Add PPLX and Jina ColBERT results

# Add PPLX and Jina ColBERT results

## Summary

This PR adds complete standard-scope HAKARI-Bench results for two multilingual
late-interaction retrieval models:

| Model | Revision | Variants | Files | Overall base nDCG@10 |
| --- | --- | --- | ---: | ---: |
| `perplexity-ai/pplx-embed-v1-late-0.6b` | `f47f3a745228a39ec262ba1613dd9d2bd5d7b3f8` | base | 563 | 0.570230 |
| `jinaai/jina-colbert-v2` | `4552c4dc1ffd7d7a635b6a41a1077fe9c9cdd974` | base, 96d, 64d | 563 | 0.551971 |

Both models have all 563 standard evaluation tasks and all 550 non-overlapping
Overall tasks.

## Official settings and validation gate

The official Hugging Face model cards, checkpoint configuration, author blog or
paper, and PyLate usage were reviewed before the full runs.

`pplx-embed-v1-late-0.6b` uses exact MaxSim over 128-dimensional token
embeddings, `[Q] ` / `[D] ` prefixes, q32/d512, query expansion, and attention
to expansion tokens. It was evaluated in fp32 with SDPA and pinned remote code.
Its NanoBEIR-en mean nDCG@10 was `0.677272`, within `0.005149` of the
`lightonai/ColBERT-Zero` reference used in the official model card.

`jina-colbert-v2` uses exact MaxSim, `[QueryMarker]` / `[DocumentMarker]`,
q32/d300, expansion-token attention, and the documented 128/96/64-dimensional
Matryoshka variants. Its standard Transformers dispatch is `eager` because the
external XLM-R class does not advertise native `flash_attention_2`; the pinned
checkpoint's own `use_flash_attn: true` path uses the installed FlashAttention
implementation. NanoBEIR-en base/96d/64d means were `0.651298`, `0.638029`, and
`0.622335`. Across the 12 tasks shared with the official BEIR table, the base
scores correlated at Pearson `0.929994` and Spearman `0.916084`.

## Runtime

- Hardware: two NVIDIA GeForce RTX 5090 GPUs, one independent worker per
disjoint task partition.
- PPLX: Python 3.12, Transformers 5.12.1, SentenceTransformers 5.4.1, PyLate
1.4.0, fp32, SDPA.
- Jina: Python 3.12, Transformers 4.57.6, SentenceTransformers 5.4.1, PyLate
1.3.4, FlashAttention 2.8.3, bf16, eager standard dispatch plus the
checkpoint-native FlashAttention path.
- Batch sizes 64 and 128 were used across resumable task partitions. Batch size
only changes encoding throughput; model weights, token lengths, prefixes,
dtype, attention, variants, and exact MaxSim scoring were unchanged.

The evaluations were driven by the reviewed local model cards with `--all` or
equivalent disjoint standard-task partitions. Existing readable result files
were skipped on resume.

## Coverage and integrity audit

- 1,126/1,126 compressed result JSON files are present and readable.
- Each model has exactly 563 unique standard task keys, with no missing, extra,
or duplicate tasks.
- Dataset revisions are resolved and internally consistent.
- All aggregate scores are finite and within `[0, 1]`.
- Every result matches its pinned model revision, dtype, attention setting,
sequence length, backend, prefix, expansion, and exact-MaxSim metadata.
- PPLX has the expected base result on every task; Jina has base, 96d, and 64d
on every task.
- A merged-DuckDB audit produced 550/550 Overall tasks for both models. Overall
base means are `0.570230` for PPLX and `0.551971` for Jina, with Borda ranks
14 and 25 respectively. Their placement above the established 350M and
smaller ColBERT baselines is consistent with the NanoBEIR-en validation and
official BEIR profiles.

## Submission checklist

- [x] Only `.json.xz` result artifacts under the two model directories are
included.
- [x] No aggregate JSON, caches, DuckDB, logs, Markdown, or scratch files are
included.
- [x] Model and dataset revisions plus runtime metadata are preserved.
- [x] All 1,126 local files passed decompression, schema, score, coverage, and
metadata validation.

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