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--- |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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tags: |
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- generated_from_trainer |
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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: clinical-ner |
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results: [] |
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widget: |
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- text: "63 year old woman with history of CAD presented to ER" |
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example_title: "Example-1" |
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- text: "63 year old woman diagnosed with CAD" |
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example_title: "Example-2" |
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- text: "A 48 year-old female presented with vaginal bleeding and abnormal Pap smears. |
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Upon diagnosis of invasive non-keratinizing SCC of the cervix, she underwent a radical hysterectomy with salpingo-oophorectomy which demonstrated positive spread to the pelvic lymph nodes and the parametrium. |
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Pathological examination revealed that the tumour also extensively involved the lower uterine segment." |
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example_title: "example 3" |
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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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# clinical-ner |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the Medical dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8058 |
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- Precision: 0.5786 |
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- Recall: 0.6683 |
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- F1: 0.6202 |
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- Accuracy: 0.8099 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 45 |
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- mixed_precision_training: Native AMP |
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### Python Code: |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("token-classification", model="blaze999/clinical-ner", aggregation_strategy='simple') |
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result = pipe('45 year old woman diagnosed with CAD') |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("blaze999/clinical-ner") |
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model = AutoModelForTokenClassification.from_pretrained("blaze999/clinical-ner") |
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``` |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 5 | 4.7713 | 0.0002 | 0.001 | 0.0004 | 0.0182 | |
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| No log | 2.0 | 10 | 4.2264 | 0.0002 | 0.0008 | 0.0003 | 0.1481 | |
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| No log | 3.0 | 15 | 3.6238 | 0.0004 | 0.0003 | 0.0003 | 0.4575 | |
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| 4.2324 | 4.0 | 20 | 2.8751 | 0.0 | 0.0 | 0.0 | 0.4734 | |
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| 4.2324 | 5.0 | 25 | 2.4550 | 0.0306 | 0.0008 | 0.0015 | 0.4739 | |
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| 4.2324 | 6.0 | 30 | 2.1920 | 0.0722 | 0.0437 | 0.0545 | 0.5007 | |
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| 4.2324 | 7.0 | 35 | 1.9841 | 0.1137 | 0.1087 | 0.1112 | 0.5392 | |
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| 2.3521 | 8.0 | 40 | 1.8153 | 0.1956 | 0.189 | 0.1922 | 0.5829 | |
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| 2.3521 | 9.0 | 45 | 1.6504 | 0.2539 | 0.2617 | 0.2578 | 0.6218 | |
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| 2.3521 | 10.0 | 50 | 1.4801 | 0.3607 | 0.3787 | 0.3695 | 0.6782 | |
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| 2.3521 | 11.0 | 55 | 1.3417 | 0.3933 | 0.433 | 0.4122 | 0.7021 | |
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| 1.6185 | 12.0 | 60 | 1.2333 | 0.4054 | 0.4795 | 0.4394 | 0.7203 | |
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| 1.6185 | 13.0 | 65 | 1.1490 | 0.4307 | 0.5125 | 0.4680 | 0.7347 | |
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| 1.6185 | 14.0 | 70 | 1.0750 | 0.4412 | 0.543 | 0.4868 | 0.7503 | |
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| 1.6185 | 15.0 | 75 | 1.0179 | 0.4816 | 0.5637 | 0.5195 | 0.7619 | |
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| 1.1438 | 16.0 | 80 | 0.9774 | 0.4899 | 0.578 | 0.5303 | 0.7689 | |
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| 1.1438 | 17.0 | 85 | 0.9475 | 0.5005 | 0.5955 | 0.5439 | 0.7743 | |
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| 1.1438 | 18.0 | 90 | 0.9192 | 0.5082 | 0.6078 | 0.5535 | 0.7788 | |
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| 1.1438 | 19.0 | 95 | 0.8923 | 0.5151 | 0.6085 | 0.5579 | 0.7828 | |
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| 0.8863 | 20.0 | 100 | 0.8691 | 0.5263 | 0.6242 | 0.5711 | 0.7882 | |
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| 0.8863 | 21.0 | 105 | 0.8604 | 0.5358 | 0.6342 | 0.5809 | 0.7907 | |
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| 0.8863 | 22.0 | 110 | 0.8474 | 0.5429 | 0.641 | 0.5879 | 0.7946 | |
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| 0.8863 | 23.0 | 115 | 0.8362 | 0.5493 | 0.644 | 0.5929 | 0.7969 | |
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| 0.7361 | 24.0 | 120 | 0.8284 | 0.5531 | 0.6512 | 0.5982 | 0.7994 | |
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| 0.7361 | 25.0 | 125 | 0.8325 | 0.5555 | 0.6565 | 0.6018 | 0.8001 | |
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| 0.7361 | 26.0 | 130 | 0.8156 | 0.5686 | 0.6562 | 0.6093 | 0.8035 | |
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| 0.7361 | 27.0 | 135 | 0.8177 | 0.5634 | 0.6625 | 0.6089 | 0.8039 | |
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| 0.6449 | 28.0 | 140 | 0.8152 | 0.5643 | 0.6567 | 0.6070 | 0.8036 | |
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| 0.6449 | 29.0 | 145 | 0.8109 | 0.5700 | 0.6647 | 0.6137 | 0.8066 | |
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| 0.6449 | 30.0 | 150 | 0.8164 | 0.5697 | 0.6653 | 0.6138 | 0.8055 | |
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| 0.6449 | 31.0 | 155 | 0.8081 | 0.5742 | 0.6627 | 0.6153 | 0.8085 | |
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| 0.5912 | 32.0 | 160 | 0.8130 | 0.5687 | 0.6677 | 0.6142 | 0.8067 | |
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| 0.5912 | 33.0 | 165 | 0.8048 | 0.5779 | 0.6637 | 0.6179 | 0.8089 | |
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| 0.5912 | 34.0 | 170 | 0.8096 | 0.5760 | 0.669 | 0.6190 | 0.8085 | |
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| 0.5912 | 35.0 | 175 | 0.8063 | 0.5790 | 0.6677 | 0.6202 | 0.8091 | |
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| 0.5625 | 36.0 | 180 | 0.8052 | 0.5755 | 0.6673 | 0.6180 | 0.8094 | |
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| 0.5625 | 37.0 | 185 | 0.8063 | 0.5753 | 0.6667 | 0.6176 | 0.8093 | |
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| 0.5625 | 38.0 | 190 | 0.8055 | 0.5783 | 0.6677 | 0.6198 | 0.8103 | |
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| 0.5625 | 39.0 | 195 | 0.8052 | 0.5792 | 0.668 | 0.6205 | 0.8099 | |
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| 0.5442 | 40.0 | 200 | 0.8052 | 0.5798 | 0.6685 | 0.6210 | 0.8097 | |
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| 0.5442 | 41.0 | 205 | 0.8055 | 0.5784 | 0.6683 | 0.6201 | 0.8098 | |
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| 0.5442 | 42.0 | 210 | 0.8056 | 0.5789 | 0.6685 | 0.6205 | 0.8100 | |
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| 0.5442 | 43.0 | 215 | 0.8057 | 0.5786 | 0.6683 | 0.6202 | 0.8100 | |
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| 0.5397 | 44.0 | 220 | 0.8057 | 0.5786 | 0.6683 | 0.6202 | 0.8099 | |
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| 0.5397 | 45.0 | 225 | 0.8058 | 0.5786 | 0.6683 | 0.6202 | 0.8099 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
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