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qanom-seq2seq-model-baseline

Text2Text Generation Transformers PyTorch
English t5 semantic-role-labeling question-answer generation Inference Endpoints text-generation-inference
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@@ -29,7 +29,7 @@ tokenizer = transformers.AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-mo
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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` 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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  ```
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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