Instructions to use timaos/try_save_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timaos/try_save_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="timaos/try_save_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("timaos/try_save_model") model = AutoModelForSequenceClassification.from_pretrained("timaos/try_save_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf507cc18894b6b5fea6aef923c87426806197b514430f667d02b734500d0568
- Size of remote file:
- 433 MB
- SHA256:
- 149631a03c28a1d6f4a13e56ecdcb603ab46ac7c3e4d5133b3bf233025fe901f
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