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.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data.jsonl filter=lfs diff=lfs merge=lfs -text

DCS_pick.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:108eafe409ce65459c8d7c3c2f834d032ea95ce6b235d31ca5597e639bdc9167
+size 199818951

Graphml.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9d0895f0fdd12d84b123636a81303529a4ab5eae17db694669128a721e9e683
+size 650777615

README.md

@@ -0,0 +1,153 @@
+---
+datasets:
+- name: sanskrit-word-segmentation
+  license: other
+  language:
+    - sa
+  task_categories:
+    - token-classification
+    - structured-prediction
+  tags:
+    - sanskrit
+    - word-segmentation
+    - morphological-analysis
+    - low-resource
+    - graph-based
+    - lemma
+    - cng
+  pretty_name: Sanskrit Word Segmentation & Morphological Candidates
+  size_categories:
+    - 100K<n<1M
+  source_datasets:
+    - original
+  annotations_creators:
+    - expert-annotated
+  multilinguality: monolingual
+  language_creators:
+    - expert
+  paperswithcode_id: graph-based-word-segmentation-sanskrit
+  citation: |
+    @inproceedings{krishna2017graph,
+      title={A Graph Based Framework for Structured Prediction Tasks in Sanskrit},
+      author={Krishna, Amrith and Goyal, Pankaj and Sharma, Dipti Misra},
+      booktitle={Proceedings of the 14th International Conference on Natural Language Processing},
+      year={2017}
+    }
+---
+
+
+# Sanskrit Word Segmentation and Morphological Candidate Dataset
+
+This dataset provides Sanskrit sentences annotated with gold-standard word segmentations, lemmas, and morphological tags based on the paper [A Dataset for Sanskrit Word Segmentation](https://aclanthology.org/W17-2214.pdf). It also includes graph-based candidate morphological analyses derived from a structured Sanskrit parser. The dataset is useful for tasks such as:
+
+- Word Segmentation  
+- Lemmatization  
+- Morphological Analysis  
+- Graph-based Disambiguation  
+- Low-resource NLP  
+
+---
+
+## 📂 Folder Structure
+
+```
+
+.
+├── data.jsonl # Final merged dataset in JSON Lines format
+├── DCS_pick.zip # Zipped DCS pickle files (.p) with gold segmentation and lemmas
+├── Graphml.zip # Zipped GraphML files (.graphml) with candidate morphological structures
+├── convert.py # Python script to convert .p and .graphml into dataset.jsonl
+├── graphFiles # Text file listing valid .graphml IDs to be processed
+├── README.md 
+
+├── Sample/ # Contains sample files and visual tools
+│ ├── DCS_999.p
+│ ├── sample_999.graphml
+│ ├── graph_reader.py # GraphML visualizer using NetworkX + matplotlib
+│ ├── pickleReader.py # Script to read and inspect a DCS .p file
+│ └── graph_output.png # Example output image from visualizing a graph
+````
+
+---
+
+## 🧠 Data Format: `dataset.jsonl`
+
+Each line is a JSON object with the following fields:
+
+```json
+{
+  "id": "100012",
+  "sentence": "tatra sarvatra vaktavyaṁ manyante śāstrakovidāḥ",
+  "gold_segments": ["tatra", "sarvatra", "vaktavya", "man", "śāstra", "kovida"],
+  "lemmas": [["tatra"], ["sarvatra"], ["vaktavya"], ["man"], ["śāstra", "kovida"]],
+  "morph_tags": [["2"], ["2"], ["71"], ["-19"], ["3", "39"]],
+  "candidates": [
+    {
+      "id": "1",
+      "word": "tatra",
+      "lemma": "tatra",
+      "morph": "adv.",
+      "cng": "2",
+      "chunk_no": "1",
+      "position": 0,
+      "length": 5,
+      "pre_verb": ""
+    },
+    ...
+  ]
+}
+````
+
+* `gold_segments`: List of correct segmented words (chunks)
+* `lemmas`: Root forms for each chunk (1-to-many possible)
+* `morph_tags`: Morphological CNG tags (per chunk)
+* `candidates`: All possible analyses from the graph representation
+
+---
+
+## How to Use
+
+### Read Dataset in Python
+
+```python
+import json
+
+with open("dataset.jsonl", "r", encoding="utf-8") as f:
+    for line in f:
+        entry = json.loads(line)
+        print(entry["id"], entry["sentence"])
+```
+
+### Visualize GraphML File (Example)
+
+```bash
+python graph_reader.py
+```
+
+For additional setup details or troubleshooting, please refer to the [original codebase on Zenodo](https://zenodo.org/records/803508).
+
+
+# Citing 
+```bibtex
+@inproceedings{krishna-etal-2017-dataset,
+    title = "A Dataset for {S}anskrit Word Segmentation",
+    author = "Krishna, Amrith  and
+      Satuluri, Pavan Kumar  and
+      Goyal, Pawan",
+    editor = "Alex, Beatrice  and
+      Degaetano-Ortlieb, Stefania  and
+      Feldman, Anna  and
+      Kazantseva, Anna  and
+      Reiter, Nils  and
+      Szpakowicz, Stan",
+    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
+    month = aug,
+    year = "2017",
+    address = "Vancouver, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W17-2214/",
+    doi = "10.18653/v1/W17-2214",
+    pages = "105--114",
+    abstract = "The last decade saw a surge in digitisation efforts for ancient manuscripts in Sanskrit. Due to various linguistic peculiarities inherent to the language, even the preliminary tasks such as word segmentation are non-trivial in Sanskrit. Elegant models for Word Segmentation in Sanskrit are indispensable for further syntactic and semantic processing of the manuscripts. Current works in word segmentation for Sanskrit, though commendable in their novelty, often have variations in their objective and evaluation criteria. In this work, we set the record straight. We formally define the objectives and the requirements for the word segmentation task. In order to encourage research in the field and to alleviate the time and effort required in pre-processing, we release a dataset of 115,000 sentences for word segmentation. For each sentence in the dataset we include the input character sequence, ground truth segmentation, and additionally lexical and morphological information about all the phonetically possible segments for the given sentence. In this work, we also discuss the linguistic considerations made while generating the candidate space of the possible segments."
+}
+```

Sample/DCS_999.p

@@ -0,0 +1,49 @@
+(i__main__
+DCS
+p0
+(dp1
+S'lemmas'
+p2
+(lp3
+(lp4
+S'madhupu\xe1\xb9\xa3pa'
+p5
+aa(lp6
+S'lodhrapu\xe1\xb9\xa3pa'
+p7
+aa(lp8
+S'v\xc4\x81naprastha'
+p9
+aa(lp10
+S'madhudruma'
+p11
+aasS'dcs_chunks'
+p12
+(lp13
+g5
+ag7
+ag9
+ag11
+asS'sent_id'
+p14
+I999
+sS'cng'
+p15
+(lp16
+(lp17
+S'29'
+p18
+aa(lp19
+S'29'
+p20
+aa(lp21
+S'29'
+p22
+aa(lp23
+S'29'
+p24
+aasS'sentence'
+p25
+S'maDupuzpo loDrapuzpo vAnaprasTo maDudrumaH    '
+p26
+sb.

Sample/graph_reader.py

@@ -0,0 +1,61 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+import pickle
+
+# ---------- Load GraphML ----------
+graph_path = "sample_999.graphml"  # Replace with your file path
+G = nx.read_graphml(graph_path)
+
+# ---------- Load DCS Pickle ----------
+class DCS:
+    def __init__(self, sent_id, sentence):
+        self.sent_id = sent_id
+        self.sentence = sentence
+        self.dcs_chunks = []
+        self.lemmas = []
+        self.cng = []
+
+pkl_path = "DCS_999.p"  # Replace with your matching pickle file
+with open(pkl_path, "rb") as f:
+    gold = pickle.load(f, encoding='utf-8')
+
+
+gold_words = set(gold.dcs_chunks)
+
+# ---------- Prepare Node Labels and Colors ----------
+node_labels = {}
+node_colors = []
+
+for node_id, data in G.nodes(data=True):
+    word = data.get("word", "")
+    node_labels[node_id] = word
+
+    if word in gold_words:
+        node_colors.append("green")  # Gold-standard segment
+    else:
+        node_colors.append("red")    # Candidate, not in gold
+
+# ---------- Prepare Edge Labels ----------
+edge_labels = {}
+for u, v, data in G.edges(data=True):
+    etype = data.get("type", "")
+    edge_labels[(u, v)] = f"type={etype}"
+
+# ---------- Draw the Graph ----------
+plt.figure(figsize=(14, 10))
+pos = nx.spring_layout(G, seed=42)
+
+nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=800, alpha=0.9)
+nx.draw_networkx_edges(G, pos, arrows=True)
+nx.draw_networkx_labels(G, pos, labels=node_labels, font_size=10)
+nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='gray', font_size=8)
+
+plt.title(f"Segmentation Graph for Sentence:\n{gold.sentence}", fontsize=14)
+plt.axis('off')
+plt.tight_layout()
+
+
+output_image_path = "graph_output.png"
+plt.savefig(output_image_path, format="png", dpi=300)
+print(f"Graph saved to {output_image_path}")
+

Sample/pickleReader.py

@@ -0,0 +1,23 @@
+import pickle
+
+#In case of any clarification, email to - [email protected]
+# The latest modifications can be found at: www.amrithkrishna.com 
+# Class definition for pickle files
+# currently the pickle files can be opened only with Python 3
+
+class DCS:
+    def __init__(self, sent_id, sentence):
+        self.sent_id = sent_id
+        self.sentence = sentence
+        self.dcs_chunks = []
+        self.lemmas = []
+        self.cng = []
+
+
+output_load = pickle.load(open('DCS_999.p', "rb"), encoding='utf-8')
+print(output_load)
+print('Sentence Id:',output_load.sent_id)
+print('Sentence:',output_load.sentence)
+print('Chunks:',output_load.dcs_chunks)
+print('Lemmas:',output_load.lemmas)
+print('Morphological class (CNG):',output_load.cng)

Sample/sample_999.graphml

@@ -0,0 +1,1229 @@
+<?xml version='1.0' encoding='utf-8'?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+  <key attr.name="key" attr.type="long" for="edge" id="d8" />
+  <key attr.name="length_word" attr.type="long" for="node" id="d6" />
+  <key attr.name="chunk_no" attr.type="long" for="node" id="d1" />
+  <key attr.name="position" attr.type="long" for="node" id="d3" />
+  <key attr.name="morph" attr.type="string" for="node" id="d5" />
+  <key attr.name="pre_verb" attr.type="string" for="node" id="d7" />
+  <key attr.name="lemma" attr.type="string" for="node" id="d4" />
+  <key attr.name="cng" attr.type="long" for="node" id="d0" />
+  <key attr.name="word" attr.type="string" for="node" id="d2" />
+  <graph edgedefault="directed">
+    <node id="1">
+      <data key="d2">maDu</data>
+      <data key="d0">3</data>
+      <data key="d4">maDu</data>
+      <data key="d7" />
+      <data key="d5">iic.</data>
+      <data key="d3">0</data>
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+      <data key="d6">5</data>
+    </node>
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+      <data key="d2">maDu</data>
+      <data key="d0">71</data>
+      <data key="d4">maDu</data>
+      <data key="d7" />
+      <data key="d5">acc. sg. n.</data>
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+    <node id="4">
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+      <data key="d6">6</data>
+    </node>
+    <node id="5">
+      <data key="d2">loDra</data>
+      <data key="d0">3</data>
+      <data key="d4">loDra</data>
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+      <data key="d5">iic.</data>
+      <data key="d3">0</data>
+      <data key="d1">2</data>
+      <data key="d6">6</data>
+    </node>
+    <node id="6">
+      <data key="d2">puzpaH</data>
+      <data key="d0">29</data>
+      <data key="d4">puzpa</data>
+      <data key="d7" />
+      <data key="d5">nom. sg. m.</data>
+      <data key="d3">5</data>
+      <data key="d1">2</data>
+      <data key="d6">6</data>
+    </node>
+    <node id="7">
+      <data key="d2">vAn</data>
+      <data key="d0">-10</data>
+      <data key="d4">vA</data>
+      <data key="d7" />
+      <data key="d5">ppr. [2] ac.</data>
+      <data key="d3">-1</data>
+      <data key="d1">3</data>
+      <data key="d6">3</data>
+    </node>
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+      <data key="d6">3</data>
+    </node>
+    <node id="9">
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+      <data key="d0">2</data>
+      <data key="d4">vA</data>
+      <data key="d7" />
+      <data key="d5">conj.</data>
+      <data key="d3">0</data>
+      <data key="d1">3</data>
+      <data key="d6">2</data>
+    </node>
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+      <data key="d0">3</data>
+      <data key="d4">vAna</data>
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+      <data key="d5">iic.</data>
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+    </node>
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+      <data key="d0">29</data>
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+      <data key="d1">3</data>
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+    <node id="27">
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+      <data key="d4">maDudruma</data>
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+      <data key="d6">10</data>
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+    </edge>
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+    </edge>
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+      <data key="d8">1</data>
+    </edge>
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+      <data key="d8">1</data>
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+      <data key="d8">1</data>
+    </edge>
+    <edge source="2" target="12">
+      <data key="d8">1</data>
+    </edge>
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+      <data key="d8">1</data>
+    </edge>
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+      <data key="d8">1</data>
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convert.py

@@ -0,0 +1,83 @@
+import pickle
+import networkx as nx
+import json
+import os
+from tqdm import tqdm
+
+# Class for reading DCS pickle files
+class DCS:
+    def __init__(self, sent_id, sentence):
+        self.sent_id = sent_id
+        self.sentence = sentence
+        self.dcs_chunks = []
+        self.lemmas = []
+        self.cng = []
+
+# === Config Paths ===
+graph_file_list = "graphFiles"
+graph_folder = "After_graphml"
+pkl_folder = "DCS_pick"
+output_jsonl = "dataset.jsonl"
+log_file = "conversion_log.txt"
+
+# === Prepare Output Files ===
+with open(output_jsonl, "w", encoding="utf-8") as out_fp, open(log_file, "w", encoding="utf-8") as log_fp:
+    with open(graph_file_list, "r") as id_file:
+        ids = [line.strip().replace(".graphml", "") for line in id_file]
+
+    success_count = 0
+    error_count = 0
+
+    for sid in tqdm(ids, desc="Converting files"):
+        try:
+            # === Load .p (pickle) file ===
+            pkl_path = os.path.join(pkl_folder, f"{sid}.p")
+            if not os.path.exists(pkl_path):
+                raise FileNotFoundError(f"Missing pickle: {pkl_path}")
+
+            with open(pkl_path, "rb") as f:
+                gold = pickle.load(f, encoding="utf-8")
+
+            # === Load .graphml file ===
+            graph_path = os.path.join(graph_folder, f"{sid}.graphml")
+            if not os.path.exists(graph_path):
+                raise FileNotFoundError(f"Missing graphml: {graph_path}")
+
+            G = nx.read_graphml(graph_path)
+
+            # === Extract candidates from graph ===
+            candidates = []
+            for node_id, node_data in G.nodes(data=True):
+                candidates.append({
+                    "id": node_id,
+                    "word": node_data.get("word", ""),
+                    "lemma": node_data.get("lemma", ""),
+                    "morph": node_data.get("morph", ""),
+                    "cng": str(node_data.get("cng", "")),
+                    "chunk_no": str(node_data.get("chunk_no", "")),
+                    "position": int(node_data.get("position", -1)),
+                    "length": int(node_data.get("length_word", -1)),
+                    "pre_verb": node_data.get("pre_verb", "")
+                })
+
+            # === Final dictionary ===
+            sample = {
+                "id": sid,
+                "sentence": gold.sentence.strip(),
+                "gold_segments": gold.dcs_chunks,
+                "lemmas": gold.lemmas,
+                "morph_tags": gold.cng,
+                "candidates": candidates
+            }
+
+            json.dump(sample, out_fp, ensure_ascii=False)
+            out_fp.write("\n")
+            success_count += 1
+
+        except Exception as e:
+            log_fp.write(f"[ERROR] {sid}: {str(e)}\n")
+            error_count += 1
+
+# === Done ===
+print(f"\n✅ Conversion complete: {success_count} samples written")
+print(f"⚠️  {error_count} samples skipped — see {log_file} for details")

data.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0b5a32738d3f8e390ea5bc2c57e553c6cdd45107441331bfd97bc8104a7e8d7
+size 653382714

dataset.py

@@ -0,0 +1,42 @@
+import json
+import datasets
+
+class SanskritWordSegmentation(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="Gold-standard Sanskrit word segmentation and morphological candidate graphs.",
+            features=datasets.Features({
+                "id": datasets.Value("string"),
+                "sentence": datasets.Value("string"),
+                "gold_segments": datasets.Sequence(datasets.Value("string")),
+                "lemmas": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+                "morph_tags": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+                "candidates": datasets.Sequence({
+                    "id": datasets.Value("string"),
+                    "word": datasets.Value("string"),
+                    "lemma": datasets.Value("string"),
+                    "morph": datasets.Value("string"),
+                    "cng": datasets.Value("string"),
+                    "chunk_no": datasets.Value("string"),
+                    "position": datasets.Value("int32"),
+                    "length": datasets.Value("int32"),
+                    "pre_verb": datasets.Value("string"),
+                }),
+            }),
+            supervised_keys=None,
+            homepage="https://zenodo.org/records/803508",
+            citation="Krishna et al. (2017), A Dataset for Sanskrit Word Segmentation",
+        )
+
+    def _split_generators(self, dl_manager):
+        data_path = dl_manager.download_and_extract("data.jsonl")
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                yield idx, json.loads(line)