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abhilash1910/albert-german-ner
albert-german-ner is a fill-mask model from abhilash1910. Use it when you need the model to fill a missing word. It is set up for transformers.
This is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.
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From the Hugging Face model README
This is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.
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'}]
For all resources , please look into huggingface.
language: