Update README.md

README.md

@@ -22,27 +22,27 @@ 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',
+    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',
@@ -51,9 +51,9 @@ language:
                      '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)
+    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. -->