readme: ---
  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.
  
  ```python
  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)
  ```