Token Classification
Transformers
PyTorch
Graphcore
roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use jimypbr/test-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jimypbr/test-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jimypbr/test-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jimypbr/test-ner") model = AutoModelForTokenClassification.from_pretrained("jimypbr/test-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9920953233908337, | |
| "eval_f1": 0.952317215994646, | |
| "eval_loss": 0.039794921875, | |
| "eval_precision": 0.9467731204258151, | |
| "eval_recall": 0.9579266240323123, | |
| "eval_runtime": 8.5576, | |
| "eval_samples": 3250, | |
| "eval_samples_per_second": 379.781, | |
| "eval_steps_per_second": 37.978, | |
| "train_loss": 0.07335638256863196, | |
| "train_runtime": 838.7854, | |
| "train_samples": 14041, | |
| "train_samples_per_second": 50.187, | |
| "train_steps_per_second": 3.137 | |
| } |