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  ---
 
 
 
 
 
 
 
 
 
 
 
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  license: mit
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - en
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+
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+ tags:
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+ - pytorch
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+ - ner
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+ - text generation
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+ - seq2seq
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+
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+ inference: false
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+
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  license: mit
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+
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+ datasets:
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+ - conll2003
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+
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+ metrics:
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+ - f1
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  ---
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+ # t5-base-qa-ner-conll
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+
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+ Unofficial implementation of [InstructionNER](https://arxiv.org/pdf/2203.03903v1.pdf).
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+ t5-base model tuned on conll2003 dataset.
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+
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+ https://github.com/ovbystrova/InstructionNER
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+
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+ ## Inference
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+ ```shell
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+ git clone https://github.com/ovbystrova/InstructionNER
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+ cd InstructionNER
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+ ```
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+
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+ ```python
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+ from instruction_ner.model import Model
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+
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+ model = Model(
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+ model_path_or_name="olgaduchovny/t5-base-ner-mit-restaurant",
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+ tokenizer_path_or_name="olgaduchovny/t5-base-mit-restaurant"
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+ )
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+
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+ options = ["LOC", "PER", "ORG", "MISC"]
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+
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+ instruction = "please extract entities and their types from the input sentence, " \
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+ "all entity types are in options"
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+
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+ text = "any asian cuisine around"
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+
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+ generation_kwargs = {
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+ "num_beams": 2,
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+ "max_length": 128
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+ }
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+
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+ pred_spans = model.predict(
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+ text=text,
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+ generation_kwargs=generation_kwargs,
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+ instruction=instruction,
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+ options=options
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+ )
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+
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+ >>> [(4, 9, 'Cuisine'), (18, 24, 'Location')]
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+ ```