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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 = "Once I visited Sovok in Nizny Novgorod. I had asian wok there. It was the best WOK i ever had"\ |
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"It was cheap but lemonades cost 5 dollars." |
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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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>>> ('sovok is a Restaurant_Name, Nizny Novgorod is a Location, asian wok is a Dish, cheap is a Price, lemonades is a Dish, 5 dollars is a Price.', |
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[(24, 38, 'Location'), |
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(46, 55, 'Dish'), |
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(100, 105, 'Price'), |
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(110, 119, 'Dish'), |
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(125, 134, 'Price')]) |
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``` |
