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--- |
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license: llama3.2 |
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language: |
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- zh |
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- en |
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- it |
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- de |
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- fr |
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- ja |
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- ko |
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base_model: |
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- meta-llama/Llama-3.2-3B |
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- lianghsun/Llama-3.2-Taiwan-3B |
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datasets: |
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- lianghsun/tw-emergency-medicine-bench |
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- lianghsun/tw-legal-nlp |
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- lianghsun/tw-legal-synthetic-qa |
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- lianghsun/tw-law-article-qa |
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- lianghsun/tw-judgment-qa |
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- lianghsun/tw-judgment-gist-chat |
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- lianghsun/tw-bar-examination-2020-chat |
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- lianghsun/tw-structured-law-article |
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- lianghsun/tw-judgment-gist-chat |
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- lianghsun/tw-contract-review-chat |
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- lianghsun/reasoning-base-20k-chat |
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- lianghsun/vulnerability-mitigation-qa-zh_tw |
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- lianghsun/tw-instruct |
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- rombodawg/Everything_Instruct_Multilingual |
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- xzuyn/manythings-translations-alpaca |
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- neural-bridge/rag-dataset-12000 |
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- minyichen/glaive_toolcall_zh_tw |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- Taiwan |
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- ROC |
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- zh-tw |
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- instruct |
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- chat |
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- llama3.2 |
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- SLM |
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model-index: |
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- name: Llama-3.2-Taiwan-3B-Instruct |
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results: |
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- task: |
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type: text-generation |
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name: Single Choice Question |
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dataset: |
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type: lianghsun/tw-legal-benchmark-v1 |
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name: tw-legal-benchmark-v1 |
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metrics: |
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- name: single choice |
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type: accuracy |
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value: 31.1 |
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- task: |
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type: text-generation |
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name: Single Choice Question |
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dataset: |
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type: lianghsun/Formosa-bench |
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name: (Society) Formosa Taiwan Knowledge Bench |
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config: society |
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split: test |
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revision: v2024.11.27 |
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metrics: |
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- name: single choice |
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type: accuracy |
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value: 60.42 |
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- task: |
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type: text-generation |
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name: Single Choice Question |
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dataset: |
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type: lianghsun/Formosa-bench |
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name: (Governmnt) Formosa Taiwan Knowledge Bench |
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config: governmnt |
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split: test |
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revision: v2024.11.27 |
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metrics: |
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- name: single choice |
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type: accuracy |
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value: 44.25 |
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- task: |
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type: text-generation |
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name: Single Choice Question |
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dataset: |
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type: lianghsun/Formosa-bench |
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name: (Geography) Formosa Taiwan Knowledge Bench |
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config: geography |
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split: test |
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revision: v2024.11.27 |
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metrics: |
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- name: single choice |
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type: accuracy |
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value: 47.54 |
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- task: |
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type: text-generation |
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name: Single Choice Question |
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dataset: |
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type: lianghsun/Formosa-bench |
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name: (History) Formosa Taiwan Knowledge Bench |
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config: history |
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split: test |
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revision: v2024.11.27 |
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metrics: |
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- name: single choice |
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type: accuracy |
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value: 60 |
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- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (geography_of_taiwan) tmmlu++ |
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config: geography_of_taiwan |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
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- name: single choice |
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type: accuracy |
|
value: 36.2 |
|
- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (dentistry) tmmlu++ |
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config: dentistry |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
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type: accuracy |
|
value: 33.83 |
|
- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (technical) tmmlu++ |
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config: technical |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
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type: accuracy |
|
value: 35.07 |
|
- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (statistics_and_machine_learning) tmmlu++ |
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config: statistics_and_machine_learning |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
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- name: single choice |
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type: accuracy |
|
value: 28.57 |
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- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (clinical_psychology) tmmlu++ |
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config: clinical_psychology |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
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type: accuracy |
|
value: 29.6 |
|
- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (tve_design) tmmlu++ |
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config: tve_design |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 38.54 |
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- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (three_principles_of_people) tmmlu++ |
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config: three_principles_of_people |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
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type: accuracy |
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value: 48.2 |
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- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (introduction_to_law) tmmlu++ |
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config: introduction_to_law |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
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type: accuracy |
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value: 29.96 |
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- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (linear_algebra) tmmlu++ |
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config: linear_algebra |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
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type: accuracy |
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value: 21.43 |
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- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (agriculture) tmmlu++ |
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config: agriculture |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
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type: accuracy |
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value: 24.5 |
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- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (jce_humanities) tmmlu++ |
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config: jce_humanities |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
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type: accuracy |
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value: 38.89 |
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- task: |
|
type: question-answering |
|
name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (music) tmmlu++ |
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config: music |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
|
type: accuracy |
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value: 25.9 |
|
- task: |
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type: question-answering |
|
name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (secondary_physics) tmmlu++ |
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config: secondary_physics |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
|
type: accuracy |
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value: 33.04 |
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- task: |
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type: question-answering |
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name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (physics) tmmlu++ |
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config: physics |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
|
type: accuracy |
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value: 27.84 |
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- task: |
|
type: question-answering |
|
name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (advance_chemistry) tmmlu++ |
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config: advance_chemistry |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
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metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 27.64 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
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dataset: |
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type: ikala/tmmluplus |
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name: (junior_science_exam) tmmlu++ |
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config: junior_science_exam |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 30.05 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
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dataset: |
|
type: ikala/tmmluplus |
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name: (veterinary_pathology) tmmlu++ |
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config: veterinary_pathology |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 25.09 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
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dataset: |
|
type: ikala/tmmluplus |
|
name: (financial_analysis) tmmlu++ |
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config: financial_analysis |
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split: test |
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 25.13 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (national_protection) tmmlu++ |
|
config: national_protection |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 42.65 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (macroeconomics) tmmlu++ |
|
config: macroeconomics |
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split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 26.76 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (politic_science) tmmlu++ |
|
config: politic_science |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 27.44 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (ttqav2) tmmlu++ |
|
config: ttqav2 |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 61.06 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (junior_chinese_exam) tmmlu++ |
|
config: junior_chinese_exam |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 30.86 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
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name: (traditional_chinese_medicine_clinical_medicine) tmmlu++ |
|
config: traditional_chinese_medicine_clinical_medicine |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 25.9 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (junior_math_exam) tmmlu++ |
|
config: junior_math_exam |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 21.71 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (auditing) tmmlu++ |
|
config: auditing |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 21.82 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (anti_money_laundering) tmmlu++ |
|
config: anti_money_laundering |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 37.31 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (pharmacology) tmmlu++ |
|
config: pharmacology |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 30.68 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (trust_practice) tmmlu++ |
|
config: trust_practice |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 28.18 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (tve_mathematics) tmmlu++ |
|
config: tve_mathematics |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 18.67 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (human_behavior) tmmlu++ |
|
config: human_behavior |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 32.04 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (pharmacy) tmmlu++ |
|
config: pharmacy |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 22.76 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (tve_chinese_language) tmmlu++ |
|
config: tve_chinese_language |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 36.65 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (optometry) tmmlu++ |
|
config: optometry |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 25.11 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (physical_education) tmmlu++ |
|
config: physical_education |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 30.73 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (organic_chemistry) tmmlu++ |
|
config: organic_chemistry |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 35.78 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (tve_natural_sciences) tmmlu++ |
|
config: tve_natural_sciences |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 33.73 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (education) tmmlu++ |
|
config: education |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 37.9 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (mechanical) tmmlu++ |
|
config: mechanical |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 42.37 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (taiwanese_hokkien) tmmlu++ |
|
config: taiwanese_hokkien |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 14.73 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (nautical_science) tmmlu++ |
|
config: nautical_science |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 30.49 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (business_management) tmmlu++ |
|
config: business_management |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 39.57 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (logic_reasoning) tmmlu++ |
|
config: logic_reasoning |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 27.34 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (marketing_management) tmmlu++ |
|
config: marketing_management |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 39.78 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (economics) tmmlu++ |
|
config: economics |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 25.95 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (basic_medical_science) tmmlu++ |
|
config: basic_medical_science |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 28.41 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (occupational_therapy_for_psychological_disorders) tmmlu++ |
|
config: occupational_therapy_for_psychological_disorders |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 35.73 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (general_principles_of_law) tmmlu++ |
|
config: general_principles_of_law |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 31.13 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (junior_chemistry) tmmlu++ |
|
config: junior_chemistry |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 24.88 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (veterinary_pharmacology) tmmlu++ |
|
config: veterinary_pharmacology |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 36.3 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (educational_psychology) tmmlu++ |
|
config: educational_psychology |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 33.52 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (finance_banking) tmmlu++ |
|
config: finance_banking |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 32.59 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (official_document_management) tmmlu++ |
|
config: official_document_management |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 32.43 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (fire_science) tmmlu++ |
|
config: fire_science |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 30.65 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (junior_social_studies) tmmlu++ |
|
config: junior_social_studies |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 47.62 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (accounting) tmmlu++ |
|
config: accounting |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 20.94 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (engineering_math) tmmlu++ |
|
config: engineering_math |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 27.18 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (education_(profession_level)) tmmlu++ |
|
config: education_(profession_level) |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 24.07 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (chinese_language_and_literature) tmmlu++ |
|
config: chinese_language_and_literature |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 27.64 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (management_accounting) tmmlu++ |
|
config: management_accounting |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 24.19 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (culinary_skills) tmmlu++ |
|
config: culinary_skills |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 39.38 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (administrative_law) tmmlu++ |
|
config: administrative_law |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 25.71 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (insurance_studies) tmmlu++ |
|
config: insurance_studies |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 33.42 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (real_estate) tmmlu++ |
|
config: real_estate |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 22.83 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (computer_science) tmmlu++ |
|
config: computer_science |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 31.61 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (taxation) tmmlu++ |
|
config: taxation |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 27.47 |
|
- task: |
|
type: question-answering |
|
name: Single Choice Question |
|
dataset: |
|
type: ikala/tmmluplus |
|
name: (trade) tmmlu++ |
|
config: trade |
|
split: test |
|
revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
metrics: |
|
- name: single choice |
|
type: accuracy |
|
value: 20.32 |
|
widget: |
|
- text: 中華民國憲法第一條 |
|
metrics: |
|
- accuracy |
|
--- |
|
|
|
# Model Card for lianghsun/Llama-3.2-Taiwan-3B-Instruct |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
<a href="https://discord.gg/fj6WbHMvfs" target="_blank">[Discord]</a> |
|
|
|
 |
|
*圖像生成來自 [OpenArt](https://openart.ai/home)：An anime-style 🦙 standing proudly atop the summit of Taiwan’s [Yushan (Jade Mountain)](https://zh.wikipedia.org/wiki/%E7%8E%89%E5%B1%B1), gazing forward.* |
|
|
|
採用 [lianghsun/Llama-3.2-Taiwan-3B](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B) 為[基礎模型（foundation model）](https://en.wikipedia.org/wiki/Foundation_model)，使用大量[中華民國台灣](https://zh.wikipedia.org/zh-tw/%E8%87%BA%E7%81%A3)的繁體中文對話集和多國語言對話集進行模型[指令微調（instruction fine-tuning）](https://www.ibm.com/topics/instruction-tuning)和多輪迭代[直接偏好優化（direct preference optimization, DPO）](https://arxiv.org/abs/2305.18290)，旨在訓練出具有中華民國台灣知識及風格的[小語言模型（small langugae model, SLM）](https://www.ibm.com/think/topics/small-language-models)之對話模型。 |
|
|
|
<details> |
|
<summary><b>Model Change Log</b></summary> |
|
|
|
| Update Date | Model Version | Key Changes | |
|
|--------------|-----------------------|-------------------------------------| |
|
| 2025/01/01 | v2025.01.01 | Fine-tuning is based on the [foundation model](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B) version v2024.12.28, and it uses self-prepared instruction datasets for this round of fine-tuning. | |
|
| 2024/12/13 | v2024.12.13 | Completed 1st round DPO training (10/10 epochs). Preparing for next round DPO training. | |
|
| 2024/11/27 | v2024.11.27 | Completed SFT training (5/5 epochs). Preparing for multi-round DPO training. | |
|
| 2024/11/25 | v2024.11.25 | Updated model version to v2024.11.25, training progressed to (3/5) epochs. Still in SFT stage, DPO training remains pending. | |
|
| 2024/11/22 | v2024.11.22 | Initial upload: Model version v2024.11.22, training completed up to (1/5) epochs. Currently trained only on SFT, DPO training not yet performed. | |
|
|
|
</details> |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
- **Developed by:** [Huang Liang Hsun](https://www.linkedin.com/in/lianghsunhuang) |
|
- **Model type:** LlamaForCausalLM |
|
- **Language(s) (NLP):** Tranditional Chinese (zh-tw), English |
|
- **License:** [llama3.2](https://huggingface.co/meta-llama/Llama-3.2-1B/blob/main/LICENSE.txt) |
|
- **Fine-tuned from model:** [lianghsun/Llama-3.2-Taiwan-3B](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B) |
|
|
|
### Model Sources |
|
|
|
<!-- Provide the basic links for the model. --> |
|
- **Repository:** [lianghsun/Llama-3.2-Taiwan-3B](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B) |
|
- **Paper:** (WIP, show me the time) |
|
- **Playground:** [🦉 Tawian SmolLM Chat](https://huggingface.co/spaces/lianghsun/tw-smol-chat) 👈🏼 來玩看看 😻 |
|
- **Demo:** |
|
```yaml |
|
user: 請介紹台灣 |
|
assistant: 台灣，位於亞洲東部，地處太平洋與菲律賓海之間，面積約36,000平方公里，人口約2,300萬，是民主自由的國家，經濟實力強勁，擁有世界第10大經濟體。台灣以美食、文化、自然美景著稱，還有豐富的歷史與傳統，吸引全球遊客。台灣語為官方語言，但中文也廣為使用，英語也常用於國際交流。台灣政治多元，執政黨為民進黨，台灣是全球科技產業的重鎮，擁有先進的製造業與服務業。台灣氣候溫暖潮濕，四季分明，夏季炎熱，冬季涼爽，雨季則在5月至10月。台灣的美食以小吃為主，如滷肉飯、珍珠 |
|
``` |
|
|
|
## Uses |
|
|
|
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
|
|
|
### Direct Use |
|
|
|
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
|
本模型已經具備有繁體中文對話能力，使用者可以直接部署推論端點使用。 |
|
|
|
### Downstream Use |
|
|
|
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
|
若需強化模型在特定領域的知識，可透過微調進一步提升其性能與專業能力。 |
|
|
|
### Out-of-Scope Use |
|
|
|
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
|
本模型旨在提供資訊，不參與任何政治或法律問題的評斷或立場表達。 |
|
|
|
## Bias, Risks, and Limitations |
|
|
|
<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
|
語言模型的生成內容可能因訓練集的多樣性而帶有偏見、特定立場，或包含與事實不符的言論，請使用者務必在使用過程中仔細確認內容的準確性與中立性。 |
|
|
|
### Recommendations |
|
|
|
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
|
|
|
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
|
|
|
## How to Get Started with the Model |
|
|
|
要使用 [vLLM Docker image](https://docs.vllm.ai/en/latest/serving/deploying_with_docker.html) 來啟動此模型，您可以按照以下操作： |
|
```bash |
|
docker run --runtime nvidia --gpus all \ |
|
-v ~/.cache/huggingface:/root/.cache/huggingface \ |
|
--env "HUGGING_FACE_HUB_TOKEN=<secret>" \ |
|
-p 8000:8000 \ |
|
--ipc=host \ |
|
vllm/vllm-openai:latest \ |
|
--model lianghsun/Llama-3.2-Taiwan-3B-Instruct |
|
``` |
|
|
|
請注意，如果想要使用不同版本的 checkpoint，請加上 `--revision <tag_name>` |
|
```bash |
|
docker run --runtime nvidia --gpus all \ |
|
-v ~/.cache/huggingface:/root/.cache/huggingface \ |
|
--env "HUGGING_FACE_HUB_TOKEN=<secret>" \ |
|
-p 8000:8000 \ |
|
--ipc=host \ |
|
vllm/vllm-openai:latest \ |
|
--model lianghsun/Llama-3.2-Taiwan-3B-Instruct --revision <tag_name> |
|
``` |
|
|
|
## Training Details |
|
|
|
### Training Data |
|
|
|
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
|
|
|
<details> |
|
<summary><b>繁體中文對話資料集</b></summary> |
|
|
|
- [lianghsun/tw-legal-nlp](https://huggingface.co/datasets/lianghsun/tw-legal-nlp) |
|
- [lianghsun/tw-legal-synthetic-qa](https://huggingface.co/datasets/lianghsun/tw-legal-synthetic-qa) |
|
- [lianghsun/tw-law-article-qa](https://huggingface.co/datasets/lianghsun/tw-law-article-qa) |
|
- [lianghsun/tw-judgment-qa](https://huggingface.co/datasets/lianghsun/tw-judgment-qa) |
|
- [lianghsun/tw-bar-examination-2020-chat](https://huggingface.co/datasets/lianghsun/tw-bar-examination-2020-chat) |
|
- [lianghsun/tw-structured-law-article](https://huggingface.co/datasets/lianghsun/tw-structured-law-article) |
|
- [lianghsun/tw-judgment-gist-chat](https://huggingface.co/datasets/lianghsun/tw-judgment-gist-chat) |
|
- [lianghsun/vulnerability-mitigation-qa-zh_tw](https://huggingface.co/datasets/lianghsun/vulnerability-mitigation-qa-zh_tw) |
|
- [lianghsun/tw-legal-qa-chat](https://huggingface.co/datasets/lianghsun/tw-legal-qa-chat) |
|
- [lianghsun/reasoning-base-20k-chat](https://huggingface.co/datasets/lianghsun/reasoning-base-20k-chat) |
|
- [lianghsun/tw-contract-review-chat](https://huggingface.co/datasets/lianghsun/tw-contract-review-chat) |
|
- [lianghsun/tw-legal-methodology-chat](https://huggingface.co/datasets/lianghsun/tw-legal-methodology-chat) |
|
- [minyichen/glaive_toolcall_zh_tw](https://huggingface.co/datasets/minyichen/glaive_toolcall_zh_tw) |
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</details> |
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|
<details> |
|
<summary><b>多國語系對話資料集</b></summary> |
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|
|
- [rombodawg/Everything_Instruct_Multilingual](https://huggingface.co/datasets/rombodawg/Everything_Instruct_Multilingual) |
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- [xzuyn/manythings-translations-alpaca](https://huggingface.co/datasets/xzuyn/manythings-translations-alpaca) |
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- [neural-bridge/rag-dataset-12000](https://huggingface.co/datasets/neural-bridge/rag-dataset-12000) |
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|
</details> |
|
|
|
### Training Procedure |
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|
|
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing |
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(WIP) |
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#### Training Hyperparameters |
|
|
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<details> |
|
<summary><b>SFT stage for v2024.11.27</b></summary> |
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|
|
**Note:** 以下包含 `v2024.11.22` 和 `v2025.11.25` 的超參數設定 |
|
- **learning_rate:** 5e-05 |
|
- **min_learning_rate:** 5e-07 |
|
- **train_batch_size:** 105 |
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- **seed:** 42 |
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- **distributed_type:** multi-GPU |
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- **num_devices:** 4 |
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- **gradient_accumulation_steps:** 50 |
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- **total_train_batch_size:** 21,000 |
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- **optimizer:** Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- **lr_scheduler_type:** cosine |
|
- **lr_scheduler_warmup_ratio:** 0.01 |
|
- **num_epochs:** 5.0 |
|
- **global_step:** 590 |
|
</details> |
|
|
|
#### Speeds, Sizes, Times |
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|
|
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
|
<details> |
|
<summary><b>SFT stage for v2024.11.27</b></summary> |
|
|
|
**Note:** 以下包含 `v2024.11.22` 和 `v2025.11.25` 的超參數設定 |
|
- **Duration**: 5 days, 16:15:11.17 |
|
- **Train runtime**: 490,511.1789 |
|
- **Train samples per second**: 25.37 |
|
- **Train steps per second**: 0.001 |
|
- **Total training FLOPs**: 26,658,386,120,540,160 |
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- **Train loss**: 0.8533 |
|
</details> |
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|
|
## Evaluation |
|
|
|
<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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<details> |
|
<summary><b>Formosa Taiwan Knowledge Bench</b></summary> |
|
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#### Testing Data |
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|
<!-- This should link to a Dataset Card if possible. --> |
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[lianghsun/Formosa-bench](https://huggingface.co/datasets/lianghsun/Formosa-bench) |
|
|
|
#### Factors |
|
|
|
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
|
|
|
[More Information Needed] |
|
|
|
#### Metrics |
|
|
|
<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
|
|
|
[More Information Needed] |
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|
|
### Results |
|
|
|
[More Information Needed] |
|
|
|
#### Summary |
|
|
|
</details> |
|
|
|
<details> |
|
<summary><b>lianghsun/tw-legal-benchmark-v1</b></summary> |
|
|
|
#### Testing Data |
|
|
|
<!-- This should link to a Dataset Card if possible. --> |
|
|
|
- **Dataset:** [lianghsun/tw-legal-benchmark-v1](https://huggingface.co/datasets/lianghsun/tw-legal-benchmark-v1) |
|
- **Revision:** 66c3a5f3ff2298f6a1cf23201070b5317bdd1893 |
|
|
|
#### Factors |
|
|
|
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
|
|
|
[More Information Needed] |
|
|
|
#### Metrics |
|
|
|
<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
|
Accuracy |
|
|
|
### Results |
|
|
|
- **Model Revision:** v2024.11.27 |
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|
|
| **Subset** | **Split** | **Score** | |
|
|--------------|-------|-------| |
|
| [lianghsun/tw-legal-benchmark-v1](https://huggingface.co/datasets/lianghsun/tw-legal-benchmark-v1/blob/main/benchmark.csv) | train | 31.1 | |
|
|
|
#### Summary |
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|
|
</details> |
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|
|
<details> |
|
<summary><b>tmmlu++</b></summary> |
|
|
|
#### Testing Data |
|
|
|
<!-- This should link to a Dataset Card if possible. --> |
|
- **Dataset:** [ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus) |
|
- **Revision:** c0e8ae955997300d5dbf0e382bf0ba5115f85e8c |
|
|
|
#### Factors |
|
|
|
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
|
[More Information Needed] |
|
|
|
#### Metrics |
|
|
|
<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
|
Accuracy |
|
|
|
### Results |
|
|
|
- **Model Revision:** v2024.11.27 |
|
|
|
| **Subset** | **Split** | **Score** | |
|
|--------------|-------|-------| |
|
| [geography_of_taiwan](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/geography_of_taiwan_test.csv) | test | 36.2 | |
|
| [dentistry](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/dentistry_test.csv) | test | 33.83 | |
|
| [technical](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/technical_test.csv) | test | 35.07 | |
|
| [statistics_and_machine_learning](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/statistics_and_machine_learning_test.csv) | test | 28.57 | |
|
| [clinical_psychology](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/clinical_psychology_test.csv) | test | 29.6 | |
|
| [tve_design](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/tve_design_test.csv) | test | 38.54 | |
|
| [three_principles_of_people](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/three_principles_of_people_test.csv) | test | 48.2 | |
|
| [introduction_to_law](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/introduction_to_law_test.csv) | test | 29.96 | |
|
| [linear_algebra](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/linear_algebra_test.csv) | test | 21.43 | |
|
| [agriculture](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/agriculture_test.csv) | test | 24.5 | |
|
| [jce_humanities](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/jce_humanities_test.csv) | test | 38.89 | |
|
| [music](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/music_test.csv) | test | 25.9 | |
|
| [secondary_physics](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/secondary_physics_test.csv) | test | 33.04 | |
|
| [physics](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/physics_test.csv) | test | 27.84 | |
|
| [advance_chemistry](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/advance_chemistry_test.csv) | test | 27.64 | |
|
| [junior_science_exam](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/junior_science_exam_test.csv) | test | 30.05 | |
|
| [veterinary_pathology](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/veterinary_pathology_test.csv) | test | 25.09 | |
|
| [financial_analysis](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/financial_analysis_test.csv) | test | 25.13 | |
|
| [national_protection](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/national_protection_test.csv) | test | 42.65 | |
|
| [macroeconomics](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/macroeconomics_test.csv) | test | 26.76 | |
|
| [politic_science](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/politic_science_test.csv) | test | 27.44 | |
|
| [ttqav2](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/ttqav2_test.csv) | test | 61.06 | |
|
| [junior_chinese_exam](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/junior_chinese_exam_test.csv) | test | 30.86 | |
|
| [traditional_chinese_medicine_clinical_medicine](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/traditional_chinese_medicine_clinical_medicine_test.csv) | test | 25.9 | |
|
| [junior_math_exam](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/junior_math_exam_test.csv) | test | 21.71 | |
|
| [auditing](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/auditing_test.csv) | test | 21.82 | |
|
| [anti_money_laundering](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/anti_money_laundering_test.csv) | test | 37.31 | |
|
| [pharmacology](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/pharmacology_test.csv) | test | 30.68 | |
|
| [trust_practice](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/trust_practice_test.csv) | test | 28.18 | |
|
| [tve_mathematics](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/tve_mathematics_test.csv) | test | 18.67 | |
|
| [human_behavior](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/human_behavior_test.csv) | test | 32.04 | |
|
| [pharmacy](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/pharmacy_test.csv) | test | 22.76 | |
|
| [tve_chinese_language](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/tve_chinese_language_test.csv) | test | 36.65 | |
|
| [optometry](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/optometry_test.csv) | test | 25.11 | |
|
| [physical_education](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/physical_education_test.csv) | test | 30.73 | |
|
| [organic_chemistry](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/organic_chemistry_test.csv) | test | 35.78 | |
|
| [tve_natural_sciences](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/tve_natural_sciences_test.csv) | test | 33.73 | |
|
| [education](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/education_test.csv) | test | 37.9 | |
|
| [mechanical](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/mechanical_test.csv) | test | 42.37 | |
|
| [taiwanese_hokkien](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/taiwanese_hokkien_test.csv) | test | 14.73 | |
|
| [nautical_science](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/nautical_science_test.csv) | test | 30.49 | |
|
| [business_management](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/business_management_test.csv) | test | 39.57 | |
|
| [logic_reasoning](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/logic_reasoning_test.csv) | test | 27.34 | |
|
| [marketing_management](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/marketing_management_test.csv) | test | 39.78 | |
|
| [economics](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/economics_test.csv) | test | 25.95 | |
|
| [basic_medical_science](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/basic_medical_science_test.csv) | test | 28.41 | |
|
| [occupational_therapy_for_psychological_disorders](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/occupational_therapy_for_psychological_disorders_test.csv) | test | 35.73 | |
|
| [general_principles_of_law](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/general_principles_of_law_test.csv) | test | 31.13 | |
|
| [junior_chemistry](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/junior_chemistry_test.csv) | test | 24.88 | |
|
| [veterinary_pharmacology](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/veterinary_pharmacology_test.csv) | test | 36.3 | |
|
| [educational_psychology](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/educational_psychology_test.csv) | test | 33.52 | |
|
| [finance_banking](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/finance_banking_test.csv) | test | 32.59 | |
|
| [official_document_management](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/official_document_management_test.csv) | test | 32.43 | |
|
| [fire_science](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/fire_science_test.csv) | test | 30.65 | |
|
| [junior_social_studies](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/junior_social_studies_test.csv) | test | 47.62 | |
|
| [accounting](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/accounting_test.csv) | test | 20.94 | |
|
| [engineering_math](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/engineering_math_test.csv) | test | 27.18 | |
|
| [education_(profession_level)](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/education_(profession_level)_test.csv) | test | 24.07 | |
|
| [chinese_language_and_literature](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/chinese_language_and_literature_test.csv) | test | 27.64 | |
|
| [management_accounting](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/management_accounting_test.csv) | test | 24.19 | |
|
| [culinary_skills](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/culinary_skills_test.csv) | test | 39.38 | |
|
| [administrative_law](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/administrative_law_test.csv) | test | 25.71 | |
|
| [insurance_studies](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/insurance_studies_test.csv) | test | 33.42 | |
|
| [real_estate](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/real_estate_test.csv) | test | 22.83 | |
|
| [computer_science](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/computer_science_test.csv) | test | 31.61 | |
|
| [taxation](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/taxation_test.csv) | test | 27.47 | |
|
| [trade](https://huggingface.co/datasets/ikala/tmmluplus/blob/main/data/trade_test.csv) | test | 20.32 | |
|
|
|
|
|
#### Summary |
|
模型版號 `v2024.11.27`，無論是基礎模型（[lianghsun/Llama-3.2-Taiwan-3B](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B)）還是指令微調模型（[lianghsun/Llama-3.2-Taiwan-3B-Instruct](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B-Instruct)），均未接受過 tmmlu++ 資料集的訓練，以確保測試的公平性。經測試，目前該模型在 tmmlu++ 上表現普遍不佳，未達及格分數，可能需要加入專業領域的資料集來強化基礎模型能力。 |
|
|
|
</details> |
|
|
|
## Model Examination [optional] |
|
|
|
<!-- Relevant interpretability work for the model goes here --> |
|
|
|
[More Information Needed] |
|
|
|
## Environmental Impact |
|
|
|
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
|
- **Hardware Type:** 🚀 |
|
- **Hours used:** ⏳⏳⌛ |
|
- **Cloud Provider:** [鴻鵠國際股份有限公司](https://www.honghutech.com/) |
|
- **Compute Region:** 🇹🇼 |
|
- **Carbon Emitted:** ♻️ |
|
|
|
## Technical Specifications |
|
|
|
### Model Architecture and Objective |
|
|
|
[More Information Needed] |
|
|
|
### Compute Infrastructure |
|
|
|
[More Information Needed] |
|
|
|
#### Hardware |
|
|
|
- **CPU count:** 32 |
|
- **Logical CPU count:** 64 |
|
- **GPU count:** 4 |
|
- **GPU type:** NVIDIA H100 NVL |
|
|
|
#### Software |
|
|
|
- **OS version:** Linux-5.15.0-124-generic-x86_64-with-glibc2.35 |
|
- **Python version:** 3.12.7 |
|
|
|
## Citation |
|
|
|
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
|
```bibtex |
|
@misc{lianghsun2024llama32taiwan3binstruct, |
|
author = {Huang, Liang Hsun}, |
|
title = {Llama-3.2-Taiwan-3B-Instruct}, |
|
year = {2024}, |
|
publisher = {Hugging Face}, |
|
howpublished = {\url{https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B-Instruct}}, |
|
note = {Accessed: 2024-11-25} |
|
} |
|
``` |
|
|
|
## Glossary [optional] |
|
|
|
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
|
N/A |
|
|
|
## More Information |
|
|
|
### Acknowledge |
|
 |
|
在此致謝[鴻鵠國際股份有限公司](https://www.honghutech.com/)蔡長明先生無償地贊助算力，以及曾經幫忙過：廖振翔、chweng、Ben、kevin、Maxxchu、Lam 和陳林彥…等朋友們，才能讓這個模型得以訓練完成，提供算力者乃人生父母。 |
|
|
|
### Usage |
|
如果你基於此指令模型進行微調，希望能不吝嗇在 **模型卡片(model card)** 裡標註 **基礎模型** 為： |
|
```yaml |
|
base_model: lianghsun/Llama-3.2-Taiwan-3B-Instruct |
|
``` |
|
|
|
標註和 ❤️ 是給予我們最大的鼓勵，謝謝。😀 |
|
|
|
## Model Card Authors |
|
|
|
[Huang Liang Hsun](https://www.linkedin.com/in/lianghsunhuang) |
|
|
|
## Model Card Contact |
|
|
|
[Huang Liang Hsun](https://www.linkedin.com/in/lianghsunhuang) |
|
|
|
### Framework versions |
|
|
|
- Transformers 4.45.2 |
|
- Pytorch 2.4.1+cu121 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.20.0 |
|
|
