Loo

75
1
1 language
—
by
Clemylia
Language Model
OTHER
New
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Early-stage
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Quick Summary

Ce document présente le modèle Loo, ses fonctionnalités, ses conditions d'utilisation et les instructions pour son déploiement et son test.

Training Data Analysis

🔴 Low Quality (3.8/10)

Researched training datasets used by Loo with quality assessment

Specialized For

general
science
multilingual

Training Datasets (2)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

🚀 Utilisation et Déploiementpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "Clemylia/Loo-Math-Generator"
TOKENIZER_ID = "Clemylia/Loo-tokenizer" 

tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID)

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