Update README.md

README.md

@@ -22,6 +22,38 @@ language:
 - **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. -->