Instructions to use KalaiselvanD/model_albert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/model_albert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/model_albert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/model_albert") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/model_albert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 680187a40ef547f7c1b2cae171034c065179f6eed2e652c76ac91440250f2d76
- Size of remote file:
- 4.86 kB
- SHA256:
- 3391b09ea9abe18198118ae8d00c31c84948318dae4823888daeb254dbb4d71a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.