metadata

language: en
datasets:
  - Satellite-Instrument-NER
widget:
  - text: Centroid Moment Tensor Global Navigation Satellite System GNSS
  - text: >-
      This paper describes the latest version of the algorithm MAIAC used for
      processing the MODIS Collection 6 data record.
  - text: >-
      We derive tropospheric column BrO during the ARCTAS and ARCPAC field
      campaigns in spring 2008 using retrievals of total column BrO from the
      satellite UV nadir sensors OMI and GOME - 2 using a radiative transfer
      model and stratospheric column BrO from a photochemical simulation.
license: mit

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)