transformers tensorboard safetensors bert token-classification generated_from_trainer dataset:transformer_dataset_ner_kaggle base_model:google-bert/bert-base-cased base_model:finetune:google-bert/bert-base-cased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-cased
datasets:
- transformer_dataset_ner_kaggle
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: huner_disease
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: transformer_dataset_ner_kaggle
      type: transformer_dataset_ner_kaggle
      config: ncbi_disease
      split: validation
      args: ncbi_disease
    metrics:
    - type: precision
      value: 0.7905582615211689
      name: Precision
    - type: recall
      value: 0.8222915042868277
      name: Recall
    - type: f1
      value: 0.8061127029608404
      name: F1
    - type: accuracy
      value: 0.9795934778779362
      name: Accuracy
---

<!-- 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. -->

# huner_disease

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the transformer_dataset_ner_kaggle dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2260
- Precision: 0.7906
- Recall: 0.8223
- F1: 0.8061
- Accuracy: 0.9796

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0651        | 1.0   | 1834  | 0.0703          | 0.6823    | 0.7880 | 0.7314 | 0.9767   |
| 0.0459        | 2.0   | 3668  | 0.0712          | 0.7470    | 0.7617 | 0.7543 | 0.9781   |
| 0.03          | 3.0   | 5502  | 0.0903          | 0.7278    | 0.8137 | 0.7684 | 0.9779   |
| 0.0177        | 4.0   | 7336  | 0.0915          | 0.7529    | 0.8055 | 0.7783 | 0.9791   |
| 0.0139        | 5.0   | 9170  | 0.1088          | 0.7346    | 0.8207 | 0.7753 | 0.9777   |
| 0.01          | 6.0   | 11004 | 0.1196          | 0.7283    | 0.8207 | 0.7718 | 0.9772   |
| 0.007         | 7.0   | 12838 | 0.1175          | 0.7615    | 0.7938 | 0.7773 | 0.9787   |
| 0.0055        | 8.0   | 14672 | 0.1488          | 0.7452    | 0.8237 | 0.7825 | 0.9783   |
| 0.0049        | 9.0   | 16506 | 0.1351          | 0.7704    | 0.8125 | 0.7909 | 0.9795   |
| 0.0042        | 10.0  | 18340 | 0.1617          | 0.7491    | 0.8184 | 0.7822 | 0.9782   |
| 0.0035        | 11.0  | 20174 | 0.1453          | 0.7557    | 0.8009 | 0.7776 | 0.9785   |
| 0.0036        | 12.0  | 22008 | 0.1662          | 0.7554    | 0.8198 | 0.7863 | 0.9777   |
| 0.0027        | 13.0  | 23842 | 0.1621          | 0.7781    | 0.8075 | 0.7925 | 0.9790   |
| 0.0027        | 14.0  | 25676 | 0.1599          | 0.7519    | 0.8110 | 0.7804 | 0.9776   |
| 0.0027        | 15.0  | 27510 | 0.1633          | 0.7710    | 0.8127 | 0.7913 | 0.9785   |
| 0.0027        | 16.0  | 29344 | 0.1674          | 0.7588    | 0.8129 | 0.7849 | 0.9780   |
| 0.0022        | 17.0  | 31178 | 0.1670          | 0.7652    | 0.8168 | 0.7902 | 0.9781   |
| 0.0021        | 18.0  | 33012 | 0.1586          | 0.7734    | 0.8159 | 0.7940 | 0.9790   |
| 0.002         | 19.0  | 34846 | 0.1650          | 0.7787    | 0.8172 | 0.7975 | 0.9795   |
| 0.0018        | 20.0  | 36680 | 0.1642          | 0.7697    | 0.8048 | 0.7868 | 0.9793   |
| 0.0017        | 21.0  | 38514 | 0.1874          | 0.7743    | 0.8176 | 0.7954 | 0.9784   |
| 0.0015        | 22.0  | 40348 | 0.1598          | 0.7647    | 0.8227 | 0.7926 | 0.9785   |
| 0.0012        | 23.0  | 42182 | 0.1819          | 0.7958    | 0.7997 | 0.7977 | 0.9793   |
| 0.0016        | 24.0  | 44016 | 0.1679          | 0.7960    | 0.8073 | 0.8016 | 0.9794   |
| 0.0013        | 25.0  | 45850 | 0.1659          | 0.7662    | 0.8147 | 0.7897 | 0.9785   |
| 0.001         | 26.0  | 47684 | 0.1774          | 0.7732    | 0.8217 | 0.7967 | 0.9789   |
| 0.0016        | 27.0  | 49518 | 0.1622          | 0.7767    | 0.8131 | 0.7945 | 0.9789   |
| 0.0007        | 28.0  | 51352 | 0.1958          | 0.7642    | 0.8223 | 0.7922 | 0.9783   |
| 0.0009        | 29.0  | 53186 | 0.1861          | 0.7764    | 0.8223 | 0.7987 | 0.9790   |
| 0.0012        | 30.0  | 55020 | 0.1917          | 0.7528    | 0.8252 | 0.7873 | 0.9774   |
| 0.0005        | 31.0  | 56854 | 0.1952          | 0.7833    | 0.8106 | 0.7967 | 0.9792   |
| 0.0009        | 32.0  | 58688 | 0.1910          | 0.7801    | 0.8149 | 0.7971 | 0.9791   |
| 0.0008        | 33.0  | 60522 | 0.1931          | 0.7737    | 0.8180 | 0.7952 | 0.9790   |
| 0.0006        | 34.0  | 62356 | 0.1902          | 0.7730    | 0.8176 | 0.7947 | 0.9788   |
| 0.0008        | 35.0  | 64190 | 0.1904          | 0.7799    | 0.8211 | 0.8    | 0.9791   |
| 0.0006        | 36.0  | 66024 | 0.1951          | 0.7844    | 0.8153 | 0.7995 | 0.9795   |
| 0.0008        | 37.0  | 67858 | 0.1943          | 0.7749    | 0.8256 | 0.7994 | 0.9791   |
| 0.0007        | 38.0  | 69692 | 0.2051          | 0.7796    | 0.8248 | 0.8016 | 0.9791   |
| 0.0004        | 39.0  | 71526 | 0.2108          | 0.7796    | 0.8223 | 0.8004 | 0.9792   |
| 0.0004        | 40.0  | 73360 | 0.2135          | 0.7788    | 0.8254 | 0.8014 | 0.9792   |
| 0.0004        | 41.0  | 75194 | 0.2028          | 0.7908    | 0.8176 | 0.8040 | 0.9798   |
| 0.0006        | 42.0  | 77028 | 0.2058          | 0.7855    | 0.8215 | 0.8031 | 0.9796   |
| 0.0005        | 43.0  | 78862 | 0.2109          | 0.7860    | 0.8254 | 0.8052 | 0.9793   |
| 0.0004        | 44.0  | 80696 | 0.2175          | 0.7784    | 0.8287 | 0.8028 | 0.9791   |
| 0.0003        | 45.0  | 82530 | 0.2206          | 0.7904    | 0.8223 | 0.8060 | 0.9795   |
| 0.0003        | 46.0  | 84364 | 0.2198          | 0.7942    | 0.8180 | 0.8059 | 0.9797   |
| 0.0004        | 47.0  | 86198 | 0.2265          | 0.7791    | 0.8233 | 0.8006 | 0.9791   |
| 0.0003        | 48.0  | 88032 | 0.2265          | 0.7825    | 0.8242 | 0.8028 | 0.9793   |
| 0.0004        | 49.0  | 89866 | 0.2260          | 0.7892    | 0.8209 | 0.8048 | 0.9794   |
| 0.0003        | 50.0  | 91700 | 0.2260          | 0.7906    | 0.8223 | 0.8061 | 0.9796   |


# Run the model
```python
from transformers import pipeline

model_checkpoint = "manibt1993/huner_disease"
token_classifier = pipeline(
    "token-classification", model=model_checkpoint, aggregation_strategy="simple"
)
token_classifier("patient has diabtes, anemia, hypertension with ckd which hurts the patient since 6 years. Patient today experience with right leg pain, fever and cough.")
```

### Model output
```python
[{'entity_group': 'Disease',
  'score': 0.69145554,
  'word': 'diabtes',
  'start': 12,
  'end': 19},
 {'entity_group': 'Disease',
  'score': 0.9955915,
  'word': 'anemia',
  'start': 21,
  'end': 27},
 {'entity_group': 'Disease',
  'score': 0.99971104,
  'word': 'hypertension',
  'start': 29,
  'end': 41},
 {'entity_group': 'Disease',
  'score': 0.9249976,
  'word': 'right leg pain',
  'start': 120,
  'end': 134},
 {'entity_group': 'Disease',
  'score': 0.9983512,
  'word': 'fever',
  'start': 136,
  'end': 141},
 {'entity_group': 'Disease',
  'score': 0.99849665,
  'word': 'cough',
  'start': 146,
  'end': 151}]
```

### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.1
