bert-base-NER
Model description
bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves F1 0.61 for the NER task. It has been trained to recognize two types of entities: instrument and satellite.
Specifically, this model is a bert-base-cased model that was fine-tuned on Satellite-Instrument-NER dataset.
Intended uses & limitations
How to use
You can use this model with Transformers pipeline for NER.
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("NahedAbdelgaber/ner_base_model")
model = AutoModelForTokenClassification.from_pretrained("NahedAbdelgaber/ner_base_model")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Centroid Moment Tensor Global Navigation Satellite System GNSS"
ner_results = nlp(example)
print(ner_results)
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