Instructions to use intelcomp/ipc_level1_D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intelcomp/ipc_level1_D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intelcomp/ipc_level1_D")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intelcomp/ipc_level1_D") model = AutoModelForSequenceClassification.from_pretrained("intelcomp/ipc_level1_D") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 600035964af6611ca496946612add2fa33577324a76a3249bccfc1df3dbffbe5
- Size of remote file:
- 2.74 kB
- SHA256:
- 95a952fd434a75f742f88bdab3305a9b515a0d70980fc9495fafda7418e65bc3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.