Instructions to use hf-internal-testing/tiny-random-AlbertForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-AlbertForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-AlbertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-AlbertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-AlbertForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09094f868757ac2ab157ff42c33fb7ce957839ffe30d03d18ca6728a372fb696
- Size of remote file:
- 15.9 MB
- SHA256:
- 435f2ae63d28e19896aa6eb854bffa2c5b7cc7fb197efb6eb75d105cfcc96ae9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.