Add dataset card

README.md

@@ -1,41 +1,123 @@
 ---
-dataset_info:
-  features:
-  - name: blake2b
-    dtype: string
-  - name: mime_type
-    dtype: string
-  - name: source_path
-    dtype: string
-  - name: source_root
-    dtype: string
-  - name: extension
-    dtype: string
-  - name: source_size_bytes
-    dtype: int64
-  - name: sample_bytes
-    dtype: int32
-  - name: tokens
-    list: int32
-  splits:
-  - name: train
-    num_bytes: 4159696849
-    num_examples: 37111
-  - name: validation
-    num_bytes: 508386684
-    num_examples: 4591
-  - name: test
-    num_bytes: 539592038
-    num_examples: 4748
-  download_size: 2315936056
-  dataset_size: 5207675571
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+language:
+- en
+license: apache-2.0
+task_categories:
+- text-classification
+tags:
+- binary-analysis
+- file-type-detection
+- mime-type
+- magic-bytes
+- cybersecurity
+size_categories:
+- 10K<n<100K
 ---
+
+# Magic-BERT Binary File Classification Dataset
+
+This dataset contains tokenized binary file samples for training MIME type classification models.
+Each sample is a 64KB chunk from the beginning of a file, tokenized using a byte-level BPE tokenizer.
+
+## Dataset Description
+
+- **Total Samples:** 46,450
+- **Number of Classes:** 125
+- **Sequence Length:** 999999
+- **Vocabulary Size:** 32,768
+
+### Splits
+
+| Split | Samples |
+|-------|---------|
+| Train | 37,111 |
+| Validation | 4,591 |
+| Test | 4,748 |
+
+## Features
+
+Each sample contains:
+
+| Feature | Type | Description |
+|---------|------|-------------|
+| `blake2b` | string | Content hash (unique sample ID) |
+| `mime_type` | string | MIME type label |
+| `source_path` | string | Original file path |
+| `source_root` | string | Dataset source |
+| `extension` | string | File extension |
+| `source_size_bytes` | int64 | Original file size |
+| `sample_bytes` | int32 | Sample size (max 64KB) |
+| `tokens` | List[int32] | Tokenized input (999999 tokens) |
+
+## MIME Type Distribution (Top 20)
+
+| MIME Type | Count | Percentage |
+|-----------|-------|------------|
+| application/octet-stream | 2,000 | 4.3% |
+| application/vnd.ms-powerpoint | 2,000 | 4.3% |
+| audio/flac | 2,000 | 4.3% |
+| text/x-c | 2,000 | 4.3% |
+| text/plain | 1,999 | 4.3% |
+| image/png | 1,964 | 4.2% |
+| application/gzip | 1,574 | 3.4% |
+| image/svg+xml | 1,486 | 3.2% |
+| text/html | 1,454 | 3.1% |
+| application/vnd.ms-excel | 1,380 | 3.0% |
+| application/javascript | 1,340 | 2.9% |
+| application/x-7z-compressed | 1,200 | 2.6% |
+| image/gif | 1,156 | 2.5% |
+| text/csv | 1,128 | 2.4% |
+| image/webp | 1,014 | 2.2% |
+| application/zlib | 987 | 2.1% |
+| text/x-lisp | 980 | 2.1% |
+| application/x-gettext-translation | 965 | 2.1% |
+| text/x-c++ | 800 | 1.7% |
+| application/zstd | 781 | 1.7% |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("your-username/magic-bert-dataset")
+
+# Access splits
+train_data = dataset["train"]
+val_data = dataset["validation"]
+test_data = dataset["test"]
+
+# Example: Get a sample
+sample = train_data[0]
+print(f"MIME type: {sample['mime_type']}")
+print(f"Tokens: {len(sample['tokens'])}")
+```
+
+## Training
+
+This dataset is designed for use with BERT-style models for binary file classification:
+
+```python
+from transformers import BertForSequenceClassification
+
+model = BertForSequenceClassification.from_pretrained(
+    "your-model",
+    num_labels=125,
+)
+```
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{magic_bert_dataset,
+  title = {Magic-BERT Binary File Classification Dataset},
+  year = {2025},
+  publisher = {HuggingFace},
+}
+```
+
+## License
+
+This dataset is released under the Apache 2.0 License.