Instructions to use raykallen/cybert_apache_parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raykallen/cybert_apache_parser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="raykallen/cybert_apache_parser")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("raykallen/cybert_apache_parser") model = AutoModelForTokenClassification.from_pretrained("raykallen/cybert_apache_parser") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5642f5993feda58521d1004025721718de67b96832d9c9ca3cbe3b9a9a5b980f
- Size of remote file:
- 431 MB
- SHA256:
- f0c5294f1653f16f6b36bb9aae2aa346b352e3799039b5865229777ab637069e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.