Instructions to use universalner/uner_dan_ddt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalner/uner_dan_ddt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="universalner/uner_dan_ddt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("universalner/uner_dan_ddt") model = AutoModelForTokenClassification.from_pretrained("universalner/uner_dan_ddt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from universalner/uner_dan_ddt: direct link, hf CLI and curl.
- Browser
- Download file 346 Bytes
-
https://huggingface.co/universalner/uner_dan_ddt/resolve/main/eval_results.json
- Command line
-
hf download hf://universalner/uner_dan_ddt/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/universalner/uner_dan_ddt/resolve/main/eval_results.json
346 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9939024390243902, | |
| "eval_f1": 0.8994708994708994, | |
| "eval_loss": 0.033292997628450394, | |
| "eval_precision": 0.9018567639257294, | |
| "eval_recall": 0.8970976253298153, | |
| "eval_runtime": 1.4533, | |
| "eval_samples": 565, | |
| "eval_samples_per_second": 388.762, | |
| "eval_steps_per_second": 24.771 | |
| } |