Instructions to use KalaiselvanD/model_albert_5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_albert_5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_albert_5000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_albert_5000") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_albert_5000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82328fc7e2e2adc950b2b707fcb31da72dd3de1cbcbf24267a39c3f7cf178ebc
- Size of remote file:
- 46.7 MB
- SHA256:
- 056ef216c2d7b8256274b3c2551c421e68b0de63df6e7f443e40769f8032e932
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