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)