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ner_base_model
NahedAbdelgaber/ner_base_model
ner_base_model is a token classification model from NahedAbdelgaber. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
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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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Oct 18, 2022
- Updated
- Nov 10, 2022
- SHA
- 77377d66
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Datasets
Model card
From the Hugging Face model README
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)
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Languages
- en
- Created
- Oct 18, 2022
- Updated
- Nov 10, 2022