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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
model = AutoModelForTokenClassification.from_pretrained('kalexa2/fabner-ner')
tokenizer = AutoTokenizer.from_pretrained('kalexa2/fabner-ner')
token_classifier = pipeline('ner',
model=model,
tokenizer=tokenizer,
aggregation_strategy="simple" )
r = token_classifier("Here, we report in-situ characterization of melt-flow dynamics in every location of the entire melt pool in laser metal additive manufacturing by populous and uniformly dispersed micro-tracers through in-situ high-resolution synchrotron x-ray imaging .")
for entity in r:
print(entity)
