--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: albert-base-v2-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9252213840603477 - name: Recall type: recall value: 0.9329732113328189 - name: F1 type: f1 value: 0.9290811285541773 - name: Accuracy type: accuracy value: 0.9848205157332728 --- # albert-base-v2-finetuned-ner This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0626 - Precision: 0.9252 - Recall: 0.9330 - F1: 0.9291 - Accuracy: 0.9848 ## 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: 64 - eval_batch_size: 64 - 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 | 220 | 0.0863 | 0.8827 | 0.8969 | 0.8898 | 0.9773 | | No log | 2.0 | 440 | 0.0652 | 0.8951 | 0.9199 | 0.9073 | 0.9809 | | 0.1243 | 3.0 | 660 | 0.0626 | 0.9191 | 0.9208 | 0.9200 | 0.9827 | | 0.1243 | 4.0 | 880 | 0.0585 | 0.9227 | 0.9281 | 0.9254 | 0.9843 | | 0.0299 | 5.0 | 1100 | 0.0626 | 0.9252 | 0.9330 | 0.9291 | 0.9848 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.8.1+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0