Token Classification
Transformers
ONNX
Safetensors
Danish
electra
punctuation-restoration
sentence-boundary-detection
truecasing
danish
Instructions to use RyeAI/ekko-pnc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RyeAI/ekko-pnc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RyeAI/ekko-pnc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RyeAI/ekko-pnc") model = AutoModelForTokenClassification.from_pretrained("RyeAI/ekko-pnc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download calibration.json from RyeAI/ekko-pnc: direct link, hf CLI and curl.
- Browser
- Download file 417 Bytes
-
https://huggingface.co/RyeAI/ekko-pnc/resolve/main/calibration.json
- Command line
-
hf download hf://RyeAI/ekko-pnc/calibration.json
-
curl -L -o calibration.json https://huggingface.co/RyeAI/ekko-pnc/resolve/main/calibration.json
417 Bytes
| { | |
| "boundary_logit_bias": 0.75, | |
| "applied_to_label_ids": [ | |
| 4, | |
| 5, | |
| 6, | |
| 7, | |
| 8, | |
| 9 | |
| ], | |
| "applied_to_labels": [ | |
| ".", | |
| ".|U", | |
| "?", | |
| "?|U", | |
| "!", | |
| "!|U" | |
| ], | |
| "classifier_bias_delta": [ | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.75, | |
| 0.75, | |
| 0.75, | |
| 0.75, | |
| 0.75, | |
| 0.75 | |
| ], | |
| "selected_on": "CoRal conversation running-text DEV (random cuts), not on any test set" | |
| } | |