--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.5180180180180181 - name: Recall type: recall value: 0.31974050046339203 - name: F1 type: f1 value: 0.39541547277936967 - name: Accuracy type: accuracy value: 0.9357035175879397 --- # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.4235 - Precision: 0.5180 - Recall: 0.3197 - F1: 0.3954 - Accuracy: 0.9357 - Corporation Precision: 0.2222 - Corporation Recall: 0.2121 - Corporation F1: 0.2171 - Creative-work Precision: 0.4462 - Creative-work Recall: 0.2042 - Creative-work F1: 0.2802 - Group Precision: 0.4030 - Group Recall: 0.1636 - Group F1: 0.2328 - Location Precision: 0.5161 - Location Recall: 0.4267 - Location F1: 0.4672 - Person Precision: 0.7747 - Person Recall: 0.4569 - Person F1: 0.5748 - Product Precision: 0.1596 - Product Recall: 0.1181 - Product F1: 0.1357 - B-corporation Precision: 0.3696 - B-corporation Recall: 0.2576 - B-corporation F1: 0.3036 - B-creative-work Precision: 0.75 - B-creative-work Recall: 0.2535 - B-creative-work F1: 0.3789 - B-group Precision: 0.5 - B-group Recall: 0.1636 - B-group F1: 0.2466 - B-location Precision: 0.6293 - B-location Recall: 0.4867 - B-location F1: 0.5489 - B-person Precision: 0.8608 - B-person Recall: 0.4755 - B-person F1: 0.6126 - B-product Precision: 0.4545 - B-product Recall: 0.1969 - B-product F1: 0.2747 - I-corporation Precision: 0.3333 - I-corporation Recall: 0.2727 - I-corporation F1: 0.3 - I-creative-work Precision: 0.4262 - I-creative-work Recall: 0.2016 - I-creative-work F1: 0.2737 - I-group Precision: 0.3478 - I-group Recall: 0.1416 - I-group F1: 0.2013 - I-location Precision: 0.5932 - I-location Recall: 0.3684 - I-location F1: 0.4545 - I-person Precision: 0.7625 - I-person Recall: 0.3631 - I-person F1: 0.4919 - I-product Precision: 0.2222 - I-product Recall: 0.1488 - I-product F1: 0.1782 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Corporation Precision | Corporation Recall | Corporation F1 | Creative-work Precision | Creative-work Recall | Creative-work F1 | Group Precision | Group Recall | Group F1 | Location Precision | Location Recall | Location F1 | Person Precision | Person Recall | Person F1 | Product Precision | Product Recall | Product F1 | B-corporation Precision | B-corporation Recall | B-corporation F1 | B-creative-work Precision | B-creative-work Recall | B-creative-work F1 | B-group Precision | B-group Recall | B-group F1 | B-location Precision | B-location Recall | B-location F1 | B-person Precision | B-person Recall | B-person F1 | B-product Precision | B-product Recall | B-product F1 | I-corporation Precision | I-corporation Recall | I-corporation F1 | I-creative-work Precision | I-creative-work Recall | I-creative-work F1 | I-group Precision | I-group Recall | I-group F1 | I-location Precision | I-location Recall | I-location F1 | I-person Precision | I-person Recall | I-person F1 | I-product Precision | I-product Recall | I-product F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:---------------------:|:------------------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|:----------------:|:-------------:|:---------:|:-----------------:|:--------------:|:----------:|:-----------------------:|:--------------------:|:----------------:|:-------------------------:|:----------------------:|:------------------:|:-----------------:|:--------------:|:----------:|:--------------------:|:-----------------:|:-------------:|:------------------:|:---------------:|:-----------:|:-------------------:|:----------------:|:------------:|:-----------------------:|:--------------------:|:----------------:|:-------------------------:|:----------------------:|:------------------:|:-----------------:|:--------------:|:----------:|:--------------------:|:-----------------:|:-------------:|:------------------:|:---------------:|:-----------:|:-------------------:|:----------------:|:------------:| | No log | 1.0 | 425 | 0.3858 | 0.4406 | 0.2576 | 0.3251 | 0.9303 | 0.0741 | 0.0606 | 0.0667 | 0.0667 | 0.0141 | 0.0233 | 0.1458 | 0.0848 | 0.1073 | 0.3829 | 0.4467 | 0.4123 | 0.7235 | 0.4452 | 0.5512 | 0.0 | 0.0 | 0.0 | 0.2391 | 0.1667 | 0.1964 | 0.0 | 0.0 | 0.0 | 0.375 | 0.0909 | 0.1463 | 0.5137 | 0.5 | 0.5068 | 0.8675 | 0.4732 | 0.6124 | 0.0 | 0.0 | 0.0 | 0.1923 | 0.0909 | 0.1235 | 0.3 | 0.0698 | 0.1132 | 0.1447 | 0.0973 | 0.1164 | 0.3636 | 0.3789 | 0.3711 | 0.7184 | 0.3720 | 0.4902 | 0.0 | 0.0 | 0.0 | | 0.199 | 2.0 | 850 | 0.4265 | 0.5295 | 0.2743 | 0.3614 | 0.9326 | 0.1444 | 0.1970 | 0.1667 | 0.4583 | 0.1549 | 0.2316 | 0.4483 | 0.0788 | 0.1340 | 0.5263 | 0.4 | 0.4545 | 0.7839 | 0.4312 | 0.5564 | 0.0714 | 0.0236 | 0.0355 | 0.2969 | 0.2879 | 0.2923 | 0.7297 | 0.1901 | 0.3017 | 0.7368 | 0.0848 | 0.1522 | 0.6635 | 0.46 | 0.5433 | 0.8981 | 0.4522 | 0.6016 | 0.5 | 0.0630 | 0.1119 | 0.2090 | 0.2545 | 0.2295 | 0.5581 | 0.1860 | 0.2791 | 0.3 | 0.0531 | 0.0902 | 0.5536 | 0.3263 | 0.4106 | 0.7619 | 0.3333 | 0.4638 | 0.1538 | 0.0496 | 0.075 | | 0.0799 | 3.0 | 1275 | 0.4235 | 0.5180 | 0.3197 | 0.3954 | 0.9357 | 0.2222 | 0.2121 | 0.2171 | 0.4462 | 0.2042 | 0.2802 | 0.4030 | 0.1636 | 0.2328 | 0.5161 | 0.4267 | 0.4672 | 0.7747 | 0.4569 | 0.5748 | 0.1596 | 0.1181 | 0.1357 | 0.3696 | 0.2576 | 0.3036 | 0.75 | 0.2535 | 0.3789 | 0.5 | 0.1636 | 0.2466 | 0.6293 | 0.4867 | 0.5489 | 0.8608 | 0.4755 | 0.6126 | 0.4545 | 0.1969 | 0.2747 | 0.3333 | 0.2727 | 0.3 | 0.4262 | 0.2016 | 0.2737 | 0.3478 | 0.1416 | 0.2013 | 0.5932 | 0.3684 | 0.4545 | 0.7625 | 0.3631 | 0.4919 | 0.2222 | 0.1488 | 0.1782 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1