Instructions to use ran/y7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ran/y7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ran/y7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ran/y7") model = AutoModelForSequenceClassification.from_pretrained("ran/y7") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d68c420394e33be27d21d1cd539bb0464f6f132bbe207057f5877e5087d8f9a
- Size of remote file:
- 438 MB
- SHA256:
- 9909347d5c4e2daeef59b68a3a9f7ee829b5ea5fda5da8c1e3d99edd3b995390
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