Instructions to use KalaiselvanD/model_albert_5000_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_albert_5000_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_albert_5000_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_albert_5000_1") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_albert_5000_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 749ca24a2bbfa4c4c388115f91d8d921ec5eb8e521c151fb1d1be08c22219984
- Size of remote file:
- 4.92 kB
- SHA256:
- 216f5e0cbfb670e20d3f7a0f738bef0ac3d5f2c77e0901e87fc673d062dc2d29
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