Duplicate from mgoin/mlperf-inference-llama3.1-8b-data

Co-authored-by: Michael Goin <mgoin@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
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+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+dataset_info:
+  features:
+  - name: dataset
+    dtype: string
+  - name: system_prompt
+    dtype: string
+  - name: question
+    dtype: string
+  - name: output
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 34308821
+    num_examples: 13368
+  download_size: 34308821
+  dataset_size: 34308821
+---
+
+# CNN Evaluation Dataset
+
+This dataset contains 13368 examples from the CNN evaluation dataset, converted to the MLPerf Inference format.
+
+## Dataset Structure
+
+- **dataset**: Source dataset name ("cnn_eval")
+- **system_prompt**: Empty string (as requested)
+- **question**: Combined instruction and input text
+- **output**: Expected output/summary
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("path/to/this/dataset")
+print(dataset)
+
+# Access the training data
+train_data = dataset["train"]
+print(f"Number of examples: {len(train_data)}")
+
+# Example usage
+example = train_data[0]
+print(f"Question: {example['question'][:100]}...")
+print(f"Output: {example['output']}")
+```
+
+## Source
+
+Converted from cnn_eval.json using the following conversion script.
+Generated dataset matches the structure of [mgoin/mlperf-inference-llama3.1-405b-data](https://huggingface.co/datasets/mgoin/mlperf-inference-llama3.1-405b-data).
+
+```python
+#!/usr/bin/env python3
+
+import json
+import os
+from datasets import Dataset, DatasetDict
+from tqdm import tqdm
+
+
+def convert_cnn_to_parquet(input_file, output_dir="cnn_dataset_parquet"):
+    """
+    Convert cnn_eval.json to HuggingFace dataset format in parquet format.
+    This creates the exact same structure as the reference dataset:
+    mgoin/mlperf-inference-llama3.1-data
+
+    Args:
+        input_file: Path to cnn_eval.json
+        output_dir: Directory to save the HuggingFace dataset in parquet format
+    """
+
+    # Load the JSON data
+    print(f"Loading data from {input_file}...")
+    with open(input_file, "r") as f:
+        data = json.load(f)
+
+    print(f"Found {len(data)} items")
+
+    # Convert to HuggingFace format
+    converted_data = []
+
+    for item in tqdm(data, desc="Converting items"):
+        # Get the instruction template
+        instruction_template = item["instruction"]["llama"]
+
+        # Substitute {input} with the actual input text
+        question = instruction_template.format(input=item["input"])
+
+        # Create the HuggingFace dataset format
+        hf_item = {
+            "dataset": "cnn_eval",  # Source dataset name
+            "system_prompt": "",  # Empty as requested
+            "question": question,  # Combined instruction + input
+            "output": item["output"],  # Expected output
+        }
+
+        converted_data.append(hf_item)
+
+    # Create HuggingFace dataset
+    dataset = Dataset.from_list(converted_data)
+
+    # Create DatasetDict with train split
+    dataset_dict = DatasetDict({"train": dataset})
+
+    # Create the data directory structure like the reference dataset
+    data_dir = os.path.join(output_dir, "data")
+    os.makedirs(data_dir, exist_ok=True)
+
+    # Save as parquet in the data directory
+    parquet_file = os.path.join(data_dir, "train-00000-of-00001.parquet")
+    print(f"Saving dataset to {parquet_file}...")
+    dataset_dict["train"].to_parquet(parquet_file)
+
+    # Create README.md similar to the reference dataset
+    readme_content = """"""
+
+    readme_file = os.path.join(output_dir, "README.md")
+    with open(readme_file, "w") as f:
+        f.write(readme_content)
+
+    print(f"Conversion complete!")
+    print(f"Parquet file saved to: {parquet_file}")
+    print(f"README.md saved to: {readme_file}")
+    print(f"Total items: {len(converted_data)}")
+    print(f"File size: {os.path.getsize(parquet_file) / 1024 / 1024:.2f} MB")
+
+    # Print a sample for verification
+    print("\nSample converted item:")
+    sample = converted_data[0]
+    print(f"Dataset: {sample['dataset']}")
+    print(f"System prompt: '{sample['system_prompt']}'")
+    print(f"Question (first 200 chars): {sample['question'][:200]}...")
+    print(f"Output (first 100 chars): {sample['output'][:100]}...")
+
+    # Print dataset info
+    print(f"\nDataset info:")
+    print(f"Features: {dataset_dict['train'].features}")
+    print(f"Number of rows: {len(dataset_dict['train'])}")
+
+    return dataset_dict
+
+
+if __name__ == "__main__":
+    # Check if cnn_eval.json exists
+    input_file = "cnn_eval.json"
+    if not os.path.exists(input_file):
+        print(f"Error: {input_file} not found in current directory")
+        exit(1)
+
+    convert_cnn_to_parquet(input_file)
+
+```

data/train-00000-of-00001.parquet

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dataset_dict.json

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+{"splits": ["train"]}

train-00000-of-00001.parquet

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train/dataset_info.json

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+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "dataset": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "system_prompt": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "question": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "output": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

train/state.json

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+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "1b412bf751b47c46",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}