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Andreas Koepf
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GALLERY.md
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GALLERY.md
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@ -33,6 +33,7 @@ This gallery shows examples from all available datasets using their default conf
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- [spell_backward](#spell_backward)
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- [sudoku](#sudoku)
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- [syllogism](#syllogism)
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- [word_ladder](#word_ladder)
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- [word_sequence_reversal](#word_sequence_reversal)
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- [word_sorting](#word_sorting)
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@ -710,19 +711,19 @@ size = 500
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Example tasks:
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```
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Example 1:
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Question: How many times does the letter "w" appear in the text: "bed and enters his mechanical dresser Two minutes later the machine deposited him all dressed"?
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Answer: 1
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Metadata: {'span_length': 15, 'target_letter': 'w', 'span': ['bed', 'and', 'enters', 'his', 'mechanical', 'dresser', 'Two', 'minutes', 'later', 'the', 'machine', 'deposited', 'him', 'all', 'dressed']}
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Question: How many times does the letter "o" appear in the text: "bed and enters his mechanical dresser Two minutes later the machine deposited him all dressed"?
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Answer: 2
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Metadata: {'span_length': 15, 'target_letter': 'o', 'span': ['bed', 'and', 'enters', 'his', 'mechanical', 'dresser', 'Two', 'minutes', 'later', 'the', 'machine', 'deposited', 'him', 'all', 'dressed']}
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Example 2:
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Question: How many times does the letter "p" appear in the text: "it into a watering place"?
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Question: How many times does the letter "c" appear in the text: "it into a watering place"?
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Answer: 1
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Metadata: {'span_length': 5, 'target_letter': 'p', 'span': ['it', 'into', 'a', 'watering', 'place']}
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Metadata: {'span_length': 5, 'target_letter': 'c', 'span': ['it', 'into', 'a', 'watering', 'place']}
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Example 3:
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Question: How many times does the letter "t" appear in the text: "readable form accessible by the widest array of equipment including outdated"?
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Answer: 5
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Metadata: {'span_length': 11, 'target_letter': 't', 'span': ['readable', 'form', 'accessible', 'by', 'the', 'widest', 'array', 'of', 'equipment', 'including', 'outdated']}
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Question: How many times does the letter "o" appear in the text: "readable form accessible by the widest array of equipment including outdated"?
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Answer: 3
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Metadata: {'span_length': 11, 'target_letter': 'o', 'span': ['readable', 'form', 'accessible', 'by', 'the', 'widest', 'array', 'of', 'equipment', 'including', 'outdated']}
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```
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@ -1443,6 +1444,38 @@ Metadata: {'premise1': 'All butterflies are tigers', 'premise2': 'No tigers are
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```
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### word_ladder
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Generates word ladder transformation tasks
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Default configuration:
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```python
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min_word_length = 3
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max_word_length = 5
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min_chain_length = -1
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max_chain_length = -1
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seed = 42
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size = 500
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```
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Example tasks:
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```
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Example 1:
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Question: Transform the word 'CEILS' into 'ANIGH' by changing one letter at a time. Each step must create a valid English word (including plurals) and keep the same word length. Show the sequence of words needed.
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Answer: CEILS,TEILS,TEINS,THINS,THIGS,THIGH,AHIGH,ANIGH
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Metadata: {'start_word': 'CEILS', 'end_word': 'ANIGH', 'word_length': 5, 'chain_length': 8}
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Example 2:
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Question: Transform the word 'KAW' into 'EFS' by changing one letter at a time. Each step must create a valid English word (including plurals) and keep the same word length. Show the sequence of words needed.
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Answer: KAW,KAS,EAS,EFS
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Metadata: {'start_word': 'KAW', 'end_word': 'EFS', 'word_length': 3, 'chain_length': 4}
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Example 3:
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Question: Transform the word 'SAUT' into 'SKER' by changing one letter at a time. Each step must create a valid English word (including plurals) and keep the same word length. Show the sequence of words needed.
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Answer: SAUT,SHUT,SHET,SKET,SKER
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Metadata: {'start_word': 'SAUT', 'end_word': 'SKER', 'word_length': 4, 'chain_length': 5}
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```
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### word_sequence_reversal
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Generates word sequence reversal tasks from text spans
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@ -1491,21 +1524,21 @@ Example tasks:
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```
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Example 1:
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Question: Sort these words in ascending order (using ASCII/Unicode ordering) and return them as a comma-separated list:
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due, ever, many, generations
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Answer: due, ever, generations, many
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Metadata: {'original_words': ['due', 'ever', 'many', 'generations'], 'transformed_words': ['due', 'ever', 'many', 'generations'], 'direction': 'ascending', 'transformation': <TextTransformation.ORIGINAL: 'original'>, 'sorted_words': ['due', 'ever', 'generations', 'many']}
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Wolcott, keep, reaching, times
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Answer: Wolcott, keep, reaching, times
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Metadata: {'original_words': ['Wolcott', 'keep', 'reaching', 'times'], 'transformed_words': ['Wolcott', 'keep', 'reaching', 'times'], 'direction': 'ascending', 'transformation': <TextTransformation.ORIGINAL: 'original'>, 'sorted_words': ['Wolcott', 'keep', 'reaching', 'times']}
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Example 2:
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Question: Sort these words in descending order (using ASCII/Unicode ordering) and return them as a comma-separated list:
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change, 250, young
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Answer: young, change, 250
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Metadata: {'original_words': ['change', '250', 'young'], 'transformed_words': ['change', '250', 'young'], 'direction': 'descending', 'transformation': <TextTransformation.ORIGINAL: 'original'>, 'sorted_words': ['young', 'change', '250']}
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took, critical, condense
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Answer: took, critical, condense
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Metadata: {'original_words': ['took', 'critical', 'condense'], 'transformed_words': ['took', 'critical', 'condense'], 'direction': 'descending', 'transformation': <TextTransformation.ORIGINAL: 'original'>, 'sorted_words': ['took', 'critical', 'condense']}
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Example 3:
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Question: Sort these words in ascending order (using ASCII/Unicode ordering) and return them as a comma-separated list:
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industry, elementary, traverse, stepped, meals, rub, resultant, etheric, irritation
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Answer: elementary, etheric, industry, irritation, meals, resultant, rub, stepped, traverse
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Metadata: {'original_words': ['industry', 'elementary', 'traverse', 'stepped', 'meals', 'rub', 'resultant', 'etheric', 'irritation'], 'transformed_words': ['industry', 'elementary', 'traverse', 'stepped', 'meals', 'rub', 'resultant', 'etheric', 'irritation'], 'direction': 'ascending', 'transformation': <TextTransformation.ORIGINAL: 'original'>, 'sorted_words': ['elementary', 'etheric', 'industry', 'irritation', 'meals', 'resultant', 'rub', 'stepped', 'traverse']}
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apartment, yellow, Just, pleasure, collapse, different, purchasers, taking, opening
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Answer: Just, apartment, collapse, different, opening, pleasure, purchasers, taking, yellow
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Metadata: {'original_words': ['apartment', 'yellow', 'Just', 'pleasure', 'collapse', 'different', 'purchasers', 'taking', 'opening'], 'transformed_words': ['apartment', 'yellow', 'Just', 'pleasure', 'collapse', 'different', 'purchasers', 'taking', 'opening'], 'direction': 'ascending', 'transformation': <TextTransformation.ORIGINAL: 'original'>, 'sorted_words': ['Just', 'apartment', 'collapse', 'different', 'opening', 'pleasure', 'purchasers', 'taking', 'yellow']}
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```
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