the-stack-smol-xs

Tasks:

Text Generation

Sub-tasks:

language-modeling

Languages:

code

Dataset card Files Files and versions

xet

Community

roisincrtai

loubnabnl HF Staff commited on 7 days ago

Commit

e2b2b21

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Duplicate from bigcode/the-stack-smol-xs

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Co-authored-by: Loubna Ben Allal <loubnabnl@users.noreply.huggingface.co>

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README.md +48 -0
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+---
+annotations_creators: []
+language_creators:
+- crowdsourced
+language: ["code"]
+multilinguality:
+- multilingual
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+---
+## Dataset Description
+A small subset  of [the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset, with 87 programming languages, each  has 100 random samples from the original dataset for visualization.
+## Languages
+The dataset contains 87 programming languages:
+````
+'ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bison', 'bluespec', 'c',
+'c++', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir',
+'elm', 'emacs-lisp','erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell','html', 'idris', 'isabelle', 'java',
+'java-server-pages', 'javascript', 'julia', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell',
+ 'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog',
+  'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme',
+  'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex',
+  'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'zig'
+`````
+## Dataset Structure
+You can specify which language you want to load, python is loaded by default:
+```python
+# to load go:
+from datasets import load_dataset
+load_dataset("bigcode/the-stack-smol-xs", "go")
+DatasetDict({
+    train: Dataset({
+        features: ['content', 'lang', 'size', 'ext', 'max_stars_count', 'avg_line_length', 'max_line_length', 'alphanum_fraction'],
+        num_rows: 100
+    })
+})
+```

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