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README.md
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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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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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inference: false
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license: mit
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datasets:
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- conll2003
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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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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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https://github.com/ovbystrova/InstructionNER
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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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```python
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from instruction_ner.model import Model
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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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options = ["LOC", "PER", "ORG", "MISC"]
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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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text = "any asian cuisine around"
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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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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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>>> [(4, 9, 'Cuisine'), (18, 24, 'Location')]
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```
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