Instructions to use hf-tiny-model-private/tiny-random-RemBertForTokenClassification 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-RemBertForTokenClassification 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-RemBertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81eea832528d786453f142741fb58816579949fd68092bdddee1ac4053d0bb5c
- Size of remote file:
- 18.3 MB
- SHA256:
- fb5371dbc001638b8d11b6c47fa8838b620eb1d2ae76cdec47210b412ff9011f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.