doc dict | metric dict | model_response dict |
|---|---|---|
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"query": "\"Gilarawan sa web site sa istasyon ang pasundayag nga \"\"daang istilong teatro sa radyo nga adunay bag-o ug makalilisang nga geeky nga spin!\"\" Sa mga nahaunang adlaw niini, ang pasundayag gipakita ra sa dugay nang internet site sa radyo nga TogiNet Radyo, usa ka site nga nag-pokus sa pakigpulong sa radyo. Sa hinapos sa 2015, gihimong subsidiyaryong estasyon sa TogiNet ang AstroNet Radio. Sa sugod, mga bag-ohan nga aktor sa tingog ang gina-feature sa programa, mga lokal sa East Texas.\"\nPangutana: Ang istasyon naghulagway sa show ingong dunay unsang klaseng spin?\n A. Mga Bag-ohan\n B. Geeky\n C. Daang istilo\n D. Lokal\nTubag:",
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"query": "Ang pagparehistro usa ka dugang kinahanglanon sa pagproseso sa bisa. Sa pila ka mga nasod, kinahanglan nimong iparehistro ang imong kaugalingon ug ang adres diin ka nagapuyo sa lokal nga mga awtoridad. Mahimo kining manginahanglan nga pun-on ang usa ka form sa lokal nga pulisya o pagbisita sa opisina sa imigrasyon. Sa daghang nasod nga adunay ingon niana nga balaud, ang mga lokal nga hotel ang mopahigayon sa pagparehistro (sigurado-a nga mangutana ka). Sa ubang mga sitwasyon, kadto lang wala nagpuyo sa akomodasyong pangturista ang kinahanglan magparehistro. Bisan pa, kini nakapahimo sa balaod nga dili tin-aw, busa hibaw-a daan.\nPangutana: Base sa istorya, unsang pahayag bahin sa kinahanglanon sa pagparehistro para sa pagproseso sa bisa ang dili tinuod?\n A. Kon nagpuyo ka sa usa ka akomodasyong pangturista, lagmit dili na nimo kinahanglang magparehistro\n B. Tanang mga nasod kinahanglang magparehistro sa lokal nga mga awtoridad\n C. Lagmit kinahanglanon ang pagbisita sa usa sa mga opisina sa imigrasyon\n D. Sa pipila ka mga lugar, ang lokal nga mga hotel ang mopahigayon sa pagparehistro\nTubag:",
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"query": "Ang mga ngipon sa usa ka triceratops makadugmok gyud dili lamang sa mga dahon apan apil ang mga gahi kaayo nga sanga ug gamot. Nagatuo ang ubang mga siyentista nga ang mga Triceratops kay mikaon og mga cycad, usa ka tanom nga komon sa Cretaceous. Kani nga mga pananom kay mura uo gamay nga kahoyng palmera nga adunay hait nga korona ug tunokon nga mga dahon. Posibleng gigamit sa usa ka Triceratops ang kusgan nga sungo niini aron kuhaon ang mga dahon sa dili pa kan-on ang punoan. Nagpatuo ang ubang mga siyentista nga kaning mga tanom kay makahilo gayod busa walay dinosaur nga mikaon niini, bisan pa kon karon ang sloth ug ubang mga hayop pananglitan ang parrot (usa ka kaliwat sa mga dinosuar) kay makakaon sa makahilong mga dahon o prutas.\nPangutana: Sumala sa istorya, unsang bahin sa cycad nga tanom ang lagmit kaonon sa Triceratops?\n A. Ang tunukong mga tanom\n B. Ang punoan\n C. Ang gamot\n D. Ang prutas\nTubag:",
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End of preview. Expand
in Data Studio
Dataset Card for Evaluation run of ljvmiranda921/msde-sft-dev
Dataset automatically created during the evaluation run of model ljvmiranda921/msde-sft-dev.
The dataset is composed of 13 configuration, each one corresponding to one of the evaluated task.
The dataset has been created from 10 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run.
To load the details from a run, you can for instance do the following:
from datasets import load_dataset
data = load_dataset("ljvmiranda921/details_msde-google_gemma-3-4b-pt-lora-4bit-tgl_10k-AyaExp",
"results",
split="train")
Latest results
These are the latest results from run 2026-01-20T11:55:09.787124(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
{
"all": {
"acc": 0.625,
"acc_stderr": 0.06527912098338669
},
"universalner_tgl_mcf|0": {
"acc": 0.625,
"acc_stderr": 0.06527912098338669
}
}
Dataset Details
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