Embeddings, metadata, and topic-cluster density map

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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+- multilingual
+tags:
+- embeddings
+- lmarena
+- chatbot-arena
+- human-preference
+size_categories:
+- 10K<n<100K
+---
+# Chatbot Arena Conversations Embeddings
+
+![chatbot-arena-conversations — topic-cluster density map](topic_map.png)
+
+Embeddings of [agie-ai/lmsys-chatbot_arena_conversations](https://huggingface.co/datasets/agie-ai/lmsys-chatbot_arena_conversations), produced with [amkdg/Qwen3-Embedding-8B-NVFP4](https://huggingface.co/amkdg/Qwen3-Embedding-8B-NVFP4) — 4096-d,
+L2-normalized `float16` (cosine = dot product).
+
+- **65,960** conversations → **65,960** vectors
+- `emb.npy` — `float16 [65960, 4096]`
+- `meta.parquet` — one row per vector, aligned with `emb.npy`: `id, uuid, tag, chunk, n_chunks, count, source_ref`
+- `manifest.json` — counts and provenance
+
+## Usage
+
+```python
+import numpy as np, pyarrow.parquet as pq
+
+emb  = np.load("emb.npy", mmap_mode="r")           # [65960, 4096] float16
+meta = pq.read_table("meta.parquet").to_pandas()   # one row per vector, aligned with emb
+
+# A conversation = consecutive rows sharing one `uuid` (`chunk == 0` marks its start);
+# conversations longer than 8192 tokens span several chunk-rows.
+starts = meta.index[meta.chunk == 0]               # first row of each conversation
+```
+
+## Source mapping
+
+Each row carries `source_ref`, the locator back into [agie-ai/lmsys-chatbot_arena_conversations](https://huggingface.co/datasets/agie-ai/lmsys-chatbot_arena_conversations) — source_ref is `<question_id>|<a|b>` — arm a or b of that battle.
+
+## Notes
+
+**Both arms** of 32,980 original LMArena human-preference battles (2023, ~20 models, real users with human votes) -> 65,960 conversations. `tag` = the model that produced the arm. Pulled from the ungated `agie-ai/lmsys-chatbot_arena_conversations` mirror of the gated original; 19 duplicate battles removed. Distinct from arena-human-preference-140K, arena-expert-5K, and search-arena-24K.

emb.npy

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manifest.json

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+{
+  "dataset": "chatbot_arena",
+  "n_vectors": 65960,
+  "n_conversations": 65960,
+  "dim": 4096,
+  "dtype": "float16",
+  "deduped_by": null,
+  "dedup": "none (all rows kept)",
+  "order": "dataset (normalized parquet row order)",
+  "src": "embeds_chatbot_arena",
+  "dataset_rows": 65960,
+  "tag_meaning": "model (arena_hard) / language (oasst) / source id (sharegpt)",
+  "source_dataset": "lmsys/chatbot_arena_conversations (via agie-ai mirror)",
+  "join": "meta.parquet rows align with emb.npy; `source_ref` -> upstream row."
+}

meta.parquet

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