metadata
configs:
- config_name: en-tl
data_files:
- split: train
path: paracore_en-tl/*.parquet
- config_name: en-zh
data_files:
- split: train
path: paracore_en-zh/*.parquet
- config_name: en-th
data_files:
- split: train
path: paracore_en-th/*.parquet
- config_name: en-ru
data_files:
- split: train
path: paracore_en-ru/*.parquet
- config_name: en-ja
data_files:
- split: train
path: paracore_en-ja/*.parquet
- config_name: en-fr
data_files:
- split: train
path: paracore_en-fr/*.parquet
- config_name: en-de
data_files:
- split: train
path: paracore_en-de/*.parquet
- config_name: en-ar
data_files:
- split: train
path: paracore_en-ar/*.parquet
ParaRater: Enhancing Cross-Lingual Transfer in LLMs with Meta-Learning
ParaRater is a data selection method that enhances cross-lingual transfer by selecting the most valuable parallel pairs, forming high-impact parallel corpora with two meta-learned raters.
ParaCore
This is the parallel data filtered by ParaRater. English data is sampled from Common Crawl. The translation is conducted using Qwen3-8B. Each language contains about 1 B tokens.