--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: [] --- # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2315 - Precision: 0.5909 - Recall: 0.6789 - F1: 0.6318 - Accuracy: 0.9259 - Adr Precision: 0.5587 - Adr Recall: 0.6872 - Adr F1: 0.6163 - Disease Precision: 0.05 - Disease Recall: 0.0312 - Disease F1: 0.0385 - Drug Precision: 0.8364 - Drug Recall: 0.9020 - Drug F1: 0.8679 - Finding Precision: 0.1389 - Finding Recall: 0.1724 - Finding F1: 0.1538 - Symptom Precision: 0.0 - Symptom Recall: 0.0 - Symptom F1: 0.0 - B-adr Precision: 0.7568 - B-adr Recall: 0.8279 - B-adr F1: 0.7907 - B-disease Precision: 0.5 - B-disease Recall: 0.0312 - B-disease F1: 0.0588 - B-drug Precision: 0.9194 - B-drug Recall: 0.9557 - B-drug F1: 0.9372 - B-finding Precision: 0.5417 - B-finding Recall: 0.4483 - B-finding F1: 0.4906 - B-symptom Precision: 0.0 - B-symptom Recall: 0.0 - B-symptom F1: 0.0 - I-adr Precision: 0.5747 - I-adr Recall: 0.6892 - I-adr F1: 0.6268 - I-disease Precision: 0.3684 - I-disease Recall: 0.2414 - I-disease F1: 0.2917 - I-drug Precision: 0.8732 - I-drug Recall: 0.9118 - I-drug F1: 0.8921 - I-finding Precision: 0.3043 - I-finding Recall: 0.2593 - I-finding F1: 0.2800 - I-symptom Precision: 0.0 - I-symptom Recall: 0.0 - I-symptom F1: 0.0 - Macro Avg F1: 0.4368 - Weighted Avg F1: 0.7182 ## 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 | Adr Precision | Adr Recall | Adr F1 | Disease Precision | Disease Recall | Disease F1 | Drug Precision | Drug Recall | Drug F1 | Finding Precision | Finding Recall | Finding F1 | Symptom Precision | Symptom Recall | Symptom F1 | B-adr Precision | B-adr Recall | B-adr F1 | B-disease Precision | B-disease Recall | B-disease F1 | B-drug Precision | B-drug Recall | B-drug F1 | B-finding Precision | B-finding Recall | B-finding F1 | B-symptom Precision | B-symptom Recall | B-symptom F1 | I-adr Precision | I-adr Recall | I-adr F1 | I-disease Precision | I-disease Recall | I-disease F1 | I-drug Precision | I-drug Recall | I-drug F1 | I-finding Precision | I-finding Recall | I-finding F1 | I-symptom Precision | I-symptom Recall | I-symptom F1 | Macro Avg F1 | Weighted Avg F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------:|:-------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------:|:---------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:---------------:|:------------:|:--------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------:|:---------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:------------:|:---------------:| | No log | 1.0 | 127 | 0.2637 | 0.5378 | 0.6338 | 0.5819 | 0.9129 | 0.4869 | 0.6451 | 0.5550 | 0.0 | 0.0 | 0.0 | 0.7828 | 0.8480 | 0.8141 | 0.125 | 0.0690 | 0.0889 | 0.0 | 0.0 | 0.0 | 0.7377 | 0.7746 | 0.7557 | 0.0 | 0.0 | 0.0 | 0.8927 | 0.9015 | 0.8971 | 1.0 | 0.0690 | 0.1290 | 0.0 | 0.0 | 0.0 | 0.4813 | 0.6362 | 0.5480 | 0.0 | 0.0 | 0.0 | 0.8719 | 0.8676 | 0.8698 | 0.1875 | 0.1111 | 0.1395 | 0.0 | 0.0 | 0.0 | 0.3339 | 0.6592 | | No log | 2.0 | 254 | 0.2329 | 0.5826 | 0.6621 | 0.6198 | 0.9242 | 0.5455 | 0.6677 | 0.6004 | 0.0455 | 0.0312 | 0.0370 | 0.8326 | 0.9020 | 0.8659 | 0.0769 | 0.0690 | 0.0727 | 0.0 | 0.0 | 0.0 | 0.7555 | 0.8075 | 0.7806 | 1.0 | 0.0312 | 0.0606 | 0.9159 | 0.9655 | 0.9400 | 0.6 | 0.3103 | 0.4091 | 0.0 | 0.0 | 0.0 | 0.5677 | 0.6819 | 0.6196 | 0.2727 | 0.2069 | 0.2353 | 0.8846 | 0.9020 | 0.8932 | 0.2667 | 0.1481 | 0.1905 | 0.0 | 0.0 | 0.0 | 0.4129 | 0.7090 | | No log | 3.0 | 381 | 0.2315 | 0.5909 | 0.6789 | 0.6318 | 0.9259 | 0.5587 | 0.6872 | 0.6163 | 0.05 | 0.0312 | 0.0385 | 0.8364 | 0.9020 | 0.8679 | 0.1389 | 0.1724 | 0.1538 | 0.0 | 0.0 | 0.0 | 0.7568 | 0.8279 | 0.7907 | 0.5 | 0.0312 | 0.0588 | 0.9194 | 0.9557 | 0.9372 | 0.5417 | 0.4483 | 0.4906 | 0.0 | 0.0 | 0.0 | 0.5747 | 0.6892 | 0.6268 | 0.3684 | 0.2414 | 0.2917 | 0.8732 | 0.9118 | 0.8921 | 0.3043 | 0.2593 | 0.2800 | 0.0 | 0.0 | 0.0 | 0.4368 | 0.7182 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0