format squad_train_model.py back into original form

paper_experiments/squad_train_model.py

@@ -0,0 +1,59 @@
+from pathlib import Path
+
+from haystack.nodes import FARMReader
+from datasets import load_dataset
+import json
+
+from haystack.utils import SquadData
+
+
+def hf_to_squad(dataset_name: str) -> str:
+    dataset = load_dataset(dataset_name, split="train").shuffle().to_dict()
+    paragraphs = []
+
+    for i in range(len(dataset["context"])):
+        if dataset["answers"][i]["text"]:
+            answers = [
+                {"text": dataset["answers"][i]["text"][0], "answer_start": dataset["answers"][i]["answer_start"][0]}]
+            impossible = False
+        else:
+            answers = []
+            impossible = True
+        paragraph = {"qas": [{"id": dataset["id"][i], "question": dataset["question"][i], "answers": answers,
+                              "is_impossible": impossible}], "context": dataset["context"][i]}
+        paragraphs.append(paragraph)
+
+    squad = {"version": "1.0", "data": [{"title": "test", "paragraphs": paragraphs}]}
+
+    filename = f"squad-{dataset_name.split('/')[-1]}"
+    with open(f"{filename}.json", "w") as f:
+        f.write(json.dumps(squad))
+    return filename
+
+
+if __name__ == '__main__':
+    config = [1_000, 10_000, 20_000]
+    model_name = "roberta-base"  # "bert-base-uncased" is a worse alternative
+    dataset_names = ["julianrisch/qa-dataset-generated-21020", "julianrisch/qa-dataset-original-21020"]
+
+    for dataset_name in dataset_names:
+        squad_filename = hf_to_squad(dataset_name)
+        dataset = SquadData.from_file(filename=f"{squad_filename}.json")
+        for num_samples in config:
+            train_filename = f"{squad_filename}-{num_samples}.json"
+            sample = dataset.sample_questions(num_samples)
+            SquadData(squad_data=sample).save(train_filename)
+
+            # Model Training
+            reader_directory = f"{squad_filename}-{num_samples}-{model_name}"
+            reader = FARMReader(model_name_or_path=model_name, return_no_answer=True, use_confidence_scores=False)
+            reader.train(data_dir="..", train_filename=train_filename, dev_split=0.1, use_gpu=True, batch_size=16, max_seq_len=384)
+            reader.save(Path(reader_directory))
+
+            # Model Evaluation
+            reader = FARMReader(reader_directory, return_no_answer=True, use_confidence_scores=False, max_seq_len=384)
+            reader_eval_results = reader.eval_on_file(data_dir="..", test_filename="dev-v2.0.json")
+            with open("log.txt", "a") as log_file:
+                log_file.write(str(reader_directory)+'\n')
+                log_file.write(str(reader_eval_results)+'\n')
+