Instructions to use intelcomp/ipc_level1_C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intelcomp/ipc_level1_C with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intelcomp/ipc_level1_C")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intelcomp/ipc_level1_C") model = AutoModelForSequenceClassification.from_pretrained("intelcomp/ipc_level1_C") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d007a98bf62ebb0facbccca1ff5b7802c00d73207b51cf5a6b190d96fea69639
- Size of remote file:
- 1.42 GB
- SHA256:
- b8fc361656e4a7ee95c333e74f4b754d639ebc194f5ce00b01aafc950b526782
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.