Instructions to use KalaiselvanD/albert_model__29_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_model__29_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_model__29_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_model__29_2") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_model__29_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adc1759c224883c0bdb98a9f1a4a84355953fac7c6a58aeb11b15cd3cac59a6f
- Size of remote file:
- 268 MB
- SHA256:
- 4e28c7103f05971ce2c39720b4a9cca51b7601e53476c983f2f511396feba163
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