Instructions to use 9pinus/macbert-base-chinese-medical-collation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 9pinus/macbert-base-chinese-medical-collation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="9pinus/macbert-base-chinese-medical-collation")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("9pinus/macbert-base-chinese-medical-collation") model = AutoModelForTokenClassification.from_pretrained("9pinus/macbert-base-chinese-medical-collation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 8.0, | |
| "eval_accuracy": 0.9994014164827584, | |
| "eval_f1": 0.0, | |
| "eval_loss": 0.003991865552961826, | |
| "eval_precision": 0.0, | |
| "eval_recall": 0.0, | |
| "eval_runtime": 375.9642, | |
| "eval_samples": 39609, | |
| "eval_samples_per_second": 105.353, | |
| "eval_steps_per_second": 6.586, | |
| "train_loss": 0.0015094334459279553, | |
| "train_runtime": 87513.7882, | |
| "train_samples": 590000, | |
| "train_samples_per_second": 53.934, | |
| "train_steps_per_second": 3.371 | |
| } |