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Update README.md

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@@ -60,7 +60,7 @@ ds = load_dataset("AdaMLLab/WebTerminal", "unfiltered")
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  ## How it was built
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- ### v0.1 Unfiltered
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  1. **Fast filter**: skip any document that doesn't contain obvious CLI indicators (`$`, `sudo`, `pip install`, `` ```bash ``, `root@`, etc.)
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  2. **Score**: remaining docs are scored (0-34) across five signals, each with a per-match point value and a cap:
@@ -77,14 +77,14 @@ Documents scoring >=5 are kept.
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  3. **Dedup**: exact dedup across both datasets using xxhash64 on full text. Removed 1,168 duplicates.
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- ### v0.2 Clean
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- The unfiltered subset is ~84-86% noise at lower score levels (5-12), which make up 93% of the data. The root cause: v0.1's scoring uses context-blind keyword matching CLI command names like `find`, `make`, `cat` appear in normal English prose, bare `$` matches currency amounts, and indented Python/SQL code gets scored as terminal content.
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  v0.2 applies a three-stage structural filter over the unfiltered data:
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- 1. **Context-aware gate**: instead of matching bare `$`, requires `$ sudo`, `$ git`, `$ docker`, etc. (dollar sign + space + known command). Eliminates ~87% of documents immediately.
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- 2. **Validation regex**: confirms a genuine structural terminal pattern exists shell prompts followed by real commands, `user@host:~$` patterns, Python REPL `>>>`, tracebacks, `` ```bash `` code blocks, Unix file permission listings, man page headers, shebangs.
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  3. **Weighted structural scoring** (`term_score_v2`): each pattern has a weight (1-3) and occurrences are capped. Documents need `term_score_v2 >= 3` to be kept.
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  | Weight | Signal | Max |
@@ -96,7 +96,7 @@ v0.2 applies a three-stage structural filter over the unfiltered data:
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  No indentation-based scoring. No context-blind command substring matching.
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- **Result**: 3.8% of the unfiltered data survives from 61.3M rows down to 2.33M rows. Quality jumps from ~15% to ~98% genuine terminal/CLI content.
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  ## Schema
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  ## How it was built
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+ ### v0.1 Unfiltered
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  1. **Fast filter**: skip any document that doesn't contain obvious CLI indicators (`$`, `sudo`, `pip install`, `` ```bash ``, `root@`, etc.)
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  2. **Score**: remaining docs are scored (0-34) across five signals, each with a per-match point value and a cap:
 
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  3. **Dedup**: exact dedup across both datasets using xxhash64 on full text. Removed 1,168 duplicates.
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+ ### v0.2 Clean
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+ The unfiltered subset is ~84-86% noise at lower score levels (5-12), which make up 93% of the data. The root cause: v0.1's scoring uses context-blind keyword matching, CLI command names like `find`, `make`, `cat` appear in normal English prose, bare `$` matches currency amounts, and indented Python/SQL code gets scored as terminal content.
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  v0.2 applies a three-stage structural filter over the unfiltered data:
85
 
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+ 1. **Context-aware**: instead of matching bare `$`, requires `$ sudo`, `$ git`, `$ docker`, etc. (dollar sign + space + known command). Eliminates ~87% of documents immediately.
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+ 2. **Validation regex**: confirms a genuine structural terminal pattern exists, shell prompts followed by real commands, `user@host:~$` patterns, Python REPL `>>>`, tracebacks, `` ```bash `` code blocks, Unix file permission listings, man page headers, shebangs.
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  3. **Weighted structural scoring** (`term_score_v2`): each pattern has a weight (1-3) and occurrences are capped. Documents need `term_score_v2 >= 3` to be kept.
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  | Weight | Signal | Max |
 
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  No indentation-based scoring. No context-blind command substring matching.
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+ **Result**: 3.8% of the unfiltered data survives, from 61.3M rows down to 2.33M rows. Quality jumps from ~15% to ~98% terminal/CLI content.
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  ## Schema
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