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", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("raykallen/cybert_apache_parser") model = AutoModelForTokenClassification.from_pretrained("raykallen/cybert_apache_parser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a7fd50644595f7e74904e309f007c39ecab34f1bbcfcdb9725790e9441d8092
- Size of remote file:
- 433 MB
- SHA256:
- 8c5495b96537487002955845de7ff2ff3a244304805902646bd95dd77bd158d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.