--- license: apache-2.0 tags: - generated_from_trainer base_model: bert-base-uncased datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: NER_Pittsburgh_TAA results: - task: type: token-classification name: Token Classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - type: precision value: 0.9429236395877203 name: Precision - type: recall value: 0.9517843159190066 name: Recall - type: f1 value: 0.9473332591025497 name: F1 - type: accuracy value: 0.9867030994328562 name: Accuracy --- # NER_Pittsburgh_TAA This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0860 - Precision: 0.9429 - Recall: 0.9518 - F1: 0.9473 - Accuracy: 0.9867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 439 | 0.0863 | 0.9437 | 0.9444 | 0.9440 | 0.9861 | | 0.0024 | 2.0 | 878 | 0.0995 | 0.9394 | 0.9442 | 0.9418 | 0.9852 | | 0.0021 | 3.0 | 1317 | 0.0904 | 0.9355 | 0.9463 | 0.9409 | 0.9856 | | 0.0012 | 4.0 | 1756 | 0.0835 | 0.9427 | 0.9514 | 0.9471 | 0.9867 | | 0.0009 | 5.0 | 2195 | 0.0860 | 0.9429 | 0.9518 | 0.9473 | 0.9867 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1