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# Ruby dataset
**Custom ruby dataset**
- rspec_dataset
**Bigcode dataset**
- ruby-dataset
- shell-dataset
- python-dataset
- sql-dataset
## rspec dataset
Specs are exclusively gathered from the 'app/services' directory within the specified repositories. This approach is employed since the majority of business logic is encapsulated within these services
```py
REPO_URLS = [
'https://github.com/diaspora/diaspora.git',
'https://github.com/mastodon/mastodon.git',
'https://github.com/gitlabhq/gitlabhq.git',
'https://github.com/discourse/discourse.git',
'https://github.com/chatwoot/chatwoot.git',
'https://github.com/opf/openproject.git',
]
```
output
```sh
Repository Avg Source Lines Avg Test Lines Test Cases
diaspora 62 156 12
mastodon 97 131 59
gitlabhq 66 154 952
discourse 188 303 49
chatwoot 63 107 50
openproject 86 178 98
------------------------------------------------------------
Total 74 159 1220
------------------------------------------------------------
# avg_source_lines = [62, 97, 66, 188, 63, 86]
# avg_test_lines = [156, 131, 154, 303, 107, 178]
# test_cases = [12, 59, 952, 49, 50, 98]
# Assuming an average of 10 tokens per line of code, which is a rough average for programming languages
# tokens_per_line = 10
# Calculating the total tokens for source and test lines
# total_source_tokens = sum([lines * tokens_per_line for lines in avg_source_lines])
# total_test_tokens = sum([lines * tokens_per_line for lines in avg_test_lines])
# Total tokens
# total_tokens = total_source_tokens + total_test_tokens
# Average tokens per test case
# avg_tokens_per_test_case = total_tokens / sum(test_cases)
# total_tokens, avg_tokens_per_test_case
# -> (15910, 13.040983606557377)
```
When you prepare data for training or inference with an LLM, each example (in this case, each test case or code snippet) needs to fit within this context window. The average tokens per test case calculated earlier (approximately 13.04 tokens) is well within the limits of LLMs
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