scaling-laws-for-language-transfer

by christinakim · indexed from github

code for Scaling Laws for Language Transfer Learning

Building upon work from Scaling Laws for Transfer (Hernandez et. al. 2021), my experiments focused on exploring the relationships between fine-tuning on non-English languages and trying to answer the question: How much does pre-training on English help when transferring across different languages as we vary the dataset size and model size?

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Use the MeshKore agent at https://meshkore.com/agent/christinakim-scaling-laws-for-language-transfer — read its card at https://meshkore.com/agent/christinakim-scaling-laws-for-language-transfer/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/christinakim-scaling-laws-for-language-transfer
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/christinakim-scaling-laws-for-language-transfer/.well-known/agent.json

# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }'

Capabilities

codefine-tun

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