Instructions to use kelvinleong/AT_TrainlabelTest_Class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kelvinleong/AT_TrainlabelTest_Class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kelvinleong/AT_TrainlabelTest_Class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kelvinleong/AT_TrainlabelTest_Class") model = AutoModelForSequenceClassification.from_pretrained("kelvinleong/AT_TrainlabelTest_Class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8869a72c0964a4fd89fc44b4098d2cf5c2044eeeaebb62c3a4f8ecba41952bd8
- Size of remote file:
- 499 MB
- SHA256:
- 7998fb167add76793d843f902f21d68961e6d03b2a148e4e8a257132a37b0fb3
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