Instructions to use KalaiselvanD/albert_test_model_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_test_model_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_test_model_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_test_model_3") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_test_model_3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9823debc6ee9e463c81e87ca4a959e5d58c417295eb6a711f71a44d91aa3bfb
- Size of remote file:
- 268 MB
- SHA256:
- 8748d4abbd8ce469220ffe14d8bbecd48cf6d5bda143cd8701adb2bb333ef669
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