kleinay
/

qanom-seq2seq-model-joint

text2text-generation

semantic-role-labeling

question-answer generation

text-generation-inference

Model card Files Files and versions

kleinay commited on Jan 5, 2022

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1e542c4

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1 Parent(s): 96a6949

typo

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README.md +3 -3

README.md CHANGED Viewed

@@ -34,7 +34,7 @@ In order to use the model for QANom parsing easily, we suggest downloading the `
 ```python
 from pipeline import QASRL_Pipeline
 pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-joint")
-pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal")
 ```
 Which will output:
 ```json
@@ -45,11 +45,11 @@ You can learn more about using `transformers.pipelines` in the [official docs](h
 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)
 ```

 ```python
 from pipeline import QASRL_Pipeline
 pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-joint")
+pipe("The student was interested in Luke 's <predicate> research about sea animals .", verb_form="research", predicate_type="nominal")
 ```
 Which will output:
 ```json
 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 sea 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 sea animals .", verb_form="research", predicate_type="nominal", num_beams=3)
 ```