Instructions to use aayush9/saved_model_AMD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aayush9/saved_model_AMD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aayush9/saved_model_AMD")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aayush9/saved_model_AMD") model = AutoModelForSequenceClassification.from_pretrained("aayush9/saved_model_AMD") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40498a5e2d34090f134e94bd2377078bb99b5c4aefec0a68f34cb81e2fcbb483
- Size of remote file:
- 438 MB
- SHA256:
- 33a8a31804fec08772042b38d20a2f8a16442c6af4024ab9d32305f672876237
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