Instructions to use kelvinleong/KT_TrainlabelTest_Class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kelvinleong/KT_TrainlabelTest_Class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kelvinleong/KT_TrainlabelTest_Class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kelvinleong/KT_TrainlabelTest_Class") model = AutoModelForSequenceClassification.from_pretrained("kelvinleong/KT_TrainlabelTest_Class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec64f84332376dbd5f061af66670d7dc80a529a66c4e8cd87c7f948510c8f0dc
- Size of remote file:
- 499 MB
- SHA256:
- 309d4292810ced3a92da5838c4f237e399602cd1aa8641150a1aae4ad4764269
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