Doc-to-LoRA release

data/build_pwc_compact.py

@@ -0,0 +1,43 @@
+import gc
+
+from datasets import Dataset, load_dataset
+from tqdm import tqdm
+
+if __name__ == "__main__":
+    ds_name = "sggetao/PwC"
+
+    for split in ["train", "test"]:
+        ctx_qa_dict = dict()
+        ds = load_dataset(ds_name, split=split)
+        print(f"Original size: {len(ds)}")
+        for i, sample in tqdm(enumerate(ds)):
+            ctx = sample["input"]
+            if ctx not in ctx_qa_dict:
+                ctx_qa_dict[ctx] = {"prompts": [], "responses": []}
+            # question = closed_qa_prompting(sample["prompt"])
+            question = sample["prompt"]
+            answer = sample["answer"]
+            ctx_qa_dict[ctx]["prompts"].append(question)
+            ctx_qa_dict[ctx]["responses"].append(answer)
+
+        print(f"Unique contexts: {len(ctx_qa_dict)}")
+        # convert ctx_qa_dict to a list of dictionaries
+        samples = [
+            {
+                "context": ctx,
+                "prompts": ctx_qa_dict[ctx]["prompts"],
+                "responses": ctx_qa_dict[ctx]["responses"],
+            }
+            for ctx in ctx_qa_dict
+        ]
+        print(f"Sampled data: {samples[0]}")
+        # breakpoint()
+        # save to a new dataset
+        ds = Dataset.from_list(samples)
+
+        save_path = f"./data/raw_datasets/pwc_compact/{split}/ds.parquet"
+        print(f"Saving dataset to {save_path}")
+        ds.to_parquet(save_path)
+        print("=" * 80)
+        del ds, samples, ctx_qa_dict
+        gc.collect()