bert-base-token-classification-for-atc-en-uwb-atcc

1.6K

2

1 language

license:apache-2.0

by

Jzuluaga

Other

OTHER

New

2K downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

Unknown

Mobile

Laptop

Server

Quick Summary

This model allow to detect speaker roles and speaker changes based on text.

Code Examples

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Writing your own inference scriptpythontransformers

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")
model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc")


##### Process text sample (from UWB-ATCC)
from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye)


[{'entity_group': 'pilot',
'score': 0.99991554,
'word': 'lining up runway three one csa five bravo b', 'start': 0, 'end': 43
},
{'entity_group': 'atco',
'score': 0.99994576,
'word': 'easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye', 'start': 44, 'end': 126
}]

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.