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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Medical-NER-finetuned-ner
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.
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
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