readme: ---
  language:
  - en
  tags:
  - semantic-role-labeling
  - question-answer generation
  - pytorch
  datasets:
  - kleinay/qanom
  ---
  
  # A Seq2Seq model for QANom parsing
  
  This is a `t5-small` pretrained model, fine-tuned on the task of generating QANom QAs. 
  
  "QANom" stands for "QASRL for Nominalizations", which is an adaptation of [QASRL (Question-Answer driven Semantic Role Labeling)](https://qasrl.org) for the nominal predicates domain. See the [QANom paper](https://aclanthology.org/2020.coling-main.274/) for details about the task. The QANom Dataset official site is a [Google drive](https://drive.google.com/drive/folders/15PHKVdPm65ysgdkV47z6J_73kETk7_of), but we also wrapped it into a [Huggingface Dataset](https://huggingface.co/datasets/biu-nlp/qanom), which is easier to plug-and-play with (check out our [HF profile](https://huggingface.co/biu-nlp) for other related datasets, such as QASRL, QAMR, QADiscourse, and QA-Align). 
  
  ## Demo
  
  Visit [our demo](https://huggingface.co/spaces/kleinay/qanom-seq2seq-demo) for interactively exploring our model!
        
  ## Usage 
  
  The model and tokenizer can be downloaded as simply as running:
  ```python
  import transformers
  model = transformers.AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2seq-model-baseline")
  tokenizer = transformers.AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-model-baseline")
  ```
  
  However, the model fine-tuning procedure involves input preprocessing (marking the predicate in the sentence, T5's "task prefix", incorporating the predicate type and/or the verbal for of the nominalization) and output postprocessing (parsing the sequence into a list of QASRL-formatted QAs).  
  In order to use the model for QANom parsing easily, we suggest downloading the [`pipeline.py`](https://huggingface.co/kleinay/qanom-seq2seq-model-baseline/blob/main/pipeline.py) file from this repository, and then use the `QASRL_Pipeline` class:
  
  ```python
  from pipeline import QASRL_Pipeline
  pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
  pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal")
  ``` 
  Which will output:
  ```json
  [{'generated_text': 'who _ _ researched something _ _ ?<extra_id_7> Luke', 
    'QAs': [{'question': 'who researched something ?', 'answers': ['Luke']}]}]
  ```   
  You can learn more about using `transformers.pipelines` in the [official docs](https://huggingface.co/docs/transformers/main_classes/pipelines).
  
  Notice that you need to specify which word in the sentence is the predicate, about which the question will interrogate. By default, you should precede the predicate with the `<predicate>` symbol, but you can also specify your own predicate marker:
  ```python
  pipe("The student was interested in Luke 's <PRED> research about see animals .", verb_form="research", predicate_type="nominal", predicate_marker="<PRED>")
  ```
  In addition, you can specify additional kwargs for controling the model's decoding algorithm:
  ```python
  pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal", num_beams=3)
  ```
  
  
              