minishlab/potion-code-16M
Updated • 10 • 2
text stringlengths 25 2.19k | embedding listlengths 768 768 |
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def get _ albums _ from _ artist ( self, artist, type = ( " album ", " single ", " appears _ on ", " compilation " ), market = none ) : q = { " include _ groups " : ", ". join ( type ), " market " : market or self. user _ market ( ), " limit " : 50 } url = " artists / { } / albums ". format ( artist ['id'] ) page = sel... | [
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def non _ dominated _ sort ( objectives ) : extended = np. tile ( objectives, ( objectives. shape [ 0 ], 1, 1 ) ) dominance = np. sum ( np. logical _ and ( np. all ( extended < = np. swapaxes ( extended, 0, 1 ), axis = 2 ), np. any ( extended < np. swapaxes ( extended, 0, 1 ), axis = 2 ) ), axis = 1 ) return objectives... | [
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def extract _ article ( self ) : # get an article html = self. download _ page ( ) soup = beautifulsoup ( html, " html5lib " ) if not soup : raise valueerror ( " sorry, i could not parse that page properly " ) # get author, title and name author _ tag = soup. find ( rel = " author " ) self. author = author _ tag and au... | [
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def test _ atomic _ id _ pattern _ nistxml _ sv _ iv _ atomic _ id _ pattern _ 1 _ 4 ( mode, save _ output, output _ format ) : assert _ bindings ( schema = " nistdata / atomic / id / schema + instance / nistschema - sv - iv - atomic - id - pattern - 1. xsd ", instance = " nistdata / atomic / id / schema + instance / n... | [
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def test _ atomic _ language _ max _ length _ nistxml _ sv _ iv _ atomic _ language _ max _ length _ 1 _ 1 ( mode, save _ output, output _ format ) : assert _ bindings ( schema = " nistdata / atomic / language / schema + instance / nistschema - sv - iv - atomic - language - maxlength - 1. xsd ", instance = " nistdata /... | [
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def _ is _ colour _ valid ( self, tag : str ) : colour _ to _ check = tag. replace ('','' ). replace ('< ','' ). replace ('> ','' ). upper ( ) return colour _ to _ check in colour _ dict | [
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def connects ( r1, s1, r2, s2, topology ) : for pkt, out _ port in r1. apply ( r1. pattern ) : new _ switch, in _ port = topology. node [ s1 ] ['port'] [ out _ port ] if new _ switch = = s2 : if r2. pattern. intersects ( pkt ) : return true return false | [
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def connect ( self, address = none, port = none ) : if not self. address : self. address = ( address, port ) elif not address and not port and not self. address : raise exception ( " address and port must be specified in " " constructor or in connect ( ) " ) self. control _ socket = ssl. wrap _ socket ( self. control _... | [
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def reviewspellcurrentcharacter ( self, inputevent ) : self. _ reviewcurrentcharacter ( inputevent, 2 ) return true | [
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def geo _ clustering _ dbscan ( df, min _ dist = 0. 6, min _ samples = 3 ) : x = df [ ['latitude ','longitude'] ]. copy ( ) distance _ matrix = squareform ( pdist ( x, ( lambda u, v : haversine ( u, v ) ) ) ) db = dbscan ( eps = min _ dist, min _ samples = min _ samples, metric ='precomputed') y _ db = db. fit _ predic... | [
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def do _ postprocesses ( self, job ) : # denoise # if self. settings. denoise _ enabled : denoise = denoise ( job = job ) denoise _ proc = self. do _ process ( obj = denoise, job = job ) if not denoise _ proc : return false # multicolumnskew # if self. settings. denoise _ enabled and self. settings. multi _ column _ sk... | [
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def poll _ for _ updates ( ) : logbuffer = next ( x for x in logging. getlogger ( ). handlers if isinstance ( x, logging. handlers. bufferinghandler ) ). buffer get _ props = lambda x : { k : v for k, v in x. _ _ dict _ _. iteritems ( ) if not k [ 0 ] = ='_'} old _ workflows = { workflow. id : get _ props ( workflow ) ... | [
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Pringled/cornstack-docs-tokenlearn Dataset Card
This dataset was created with Tokenlearn for training Model2Vec models on code retrieval. It contains mean token embeddings produced by nomic-ai/CodeRankEmbed, used as training targets for static embedding distillation.
The dataset contains code documents from CornStack across 6 programming languages (50,000 rows per language, 300,000 total).
Dataset Details
| Field | Value |
|---|---|
| Source | CornStack (nomic-ai) |
| Embedding model | nomic-ai/CodeRankEmbed |
| Embedding dimension | 768 |
| Languages | Python, Java, PHP, Go, JavaScript, Ruby |
| Rows per language | 50,000 |
| Total rows | 300,000 |
| Field | document |
Source Datasets
| Language | Source |
|---|---|
python |
nomic-ai/cornstack-python-v1 |
java |
nomic-ai/cornstack-java-v1 |
php |
nomic-ai/cornstack-php-v1 |
go |
nomic-ai/cornstack-go-v1 |
javascript |
nomic-ai/cornstack-javascript-v1 |
ruby |
nomic-ai/cornstack-ruby-v1 |
Dataset Structure
| Column | Type | Description |
|---|---|---|
text |
string |
Truncated input text (tokenizer max length 512) |
embedding |
list[float32] |
Mean token embedding from nomic-ai/CodeRankEmbed, excluding BOS/EOS tokens |
Usage
Load a single language config:
from datasets import load_dataset
# Load Python code documents
dataset = load_dataset("Pringled/cornstack-docs-tokenlearn", name="python")
# Load all languages and concatenate
from datasets import concatenate_datasets
all_langs = concatenate_datasets([
load_dataset("Pringled/cornstack-docs-tokenlearn", name=lang)["train"]
for lang in ["python", "java", "php", "go", "javascript", "ruby"]
])
Creation
Featurized from CornStack using nomic-ai/CodeRankEmbed with mean token pooling (BOS/EOS excluded). Two sampling seeds (42 and 100) were used with a 10k streaming shuffle buffer to maximise diversity. Texts are truncated to 512 tokens.
Library Authors
Tokenlearn was developed by the Minish team consisting of Stephan Tulkens and Thomas van Dongen.
Citation
@software{minishlab2024model2vec,
author = {Stephan Tulkens and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
publisher = {Zenodo},
doi = {10.5281/zenodo.17270888},
url = {https://github.com/MinishLab/model2vec},
license = {MIT}
}
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