Create README.md

README.md

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+---
+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)
+```