Instructions to use jinbbong/kbert_base_esg_e3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinbbong/kbert_base_esg_e3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jinbbong/kbert_base_esg_e3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jinbbong/kbert_base_esg_e3") model = AutoModelForMaskedLM.from_pretrained("jinbbong/kbert_base_esg_e3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jinbbong/kbert_base_esg_e3: direct link, hf CLI and curl.
- Browser
- Download file 1.97 kB
-
https://huggingface.co/jinbbong/kbert_base_esg_e3/resolve/main/training_args.bin
- Command line
-
hf download hf://jinbbong/kbert_base_esg_e3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jinbbong/kbert_base_esg_e3/resolve/main/training_args.bin
1.97 kB
- Xet hash:
- 0ee286aa8b52cc70f60c20e3e76987ffbd31bc85cbb23fba4f120f2722f814d6
- Size of remote file:
- 1.97 kB
- SHA256:
- a07f8a55d9656704f814755ba4ffc0a6708d0e47522d4c022f5951ef34cb4031
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