Instructions to use ran/h1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ran/h1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ran/h1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ran/h1") model = AutoModelForSequenceClassification.from_pretrained("ran/h1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a02a5d58620cd765b308a11a19a9e3abd544892173442a54aaaa28616303044d
- Size of remote file:
- 438 MB
- SHA256:
- 6d1df778888df68ba9e7975a09040cbeefbe183ffd6a346db11856357aabc2eb
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