Instructions to use LenguajeNaturalAI/tiny-albert-testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LenguajeNaturalAI/tiny-albert-testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LenguajeNaturalAI/tiny-albert-testing")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LenguajeNaturalAI/tiny-albert-testing") model = AutoModel.from_pretrained("LenguajeNaturalAI/tiny-albert-testing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14cf8781071705d1f33faf6ecd6885c9e94db8abcfe3d8439195e300d6f885c5
- Size of remote file:
- 19.4 MB
- SHA256:
- f9a5f25caa1160276c44f2e942ccb63dc3e3a54e87748e1ed62dc8454c81cc55
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.