Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use Pontonkid/Biomed_bert-base-uncased-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pontonkid/Biomed_bert-base-uncased-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Pontonkid/Biomed_bert-base-uncased-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Pontonkid/Biomed_bert-base-uncased-NER") model = AutoModelForTokenClassification.from_pretrained("Pontonkid/Biomed_bert-base-uncased-NER") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.4705882352941178, | |
| "eval_steps": 500, | |
| "global_step": 1000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.74, | |
| "learning_rate": 3.774509803921569e-05, | |
| "loss": 0.2314, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 1.47, | |
| "learning_rate": 2.5490196078431373e-05, | |
| "loss": 0.1472, | |
| "step": 1000 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 2040, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "total_flos": 1149951689880.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |