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--- |
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license: mit |
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base_model: Clinical-AI-Apollo/Medical-NER |
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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: Medical-NER-finetuned-ner |
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results: [] |
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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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# Medical-NER-finetuned-ner |
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This model is a fine-tuned version of [Clinical-AI-Apollo/Medical-NER](https://huggingface.co/Clinical-AI-Apollo/Medical-NER) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2045 |
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- Precision: 0.9394 |
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- Recall: 0.9282 |
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- F1: 0.9338 |
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- Accuracy: 0.9296 |
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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-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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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 | 0.37 | 100 | 0.4486 | 0.8318 | 0.8662 | 0.8486 | 0.8331 | |
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| No log | 0.75 | 200 | 0.3747 | 0.8608 | 0.8834 | 0.8720 | 0.8646 | |
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| No log | 1.12 | 300 | 0.3245 | 0.8801 | 0.8932 | 0.8866 | 0.8828 | |
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| No log | 1.49 | 400 | 0.2846 | 0.9128 | 0.9038 | 0.9083 | 0.9028 | |
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| 0.4808 | 1.87 | 500 | 0.2554 | 0.9199 | 0.9067 | 0.9133 | 0.9083 | |
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| 0.4808 | 2.24 | 600 | 0.2480 | 0.9270 | 0.9073 | 0.9171 | 0.9102 | |
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| 0.4808 | 2.61 | 700 | 0.2269 | 0.9271 | 0.9172 | 0.9221 | 0.9171 | |
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| 0.4808 | 2.99 | 800 | 0.2319 | 0.9270 | 0.9089 | 0.9179 | 0.9129 | |
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| 0.4808 | 3.36 | 900 | 0.2303 | 0.9284 | 0.9088 | 0.9185 | 0.9133 | |
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| 0.2633 | 3.73 | 1000 | 0.2246 | 0.9311 | 0.9111 | 0.9210 | 0.9155 | |
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| 0.2633 | 4.1 | 1100 | 0.2120 | 0.9343 | 0.9218 | 0.9280 | 0.9236 | |
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| 0.2633 | 4.48 | 1200 | 0.2111 | 0.9361 | 0.9222 | 0.9291 | 0.9243 | |
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| 0.2633 | 4.85 | 1300 | 0.2152 | 0.9320 | 0.9185 | 0.9252 | 0.9208 | |
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| 0.2633 | 5.22 | 1400 | 0.2068 | 0.9333 | 0.9227 | 0.9280 | 0.9239 | |
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| 0.2218 | 5.6 | 1500 | 0.2070 | 0.9360 | 0.9256 | 0.9308 | 0.9267 | |
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| 0.2218 | 5.97 | 1600 | 0.2045 | 0.9394 | 0.9282 | 0.9338 | 0.9296 | |
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| 0.2218 | 6.34 | 1700 | 0.2020 | 0.9357 | 0.9275 | 0.9316 | 0.9284 | |
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| 0.2218 | 6.72 | 1800 | 0.2054 | 0.9354 | 0.9227 | 0.9290 | 0.9246 | |
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| 0.2218 | 7.09 | 1900 | 0.2053 | 0.9372 | 0.9253 | 0.9312 | 0.9269 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.15.2 |
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