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Medical-NER-finetuned-ner

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+ ---
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+ license: mit
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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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+
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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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+ # Medical-NER-finetuned-ner
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+
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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: 0.3114
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+ - Precision: 0.7903
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+ - Recall: 0.9005
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+ - F1: 0.8418
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+ - Accuracy: 0.9313
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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: 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: cosine
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+ - num_epochs: 20
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+
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+ ### Training results
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+
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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 | 90 | 0.9174 | 0.4239 | 0.3613 | 0.3901 | 0.7448 |
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+ | No log | 2.0 | 180 | 0.6814 | 0.5257 | 0.5521 | 0.5386 | 0.7899 |
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+ | No log | 3.0 | 270 | 0.6262 | 0.5383 | 0.7265 | 0.6184 | 0.7974 |
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+ | No log | 4.0 | 360 | 0.4934 | 0.6065 | 0.7291 | 0.6622 | 0.8434 |
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+ | No log | 5.0 | 450 | 0.5071 | 0.6102 | 0.7946 | 0.6903 | 0.8431 |
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+ | 0.7847 | 6.0 | 540 | 0.4195 | 0.6863 | 0.7963 | 0.7372 | 0.8744 |
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+ | 0.7847 | 7.0 | 630 | 0.4215 | 0.6850 | 0.8386 | 0.7541 | 0.8816 |
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+ | 0.7847 | 8.0 | 720 | 0.3807 | 0.7287 | 0.8440 | 0.7822 | 0.8985 |
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+ | 0.7847 | 9.0 | 810 | 0.3474 | 0.7383 | 0.8479 | 0.7893 | 0.9079 |
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+ | 0.7847 | 10.0 | 900 | 0.3259 | 0.7583 | 0.8679 | 0.8094 | 0.9135 |
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+ | 0.7847 | 11.0 | 990 | 0.3428 | 0.7595 | 0.8812 | 0.8158 | 0.9151 |
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+ | 0.2288 | 12.0 | 1080 | 0.3469 | 0.7568 | 0.8821 | 0.8147 | 0.9147 |
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+ | 0.2288 | 13.0 | 1170 | 0.3211 | 0.7790 | 0.8880 | 0.8299 | 0.9257 |
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+ | 0.2288 | 14.0 | 1260 | 0.3217 | 0.7847 | 0.8909 | 0.8344 | 0.9271 |
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+ | 0.2288 | 15.0 | 1350 | 0.2944 | 0.7952 | 0.8941 | 0.8418 | 0.9321 |
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+ | 0.2288 | 16.0 | 1440 | 0.3244 | 0.7822 | 0.8986 | 0.8364 | 0.9275 |
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+ | 0.1273 | 17.0 | 1530 | 0.3153 | 0.7911 | 0.9012 | 0.8426 | 0.9307 |
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+ | 0.1273 | 18.0 | 1620 | 0.3198 | 0.7874 | 0.9005 | 0.8402 | 0.9298 |
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+ | 0.1273 | 19.0 | 1710 | 0.3109 | 0.7911 | 0.9012 | 0.8426 | 0.9315 |
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+ | 0.1273 | 20.0 | 1800 | 0.3114 | 0.7903 | 0.9005 | 0.8418 | 0.9313 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.30.0
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.13.3