Instructions to use hf-tiny-model-private/tiny-random-RobertaForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-RobertaForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-RobertaForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RobertaForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-RobertaForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d8564868062ae49463bbb0bbf0dc34d791ee4e62c0d302cbe935f8c275752a0
- Size of remote file:
- 349 kB
- SHA256:
- 8da2cf7c1a49decf6291b1cb3c1283a1a8658f5bfe209e5cafc4aaadb4e91487
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.