readme: ---
  language: 
    - en
  
  tags:
  - pytorch
  - ner
  - text generation
  - seq2seq
  
  inference: false
  
  license: mit
  
  datasets:
  - conll2003
  
  metrics:
  - f1
  ---
  # t5-base-qa-ner-conll
  
  Unofficial implementation of [InstructionNER](https://arxiv.org/pdf/2203.03903v1.pdf).
  t5-base model tuned on conll2003 dataset.
  
  https://github.com/ovbystrova/InstructionNER 
  
  ## Inference 
  ```shell
  git clone https://github.com/ovbystrova/InstructionNER 
  cd InstructionNER
  ```
  
  ```python
  from instruction_ner.model import Model
  model = Model(
      model_path_or_name="olgaduchovny/t5-base-ner-mit-movie",
      tokenizer_path_or_name="olgaduchovny/t5-base-ner-mit-movie"
  )
  options = [
          "ACTOR",
          "AWARD",
          "CHARACTER",
          "DIRECTOR",
          "GENRE",
          "OPINION",
          "ORIGIN",
          "PLOT",
          "QUOTE",
          "RELATIONSHIP",
          "SOUNDTRACK",
          "YEAR"
      ]
  instruction = "please extract entities and their types from the input sentence, " \
                "all entity types are in options"
  text = "are there any good romantic comedies out right now"
  generation_kwargs = {
      "num_beams": 2,
      "max_length": 128
  }
  pred_spans = model.predict(
      text=text,
      generation_kwargs=generation_kwargs,
      instruction=instruction,
      options=options
  )
  >>> [(19, 36, 'GENRE'), (41, 50, 'YEAR')]
  ```