Instructions to use Tommert25/RobBERTBestModelOct11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tommert25/RobBERTBestModelOct11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Tommert25/RobBERTBestModelOct11")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Tommert25/RobBERTBestModelOct11") model = AutoModelForTokenClassification.from_pretrained("Tommert25/RobBERTBestModelOct11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3b3782210cf571d0a01fc298cf972a8c98beb349343cf2c9cf40df169948da1
- Size of remote file:
- 4.09 kB
- SHA256:
- ecf9022ed9e6f221c4fe74d475a0ea923ef6f426d020654deccbde893255d059
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.