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README.md

@@ -11,11 +11,12 @@ pinned: false
 
 # Mecari (Japanese Morphological Analysis with Graph Neural Networks)
 
-## Training
+## Demo
+You can try Mecari in https://huggingface.co/spaces/zbller/Mecari
 
-### Overview
+## Overview
 
-Mecari [1] is a GNN‑based Japanese morphological analyzer. It supports training from partially annotated graphs (only '+'/'-' where available; '?' is ignored) and aims for fast training and inference.
+Mecari [1] is Google's GNN(Graph Neural Network)‑based Japanese morphological analyzer. It supports training from partially annotated graphs (only '+'/'-' where available; '?' is ignored) and aims for fast training and inference.
 
 <p align="center">
   <img src="abst.png" alt="Overview" width="70%" />
@@ -39,14 +40,15 @@ Nodes are featurized with JUMAN++‑style unigram features, edges are modeled as
 ### Inference
 Use the model’s node scores and run Viterbi to search the optimal non‑overlapping path.
 
-## Results (KWDLC test)
+### Results (KWDLC test)
 
 - Trained model (sample_model): Seg F1 0.9725, POS F1 0.9562
 - MeCab (JUMANDIC) baseline:   Seg F1 0.9677, POS F1 0.9465
 
 The GATv2 model trained with this repository (current code and `configs/gatv2.yaml`) using the official KWDLC split outperforms MeCab on both segmentation and POS accuracy.
 
-## Tested Environment
+## Environmental Setup
+### Tested Environment
 
 - OS: Ubuntu 24.04.3 LTS (Noble Numbat)
 - Python: 3.11.3
@@ -55,7 +57,7 @@ The GATv2 model trained with this repository (current code and `configs/gatv2.ya
 - MeCab (binary): 0.996
 - JUMANDIC: `/var/lib/mecab/dic/juman-utf8`
 
-## MeCab Setup (Ubuntu 24.04)
+### MeCab Setup (Ubuntu 24.04)
 1) Install packages (includes the JUMANDIC dictionary)
 
 ```bash
@@ -70,7 +72,7 @@ mecab -v                       # e.g., mecab of 0.996
 test -d /var/lib/mecab/dic/juman-utf8 && echo "JUMANDIC OK"
 ```
 
-## Project Setup
+### Project Setup
 
 ```bash
 # Install uv if needed
@@ -146,8 +148,8 @@ python evaluate.py --max-samples 50 \
 CC BY‑NC 4.0 (non‑commercial use only)
 
 ## Acknowledgments
-- [1] Technical inspiration: Mecari, a morphological analysis system developed by Google, as described in “Data processing for Japanese text‑to‑pronunciation models” by G. Mazovetskiy and T. Kudo (NLP2024 Workshop on Japanese Language Resources). URL: https://jedworkshop.github.io/JLR2024/materials/b-2.pdf (pp. 19–23)
-- [2] Graph architecture: Brody, Shaked, Uri Alon, and Eran Yahav. "HOW ATTENTIVE ARE GRAPH ATTENTION NETWORKS?." 10th International Conference on Learning Representations, ICLR 2022. 2022.
+- [1] “Data processing for Japanese text‑to‑pronunciation models”, Gleb Mazovetskiy, Taku Kudo, NLP2024 Workshop on Japanese Language Resources, URL: https://jedworkshop.github.io/JLR2024/materials/b-2.pdf (pp. 19–23)
+- [2] "HOW ATTENTIVE ARE GRAPH ATTENTION NETWORKS?.", Graph architecture: Brody, Shaked, Uri Alon, and Eran Yahav, 10th International Conference on Learning Representations, ICLR 2022. 2022.
 
 ## Disclaimer
 - Independent academic implementation for educational and research purposes.

app.py

@@ -171,4 +171,4 @@ with gr.Blocks(theme=gr.themes.Soft(), css=FONT_CSS) as demo:
     _warmup()
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7863")))
+    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))

mecari/analyzers/mecab.py

@@ -129,8 +129,7 @@ class MeCabAnalyzer:
             node_id, surface, features, sb, eb = parts[0], parts[1], parts[2], parts[3], parts[4]
             if surface in ("BOS", "EOS"):
                 continue
-            if not surface.strip():
-                continue
+            
             try:
                 start_byte = int(sb)
                 end_byte = int(eb)
@@ -168,4 +167,47 @@ class MeCabAnalyzer:
                 continue
             seen.add(key)
             out.append(m)
+        # Synthesize whitespace nodes for gaps if MeCab didn't output them
+        if text:
+            existing_ws_spans = set()
+            for m in out:
+                surf = m.get("surface", "")
+                if surf.strip() == "":
+                    cs = m.get("start_pos", 0)
+                    ce = m.get("end_pos", 0)
+                    existing_ws_spans.add((cs, ce))
+
+            i = 0
+            n = len(text)
+            while i < n:
+                ch = text[i]
+                if ch.isspace():
+                    j = i + 1
+                    while j < n and text[j].isspace():
+                        j += 1
+                    span = (i, j)
+                    if span not in existing_ws_spans:
+                        ws = text[i:j]
+                        wm = {
+                            "surface": ws,
+                            "pos": "特殊",
+                            "pos_detail1": "空白",
+                            "pos_detail2": "*",
+                            "pos_detail3": "*",
+                            "base_form": "",
+                            "reading": "",
+                            "inflection_type": "*",
+                            "inflection_form": "*",
+                            "start_pos": i,
+                            "end_pos": j,
+                            "annotation": "?",
+                            "node_id": f"WS_{i}_{j}",
+                        }
+                        key = signature_key(wm)
+                        if key not in seen:
+                            seen.add(key)
+                            out.append(wm)
+                    i = j
+                else:
+                    i += 1
         return out

pyproject.toml

@@ -4,6 +4,7 @@ version = "0.1.0"
 description = "Japanese morphological analysis using Graph Neural Networks"
 readme = "README.md"
 requires-python = ">=3.11,<3.12"
+license = { file = "LICENSE" }
 dependencies = [
     "torch>=2.2,<2.3",
     "pytorch-lightning>=2.0.0",

readme2.md

@@ -1,26 +1,8 @@
 # Mecari (Japanese Morphological Analysis with Graph Neural Networks)
-
-本ドキュメントは既存の README を整理し、セットアップや再現、評価の手順、既知の制限やトラブルシュートを強化した改訂版です。
-
-- Demo: https://huggingface.co/spaces/zbller/Mecari （推論デモ。CPU/ブラウザ実行、学習は未対応）
-
-## 目次
-- 概要
-- 結果（KWDLC）
-- 動作環境
-- MeCab のセットアップ
-- プロジェクトセットアップ（uv / pip）
-- クイックスタート（形態素解析）
-- 学習と評価（KWDLC）
-- コンフィグ概要
-- 実験ディレクトリと再開
-- トラブルシュート
-- 既知の制限
-- ライセンスと謝辞
-- 参考文献
+- Demo: https://huggingface.co/spaces/zbller/Mecari
 
 ## 概要
-Mecari は、公開研究 [1] のアイデアに着想を得た GNN（Graph Neural Network）ベースの日本語形態素解析器です。MeCab（JUMANDIC）の候補からグラフを構築し、部分アノテーション（'+'/'-'、'?'は学習で無視）で学習可能、学習・推論の高速化を重視しています。ノードには JUMAN++ 風のユニグラム特徴量、エッジは無向（双方向）として扱い、GATv2 [2] を学習します。推論ではノードスコアに基づき Viterbi で非重複の最適経路を探索します。
+Mecari は、Googleの発表 [1] に着想を得た GNN（Graph Neural Network）ベースの日本語形態素解析器です。MeCab（JUMANDIC）の候補からグラフを構築し、部分アノテーション（'+'/'-'、'?'は学習で無視）で学習可能、学習・推論の高速化を重視しています。ノードには JUMAN++ 風のユニグラム特徴量、エッジは無向（双方向）として扱い、GATv2 [2] を学習します。推論ではノードスコアに基づき Viterbi で非重複の最適経路を探索します。
 
 ## 結果（KWDLC）
 - 学習済み（`sample_model`）: Seg F1 0.9725, POS F1 0.9562
@@ -29,8 +11,6 @@ Mecari は、公開研究 [1] のアイデアに着想を得た GNN（Graph Neur
 再現メモ
 - データ: 公式 KWDLC の `dev.id`/`test.id` 分割を使用
 - コンフィグ: `configs/gatv2.yaml`（`extends: base.yaml`）
-- 乱数: `training.seed: 42`
-- 決定性: 既定は `deterministic: false`（高速重視）。厳密再現性が必要なら true を推奨（速度低下に注意）
 
 ## 動作環境
 - OS: Ubuntu 24.04.3 LTS（確認済）