German NER Albert Model For Token Classification

This is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.

Model Specifications

Usage Specifications

This model is trained on Tensorflow version and is compatible with the 'ner' pipeline of huggingface.

from transformers import AutoTokenizer,TFAutoModelForTokenClassification
from transformers import pipeline

model=TFAutoModelForTokenClassification.from_pretrained('abhilash1910/albert-german-ner')
tokenizer=AutoTokenizer.from_pretrained('abhilash1910/albert-german-ner')
ner_model = pipeline('ner', model=model, tokenizer=tokenizer)
seq='Berlin ist die Hauptstadt von Deutschland'
ner_model(seq)

The Tensorflow version of Albert is used for training the model and the output for the above mentioned segment is as follows:

[{'entity': 'B-PERderiv',
  'index': 1,
  'score': 0.09580112248659134,
  'word': '▁berlin'},
 {'entity': 'B-ORGpart',
  'index': 2,
  'score': 0.08364498615264893,
  'word': '▁is'},
 {'entity': 'B-LOCderiv',
  'index': 3,
  'score': 0.07593920826911926,
  'word': 't'},
 {'entity': 'B-PERderiv',
  'index': 4,
  'score': 0.09574996680021286,
  'word': '▁die'},
 {'entity': 'B-LOCderiv',
  'index': 5,
  'score': 0.07097965478897095,
  'word': '▁'},
 {'entity': 'B-PERderiv',
  'index': 6,
  'score': 0.07122448086738586,
  'word': 'haupt'},
 {'entity': 'B-PERderiv',
  'index': 7,
  'score': 0.12397754937410355,
  'word': 'stadt'},
 {'entity': 'I-OTHderiv',
  'index': 8,
  'score': 0.0818650871515274,
  'word': '▁von'},
 {'entity': 'I-LOCderiv',
  'index': 9,
  'score': 0.08271490037441254,
  'word': '▁'},
 {'entity': 'B-LOCderiv',
  'index': 10,
  'score': 0.08616268634796143,
  'word': 'deutschland'}]

Resources

For all resources , please look into huggingface.

language: