Instructions to use owen198/esgbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use owen198/esgbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="owen198/esgbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("owen198/esgbert") model = AutoModelForSequenceClassification.from_pretrained("owen198/esgbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from owen198/esgbert: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/owen198/esgbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://owen198/esgbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/owen198/esgbert/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 85ea67c8073df99b9c3a62c7da558811e1f0aafcc897d8975ff1b4cf66274615
- Size of remote file:
- 433 MB
- SHA256:
- fa989cb633d9244fab60881222f38a752355e02df68215e0db38e6e53b6c4aa2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.