Token Classification
Transformers
Safetensors
Ukrainian
xlm-roberta
feature-extraction
ukrainian
stress
homograph
tts
Instructions to use aloudreader/uk-stress-token-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloudreader/uk-stress-token-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="aloudreader/uk-stress-token-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aloudreader/uk-stress-token-classifier") model = AutoModel.from_pretrained("aloudreader/uk-stress-token-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download report.json from aloudreader/uk-stress-token-classifier: direct link, hf CLI and curl.
- Browser
- Download file 353 Bytes
-
https://huggingface.co/aloudreader/uk-stress-token-classifier/resolve/main/report.json
- Command line
-
hf download hf://aloudreader/uk-stress-token-classifier/report.json
-
curl -L -o report.json https://huggingface.co/aloudreader/uk-stress-token-classifier/resolve/main/report.json
353 Bytes
| { | |
| "all": { | |
| "rows": 66630, | |
| "accuracy": 0.85002251238181, | |
| "first_candidate": 0.5555605583070689 | |
| }, | |
| "unseen form": { | |
| "rows": 30754, | |
| "accuracy": 0.7447486505820381, | |
| "first_candidate": 0.5997593808935423 | |
| }, | |
| "seen form": { | |
| "rows": 35876, | |
| "accuracy": 0.9402664734084067, | |
| "first_candidate": 0.5176719812688148 | |
| } | |
| } |