Instructions to use kelvinleong/KT_TrainlabelTest2_Class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kelvinleong/KT_TrainlabelTest2_Class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kelvinleong/KT_TrainlabelTest2_Class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kelvinleong/KT_TrainlabelTest2_Class") model = AutoModelForSequenceClassification.from_pretrained("kelvinleong/KT_TrainlabelTest2_Class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a83a42730f799e22c21363fa28e2180ce253871b87da7069984e05b9de061d8a
- Size of remote file:
- 499 MB
- SHA256:
- 6eb373f0390a161b9a865d019b6592f766e5a26ffb102e8a7851f52dcc5bccd6
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