transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:google-bert/bert-base-cased base_model:finetune:google-bert/bert-base-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: google-bert/bert-base-cased
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
widget:
- text: Patient X presented with symptoms of persistent cough, fever, and fatigue,
    suggestive of a respiratory infection. Upon examination, a chest X-ray revealed
    infiltrates consistent with pneumonia. Treatment was initiated with antibiotics.
    Additionally, the patient reported a history of hypertension and was prescribed
    medication for blood pressure management. During follow-up visits, the patient
    complained of worsening shortness of breath and chest pain. Further investigations
    led to a diagnosis of congestive heart failure. Treatment was adjusted accordingly,
    including diuretics and ACE inhibitors. The patient's EHR also documents previous
    episodes of asthma exacerbations, for which bronchodilators were prescribed.
- text: Patient Y presented with abdominal pain, bloating, and diarrhea, indicative
    of gastrointestinal distress. Laboratory tests revealed elevated liver enzymes
    and bilirubin levels, suggesting a hepatobiliary disorder. Further imaging studies
    confirmed the presence of gallstones and evidence of cholecystitis. The patient
    underwent a laparoscopic cholecystectomy for gallbladder removal. Additionally,
    the EHR notes a history of type 2 diabetes mellitus, managed with oral hypoglycemic
    agents and lifestyle modifications.
- text: Patient Z visited the emergency department complaining of severe headache,
    photophobia, and nausea. Neurological examination revealed signs of meningeal
    irritation, prompting suspicion of meningitis. Lumbar puncture confirmed the diagnosis,
    with cerebrospinal fluid analysis showing elevated white blood cell count and
    protein levels. The patient was started on intravenous antibiotics and supportive
    therapy. The EHR also documents a past medical history of rheumatoid arthritis,
    for which the patient receives disease-modifying antirheumatic drugs (DMARDs).
- text: Patient W presented with joint pain, swelling, and morning stiffness affecting
    multiple joints, suggestive of inflammatory arthritis. Physical examination and
    laboratory tests supported the diagnosis of rheumatoid arthritis, with positive
    rheumatoid factor and anti-cyclic citrullinated peptide (anti-CCP) antibodies.
    Treatment was initiated with disease-modifying antirheumatic drugs (DMARDs) and
    corticosteroids to alleviate symptoms and prevent joint damage. Additionally,
    the EHR documents a history of asthma, managed with inhaled corticosteroids and
    bronchodilators.
- text: Patient V sought medical attention for recurrent episodes of chest pain, dyspnea,
    and palpitations. Cardiac evaluation revealed signs of myocardial ischemia on
    electrocardiogram and elevated cardiac enzymes, indicating acute coronary syndrome.
    The patient underwent coronary angiography, which confirmed significant coronary
    artery disease requiring percutaneous coronary intervention (PCI) with stent placement.
    Furthermore, the EHR notes a past medical history of systemic lupus erythematosus
    (SLE), managed with immunosuppressive therapy and hydroxychloroquine for disease
    control.
model-index:
- name: Disease_Identification_SonatafyAI_BERT_v1
  results: []
---

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should probably proofread and complete it, then remove this comment. -->

# Disease_Identification_SonatafyAI_BERT_v1

This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the [ncbi_disease](https://huggingface.co/datasets/ncbi_disease) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1063
- Precision: 0.8247
- Recall: 0.8729
- F1: 0.8481
- Accuracy: 0.9840

## 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: 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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1208        | 1.0   | 680  | 0.0579          | 0.7339    | 0.8272 | 0.7778 | 0.9816   |
| 0.0438        | 2.0   | 1360 | 0.0616          | 0.7785    | 0.8755 | 0.8242 | 0.9836   |
| 0.0171        | 3.0   | 2040 | 0.0736          | 0.7949    | 0.8272 | 0.8107 | 0.9822   |
| 0.0096        | 4.0   | 2720 | 0.0857          | 0.8173    | 0.8640 | 0.8400 | 0.9844   |
| 0.0069        | 5.0   | 3400 | 0.0986          | 0.8050    | 0.8551 | 0.8293 | 0.9833   |
| 0.0016        | 6.0   | 4080 | 0.1055          | 0.8068    | 0.8755 | 0.8397 | 0.9834   |
| 0.0009        | 7.0   | 4760 | 0.1063          | 0.8247    | 0.8729 | 0.8481 | 0.9840   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
