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bert-base-finetuned-ner-covidmed-v2

Token Classification
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bert
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  1. README.md +112 -0
  2. config.json +71 -0
  3. model.safetensors +3 -0
  4. special_tokens_map.json +7 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +56 -0
  7. training_args.bin +3 -0
  8. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: google-bert/bert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: bert-base-finetuned-ner-covidmed-v2
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+ results: []
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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-base-finetuned-ner-covidmed-v2
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+
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+ This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2063
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+ - Accuracy: 0.9462
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+ - Precision: 0.7743
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+ - Recall: 0.7764
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+ - F1: 0.7662
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+ - Age Precision: 0.8797
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+ - Age Recall: 0.9553
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+ - Age F1-score: 0.9160
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+ - Date Precision: 0.9645
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+ - Date Recall: 0.9867
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+ - Date F1-score: 0.9755
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+ - Gender Precision: 0.9151
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+ - Gender Recall: 0.9329
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+ - Gender F1-score: 0.9239
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+ - Job Precision: 0.4643
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+ - Job Recall: 0.1503
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+ - Job F1-score: 0.2271
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+ - Location Precision: 0.7505
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+ - Location Recall: 0.8372
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+ - Location F1-score: 0.7915
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+ - Name Precision: 0.8225
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+ - Name Recall: 0.7579
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+ - Name F1-score: 0.7889
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+ - Organization Precision: 0.5831
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+ - Organization Recall: 0.6822
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+ - Organization F1-score: 0.6288
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+ - Patient Id Precision: 0.9330
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+ - Patient Id Recall: 0.9800
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+ - Patient Id F1-score: 0.9560
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+ - Symptom And Disease Precision: 0.6264
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+ - Symptom And Disease Recall: 0.6937
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+ - Symptom And Disease F1-score: 0.6583
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+ - Transportation Precision: 0.8042
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+ - Transportation Recall: 0.7876
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+ - Transportation F1-score: 0.7958
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+ - Micro avg Precision: 0.7994
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+ - Micro avg Recall: 0.8551
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+ - Micro avg F1-score: 0.8263
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+ - Macro avg Precision: 0.7743
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+ - Macro avg Recall: 0.7764
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+ - Macro avg F1-score: 0.7662
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+ - Weighted avg Precision: 0.8004
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+ - Weighted avg Recall: 0.8551
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+ - Weighted avg F1-score: 0.8250
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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: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 7
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Age Precision | Age Recall | Age F1-score | Date Precision | Date Recall | Date F1-score | Gender Precision | Gender Recall | Gender F1-score | Job Precision | Job Recall | Job F1-score | Location Precision | Location Recall | Location F1-score | Name Precision | Name Recall | Name F1-score | Organization Precision | Organization Recall | Organization F1-score | Patient Id Precision | Patient Id Recall | Patient Id F1-score | Symptom And Disease Precision | Symptom And Disease Recall | Symptom And Disease F1-score | Transportation Precision | Transportation Recall | Transportation F1-score | Micro avg Precision | Micro avg Recall | Micro avg F1-score | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------------:|:----------:|:------------:|:--------------:|:-----------:|:-------------:|:----------------:|:-------------:|:---------------:|:-------------:|:----------:|:------------:|:------------------:|:---------------:|:-----------------:|:--------------:|:-----------:|:-------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:-----------------:|:-------------------:|:-----------------------------:|:--------------------------:|:----------------------------:|:------------------------:|:---------------------:|:-----------------------:|:-------------------:|:----------------:|:------------------:|:-------------------:|:----------------:|:------------------:|:----------------------:|:-------------------:|:---------------------:|
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+ | No log | 1.0 | 79 | 0.5109 | 0.8608 | 0.6258 | 0.4426 | 0.4738 | 0.9455 | 0.8643 | 0.9031 | 0.9407 | 0.9692 | 0.9547 | 1.0 | 0.4026 | 0.5741 | 0.0 | 0.0 | 0.0 | 0.4126 | 0.6039 | 0.4903 | 0.9479 | 0.2862 | 0.4396 | 0.0620 | 0.0506 | 0.0557 | 0.7840 | 0.9666 | 0.8658 | 0.2288 | 0.0546 | 0.0881 | 0.9362 | 0.2280 | 0.3667 | 0.5747 | 0.6091 | 0.5914 | 0.6258 | 0.4426 | 0.4738 | 0.5763 | 0.6091 | 0.5655 |
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+ | No log | 2.0 | 158 | 0.3149 | 0.9142 | 0.6666 | 0.6617 | 0.6599 | 0.8715 | 0.9210 | 0.8956 | 0.9433 | 0.9752 | 0.9590 | 0.9499 | 0.8615 | 0.9035 | 0.0 | 0.0 | 0.0 | 0.6003 | 0.7613 | 0.6713 | 0.8434 | 0.7453 | 0.7913 | 0.3226 | 0.3774 | 0.3479 | 0.8531 | 0.9791 | 0.9118 | 0.4833 | 0.4208 | 0.4499 | 0.7986 | 0.5751 | 0.6687 | 0.6936 | 0.7676 | 0.7287 | 0.6666 | 0.6617 | 0.6599 | 0.6905 | 0.7676 | 0.7238 |
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+ | No log | 3.0 | 237 | 0.2443 | 0.9324 | 0.7024 | 0.7168 | 0.7082 | 0.8834 | 0.9244 | 0.9034 | 0.9594 | 0.9867 | 0.9729 | 0.9368 | 0.8983 | 0.9171 | 0.0 | 0.0 | 0.0 | 0.6713 | 0.8095 | 0.7340 | 0.8293 | 0.7484 | 0.7868 | 0.4736 | 0.5110 | 0.4916 | 0.9115 | 0.9761 | 0.9427 | 0.5902 | 0.625 | 0.6071 | 0.7688 | 0.6891 | 0.7268 | 0.7539 | 0.8191 | 0.7851 | 0.7024 | 0.7168 | 0.7082 | 0.7491 | 0.8191 | 0.7812 |
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+ | No log | 4.0 | 316 | 0.2329 | 0.9347 | 0.6945 | 0.7329 | 0.7118 | 0.8716 | 0.9450 | 0.9068 | 0.9640 | 0.9867 | 0.9752 | 0.9194 | 0.9134 | 0.9164 | 0.0 | 0.0 | 0.0 | 0.6822 | 0.8325 | 0.7499 | 0.8253 | 0.7579 | 0.7902 | 0.5069 | 0.5227 | 0.5147 | 0.9277 | 0.9786 | 0.9524 | 0.5454 | 0.6822 | 0.6062 | 0.7026 | 0.7098 | 0.7062 | 0.7541 | 0.8367 | 0.7933 | 0.6945 | 0.7329 | 0.7118 | 0.7520 | 0.8367 | 0.7905 |
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+ | No log | 5.0 | 395 | 0.2173 | 0.9419 | 0.7434 | 0.7682 | 0.7441 | 0.8366 | 0.9674 | 0.8972 | 0.9634 | 0.9867 | 0.9749 | 0.8912 | 0.9394 | 0.9146 | 0.4186 | 0.1040 | 0.1667 | 0.7247 | 0.8298 | 0.7737 | 0.8114 | 0.7579 | 0.7837 | 0.5401 | 0.6719 | 0.5988 | 0.9129 | 0.9830 | 0.9467 | 0.5894 | 0.6963 | 0.6384 | 0.7461 | 0.7461 | 0.7461 | 0.7730 | 0.8519 | 0.8106 | 0.7434 | 0.7682 | 0.7441 | 0.7756 | 0.8519 | 0.8096 |
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+ | No log | 6.0 | 474 | 0.2085 | 0.9443 | 0.7635 | 0.7745 | 0.7595 | 0.8719 | 0.9588 | 0.9133 | 0.9640 | 0.9867 | 0.9752 | 0.9153 | 0.9351 | 0.9251 | 0.4340 | 0.1329 | 0.2035 | 0.7369 | 0.8356 | 0.7832 | 0.8225 | 0.7579 | 0.7889 | 0.5739 | 0.6900 | 0.6266 | 0.9282 | 0.9800 | 0.9534 | 0.6085 | 0.6963 | 0.6494 | 0.7801 | 0.7720 | 0.7760 | 0.7887 | 0.8550 | 0.8205 | 0.7635 | 0.7745 | 0.7595 | 0.7907 | 0.8550 | 0.8196 |
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+ | 0.2745 | 7.0 | 553 | 0.2063 | 0.9462 | 0.7743 | 0.7764 | 0.7662 | 0.8797 | 0.9553 | 0.9160 | 0.9645 | 0.9867 | 0.9755 | 0.9151 | 0.9329 | 0.9239 | 0.4643 | 0.1503 | 0.2271 | 0.7505 | 0.8372 | 0.7915 | 0.8225 | 0.7579 | 0.7889 | 0.5831 | 0.6822 | 0.6288 | 0.9330 | 0.9800 | 0.9560 | 0.6264 | 0.6937 | 0.6583 | 0.8042 | 0.7876 | 0.7958 | 0.7994 | 0.8551 | 0.8263 | 0.7743 | 0.7764 | 0.7662 | 0.8004 | 0.8551 | 0.8250 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.51.3
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.6.0
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+ - Tokenizers 0.21.1
config.json ADDED
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