clinical-ner
This model is a fine-tuned version of microsoft/deberta-v3-base on the Medical dataset. It achieves the following results on the evaluation set:
- Loss: 0.8058
- Precision: 0.5786
- Recall: 0.6683
- F1: 0.6202
- Accuracy: 0.8099
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 45
- mixed_precision_training: Native AMP
Python Code:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="blaze999/clinical-ner", aggregation_strategy='simple')
result = pipe('45 year old woman diagnosed with CAD')
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("blaze999/clinical-ner")
model = AutoModelForTokenClassification.from_pretrained("blaze999/clinical-ner")
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 5 | 4.7713 | 0.0002 | 0.001 | 0.0004 | 0.0182 |
No log | 2.0 | 10 | 4.2264 | 0.0002 | 0.0008 | 0.0003 | 0.1481 |
No log | 3.0 | 15 | 3.6238 | 0.0004 | 0.0003 | 0.0003 | 0.4575 |
4.2324 | 4.0 | 20 | 2.8751 | 0.0 | 0.0 | 0.0 | 0.4734 |
4.2324 | 5.0 | 25 | 2.4550 | 0.0306 | 0.0008 | 0.0015 | 0.4739 |
4.2324 | 6.0 | 30 | 2.1920 | 0.0722 | 0.0437 | 0.0545 | 0.5007 |
4.2324 | 7.0 | 35 | 1.9841 | 0.1137 | 0.1087 | 0.1112 | 0.5392 |
2.3521 | 8.0 | 40 | 1.8153 | 0.1956 | 0.189 | 0.1922 | 0.5829 |
2.3521 | 9.0 | 45 | 1.6504 | 0.2539 | 0.2617 | 0.2578 | 0.6218 |
2.3521 | 10.0 | 50 | 1.4801 | 0.3607 | 0.3787 | 0.3695 | 0.6782 |
2.3521 | 11.0 | 55 | 1.3417 | 0.3933 | 0.433 | 0.4122 | 0.7021 |
1.6185 | 12.0 | 60 | 1.2333 | 0.4054 | 0.4795 | 0.4394 | 0.7203 |
1.6185 | 13.0 | 65 | 1.1490 | 0.4307 | 0.5125 | 0.4680 | 0.7347 |
1.6185 | 14.0 | 70 | 1.0750 | 0.4412 | 0.543 | 0.4868 | 0.7503 |
1.6185 | 15.0 | 75 | 1.0179 | 0.4816 | 0.5637 | 0.5195 | 0.7619 |
1.1438 | 16.0 | 80 | 0.9774 | 0.4899 | 0.578 | 0.5303 | 0.7689 |
1.1438 | 17.0 | 85 | 0.9475 | 0.5005 | 0.5955 | 0.5439 | 0.7743 |
1.1438 | 18.0 | 90 | 0.9192 | 0.5082 | 0.6078 | 0.5535 | 0.7788 |
1.1438 | 19.0 | 95 | 0.8923 | 0.5151 | 0.6085 | 0.5579 | 0.7828 |
0.8863 | 20.0 | 100 | 0.8691 | 0.5263 | 0.6242 | 0.5711 | 0.7882 |
0.8863 | 21.0 | 105 | 0.8604 | 0.5358 | 0.6342 | 0.5809 | 0.7907 |
0.8863 | 22.0 | 110 | 0.8474 | 0.5429 | 0.641 | 0.5879 | 0.7946 |
0.8863 | 23.0 | 115 | 0.8362 | 0.5493 | 0.644 | 0.5929 | 0.7969 |
0.7361 | 24.0 | 120 | 0.8284 | 0.5531 | 0.6512 | 0.5982 | 0.7994 |
0.7361 | 25.0 | 125 | 0.8325 | 0.5555 | 0.6565 | 0.6018 | 0.8001 |
0.7361 | 26.0 | 130 | 0.8156 | 0.5686 | 0.6562 | 0.6093 | 0.8035 |
0.7361 | 27.0 | 135 | 0.8177 | 0.5634 | 0.6625 | 0.6089 | 0.8039 |
0.6449 | 28.0 | 140 | 0.8152 | 0.5643 | 0.6567 | 0.6070 | 0.8036 |
0.6449 | 29.0 | 145 | 0.8109 | 0.5700 | 0.6647 | 0.6137 | 0.8066 |
0.6449 | 30.0 | 150 | 0.8164 | 0.5697 | 0.6653 | 0.6138 | 0.8055 |
0.6449 | 31.0 | 155 | 0.8081 | 0.5742 | 0.6627 | 0.6153 | 0.8085 |
0.5912 | 32.0 | 160 | 0.8130 | 0.5687 | 0.6677 | 0.6142 | 0.8067 |
0.5912 | 33.0 | 165 | 0.8048 | 0.5779 | 0.6637 | 0.6179 | 0.8089 |
0.5912 | 34.0 | 170 | 0.8096 | 0.5760 | 0.669 | 0.6190 | 0.8085 |
0.5912 | 35.0 | 175 | 0.8063 | 0.5790 | 0.6677 | 0.6202 | 0.8091 |
0.5625 | 36.0 | 180 | 0.8052 | 0.5755 | 0.6673 | 0.6180 | 0.8094 |
0.5625 | 37.0 | 185 | 0.8063 | 0.5753 | 0.6667 | 0.6176 | 0.8093 |
0.5625 | 38.0 | 190 | 0.8055 | 0.5783 | 0.6677 | 0.6198 | 0.8103 |
0.5625 | 39.0 | 195 | 0.8052 | 0.5792 | 0.668 | 0.6205 | 0.8099 |
0.5442 | 40.0 | 200 | 0.8052 | 0.5798 | 0.6685 | 0.6210 | 0.8097 |
0.5442 | 41.0 | 205 | 0.8055 | 0.5784 | 0.6683 | 0.6201 | 0.8098 |
0.5442 | 42.0 | 210 | 0.8056 | 0.5789 | 0.6685 | 0.6205 | 0.8100 |
0.5442 | 43.0 | 215 | 0.8057 | 0.5786 | 0.6683 | 0.6202 | 0.8100 |
0.5397 | 44.0 | 220 | 0.8057 | 0.5786 | 0.6683 | 0.6202 | 0.8099 |
0.5397 | 45.0 | 225 | 0.8058 | 0.5786 | 0.6683 | 0.6202 | 0.8099 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
- Downloads last month
- 5
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for blaze999/clinical-ner
Base model
microsoft/deberta-v3-base
Finetuned
this model