Instructions to use Swarnava/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Swarnava/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Swarnava/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Swarnava/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("Swarnava/tmp_trainer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f692c1d4c82a65644056fc1d5648c8841e405e99b6502bb877382622890bbe4a
- Size of remote file:
- 268 MB
- SHA256:
- d86c88b56d8d239cc4531465ceec804f3a06325f5bcfc85d8f1713a346f4bc0f
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