/kbert_base_esg_e10/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- 72760822b6a6c711d8bd87400156d32749c6a49b3f9c48e63ac5cd96065fb557
- Size of remote file:
- 2.8 kB
- SHA256:
- 4330f3214a77045d82fd2e8b9dc7a7c8c94b92d56475e3e3fe27593324cd3c88
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