README.md

---
pretty_name: Fluid 2 — Dictation Cleanup Eval
language:
- en
license: other
task_categories:
- text-generation
size_categories:
- 1K<n<10K
tags:
- asr-correction
- dictation
- text-correction
- evaluation
- fluid-2
configs:
- config_name: default
  default: true
  data_files:
  - split: eval
    path: data/eval-*.parquet
---

# Fluid 2 — dictation cleanup eval

This repository contains **7,161 text-only voice-dictation cleanup evaluation
rows** for like-for-like model comparison. No audio is included or fetched.

| Split | Rows | Documents | Audio |
| --- | ---: | ---: | --- |
| `eval` | 7,161 | 2,718 | Not included |

## What this benchmark tests

The benchmark measures whether a model can turn noisy voice dictation into the
intended written text without answering it or adding content. The rows cover:

- local ASR, spelling, capitalization, punctuation, and grammar repair;
- filler removal, abandoned starts, cancellations, and correction chains;
- spoken punctuation plus line, paragraph, and list formatting;
- short notes and questions through long, multi-paragraph passages; and
- dictation with no surrounding context, one-sided context, or both previous
  and following context fields.

The 121 empty-target rows represent fully cancelled dictation and test whether
the model stops without emitting cleaned text.

## Measured content distribution

The following counts are computed over all 7,161 rows. Target-length counts
include the 121 empty targets.

| Clean-target length | Rows | Share |
| --- | ---: | ---: |
| Empty | 121 | 1.69% |
| 1–50 characters | 3,198 | 44.66% |
| 51–150 characters | 2,601 | 36.32% |
| 151–500 characters | 1,123 | 15.68% |
| 501+ characters | 118 | 1.65% |

| Available context | Rows | Share |
| --- | ---: | ---: |
| Neither side | 2,069 | 28.89% |
| Previous only | 2,665 | 37.22% |
| Following only | 655 | 9.15% |
| Previous and following | 1,772 | 24.75% |

The median ASR input is 14 words (95th percentile: 53; maximum: 596). Among
non-empty targets, the median is 11 words (95th percentile: 44; maximum: 427).
Of the 7,040 non-empty targets, 6,764 (96.08%) differ exactly from their ASR
input; 5,068 are shorter, 1,389 are longer, and 583 have the same character
length while still potentially differing in content.

Formatting behaviors are materially represented and the categories overlap:
1,090 targets contain a line break, 562 contain a blank-line paragraph break,
203 contain a Markdown-style list item, and 423 end in a question mark.

## Schema and loading

Only the seven columns consumed by text SFT and evaluation are present:

- `row_id`, `job_id`, `document_id`
- `prev_text`, `post_text`, `asr_text`, `clean_target`

```python
from datasets import load_dataset

eval_rows = load_dataset(
    "johnbean393/fluid-2-sft-eval",
    split="eval",
)
assert len(eval_rows) == 7_161
```

No `audio`, sampling-rate, duration, or other waveform-bearing column is
included, so ordinary loading cannot fetch or decode audio.

## Prompt templates

The Fluid 2 Beta rows are rendered as a raw completion prompt; do not apply a
chat template:

```text
<|dictation_clean_v1|>
<|start_prev_text|>{prev_text}<|end_prev_text|>
<|start_post_text|>{post_text}<|end_post_text|>
<|start_asr_text|>{asr_text}<|end_asr_text|>
<|start_target_text|>
```

The model generates `clean_target` and terminates at
`<|end_target_text|>`.

The instruction-tuned prompt-only Qwen and Gemma references, their Fluid
fine-tunes, DeepSeek V4 Flash, and GPT-5.6 Luna instead use chat messages. Qwen,
Gemma, and the Fluid fine-tunes use each GGUF's native template; DeepSeek uses
its official chat-completions API; Luna uses the local Codex Responses API. All
receive the same private system instruction and the row's `asr_text` verbatim as
the user message. The private instruction is intentionally not reproduced in
this public repository.

## Metrics

The Fluid 2 Beta rows use SGLang raw-completion inference. Instruction-tuned
Qwen/Gemma comparison rows use llama.cpp chat-completion inference with their
native GGUF templates and reasoning disabled during generation. DeepSeek uses
the official API with thinking disabled. Those paths use temperature-zero
decoding. Luna uses the local Codex Responses API with reasoning effort set to
none and proxy/model sampling defaults. The public scoreboard reports strict
exact match and character error rate (CER), while the audit also tracks
whitespace-normalized exact match, improvement over the raw-ASR copy baseline,
empty/control-token failures, and context-bucket breakdowns. Strict metrics
remain case-, punctuation-, and formatting-sensitive because those are core
behaviors of the cleanup model.

Rows with an empty `clean_target` supervise only the terminal control token.
The harness reports their empty-completion/stop behavior separately and excludes
them from the EM and CER denominators. Generations that reach the configured
completion-token limit are likewise reported separately and excluded from those
text-quality scores. Throughput and inference-failure counts still cover the full
7,161-request census.

<!-- FLUID2_EVAL_RESULTS_START -->
## Published model results

Fluid 2 Beta results use deterministic greedy SGLang raw-completion inference. The prompt-only Qwen and Gemma references and their Fluid fine-tunes use deterministic greedy llama.cpp chat-completion inference with each GGUF's native chat template and reasoning disabled at generation. DeepSeek V4 Flash uses its official chat-completions API with thinking disabled. GPT-5.6 Luna uses the local Codex Responses API with reasoning effort set to none. All nine models receive the same 7,161-row text-only development set; every chat model receives the same fixed private system instruction and verbatim `asr_text` user message. EM and CER exclude both EOS-only rows and generation-capped rows; the exclusion counts are shown explicitly.

| Model | Scored rows | EOS-only excluded | Capped excluded | EM | CER | Evaluated revision |
|---|---:|---:|---:|---:|---:|---|
| [Fluid 2 Qwen3.5 0.8B Beta](https://huggingface.co/johnbean393/fluid-2-qwen3.5-0.8b-beta) | 7,010 | 121 | 30 | 27.6605% | 17.8294% | `e42c24cc3b71` |
| [Fluid 2 Qwen3.5 2B Beta](https://huggingface.co/johnbean393/fluid-2-qwen3.5-2b-beta) | 7,016 | 121 | 24 | 30.3449% | 17.4199% | `f71a0445a8f1` |
| [Fluid 2 Qwen3.5 4B Beta](https://huggingface.co/johnbean393/fluid-2-qwen3.5-4b-beta) | 7,024 | 121 | 16 | 33.5849% | 16.4947% | `f6e0aaa0dac4` |
| [Qwen3.5-2B (prompt only)](https://huggingface.co/Qwen/Qwen3.5-2B) | 6,996 | 121 | 44 | 4.4025% | 47.9775% | `15852e8c1636` |
| [Fluid-1 Mini](https://huggingface.co/altic-dev/fluid-1-mini) | 7,023 | 121 | 17 | 5.2684% | 28.1837% | `fc9e22028bf2` |
| [Gemma 4 E2B IT (prompt only)](https://huggingface.co/google/gemma-4-E2B-it) | 7,034 | 121 | 6 | 5.7293% | 42.3142% | `3e22461f65e8` |
| [FluidIntelligence](https://huggingface.co/altic-dev/FluidIntelligence) | 7,023 | 121 | 17 | 5.8522% | 27.5391% | `0980ee1c2019` |
| [DeepSeek V4 Flash (prompt only)](https://api-docs.deepseek.com/quick_start/pricing) | 7,033 | 121 | 7 | 6.3984% | 46.1081% | `a26a7955944d` |
| [GPT-5.6 Luna (prompt only)](https://developers.openai.com/) | 6,984 | 121 | 57 | 6.2285% | 32.8253% | `gpt-5.6-luna` |
| Raw `asr_text` copy baseline (0.8B cohort) | 7,010 | 121 | 30* | 3.9372% | 39.0639% | — |

The llama.cpp comparison runs use `temperature=0`, `top_k=1`, `top_p=1`, `min_p=0`, a fixed request seed, a 4,096-token completion limit, no speculative decoding, and `--reasoning off --reasoning-budget 0`.

The DeepSeek run uses `deepseek-v4-flash`, `temperature=0`, `top_p=1`, a 4,096-token completion limit, and the official `thinking: {type: disabled}` switch. Its evaluated revision is the API-returned system fingerprint.

The GPT-5.6 Luna run uses the local Codex Responses API, model `gpt-5.6-luna`, proxy/model sampling defaults with no sampling override, `reasoning.effort=none`, a 4,096-token output limit, and a 64-request client-side in-flight ceiling. At the requested early stop, the 57 unreturned proxy-timeout rows are treated as capped exclusions; one of those rows is also EOS-only, so the exclusion columns are not disjoint for this run.

\* The raw-ASR copy baseline uses the identical 7,010-row scoring cohort as the 0.8B model. Its capped-excluded count is inherited from the 0.8B generation outcomes; the copy baseline itself does not generate or hit a token cap.

## Paired prompt-only comparison

Each delta below is recomputed on the identical intersection of rows eligible for both models: empty-target rows and any row capped by either member of the pair are excluded. Positive EM gain and positive relative CER reduction indicate improvement by the Fluid fine-tune.

| Reference → Fluid fine-tune | Common scored rows | Union capped excluded | Reference EM | Fine-tuned EM | EM gain | Reference CER | Fine-tuned CER | Relative CER reduction |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B) → [altic-dev/fluid-1-mini](https://huggingface.co/altic-dev/fluid-1-mini) | 6,994 | 46 | 4.4038% | 5.2902% | +0.8865 pp | 47.9434% | 28.1521% | +41.2806% |
| [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) → [altic-dev/FluidIntelligence](https://huggingface.co/altic-dev/FluidIntelligence) | 7,022 | 18 | 5.7391% | 5.8530% | +0.1139 pp | 41.9034% | 27.5406% | +34.2760% |

Both comparisons are size- and family-matched: Qwen 3.5 2B and Fluid-1 Mini are evaluated at Q6_K, while Gemma 4 E2B and FluidIntelligence are evaluated at Q4_K_M.

<!-- FLUID2_EVAL_RESULTS_END -->