Instructions to use jinbbong/kbert_base_esg_e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinbbong/kbert_base_esg_e5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jinbbong/kbert_base_esg_e5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jinbbong/kbert_base_esg_e5") model = AutoModelForMaskedLM.from_pretrained("jinbbong/kbert_base_esg_e5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jinbbong/kbert_base_esg_e5: direct link, hf CLI and curl.
- Browser
- Download file 603 MB
-
https://huggingface.co/jinbbong/kbert_base_esg_e5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jinbbong/kbert_base_esg_e5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jinbbong/kbert_base_esg_e5/resolve/main/pytorch_model.bin
603 MB
- Xet hash:
- 315d5fdef14e844cb374dfa770516d99aa6566b60712cf6fb64bbc402e5fa7ce
- Size of remote file:
- 603 MB
- SHA256:
- 52150ce0036b7b636133cb5745af380a92be2ad104e44e798b3902a0253dffcf
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