de dataset:bigbio/muchmore license:afl-3.0 region:us
---
datasets:
- bigbio/muchmore
language:
- de
license: afl-3.0
---
---

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Finetuned from model: bert-base-german-cased

### Model Sources

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- **Repository: https://github.com/sitingGZ/bert-sner
- **Paper : [BERT-SNER](https://aclanthology.org/2023.clinicalnlp-1.31/)
- **Demo (Coming soon)

## Uses
    import sys
    sys.path.append('modules')

    import torch
    from transformers import AutoConfig, AutoTokenizer, AutoModelForMaskedLM, EncoderDecoderConfig
    from BERT2span_semantic_disam import BERT2span
    from helpers import load_config, set_seed
    from inference import final_label_results_rescaled

    base_name =  "bert-base-german-cased"
    configs = load_config('configs/step3_gpu_span_semantic_group.yaml')
    tokenizer = AutoTokenizer.from_pretrained(base_name)
    bertMLM = AutoModelForMaskedLM.from_pretrained(base_name)
    bert_sner = BERT2span(configs, bertMLM, tokenizer)

    checkpoint_path = "checkpoints/german_bert_ex4cds_500_semantic_term.ckpt"
    state_dict = torch.load(checkpoint_path, map_location=torch.device('cpu'))
    bert_sner.load_state_dict(state_dict)
    bert_sner.eval()

    suggested_terms = {'Condition': 'Zeichen oder Symptom',
                   'DiagLab': 'Diagnostisch und Laborverfahren',
                    'LabValues': 'Klinisches Attribut',
                     'HealthState': 'Gesunder Zustand',
                     'Measure': 'Quantitatives Konzept',
                     'Medication': 'Pharmakologische Substanz',
                     'Process': 'Physiologische Funktion',
                     'TimeInfo': 'Zeitliches Konzept'}

    words = "Aktuell Infekt mit Nachweis von E Coli und Pseudomonas im TBS- CRP 99mg/dl".split()
    words_list = [words]
    heatmaps, ner_results = final_label_results_rescaled(words_list, tokenizer, bert_sner, suggested_terms, threshold=0.5)

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### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]



## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model



[More Information Needed]

## Training Details

### Training Data

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[More Information Needed]

### Training Procedure 

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#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

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[More Information Needed]

### Results

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#### Summary



## Model Examination [optional]

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[More Information Needed]

