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german-bert-clinical-ner

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german-bert-clinical-ner
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---
license: afl-3.0
datasets:
- bigbio/muchmore
language:
- de
---
---

### Model Description

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

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

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **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, berst_sner, suggested_terms, threshold=0.5)

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### 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

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

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

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]