Super-squash branch 'main' using huggingface_hub

.gitattributes

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README.md

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+---
+language:
+- en
+license: mit
+size_categories:
+- 10K<n<100K
+task_categories:
+- text2text-generation
+dataset_info:
+- config_name: default
+  features:
+  - name: pageid
+    dtype: int64
+  - name: title
+    dtype: string
+  - name: revid
+    dtype: int64
+  - name: description
+    dtype: string
+  - name: categories
+    sequence: string
+  - name: inputs
+    dtype: string
+  - name: targets
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 829905155
+    num_examples: 44754
+  download_size: 489718761
+  dataset_size: 829905155
+- config_name: instruct
+  features:
+  - name: inputs
+    dtype: string
+  - name: targets
+    dtype: string
+  - name: _task_name
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 815113125
+    num_examples: 44754
+  download_size: 480497634
+  dataset_size: 815113125
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+- config_name: instruct
+  data_files:
+  - split: train
+    path: instruct/train-*
+source_datasets: euirim/goodwiki
+---
+
+# goodwiki-text2text-completion
+
+Wikipedia articles split on paragraphs and/or markdown headers pseudo-randomly.
+
+- inputs/outputs are in columns `inputs`/`targets`
+- see the `instruct` config with prompts prepended to the text in `inputs`
+- Source data: `euirim/goodwiki`
+
+
+```yml
+dataset_info:
+  features:
+  - name: pageid
+    dtype: int64
+  - name: title
+    dtype: string
+  - name: revid
+    dtype: int64
+  - name: description
+    dtype: string
+  - name: categories
+    sequence: string
+  - name: inputs
+    dtype: string
+  - name: targets
+    dtype: string
+```

data/train-00000-of-00002.parquet

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data/train-00001-of-00002.parquet

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dataset-preprocessor.py

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+from datasets import load_dataset
+import re
+import random
+
+def split_into_paragraphs(text):
+    # Split by markdown headers or double newlines
+    paragraphs = re.split(r'\n\n|(?=^#)', text, flags=re.MULTILINE)
+    return [p.strip() for p in paragraphs if p.strip()]
+
+def create_input_output_pairs(example):
+    paragraphs = example['paragraphs']
+    n_paragraphs = len(paragraphs)
+    
+    # Randomly select about half of the paragraphs for input
+    n_input = max(1, random.randint(n_paragraphs // 2 - 1, n_paragraphs // 2 + 1))
+    
+    input_paragraphs = paragraphs[:n_input]
+    output_paragraphs = paragraphs[n_input:]
+    
+    return {
+        'inputs': ' '.join(input_paragraphs),
+        'targets': ' '.join(output_paragraphs)
+    }
+
+def preprocess_dataset(dataset_name, text_column='text'):
+    # Load the dataset
+    dataset = load_dataset(dataset_name)
+    
+    # Split text into paragraphs
+    dataset = dataset.map(
+        lambda example: {'paragraphs': split_into_paragraphs(example[text_column])},
+        remove_columns=[text_column]
+    )
+    
+    # Create input-output pairs
+    preprocessed_dataset = dataset.map(
+        create_input_output_pairs,
+        remove_columns=['paragraphs']
+    )
+    
+    return preprocessed_dataset
+
+# Usage example
+if __name__ == "__main__":
+    # Replace 'your_dataset' with the actual dataset name
+    dataset_name = 'your_dataset'
+    
+    preprocessed_dataset = preprocess_dataset(dataset_name)
+    
+    # Print some examples
+    print(preprocessed_dataset['train'][:5])
+    
+    # Save the preprocessed dataset
+    preprocessed_dataset.save_to_disk("preprocessed_dataset")

instruct/train-00000-of-00002.parquet

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instruct/train-00001-of-00002.parquet

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