Instructions to use hf-internal-testing/tiny-random-RobertaForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RobertaForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-RobertaForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RobertaForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-RobertaForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bcdd13ff132898886227982eeadda4b41f749e9448370cfb45c3b2d2715bf3b
- Size of remote file:
- 349 kB
- SHA256:
- 43aa1199b7b09e83b2d14630bf7497ba662d01db4a7f9eed778dfaf5d9c52dca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.