Instructions to use KalaiselvanD/albert_test_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_test_model_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_test_model_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_test_model_2") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_test_model_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eba711c2688fc4ecf6f2280eb6dea0f47ff96be2f48171aaef20f74673b42936
- Size of remote file:
- 268 MB
- SHA256:
- 5a0ac5d134389430da656062db0cae662383c9a7756692b5abf224ee3491e800
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