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bert-finetuned-ner

Token Classification
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - conll2003
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: bert-finetuned-ner
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: conll2003
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+ type: conll2003
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+ config: conll2003
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+ split: train
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+ args: conll2003
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.9427525378598769
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+ - name: Recall
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+ type: recall
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+ value: 0.9533826994278021
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+ - name: F1
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+ type: f1
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+ value: 0.9480378211028366
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9866957084829575
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # bert-finetuned-ner
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+
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+ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1128
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+ - Precision: 0.9428
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+ - Recall: 0.9534
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+ - F1: 0.9480
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+ - Accuracy: 0.9867
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 15
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0937 | 1.0 | 1756 | 0.0660 | 0.9179 | 0.9332 | 0.9255 | 0.9825 |
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+ | 0.0378 | 2.0 | 3512 | 0.0766 | 0.9246 | 0.9451 | 0.9348 | 0.9843 |
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+ | 0.0245 | 3.0 | 5268 | 0.0667 | 0.9241 | 0.9409 | 0.9325 | 0.9843 |
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+ | 0.017 | 4.0 | 7024 | 0.0712 | 0.9343 | 0.9505 | 0.9424 | 0.9863 |
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+ | 0.0143 | 5.0 | 8780 | 0.0898 | 0.9366 | 0.9492 | 0.9428 | 0.9855 |
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+ | 0.0049 | 6.0 | 10536 | 0.0964 | 0.9294 | 0.9482 | 0.9387 | 0.9853 |
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+ | 0.0039 | 7.0 | 12292 | 0.1001 | 0.9353 | 0.9512 | 0.9432 | 0.9860 |
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+ | 0.0036 | 8.0 | 14048 | 0.1002 | 0.9388 | 0.9522 | 0.9454 | 0.9862 |
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+ | 0.0018 | 9.0 | 15804 | 0.1049 | 0.9363 | 0.9495 | 0.9428 | 0.9861 |
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+ | 0.0019 | 10.0 | 17560 | 0.1191 | 0.9375 | 0.9497 | 0.9436 | 0.9849 |
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+ | 0.0008 | 11.0 | 19316 | 0.1083 | 0.9396 | 0.9530 | 0.9463 | 0.9864 |
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+ | 0.0003 | 12.0 | 21072 | 0.1064 | 0.9419 | 0.9530 | 0.9475 | 0.9864 |
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+ | 0.0004 | 13.0 | 22828 | 0.1091 | 0.9448 | 0.9527 | 0.9487 | 0.9865 |
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+ | 0.0006 | 14.0 | 24584 | 0.1132 | 0.9464 | 0.9542 | 0.9503 | 0.9867 |
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+ | 0.0004 | 15.0 | 26340 | 0.1128 | 0.9428 | 0.9534 | 0.9480 | 0.9867 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.23.1
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.6.1
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+ - Tokenizers 0.13.1