Instructions to use sm3455/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sm3455/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sm3455/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sm3455/results") model = AutoModelForSequenceClassification.from_pretrained("sm3455/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78f1869420f1fc9452365107c39d120031c89dbe4e37a4597cd6a352219213cf
- Size of remote file:
- 5.3 kB
- SHA256:
- ce61c669743c6c9fccfa13ce7cbbb144c1e1af4caf4ea98762436657761c58e1
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