Instructions to use celential/erc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use celential/erc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="celential/erc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("celential/erc") model = AutoModelForSequenceClassification.from_pretrained("celential/erc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from celential/erc: direct link, hf CLI and curl.
- Browser
- Download file 46.7 MB
-
https://huggingface.co/celential/erc/resolve/main/model.safetensors
- Command line
-
hf download hf://celential/erc/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/celential/erc/resolve/main/model.safetensors
46.7 MB
- Xet hash:
- ab207126dc07899fbdbe9c64276cdc2b326d84fc4a8a2ebccdd03829b1f2419f
- Size of remote file:
- 46.7 MB
- SHA256:
- 02709c535de49ef27517ba6233d7e80f829a5d780df0ab60d2b858e8aa36e5bd
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