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docs: add condition-3-zh-5k to README (schema, configs, usage)

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@@ -231,15 +231,18 @@ Keyword-swapped variants are produced using [Legesher](https://github.com/legesh
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  ## Available Configs
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- | Config | Condition | Language | Description |
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- | ---------------- | --------------------- | -------- | ------------------------------------------------------------------------------------------------ |
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- | `condition-1-en` | Condition 1 (control) | English | Unmodified filtered Python from The Stack Dedup |
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- | `condition-2-ur` | Condition 2 | Urdu | Keyword-swapped Python -- 37 keywords, 72 builtins, 66 exceptions translated via Legesher v0.7.3 |
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- | `condition-2-zh` | Condition 2 | Chinese | Keyword-swapped Python -- same transpilation method |
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- | `condition-2-es` | Condition 2 | Spanish | Keyword-swapped Python -- same transpilation method |
 
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  ## Schema
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  | Column | Type | Description |
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  | ------------- | ------ | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
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  | `code` | string | Python source code. For condition-2 configs, this is the transpiled (keyword-swapped) version. For condition-1, this is the original English source. |
@@ -249,16 +252,31 @@ Keyword-swapped variants are produced using [Legesher](https://github.com/legesh
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  | `license` | string | SPDX license identifier for the source file. |
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  | `token_count` | int64 | Token count computed using the CohereLabs/tiny-aya-base tokenizer. |
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  ## Experimental Conditions
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  The Language Decoded experiment uses a ladder of six conditions to isolate the mechanism behind code's reasoning benefit. This dataset currently provides data for conditions 1 and 2:
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- | Condition | Name | Purpose |
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- | --------------- | -------------------- | -------------------------------------------------------------------------- |
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- | Baseline | No fine-tuning | Establishes the performance floor |
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- | Condition 1 | English code | Tests whether code fine-tuning helps at all (replicates Aryabumi et al.) |
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- | Condition 2 | Keyword-swapped code | Tests whether the _language_ of keywords matters for the reasoning benefit |
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- | Conditions 3--6 | (planned) | Additional controls not yet included in this dataset |
 
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  ## Usage
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@@ -273,9 +291,15 @@ ds = load_dataset("legesher/language-decoded-data", "condition-2-ur")
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  ds = load_dataset("legesher/language-decoded-data", "condition-2-zh")
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  ds = load_dataset("legesher/language-decoded-data", "condition-2-es")
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  # Access splits
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  train = ds["train"]
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  val = ds["validation"]
 
 
 
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  ```
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  ## Technical Details
 
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  ## Available Configs
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+ | Config | Condition | Language | Description |
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+ | --------------------- | --------------------- | -------- | ------------------------------------------------------------------------------------------------ |
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+ | `condition-1-en` | Condition 1 (control) | English | Unmodified filtered Python from The Stack Dedup |
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+ | `condition-2-ur` | Condition 2 | Urdu | Keyword-swapped Python -- 37 keywords, 72 builtins, 66 exceptions translated via Legesher v0.7.3 |
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+ | `condition-2-zh` | Condition 2 | Chinese | Keyword-swapped Python -- same transpilation method |
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+ | `condition-2-es` | Condition 2 | Spanish | Keyword-swapped Python -- same transpilation method |
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+ | `condition-3-zh-5k` | Condition 3 | Chinese | Blended: 3,486 native Chinese code + 1,514 transpiled Python (see Condition 3 section below) |
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  ## Schema
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+ ### Conditions 1--2
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+
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  | Column | Type | Description |
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  | ------------- | ------ | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
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  | `code` | string | Python source code. For condition-2 configs, this is the transpiled (keyword-swapped) version. For condition-1, this is the original English source. |
 
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  | `license` | string | SPDX license identifier for the source file. |
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  | `token_count` | int64 | Token count computed using the CohereLabs/tiny-aya-base tokenizer. |
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+ ### Condition 3
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+
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+ Condition 3 blends native Chinese code with transpiled code and adds a `source_type` column to distinguish them. `code_en` is populated for transpiled rows (keeping them in sync with conditions 1--2) but null for native code rows, which have no English equivalent.
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+
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+ | Column | Type | Description |
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+ | ------------- | ----------- | ---------------------------------------------------------------------------------- |
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+ | `file_path` | string | File identifier (native filename or transpiled file path) |
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+ | `code` | string | The code content (native or transpiled) |
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+ | `code_en` | string/null | English original -- populated for transpiled rows, null for native code rows |
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+ | `language` | string | ISO 639-1 language code (`zh`) |
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+ | `license` | string | Source license (SPDX identifier, `UNKNOWN`, or `varies`) |
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+ | `token_count` | int64 | Token count computed using the CohereLabs/tiny-aya-base tokenizer |
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+ | `source_type` | string | `"native"` (natively Chinese-authored) or `"transpiled"` (keyword-swapped English) |
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+
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  ## Experimental Conditions
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  The Language Decoded experiment uses a ladder of six conditions to isolate the mechanism behind code's reasoning benefit. This dataset currently provides data for conditions 1 and 2:
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+ | Condition | Name | Purpose |
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+ | --------------- | -------------------- | ----------------------------------------------------------------------------------------- |
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+ | Baseline | No fine-tuning | Establishes the performance floor |
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+ | Condition 1 | English code | Tests whether code fine-tuning helps at all (replicates Aryabumi et al.) |
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+ | Condition 2 | Keyword-swapped code | Tests whether the _language_ of keywords matters for the reasoning benefit |
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+ | Condition 3 | Mixed native sources | Tests whether diverse native-language code adds value beyond keyword swapping |
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+ | Conditions 4--6 | (planned) | Additional controls not yet included in this dataset |
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  ## Usage
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  ds = load_dataset("legesher/language-decoded-data", "condition-2-zh")
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  ds = load_dataset("legesher/language-decoded-data", "condition-2-es")
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+ # Load blended native + transpiled (condition 3)
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+ ds = load_dataset("legesher/language-decoded-data", "condition-3-zh-5k")
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+
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  # Access splits
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  train = ds["train"]
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  val = ds["validation"]
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+
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+ # Filter condition-3 by source type
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+ native_only = train.filter(lambda x: x["source_type"] == "native")
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  ```
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  ## Technical Details