--- license: afl-3.0 datasets: - bigbio/muchmore language: - de --- --- ### Model Description - **Finetuned from model: bert-base-german-cased ### Model Sources [optional] - **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) ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations 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 [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed]