
### NER model trained on BERT 

MODEL used for fine tuning is GBERT Large by deepset.ai



## Test
Accuracy: 98 \
F1: 84.1 \
Precision: 82.7 \
Recall: 85.5

## Model inferencing:
```python
!pip install -q transformers
from transformers import pipeline

ner = pipeline(
    "ner",
    model="Sahajtomar/NER_legal_de",
    tokenizer="Sahajtomar/NER_legal_de")
    
 nlp_ner("Für eine Zuständigkeit des Verwaltungsgerichts Berlin nach § 52 Nr. 1 bis 4 VwGO hat der \
         Antragsteller keine Anhaltspunkte vorgetragen .")
         
```

{"language": "de", "tags": ["pytorch", "tf", "bert", "NER"], "datasets": ["legal entity recognition"]}
transformers pytorch tf jax bert token-classification NER de autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #bert #token-classification #NER #de #autotrain_compatible #endpoints_compatible #region-us 


### NER model trained on BERT 

MODEL used for fine tuning is GBERT Large by URL



## Test
Accuracy: 98 \
F1: 84.1 \
Precision: 82.7 \
Recall: 85.5

## Model inferencing:


### NER model trained on BERT 

MODEL used for fine tuning is GBERT Large by URL ## Test
Accuracy: 98 \
F1: 84.1 \
Precision: 82.7 \
Recall: 85.5 ## Model inferencing:
TAGS
#transformers #pytorch #tf #jax #bert #token-classification #NER #de #autotrain_compatible #endpoints_compatible #region-us 
 ### NER model trained on BERT 

MODEL used for fine tuning is GBERT Large by URL ## Test
Accuracy: 98 \
F1: 84.1 \
Precision: 82.7 \
Recall: 85.5 ## Model inferencing:
passage: TAGS
#transformers #pytorch #tf #jax #bert #token-classification #NER #de #autotrain_compatible #endpoints_compatible #region-us 
### NER model trained on BERT 

MODEL used for fine tuning is GBERT Large by URL## Test
Accuracy: 98 \
F1: 84.1 \
Precision: 82.7 \
Recall: 85.5## Model inferencing: