Instructions to use mudit23/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mudit23/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mudit23/test_model", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mudit23/test_model") model = AutoModelForSequenceClassification.from_pretrained("mudit23/test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46a9ea1604b5a165f81158b32d0acb5ce0c21740517cb90ea151f2710abdfd4a
- Size of remote file:
- 268 MB
- SHA256:
- 2b963330fb40e95146c87e0829aed34641b2bdf8f999b06bfc40e80cff193e75
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