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Medical-NER-finetuned-ner

Token Classification
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deberta-v2
Generated from Trainer
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README.md CHANGED
@@ -5,9 +5,36 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - maccrobat_biomedical_ner
 
 
 
 
 
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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
@@ -16,6 +43,12 @@ 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 the maccrobat_biomedical_ner dataset.
 
 
 
 
 
 
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  ## Model description
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@@ -34,7 +67,7 @@ More information needed
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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: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -42,6 +75,42 @@ The following hyperparameters were used during training:
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  - lr_scheduler_type: linear
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  - num_epochs: 30
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  ### Framework versions
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  - Transformers 4.39.3
 
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  - generated_from_trainer
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  datasets:
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  - maccrobat_biomedical_ner
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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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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: maccrobat_biomedical_ner
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+ type: maccrobat_biomedical_ner
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.842486314674201
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+ - name: Recall
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+ type: recall
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+ value: 0.8537938439513243
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+ - name: F1
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+ type: f1
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+ value: 0.8481023908985867
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9046288534972525
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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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  # 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 the maccrobat_biomedical_ner dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5635
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+ - Precision: 0.8425
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+ - Recall: 0.8538
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+ - F1: 0.8481
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+ - Accuracy: 0.9046
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 8.26814930103799e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  - lr_scheduler_type: linear
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  - num_epochs: 30
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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 | 20 | 0.3925 | 0.8364 | 0.8307 | 0.8335 | 0.8912 |
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+ | No log | 2.0 | 40 | 0.3671 | 0.8266 | 0.8529 | 0.8395 | 0.8954 |
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+ | No log | 3.0 | 60 | 0.4077 | 0.8073 | 0.8388 | 0.8227 | 0.8843 |
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+ | No log | 4.0 | 80 | 0.3630 | 0.8531 | 0.8463 | 0.8497 | 0.9045 |
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+ | No log | 5.0 | 100 | 0.3717 | 0.8413 | 0.8484 | 0.8449 | 0.9017 |
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+ | No log | 6.0 | 120 | 0.3721 | 0.8433 | 0.8425 | 0.8429 | 0.9015 |
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+ | No log | 7.0 | 140 | 0.3679 | 0.8553 | 0.8529 | 0.8541 | 0.9069 |
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+ | No log | 8.0 | 160 | 0.3840 | 0.8394 | 0.8504 | 0.8449 | 0.9012 |
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+ | No log | 9.0 | 180 | 0.4124 | 0.8430 | 0.8520 | 0.8475 | 0.9040 |
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+ | No log | 10.0 | 200 | 0.4328 | 0.8358 | 0.8450 | 0.8404 | 0.9004 |
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+ | No log | 11.0 | 220 | 0.4395 | 0.8395 | 0.8552 | 0.8473 | 0.9033 |
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+ | No log | 12.0 | 240 | 0.4490 | 0.8399 | 0.8490 | 0.8444 | 0.9011 |
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+ | No log | 13.0 | 260 | 0.4592 | 0.8411 | 0.8497 | 0.8454 | 0.9027 |
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+ | No log | 14.0 | 280 | 0.4623 | 0.8435 | 0.8525 | 0.8480 | 0.9047 |
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+ | No log | 15.0 | 300 | 0.4858 | 0.8416 | 0.8540 | 0.8478 | 0.9040 |
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+ | No log | 16.0 | 320 | 0.4986 | 0.8393 | 0.8499 | 0.8446 | 0.9019 |
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+ | No log | 17.0 | 340 | 0.5152 | 0.8367 | 0.8474 | 0.8420 | 0.9012 |
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+ | No log | 18.0 | 360 | 0.5138 | 0.8474 | 0.8508 | 0.8491 | 0.9055 |
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+ | No log | 19.0 | 380 | 0.5414 | 0.8384 | 0.8488 | 0.8436 | 0.9015 |
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+ | No log | 20.0 | 400 | 0.5483 | 0.8401 | 0.8508 | 0.8454 | 0.9029 |
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+ | No log | 21.0 | 420 | 0.5465 | 0.8386 | 0.8454 | 0.8420 | 0.9008 |
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+ | No log | 22.0 | 440 | 0.5463 | 0.8410 | 0.8520 | 0.8465 | 0.9034 |
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+ | No log | 23.0 | 460 | 0.5434 | 0.8441 | 0.8545 | 0.8493 | 0.9053 |
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+ | No log | 24.0 | 480 | 0.5516 | 0.8439 | 0.8493 | 0.8466 | 0.9041 |
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+ | 0.1398 | 25.0 | 500 | 0.5618 | 0.8398 | 0.8518 | 0.8458 | 0.9032 |
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+ | 0.1398 | 26.0 | 520 | 0.5583 | 0.8428 | 0.8550 | 0.8489 | 0.9046 |
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+ | 0.1398 | 27.0 | 540 | 0.5632 | 0.8427 | 0.8524 | 0.8475 | 0.9042 |
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+ | 0.1398 | 28.0 | 560 | 0.5674 | 0.8393 | 0.8522 | 0.8457 | 0.9029 |
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+ | 0.1398 | 29.0 | 580 | 0.5625 | 0.8429 | 0.8527 | 0.8478 | 0.9046 |
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+ | 0.1398 | 30.0 | 600 | 0.5635 | 0.8425 | 0.8538 | 0.8481 | 0.9046 |
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  ### Framework versions
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  - Transformers 4.39.3
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