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 pytorch_model.bin from celential/erc: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/celential/erc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://celential/erc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/celential/erc/resolve/main/pytorch_model.bin
46.8 MB
- Xet hash:
- 8dfdf1c6c4f2462bf3e2c0ca6a7066c71312a7853f4071e64ae999bb7cc4823d
- Size of remote file:
- 46.8 MB
- SHA256:
- 245cdd4cf6fac0874608abea26a6e75e97e2460a5f5f6b4a4b2fae234f9a0dbc
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