Add script to generate embeddings from datasets

.gitignore

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+*_debug/*

Generate.md

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+
+# create embeddings
+
+### multilingual-e5-small + passage
+
+```
+python datasets_to_embs.py \
+  --target="data" \
+  --model_name="intfloat/multilingual-e5-small" \
+  --output_name="multilingual-e5-small-passage" \
+  --input_prefix="passage: "
+python datasets_to_embs.py \
+  --target="chunked" \
+  --model_name="intfloat/multilingual-e5-small" \
+  --output_name="multilingual-e5-small-passage" \
+  --input_prefix="passage: "
+```
+
+### multilingual-e5-small + query
+
+```
+python datasets_to_embs.py \
+  --target="data" \
+  --model_name="intfloat/multilingual-e5-small" \
+  --output_name="multilingual-e5-small-query" \
+  --input_prefix="query: "
+python datasets_to_embs.py \
+  --target="chunked" \
+  --model_name="intfloat/multilingual-e5-small" \
+  --output_name="multilingual-e5-small-query" \
+  --input_prefix="query: "
+``````

datasets_to_embs.py

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+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Generator
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from tqdm import tqdm
+import torch
+import argparse
+from pathlib import Path
+from datasets import load_dataset
+
+parser = argparse.ArgumentParser(description="Convert datasets to embeddings")
+parser.add_argument(
+    "-t",
+    "--target",
+    type=str,
+    required=True,
+    choices=["data", "chunked"],
+    help="target dataset, data or chunked",
+)
+
+parser.add_argument(
+    "-d",
+    "--debug",
+    action="store_true",
+    help="debug mode, use small dataset",
+)
+# model_name
+parser.add_argument(
+    "-m",
+    "--model_name",
+    type=str,
+    required=True,
+    help="huggingface model name",
+)
+# input_prefix
+parser.add_argument(
+    "-i",
+    "--input_prefix",
+    type=str,
+    required=False,
+    default="",
+    help="input prefix",
+)
+# max_seq_length
+parser.add_argument(
+    "-l",
+    "--max_seq_length",
+    type=int,
+    required=False,
+    default=512,
+    help="max sequence length",
+)
+# output_name
+parser.add_argument(
+    "-o",
+    "--output_name",
+    type=str,
+    required=True,
+    help="output dir",
+)
+args = parser.parse_args()
+
+
+@dataclass
+class EmbConfig:
+    model_name: str
+    input_prefix: str
+    max_seq_length: int
+
+
+args = parser.parse_args()
+
+target_local_ds = args.target
+
+EMB_CONFIG = EmbConfig(
+    model_name=args.model_name,
+    input_prefix=args.input_prefix,
+    max_seq_length=args.max_seq_length,
+)
+embs_dir = f"embs{'_debug' if args.debug else ''}"
+
+output_embs_path = Path("/".join([embs_dir, args.output_name, target_local_ds]))
+output_embs_path.mkdir(parents=True, exist_ok=True)
+
+print("output path:", output_embs_path)
+
+MODEL = SentenceTransformer(EMB_CONFIG.model_name)
+MODEL.max_seq_length = EMB_CONFIG.max_seq_length
+
+
+def to_embs(texts: list[str], group_size=1024) -> Generator[np.ndarray, None, None]:
+    group = []
+    for text in texts:
+        group.append(text)
+        if len(group) == group_size:
+            embeddings = MODEL.encode(
+                group,
+                normalize_embeddings=True,
+                show_progress_bar=False,
+            )
+            yield embeddings  # type: ignore
+            group = []
+    if len(group) > 0:
+        embeddings = MODEL.encode(
+            group, normalize_embeddings=True, show_progress_bar=False
+        )
+        yield embeddings  # type: ignore
+
+
+def _to_data_text(
+    data, prefix=EMB_CONFIG.input_prefix, max_len=int(EMB_CONFIG.max_seq_length * 1.5)
+):
+    return (prefix + data["title"] + "\n" + data["text"])[0:max_len]
+
+
+def _to_chunk_text(
+    data, prefix=EMB_CONFIG.input_prefix, max_len=int(EMB_CONFIG.max_seq_length * 1.5)
+):
+    return (prefix + data["title"] + "\n" + data["overlap_text"] + data["text"])[
+        :max_len
+    ]
+
+
+def ds_to_embs(
+    ds,
+    text_fn,
+    group_size: int,
+):
+    texts = []
+    total = len(ds)
+    pbar = tqdm(total=total)
+    # text は group_size 件ごとに処理する
+    for i in range(0, total, group_size):
+        texts = []
+        for data in ds.select(range(i, min(i + group_size, total))):
+            data: dict = data
+            text = text_fn(data)
+            texts.append(text)
+        embs = []
+        for group_embs in to_embs(texts):
+            embs.append(group_embs)
+            pbar.update(len(group_embs))
+        embs = np.concatenate(embs)
+        yield embs, i, pbar
+
+
+if torch.cuda.is_available():
+    print("use cuda")
+    MODEL.to("cuda")
+else:
+    print("!! Warning: use cpu")
+
+ds = load_dataset(args.target)["train"]  # type: ignore
+to_text = _to_data_text if args.target == "data" else _to_chunk_text
+
+if args.debug:
+    print("debug mode")
+    ds = ds.select(range(19998))  # type: ignore
+    print("small dataset len:", len(ds))
+    group_size = 10000
+else:
+    print("dataset len:", len(ds))
+    group_size = 100_000
+
+for embs, idx, pbar in ds_to_embs(ds, to_text, group_size=group_size):
+    filename = f"{idx}.npz"
+    filepath = output_embs_path / filename
+    pbar.desc = f"saving...: {str(filepath)}"
+    np.savez_compressed(filepath, embs=embs.astype(np.float16))
+    pbar.desc = ""