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--- |
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language: |
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- en |
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tags: |
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- semantic-role-labeling |
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- question-answer generation |
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- pytorch |
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datasets: |
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- kleinay/qanom |
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--- |
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# A Seq2Seq model for QANom parsing |
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This is a `t5-small` pretrained model, fine-tuned on the task of generating QANom QAs. |
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"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). |
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## Demo |
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Visit [our demo](https://huggingface.co/spaces/kleinay/qanom-seq2seq-demo) for interactively exploring our model! |
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## Usage |
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The model and tokenizer can be downloaded as simply as running: |
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```python |
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import transformers |
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model = transformers.AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2seq-model-baseline") |
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tokenizer = transformers.AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-model-baseline") |
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``` |
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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). |
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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: |
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```python |
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from pipeline import QASRL_Pipeline |
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pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline") |
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pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal") |
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``` |
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Which will output: |
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```json |
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[{'generated_text': 'who _ _ researched something _ _ ?<extra_id_7> Luke', |
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'QAs': [{'question': 'who researched something ?', 'answers': ['Luke']}]}] |
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``` |
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You can learn more about using `transformers.pipelines` in the [official docs](https://huggingface.co/docs/transformers/main_classes/pipelines). |
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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: |
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```python |
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pipe("The student was interested in Luke 's <PRED> research about see animals .", verb_form="research", predicate_type="nominal", predicate_marker="<PRED>") |
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``` |
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In addition, you can specify additional kwargs for controling the model's decoding algorithm: |
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```python |
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pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal", num_beams=3) |
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``` |
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