Transformers
PyTorch
English
t5
text2text-generation
semantic-role-labeling
question-answer generation
text-generation-inference
Instructions to use kleinay/qanom-seq2seq-model-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kleinay/qanom-seq2seq-model-baseline with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-model-baseline") model = AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2seq-model-baseline", device_map="auto") - Notebooks
- Google Colab
- Kaggle
fix source prefix
Browse files- pipeline.py +2 -2
pipeline.py
CHANGED
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@@ -125,9 +125,9 @@ class QASRL_Pipeline(Text2TextGenerationPipeline):
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| 125 |
def _get_source_prefix(self, predicate_type: Optional[str]):
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| 126 |
if not self.is_t5_model or self.data_args.source_prefix is None:
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| 127 |
return ''
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| 128 |
-
if "
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| 129 |
if predicate_type is None:
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| 130 |
-
raise ValueError("source_prefix includes '
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| 131 |
if self.data_args.source_prefix == "Generate QAs for <predicate_type> QASRL: ": # backwrad compatibility - "Generate QAs for <predicate_type> QASRL: " alone was a sign for a longer prefix
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| 132 |
return f"Generate QAs for {predicate_type} QASRL: "
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| 133 |
else:
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| 125 |
def _get_source_prefix(self, predicate_type: Optional[str]):
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| 126 |
if not self.is_t5_model or self.data_args.source_prefix is None:
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| 127 |
return ''
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| 128 |
+
if "<predicate_type>" in self.data_args.source_prefix:
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| 129 |
if predicate_type is None:
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| 130 |
+
raise ValueError("source_prefix includes '<predicate_type>' but input has no `predicate_type`.")
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| 131 |
if self.data_args.source_prefix == "Generate QAs for <predicate_type> QASRL: ": # backwrad compatibility - "Generate QAs for <predicate_type> QASRL: " alone was a sign for a longer prefix
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| 132 |
return f"Generate QAs for {predicate_type} QASRL: "
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| 133 |
else:
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