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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9505525b0ca6aa0139c9a58aec0da80023e5fee7017f4bbcf6c73e181221191f
- Size of remote file:
- 268 MB
- SHA256:
- 07af0e24aa169338fc4082c35a6a6553b2098f7b8d1f322693f0fdec4c84db35
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.