Supporting Seq2Seq model for question answering task (#13432)

* Add seq2seq example for QnA on SQuAD Dataset.

* Changes from review - Fixing styling mistakes.

* Added how to example in README, simplified the access to dataset's preprocess function.

* Added tests for the seq2seq QA example.

* Change dataset column name to fix tests.

* Fix test command mistake.

* Add missing argument 'ignore_pad_token_for_loss' from DataTrainingArguments.

* Add missing argument 'num_beams' from DataTrainingArguments.

* Fix processing of output predicted token ids so that tokenizer decode gets appropriate input. Updated assertion conditions on the tests.

examples/pytorch/question-answering/README.md

@@ -57,6 +57,28 @@ f1 = 88.52
 exact_match = 81.22
 ```
 
+### Fine-tuning T5 on SQuAD2.0
+
+This example code fine-tunes T5 on the SQuAD2.0 dataset.
+
+```bash
+python run_seq2seq_qa.py \
+  --model_name_or_path t5-small \
+  --dataset_name squad_v2 \
+  --context_column context \
+  --question_column question \
+  --answer_column answer \
+  --do_train \
+  --do_eval \
+  --per_device_train_batch_size 12 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 2 \
+  --max_seq_length 384 \
+  --doc_stride 128 \
+  --output_dir /tmp/debug_seq2seq_squad/
+```
+
+
 #### Distributed training
 
 Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:

examples/pytorch/test_examples.py

@@ -57,6 +57,7 @@ if SRC_DIRS is not None:
     import run_mlm
     import run_ner
     import run_qa as run_squad
+    import run_seq2seq_qa as run_squad_seq2seq
     import run_speech_recognition_ctc
     import run_summarization
     import run_swag
@@ -244,6 +245,40 @@ class ExamplesTests(TestCasePlus):
             self.assertGreaterEqual(result["eval_f1"], 30)
             self.assertGreaterEqual(result["eval_exact"], 30)
 
+    def test_run_squad_seq2seq(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_seq2seq_qa.py
+            --model_name_or_path t5-small
+            --context_column context
+            --question_column question
+            --answer_column answers
+            --version_2_with_negative
+            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
+            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
+            --output_dir {tmp_dir}
+            --overwrite_output_dir
+            --max_steps=10
+            --warmup_steps=2
+            --do_train
+            --do_eval
+            --learning_rate=2e-4
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=1
+            --predict_with_generate
+        """.split()
+
+        with patch.object(sys, "argv", testargs):
+            run_squad_seq2seq.main()
+            result = get_results(tmp_dir)
+            self.assertGreaterEqual(result["eval_rouge1"], 10)
+            self.assertGreaterEqual(result["eval_rouge2"], 10)
+            self.assertGreaterEqual(result["eval_rougeL"], 10)
+            self.assertGreaterEqual(result["eval_rougeLsum"], 10)
+
     def test_run_swag(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)