Instructions to use KalaiselvanD/albert_test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/albert_test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/albert_test_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/albert_test_model") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/albert_test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5ff4c8ab02be7e456a5b73258279215b2b473c02abb25269bcf8ebc8e7452e8
- Size of remote file:
- 268 MB
- SHA256:
- ce768eaf40dbe86ee42b872d3545db96cfba7bf570c8d3784493398da70ed47f
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