adjusted preview

README.md

@@ -22,6 +22,14 @@ You can find more about our subnet on GitHub [here](https://github.com/afterpart
 
 ---
 
+## Full Vectors Access
+
+➡️ **Download the full uncompressed conversation tags embeddings** from [here](https://huggingface.co/datasets/ReadyAi/5000-podcast-conversations-with-metadata-and-embedding-dataset/tree/main/data)
+
+For large-scale processing and fine-tuning.
+
+---
+
 ## 📋 Dataset Overview
 
 This dataset contains **annotated conversation transcripts** with:

conversations_train.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9477b7504b3e6efa6911eff198e86cd1cc7262e0e1ef39db78b26041cd5e8b31
-size 149841685

dataset.py

@@ -1,103 +1,38 @@
-import glob
-
-import datasets
 import pandas as pd
 from datasets import DatasetInfo, Features, GeneratorBasedBuilder, Sequence, Split, SplitGenerator, Value
 
 
 class PodcastConversationsWithMetadataAndEmbedding(GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="conversations", version=datasets.Version("1.0.0"), description="Transcripts with speaker metadata"),
-        datasets.BuilderConfig(name="full", version=datasets.Version("1.0.0"), description="Tag embeddings for all conversations"),
-        datasets.BuilderConfig(name="conversations_to_tags", version=datasets.Version("1.0.0"), description="Tag predictions with similarity scores"),
-        datasets.BuilderConfig(name="tag_to_id", version=datasets.Version("1.0.0"), description="Dictionary of tag IDs"),
-    ]
-
     def _info(self):
-        if self.config.name == "conversations":
-            features = Features(
+        return DatasetInfo(
+            features=Features(
                 {
                     "c_guid": Value("string"),
                     "participants": Sequence(Value("string")),
-                    "transcript": Sequence(
-                        {
-                            "chunk": Value("string"),
-                            "speaker": Value("string"),
-                            "text": Value("string"),
-                        }
-                    ),
-                }
-            )
-        elif self.config.name == "full":
-            features = Features(
-                {
-                    "c_guid": Value("string"),
-                    "tag_id": Value("int64"),
-                    "tag": Value("string"),
-                    "vector": Sequence(Value("float32")),
-                }
-            )
-        elif self.config.name == "conversations_to_tags":
-            features = Features({"c_guid": Value("string"), "tag_id": Value("int64"), "tag": Value("string")})
-        elif self.config.name == "tag_to_id":
-            features = Features(
-                {
-                    "tag_id": Value("int64"),
-                    "tag": Value("string"),
+                    "transcript": Sequence({"chunk": Value("string"), "speaker": Value("string"), "text": Value("string")}),
                 }
             )
-        else:
-            raise ValueError(f"Unknown config name: {self.config.name}")
-
-        return DatasetInfo(
-            features=features,
-            description=f"Split for {self.config.name} of the podcast conversation dataset.",
         )
 
     def _split_generators(self, dl_manager):
-        if self.config.name == "conversations":
-            return [
-                SplitGenerator(
-                    name=Split.TRAIN,
-                    gen_kwargs={"filepaths": ["conversations_train.parquet"]},
-                )
-            ]
-        elif self.config.name == "full":
-            return [
-                SplitGenerator(
-                    name=Split.TRAIN,
-                    gen_kwargs={"filepaths": sorted(glob.glob("data/bittensor-conversational-tags-and-embeddings-part-*.parquet"))},
-                ),
-            ]
-        elif self.config.name == "conversations_to_tags":
-            return [
-                SplitGenerator(
-                    name=Split.TRAIN,
-                    gen_kwargs={"filepaths": ["conversations_to_tags.parquet"]},
-                ),
-            ]
-        elif self.config.name == "tag_to_id":
-            return [
-                SplitGenerator(
-                    name=Split.TRAIN,
-                    gen_kwargs={"filepaths": ["tag_to_id.parquet"]},
-                ),
-            ]
-        else:
-            raise ValueError(f"Unknown config name: {self.config.name}")
-
-    def _generate_examples(self, filepaths):
-        for path in filepaths:
-            df = pd.read_parquet(path)
-
-            for idx, row in df.iterrows():
-                record = row.to_dict()
-
-                # Normalize vector field if it exists
-                if "vector" in record:
-                    if isinstance(record["vector"], (list, tuple)):
-                        record["vector"] = list(map(float, record["vector"]))
-                    else:
-                        record["vector"] = []
-
-                yield f"{path}-{idx}", record
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"filepath": "conversations.parquet"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        df = pd.read_parquet(filepath)
+
+        for idx, row in df.iterrows():
+            record = row.to_dict()
+
+            # Normalize vector field if it exists
+            if "vector" in record:
+                if isinstance(record["vector"], (list, tuple)):
+                    record["vector"] = list(map(float, record["vector"]))
+                else:
+                    record["vector"] = []
+
+            yield idx, record