adding parquet files for default loader

README.md

@@ -1,8 +1,117 @@
+---
+language:
+- en
+- gu
+- hi
+- mr
+- ta
+- te
+- et
+- ne
+- si
+license: other
+license_name: cc-by-sa-4.0
+license_link: LICENSE
+tags:
+- nlp
+- machine-translation
+- quality-estimation
+- translation-quality-estimation
+- low-resource
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language_details: English, Gujarati, Hindi, Marathi, Tamil, Telugu, Estonian, Nepali, Sinhala
+pretty_name: Low-resource QE-DA
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other
+configs:
+- config_name: engu
+  data_files:
+  - split: train
+    path: train.engu.df.short.tsv
+  - split: validation
+    path: dev.engu.df.short.tsv
+  - split: test
+    path: test.engu.df.short.tsv
+- config_name: enhi
+  data_files:
+  - split: train
+    path: train.enhi.df.short.tsv
+  - split: validation
+    path: dev.enhi.df.short.tsv
+  - split: test
+    path: test.enhi.df.short.tsv
+- config_name: enmr
+  data_files:
+  - split: train
+    path: train.enmr.df.short.tsv
+  - split: validation
+    path: dev.enmr.df.short.tsv
+  - split: test
+    path: test.enmr.df.short.tsv
+- config_name: enta
+  data_files:
+  - split: train
+    path: train.enta.df.short.tsv
+  - split: validation
+    path: dev.enta.df.short.tsv
+  - split: test
+    path: test.enta.df.short.tsv
+- config_name: ente
+  data_files:
+  - split: train
+    path: train.ente.df.short.tsv
+  - split: validation
+    path: dev.ente.df.short.tsv
+  - split: test
+    path: test.ente.df.short.tsv
+- config_name: eten
+  data_files:
+  - split: train
+    path: train.eten.df.short.tsv
+  - split: validation
+    path: dev.eten.df.short.tsv
+  - split: test
+    path: test.eten.df.short.tsv
+- config_name: neen
+  data_files:
+  - split: train
+    path: train.neen.df.short.tsv
+  - split: validation
+    path: dev.neen.df.short.tsv
+  - split: test
+    path: test.neen.df.short.tsv
+- config_name: sien
+  data_files:
+  - split: train
+    path: train.sien.df.short.tsv
+  - split: validation
+    path: dev.sien.df.short.tsv
+  - split: test
+    path: test.sien.df.short.tsv
+- config_name: multilingual
+  data_files:
+  - split: train
+    path: train.*.df.short.tsv
+  - split: validation
+    path: dev.*.df.short.tsv
+  - split: test
+    path: test.*.df.short.tsv
+---
+
 # Low-resource QE-DA Dataset
 
 Direct Assessment (DA) quality estimation data for English→Indic (Gujarati, Hindi, Marathi, Tamil, Telugu) and related Estonian/Nepali/Sinhala pairs, released with the ALOPE work on LLM-based QE.
 
-- **Paper**: Sindhujan, A., Qian, S., Matthew, C.C.C., Orasan, C., and Kanojia, D. (2024). *ALOPE: Adaptive Layer Optimization for Translation Quality Estimation using Large Language Models.* In Second Conference on Language Modeling.
+- **Paper**: Sindhujan, A., Qian, S., Matthew, C.C.C., Orasan, C., and Kanojia, D. (2024). *ALOPE: Adaptive Layer Optimization for Translation Quality Estimation using Large Language Models.* In Second Conference on Language Modeling. ([arXiv](https://arxiv.org/html/2508.07484v1))
 - **Task**: Sentence-level quality estimation with human DA scores and z-scores; some splits include model scores and PE strings.
 - **Format**: Three splits (`train`, `dev`, `test`) per language pair, TSV with columns `index`, `original`, `translation`, `scores`, `mean`, `z_scores`, `z_mean` (+ optional `model_scores`, `PE`).
 

parquet/engu/dev.parquet

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parquet/engu/test.parquet

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parquet/engu/train.parquet

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+size 1471469

parquet/enhi/dev.parquet

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+size 259616

parquet/enhi/test.parquet

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parquet/enhi/train.parquet

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parquet/enmr/dev.parquet

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parquet/enmr/test.parquet

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parquet/enmr/train.parquet

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parquet/enta/dev.parquet

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parquet/enta/test.parquet

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parquet/enta/train.parquet

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parquet/ente/dev.parquet

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parquet/ente/test.parquet

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parquet/ente/train.parquet

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parquet/eten/dev.parquet

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parquet/eten/test.parquet

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parquet/multilingual/dev.parquet

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parquet/multilingual/test_engu.parquet

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parquet/multilingual/test_enhi.parquet

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