Instructions to use Neurona/cpener-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neurona/cpener-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Neurona/cpener-test")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Neurona/cpener-test") model = AutoModelForTokenClassification.from_pretrained("Neurona/cpener-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8ccfe8a9d08d61149fda1948a5e9630f1dc54f50e01ecf622c126fa3bc2c0f7
- Size of remote file:
- 266 MB
- SHA256:
- 1f13c87057f0968133c6c422cb5c170614c7e2fa1895092abdd4033f48ad24af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.