Duplicate from honicky/hdfs-logs-encoded-blocks

Co-authored-by: RJ Honicky <honicky@users.noreply.huggingface.co>

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README.md

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+---
+language: en
+tags:
+- log-analysis
+- hdfs
+- anomaly-detection
+license: mit
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
+dataset_info:
+  features:
+  - name: event_encoded
+    dtype: string
+  - name: tokenized_block
+    sequence: int64
+  - name: block_id
+    dtype: string
+  - name: label
+    dtype: string
+  - name: __index_level_0__
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 1159074302
+    num_examples: 460048
+  - name: validation
+    num_bytes: 145089712
+    num_examples: 57506
+  - name: test
+    num_bytes: 144844752
+    num_examples: 57507
+  download_size: 173888975
+  dataset_size: 1449008766
+---
+
+# HDFS Logs Train/Val/Test Splits
+
+This dataset contains preprocessed HDFS log sequences split into train, validation, and test sets for anomaly detection tasks.
+
+## Dataset Description
+
+The dataset is derived from the HDFS log dataset, which contains system logs from a Hadoop Distributed File System (HDFS).
+Each sequence represents a block of log messages, labeled as either normal or anomalous. The dataset has been preprocessed
+using the Drain algorithm to extract structured fields and identify event types. 
+
+### Data Fields
+
+- `block_id`: Unique identifier for each HDFS block, used to group log messages into blocks
+- `event_encoded`: The preprocessed log sequence with event IDs and parameters
+- `tokenized_block`: The tokenized log sequence, used for training
+- `label`: Classification label ('Normal' or 'Anomaly')
+
+### Data Splits
+
+- Training set: 460,049 sequences (80%)
+- Validation set: 57,506 sequences (10%)
+- Test set: 57,506 sequences (10%)
+
+The splits are stratified by the Label field to maintain class distribution across splits.
+
+## Source Data
+
+Original data source: https://zenodo.org/records/8196385/files/HDFS_v1.zip?download=1
+
+## Preprocessing
+
+We preprocess the logs using the Drain algorithm to extract structured fields and identify event types.
+We then encode the logs using a pretrained tokenizer and add special tokens to separate event types. This
+dataset should be immediately usable for training and testing models for log-based anomaly detection.
+
+## Intended Uses
+
+This dataset is designed for:
+- Training log anomaly detection models
+- Evaluating log sequence prediction models
+- Benchmarking different approaches to log-based anomaly detection
+
+see [honicky/pythia-14m-hdfs-logs](https://huggingface.co/honicky/pythia-14m-hdfs-logs) for an example model.
+
+## Citation
+
+If you use this dataset, please cite the original HDFS paper:
+```bibtex
+@inproceedings{xu2009detecting,
+  title={Detecting large-scale system problems by mining console logs},
+  author={Xu, Wei and Huang, Ling and Fox, Armando and Patterson, David and Jordan, Michael I},
+  booktitle={Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles},
+  pages={117--132},
+  year={2009}
+}
+```

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