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", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intelcomp/ipc_level1_D") model = AutoModelForSequenceClassification.from_pretrained("intelcomp/ipc_level1_D", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5ec636fe8cfdbbac9c943e8e9a0f95e2b756ad6e2e5e277b88d23ed8c5c010a
- Size of remote file:
- 1.42 GB
- SHA256:
- a8a640516d65fcf751765260f1c8df28dd738fb5dd898583fa1610b231923745
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