Instructions to use hf-internal-testing/tiny-random-MegaForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MegaForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-MegaForTokenClassification")# Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-MegaForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2dba180db6c84963b82746ecdc7a52085daa4dbd025dcd15d1db5903be7102b
- Size of remote file:
- 406 kB
- SHA256:
- 5351b8e03e0b3bd6795b55b92f17b30b56b3571061c5a40564a19d758f0ea636
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.