Create README.md

README.md

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+---
+language:
+- en
+tags:
+- semantic-role-labeling
+- question-answer generation
+- pytorch
+datasets:
+- kleinay/qanom
+---
+
+# A Seq2Seq model for QANom parsing
+
+This is a `t5-small` pretrained model, fine-tuned on the task of generating QANom QAs. 
+
+"QANom" stands for "QASRL for Nominalizations", which is an adaptation of [QASRL (Question-Answer driven Semantic Role Labeling)](www.qasrl.org) for the nominal predicates domain. See the [QANom paper](https://aclanthology.org/2020.coling-main.274/) for details about the task. The QANom Dataset official site is a [Google drive](https://drive.google.com/drive/folders/15PHKVdPm65ysgdkV47z6J_73kETk7_of), but we also wrapped it into a [Huggingface Dataset](https://huggingface.co/datasets/biu-nlp/qanom), which is easier to plug-and-play with (check out our [HF profile](https://huggingface.co/biu-nlp) for other related datasets, such as QASRL, QAMR, QADiscourse, and QA-Align). 
+
+## Demo
+
+Visit [our demo](https://huggingface.co/spaces/kleinay/qanom-seq2seq-demo) for interactively exploring our model!
+      
+## Usage 
+
+The model and tokenizer can be downloaded as simply as running:
+```python
+import transformers
+model = transformers.AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2seq-model-baseline")
+tokenizer = transformers.AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-model-baseline")
+```
+
+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).  
+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:
+
+```python
+from pipeline import QASRL_Pipeline
+pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
+pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal")
+``` 
+Which will output:
+```json
+[{'generated_text': 'who _ _ researched something _ _ ?<extra_id_7> Luke', 
+  'QAs': [{'question': 'who researched something ?', 'answers': ['Luke']}]}]
+```   
+
+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)
+```
+ 
+