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@@ -28,7 +28,7 @@ model = transformers.AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2se
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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-joint/blob/main/pipeline.py) file from this repository, and then use the `QASRL_Pipeline` class:
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  ```python
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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 form 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-joint/blob/main/pipeline.py) file from this repository, and then use the `QASRL_Pipeline` class:
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  ```python