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--- |
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language: en |
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datasets: |
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- Satellite-Instrument-NER |
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widget: |
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- text: "Centroid Moment Tensor Global Navigation Satellite System GNSS" |
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- text: "This paper describes the latest version of the algorithm MAIAC used for processing the MODIS Collection 6 data record." |
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- 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." |
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license: mit |
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--- |
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# bert-base-NER |
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## Model description |
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**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. |
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Specifically, this model is a *bert-base-cased* model that was fine-tuned on Satellite-Instrument-NER dataset. |
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## Intended uses & limitations |
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#### How to use |
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You can use this model with Transformers *pipeline* for NER. |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("NahedAbdelgaber/ner_base_model") |
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model = AutoModelForTokenClassification.from_pretrained("NahedAbdelgaber/ner_base_model") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Centroid Moment Tensor Global Navigation Satellite System GNSS" |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
