Instructions to use universalner/uner_qaf_ara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalner/uner_qaf_ara with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="universalner/uner_qaf_ara")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("universalner/uner_qaf_ara") model = AutoModelForTokenClassification.from_pretrained("universalner/uner_qaf_ara", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9547511312217195, | |
| "eval_f1": 0.7223587223587223, | |
| "eval_loss": 0.18850649893283844, | |
| "eval_precision": 0.7241379310344828, | |
| "eval_recall": 0.7205882352941176, | |
| "eval_runtime": 0.4056, | |
| "eval_samples": 140, | |
| "eval_samples_per_second": 345.174, | |
| "eval_steps_per_second": 22.19, | |
| "predict_accuracy": 0.9545663148233134, | |
| "predict_f1": 0.7088607594936709, | |
| "predict_loss": 0.1624361276626587, | |
| "predict_precision": 0.6965174129353234, | |
| "predict_recall": 0.7216494845360825, | |
| "predict_runtime": 0.4848, | |
| "predict_samples_per_second": 301.185, | |
| "predict_steps_per_second": 20.629, | |
| "train_loss": 0.23982335650731648, | |
| "train_runtime": 63.3537, | |
| "train_samples": 1004, | |
| "train_samples_per_second": 79.238, | |
| "train_steps_per_second": 4.972 | |
| } |