Datasets:
Tasks:
Translation
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
Tags:
code
License:
File size: 1,307 Bytes
6b2faea be4e835 6b2faea a8b685c 6b2faea b50c919 6b2faea acc6b3a 6b2faea acc6b3a 6b2faea b50c919 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
---
license: mit
task_categories:
- translation
language:
- en
tags:
- code
pretty_name: Base64 decode version1
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data/data.jsonl
---
# Dataset: Base64 decode version1
This dataset is for improving base64 decoding capabilities.
The number of bytes that are in the base64 encoded data spans between 0..127 bytes.
`GPT 4o` is great at base64 decoding.
However `llama3` is terrible at base64 decoding.
Short examples of what `data.jsonl` looks like:
```text
{"instruction": "Transform base64 to HEX", "input": "464pNBlIObA=", "output": "e3ae2934194839b0"}
{"instruction": "Decode Base64 to json", "input": "NQ==", "output": "[53]"}
{"instruction": "Base64 to Hexadecimal", "input": "ax0WaQ==", "output": "6b1d1669"}
{"instruction": "convert base64 to Hexadecimal", "input": "8X43", "output": "f17e37"}
{"instruction": "Change base64 to JSON", "input": "7MmBZO4=", "output": "[236,201,129,100,238]"}
{"instruction": "Json from Base64", "input": "ytBBCmPRA6De+Ow=", "output": "[202,208,65,10,99,209,3,160,222,248,236]"}
{"instruction": "BASE64 to Hex", "input": "m/A=", "output": "9bf0"}
```
# Generate dataset
```
PROMPT> python generate_dataset.py
```
This creates the `data.jsonl` file. |