Transformers
PyTorch
English
t5
text2text-generation
semantic-role-labeling
question-answer generation
text-generation-inference
Instructions to use kleinay/qanom-seq2seq-model-joint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kleinay/qanom-seq2seq-model-joint with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-model-joint") model = AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2seq-model-joint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
fix mode name
Browse files
README.md
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@@ -33,7 +33,7 @@ In order to use the model for QANom parsing easily, we suggest downloading the `
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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-
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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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```python
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from pipeline import QASRL_Pipeline
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pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-joint")
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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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