metadata

license: mit
base_model: Clinical-AI-Apollo/Medical-NER
tags:
  - generated_from_trainer
datasets:
  - maccrobat_biomedical_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Medical-NER-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: maccrobat_biomedical_ner
          type: maccrobat_biomedical_ner
          config: default
          split: train
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.842486314674201
          - name: Recall
            type: recall
            value: 0.8537938439513243
          - name: F1
            type: f1
            value: 0.8481023908985867
          - name: Accuracy
            type: accuracy
            value: 0.9046288534972525

Medical-NER-finetuned-ner

This model is a fine-tuned version of Clinical-AI-Apollo/Medical-NER on the maccrobat_biomedical_ner dataset. It achieves the following results on the evaluation set:

Loss: 0.5635
Precision: 0.8425
Recall: 0.8538
F1: 0.8481
Accuracy: 0.9046

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: 8.26814930103799e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	20	0.3925	0.8364	0.8307	0.8335	0.8912
No log	2.0	40	0.3671	0.8266	0.8529	0.8395	0.8954
No log	3.0	60	0.4077	0.8073	0.8388	0.8227	0.8843
No log	4.0	80	0.3630	0.8531	0.8463	0.8497	0.9045
No log	5.0	100	0.3717	0.8413	0.8484	0.8449	0.9017
No log	6.0	120	0.3721	0.8433	0.8425	0.8429	0.9015
No log	7.0	140	0.3679	0.8553	0.8529	0.8541	0.9069
No log	8.0	160	0.3840	0.8394	0.8504	0.8449	0.9012
No log	9.0	180	0.4124	0.8430	0.8520	0.8475	0.9040
No log	10.0	200	0.4328	0.8358	0.8450	0.8404	0.9004
No log	11.0	220	0.4395	0.8395	0.8552	0.8473	0.9033
No log	12.0	240	0.4490	0.8399	0.8490	0.8444	0.9011
No log	13.0	260	0.4592	0.8411	0.8497	0.8454	0.9027
No log	14.0	280	0.4623	0.8435	0.8525	0.8480	0.9047
No log	15.0	300	0.4858	0.8416	0.8540	0.8478	0.9040
No log	16.0	320	0.4986	0.8393	0.8499	0.8446	0.9019
No log	17.0	340	0.5152	0.8367	0.8474	0.8420	0.9012
No log	18.0	360	0.5138	0.8474	0.8508	0.8491	0.9055
No log	19.0	380	0.5414	0.8384	0.8488	0.8436	0.9015
No log	20.0	400	0.5483	0.8401	0.8508	0.8454	0.9029
No log	21.0	420	0.5465	0.8386	0.8454	0.8420	0.9008
No log	22.0	440	0.5463	0.8410	0.8520	0.8465	0.9034
No log	23.0	460	0.5434	0.8441	0.8545	0.8493	0.9053
No log	24.0	480	0.5516	0.8439	0.8493	0.8466	0.9041
0.1398	25.0	500	0.5618	0.8398	0.8518	0.8458	0.9032
0.1398	26.0	520	0.5583	0.8428	0.8550	0.8489	0.9046
0.1398	27.0	540	0.5632	0.8427	0.8524	0.8475	0.9042
0.1398	28.0	560	0.5674	0.8393	0.8522	0.8457	0.9029
0.1398	29.0	580	0.5625	0.8429	0.8527	0.8478	0.9046
0.1398	30.0	600	0.5635	0.8425	0.8538	0.8481	0.9046

Framework versions

Transformers 4.39.3
Pytorch 2.2.1
Datasets 2.18.0
Tokenizers 0.15.2