Instructions to use rajendrabaskota/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajendrabaskota/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rajendrabaskota/tmp_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rajendrabaskota/tmp_trainer") model = AutoModelForSequenceClassification.from_pretrained("rajendrabaskota/tmp_trainer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2c67f34f718c916fa4a223cfc68a640a8358b9252f10696e0d4c4fd0c1f27c6
- Size of remote file:
- 499 MB
- SHA256:
- c64cb3bdce9c09d8d5022b8492348ed0b2903c394fc2aae9dc972fea42233027
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