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Japanese Language Model Financial Evaluation Harness

This is a harness for Japanese language model evaluation in the financial domain.

0-shot Leaderboard

Model Ave. chabsa cma_basics cpa_audit fp2 security_sales_1 prompt
anthropic/claude-3-5-sonnet 77.02 93.43 81.58 61.81 72.84 75.44 default
nvidia/nemotron-4-340b-instruct 70.31 91.93 86.84 40.70 56.63 75.44 default
Qwen/Qwen2-72B 69.35 92.64 84.21 49.50 52.00 68.42 default
Qwen/Qwen2-72B-Instruct 67.71 92.18 84.21 43.72 51.79 66.67 default
openai/gpt-4-32k 66.27 93.16 81.58 37.44 50.74 68.42 default
openai/gpt-4 66.07 93.20 78.95 37.69 50.32 70.18 default
anthropic/claude-3-opus 65.81 93.04 71.05 42.71 55.58 66.67 default
openai/gpt-4o 65.26 90.93 76.32 53.02 39.37 66.67 default
openai/gpt-4-turbo 64.59 92.86 76.32 36.18 50.95 66.67 default
gemini/gemini-1.5-flash 63.10 92.36 71.05 35.93 49.47 66.67 default
anthropic/claude-3-sonnet 61.59 89.70 71.05 38.44 42.11 66.67 default
Qwen/Qwen1.5-72B-Chat 59.62 92.15 71.05 31.41 36.84 66.67 default
Qwen/Qwen2-57B-A14B 59.45 90.52 78.95 24.62 40.00 63.16 default
Qwen/Qwen2-57B-A14B-Instruct 59.40 91.03 73.68 27.39 40.00 64.91 1.0-0.1.2
Qwen/Qwen-72B 59.08 89.46 76.32 28.64 39.58 61.40 1.0-0.1.2
Qwen/Qwen1.5-72B 58.82 90.77 71.05 26.38 37.47 68.42 1.0-0.1
meta-llama/Meta-Llama-3-70B-Instruct 58.48 90.61 76.32 29.90 42.95 52.63 1.0-0.2.1
tokyotech-llm/Swallow-70b-NVE-instruct-hf 58.32 90.72 63.16 21.11 53.47 63.16 default
gemini/gemini-1.5-pro 57.94 59.95 68.42 39.70 49.68 71.93 default
Qwen/Qwen-72B-Chat 57.33 92.10 71.05 25.38 40.21 57.89 1.0-0.1.2
meta-llama/Meta-Llama-3-70B 56.87 90.19 73.68 24.87 37.68 57.89 1.0-0.1.2
tokyotech-llm/Swallow-70b-NVE-hf 56.26 86.42 60.53 20.10 52.84 61.40 default
pfnet/plamo-1.0-prime-beta 55.24 89.37 60.53 21.86 41.26 63.16 default
anthropic/claude-3-haiku 55.15 82.25 73.68 29.90 37.26 52.63 default
tokyotech-llm/Swallow-70b-hf 54.86 89.28 68.42 19.85 45.89 50.88 default
Qwen/Qwen1.5-32B-Chat 54.51 91.52 57.89 25.38 38.11 59.65 1.0-0.1.2
tokyotech-llm/Swallow-70b-instruct-hf 54.46 91.36 65.79 20.35 45.68 49.12 default
Qwen/Qwen2-7B-Instruct 53.78 91.94 60.53 25.13 35.16 56.14 1.0-0.2.1
tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1 53.50 88.64 65.79 20.10 31.58 61.40 1.0-0.1.2
Qwen/Qwen1.5-32B 53.34 91.37 68.42 27.89 29.89 49.12 default
Qwen/Qwen2-7B 53.28 90.73 65.79 24.12 31.37 54.39 1.0-0.1.2
Qwen/Qwen1.5-14B-Chat 52.82 90.43 57.89 25.63 35.79 54.39 1.0-0.1.2
pfnet/nekomata-14b-pfn-qfin 52.74 88.87 47.37 25.13 39.16 63.16 1.0-0.2.1
Qwen/Qwen1.5-14B 52.20 84.55 65.79 20.60 33.89 56.14 1.0-0.1.2
karakuri-ai/karakuri-lm-8x7b-instruct-v0.1 51.63 83.87 57.89 16.33 40.42 59.65 1.0-0.2.1
pfnet/nekomata-14b-pfn-qfin-inst-merge 51.12 88.93 50.00 24.62 37.68 54.39 1.0-0.2.1
rinna/nekomata-14b-instruction 50.91 89.40 52.63 20.35 36.00 56.14 1.0-0.2.1
mistralai/Mixtral-8x7B-Instruct-v0.1 50.63 91.02 57.89 24.37 30.74 49.12 1.0-0.2
gemini/gemini-1.0-pro 50.52 78.94 55.26 23.37 40.63 54.39 default
rinna/nekomata-14b 50.46 85.88 63.16 20.60 31.79 50.88 1.0-0.1.2
Qwen/Qwen-14B 50.30 86.14 63.16 19.10 32.21 50.88 1.0-0.1.2
openai/gpt-35-turbo 50.27 89.98 52.63 18.09 29.26 61.40 default
karakuri-ai/karakuri-lm-8x7b-chat-v0.1 50.00 85.19 60.53 19.85 37.05 47.37 1.0-0.2.1
Qwen/Qwen1.5-7B-Chat 49.73 86.27 50.00 24.87 31.37 56.14 1.0-0.2.1
Qwen/Qwen-14B-Chat 49.13 91.03 55.26 16.83 29.89 52.63 default
stabilityai/japanese-stablelm-instruct-beta-70b 47.93 84.77 42.11 19.85 33.26 59.65 1.0-0.1.2
rinna/nekomata-7b-instruction 47.75 86.71 44.74 17.34 30.32 59.65 default
Qwen/Qwen1.5-MoE-A2.7B-Chat 46.64 82.10 42.11 22.86 28.21 57.89 1.0-0.1
Qwen/Qwen-7B 45.99 82.30 47.37 19.60 31.58 49.12 1.0-0.1.2
mistralai/Mistral-7B-Instruct-v0.2 45.80 87.59 39.47 17.84 29.68 54.39 default
SakanaAI/EvoLLM-JP-v1-7B 45.74 88.40 39.47 13.32 31.37 56.14 1.0-0.2.1
Xwin-LM/Xwin-LM-70B-V0.1 45.65 87.58 39.47 16.58 32.00 52.63 1.0-0.5
Qwen/Qwen-7B-Chat 45.33 85.40 47.37 19.85 28.42 45.61 1.0-0.1.2
Rakuten/RakutenAI-7B-instruct 44.96 74.98 50.00 17.84 32.84 49.12 default
meta-llama/Meta-Llama-3-8B-Instruct 44.70 86.77 39.47 16.83 33.05 47.37 1.0-0.2.1
karakuri-ai/karakuri-lm-70b-chat-v0.1 44.59 88.59 36.84 18.09 30.32 49.12 1.0-0.2.1
SakanaAI/EvoLLM-JP-A-v1-7B 44.51 86.82 55.26 13.82 26.32 40.35 1.0-0.3
mistralai/Mixtral-8x7B-v0.1 44.29 89.39 42.11 15.58 25.26 49.12 default
meta-llama/Llama-2-70b-chat-hf 44.23 85.67 44.74 17.09 26.32 47.37 1.0-0.1
Qwen/Qwen1.5-7B 43.99 85.54 39.47 18.09 29.47 47.37 1.0-0.1.2
Qwen/Qwen1.5-MoE-A2.7B 43.12 69.29 42.11 21.61 28.21 54.39 1.0-0.1
stabilityai/japanese-stablelm-base-beta-70b 43.11 79.05 36.84 16.08 25.68 57.89 1.0-0.1.2
Qwen/Qwen1.5-4B 42.68 82.82 42.11 13.82 29.05 45.61 1.0-0.1.2
rinna/llama-3-youko-8b 42.54 79.22 42.11 17.84 29.68 43.86 default
Qwen/Qwen2-1.5B 42.21 77.46 44.74 13.82 25.89 49.12 1.0-0.1.2
Qwen/Qwen2-1.5B-Instruct 42.20 74.08 44.74 13.57 29.47 49.12 default
meta-llama/Meta-Llama-3-8B 42.13 85.77 36.84 19.85 26.11 42.11 default
meta-llama/Llama-2-70b-hf 41.96 84.07 34.21 16.83 29.05 45.61 1.0-0.1.2
sbintuitions/sarashina2-13b 41.79 82.84 26.32 19.10 26.32 54.39 1.0-0.1.2
cyberagent/calm2-7b-chat-dpo-experimental 41.71 77.96 34.21 15.83 29.68 50.88 1.0-0.1
rinna/nekomata-7b 41.55 81.34 31.58 20.85 24.84 49.12 default
stabilityai/japanese-stablelm-instruct-gamma-7b 41.46 79.09 31.58 17.34 33.68 45.61 1.0-0.2.1
tokyotech-llm/Swallow-MS-7b-v0.1 41.37 79.22 23.68 17.09 25.47 61.40 1.0-0.2.1
llm-jp/llm-jp-13b-instruct-full-jaster-v1.0 41.36 84.48 34.21 21.11 23.16 43.86 1.0-0.1
Qwen/Qwen1.5-4B-Chat 41.26 78.40 39.47 13.57 29.26 45.61 1.0-0.1.2
llm-jp/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0 41.10 82.28 28.95 13.57 26.32 54.39 1.0-0.3
karakuri-ai/karakuri-lm-70b-v0.1 41.04 58.60 39.47 18.09 31.16 57.89 default
tokyotech-llm/Swallow-7b-NVE-hf 41.03 81.34 39.47 20.10 27.37 36.84 1.0-0.1
mosaicml/mpt-30b-instruct 40.95 83.25 34.21 19.60 27.37 40.35 default
Fugaku-LLM/Fugaku-LLM-13B-instruct 40.90 81.91 42.11 12.81 23.79 43.86 1.0-0.1
meta-llama/Llama-2-7b-chat-hf 40.67 80.32 28.95 19.85 23.37 50.88 default
elyza/ELYZA-japanese-Llama-2-7b-instruct 40.59 81.39 36.84 18.84 23.79 42.11 default
sbintuitions/sarashina2-7b 40.51 85.12 39.47 12.56 25.05 40.35 1.0-0.1
rinna/youri-7b-chat 40.40 85.08 26.32 17.84 27.16 45.61 default
meta-llama/Llama-2-13b-chat-hf 40.29 80.36 39.47 13.82 25.68 42.11 1.0-0.1
Rakuten/RakutenAI-7B 40.29 71.87 31.58 15.33 31.79 50.88 1.0-0.1
tokyotech-llm/Swallow-13b-instruct-hf 40.24 80.08 42.11 13.82 24.84 40.35 1.0-0.2
stabilityai/japanese-stablelm-base-gamma-7b 40.17 74.80 31.58 18.34 30.53 45.61 1.0-0.2.1
lmsys/vicuna-7b-v1.5-16k 39.91 79.91 28.95 16.33 25.26 49.12 1.0-0.1
cyberagent/calm2-7b 39.80 78.27 31.58 16.58 26.95 45.61 1.0-0.1
elyza/ELYZA-japanese-Llama-2-7b 39.78 79.76 36.84 13.82 24.63 43.86 default
cyberagent/calm2-7b-chat 39.68 79.97 31.58 16.83 24.42 45.61 1.0-0.2
Xwin-LM/Xwin-LM-7B-V0.2 39.62 67.64 34.21 17.59 27.79 50.88 1.0-0.2.1
tokyotech-llm/Swallow-7b-NVE-instruct-hf 39.56 74.24 34.21 18.34 27.16 43.86 1.0-0.1
tokyotech-llm/Swallow-13b-NVE-hf 39.49 60.92 31.58 15.08 32.00 57.89 1.0-0.1
rinna/youri-7b-instruction 39.47 78.82 36.84 19.10 24.00 38.60 1.0-0.3
elyza/ELYZA-japanese-Llama-2-13b-instruct 39.42 73.46 34.21 14.32 29.47 45.61 1.0-0.1
lmsys/vicuna-13b-v1.3 39.20 78.86 31.58 16.58 23.37 45.61 1.0-0.2
elyza/ELYZA-japanese-Llama-2-13b-fast-instruct 39.08 55.28 47.37 18.84 26.53 47.37 1.0-0.1
rinna/japanese-gpt-neox-3.6b-instruction-ppo 38.90 73.66 34.21 14.07 26.95 45.61 default
mistralai/Mistral-7B-Instruct-v0.1 38.86 79.85 31.58 14.82 24.21 43.86 default
lmsys/vicuna-7b-v1.3 38.51 76.81 23.68 15.08 26.11 50.88 1.0-0.1
elyza/ELYZA-japanese-Llama-2-13b 38.43 76.69 36.84 14.07 24.21 40.35 default
mosaicml/mpt-30b-chat 38.30 74.85 26.32 18.34 24.63 47.37 default
lmsys/vicuna-33b-v1.3 38.28 66.31 26.32 17.59 25.05 56.14 1.0-0.1
rinna/bilingual-gpt-neox-4b-instruction-sft 38.17 77.67 23.68 17.59 26.32 45.61 default
stabilityai/japanese-stablelm-3b-4e1t-instruct 38.13 68.37 34.21 16.33 26.11 45.61 1.0-0.1
stabilityai/japanese-stablelm-instruct-ja_vocab-beta-7b 38.06 75.29 28.95 15.83 24.63 45.61 1.0-0.1.2
lmsys/longchat-7b-v1.5-32k 37.89 79.53 31.58 14.07 25.68 38.60 1.0-0.2.1
llm-jp/llm-jp-13b-v2.0 37.82 71.12 34.21 16.33 23.58 43.86 1.0-0.6
rinna/japanese-gpt-neox-3.6b-instruction-sft 37.73 73.00 23.68 18.84 24.00 49.12 1.0-0.2.1
openai/text-davinci-003 37.68 53.92 44.74 17.59 26.53 45.61 default
tokyotech-llm/Swallow-13b-hf 37.54 61.28 28.95 16.08 25.26 56.14 1.0-0.1
mistralai/Mistral-7B-v0.1 37.45 74.75 26.32 17.34 26.74 42.11 1.0-0.1.2
rinna/youri-7b 37.39 68.04 31.58 19.85 27.16 40.35 1.0-0.1
mosaicml/mpt-30b 37.35 76.95 23.68 16.83 27.16 42.11 1.0-0.2.1
tokyotech-llm/Swallow-7b-plus-hf 37.25 79.04 31.58 12.81 24.21 38.60 1.0-0.1.2
moneyforward/houou-instruction-7b-v3 37.22 73.42 26.32 16.58 25.89 43.86 1.0-0.1.2
Rakuten/RakutenAI-7B-chat 37.21 61.30 26.32 17.34 32.00 49.12 1.0-0.3
Qwen/Qwen1.5-1.8B 37.03 69.33 28.95 19.10 25.68 42.11 1.0-0.1
google/recurrentgemma-2b-it 36.94 61.04 36.84 17.84 23.37 45.61 1.0-0.2.1
google/gemma-2b 36.93 67.09 28.95 15.08 24.42 49.12 1.0-0.6
meta-llama/Llama-2-7b-hf 36.89 71.97 31.58 13.82 26.74 40.35 1.0-0.2
llm-jp/llm-jp-1.3b-v1.0 36.81 57.66 31.58 18.34 27.37 49.12 1.0-0.1
google/gemma-1.1-2b-it 36.47 61.68 34.21 13.32 24.00 49.12 1.0-0.2.1
stabilityai/japanese-stablelm-base-beta-7b 36.36 62.03 36.84 15.33 25.47 42.11 1.0-0.1.2
matsuo-lab/weblab-10b 36.31 69.82 31.58 13.82 24.21 42.11 default
rinna/bilingual-gpt-neox-4b-instruction-ppo 36.23 74.15 23.68 15.33 25.89 42.11 1.0-0.1
google/gemma-2b-it 36.17 66.75 28.95 15.33 24.21 45.61 1.0-0.1
moneyforward/houou-instruction-7b-v2 36.15 72.26 28.95 14.82 26.11 38.60 1.0-0.1
sbintuitions/sarashina1-7b 36.11 58.91 39.47 13.82 22.74 45.61 1.0-0.1
stockmark/stockmark-100b-instruct-v0.1 36.09 73.46 26.32 14.07 22.74 43.86 default
rinna/japanese-gpt-neox-3.6b-instruction-sft-v2 36.06 68.52 21.05 17.59 24.00 49.12 1.0-0.2.1
stabilityai/japanese-stablelm-base-ja_vocab-beta-7b 36.02 63.14 36.84 13.82 24.21 42.11 default
Qwen/Qwen1.5-1.8B-Chat 35.98 65.54 26.32 16.83 27.37 43.86 1.0-0.2
moneyforward/houou-instruction-7b-v1 35.45 66.86 26.32 16.33 27.37 40.35 1.0-0.1
llm-jp/llm-jp-13b-instruct-full-dolly-oasst-v1.0 35.40 66.91 23.68 13.07 24.21 49.12 1.0-0.6
lmsys/vicuna-13b-v1.5-16k 35.36 69.08 26.32 13.82 25.47 42.11 1.0-0.2
stockmark/stockmark-13b 35.33 59.20 31.58 15.83 24.42 45.61 1.0-0.1
pfnet/plamo-13b-instruct 35.27 63.10 26.32 16.08 25.26 45.61 1.0-0.6
stockmark/stockmark-13b-instruct 34.98 54.32 28.95 15.83 28.42 47.37 1.0-0.1
stockmark/stockmark-100b 34.97 68.63 26.32 13.82 24.00 42.11 default
tokyotech-llm/Swallow-7b-instruct-hf 34.88 49.40 31.58 20.60 25.47 47.37 default
cyberagent/open-calm-large 34.81 53.58 28.95 16.83 23.79 50.88 1.0-0.1
meta-llama/Llama-2-13b-hf 34.75 56.30 36.84 13.32 26.95 40.35 1.0-0.2.1
llm-jp/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0 34.63 56.84 31.58 16.33 26.32 42.11 1.0-0.5
stabilityai/japanese-stablelm-3b-4e1t-base 34.58 52.32 34.21 15.58 26.95 43.86 1.0-0.1
elyza/ELYZA-japanese-Llama-2-7b-fast 34.49 37.54 36.84 17.59 26.11 54.39 1.0-0.1
llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0 34.40 52.96 28.95 18.59 25.89 45.61 1.0-0.5
llm-jp/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0 34.35 58.90 31.58 17.84 24.84 38.60 1.0-0.5
pfnet/plamo-13b 34.26 59.69 28.95 12.81 24.21 45.61 1.0-0.6
stabilityai/japanese-stablelm-instruct-alpha-7b 34.20 53.43 26.32 15.83 26.32 49.12 1.0-0.3
elyza/ELYZA-japanese-Llama-2-13b-fast 34.06 59.12 31.58 14.82 24.42 40.35 default
stabilityai/japanese-stablelm-instruct-beta-7b 33.87 53.64 36.84 13.82 22.95 42.11 1.0-0.2
rinna/bilingual-gpt-neox-4b 33.79 58.63 31.58 14.82 23.58 40.35 1.0-0.4
Qwen/Qwen2-0.5B-Instruct 33.72 55.33 28.95 15.08 21.89 47.37 1.0-0.6
sbintuitions/sarashina1-13b 33.70 45.20 36.84 16.83 24.00 45.61 1.0-0.2.1
rinna/japanese-gpt-neox-3.6b 33.57 45.72 23.68 14.57 24.21 59.65 1.0-0.5
Xwin-LM/Xwin-LM-13B-V0.2 33.56 40.33 42.11 15.83 25.68 43.86 1.0-0.1
sbintuitions/sarashina1-65b 33.55 57.20 21.05 14.82 29.05 45.61 1.0-0.1
pfnet/plamo-13b-instruct-nc 33.18 54.15 23.68 16.33 26.11 45.61 1.0-0.6
Fugaku-LLM/Fugaku-LLM-13B 32.89 55.36 28.95 12.06 24.21 43.86 1.0-0.6
google/gemma-7b-it 32.41 53.15 26.32 17.34 23.16 42.11 default
llm-jp/llm-jp-13b-v1.0 32.36 60.76 21.05 13.07 24.84 42.11 1.0-0.6
elyza/ELYZA-japanese-Llama-2-7b-fast-instruct 32.18 36.16 39.47 18.59 26.32 40.35 1.0-0.1.2
line-corporation/japanese-large-lm-1.7b 32.10 46.77 34.21 13.82 23.58 42.11 1.0-0.4
cyberagent/open-calm-medium 32.02 49.12 26.32 13.32 24.00 47.37 1.0-0.2.1
google/recurrentgemma-2b 31.84 49.51 26.32 15.08 24.42 43.86 1.0-0.6
google/gemma-7b 31.75 48.91 23.68 16.33 24.21 45.61 1.0-0.3
tokyotech-llm/Swallow-7b-hf 31.59 42.00 28.95 16.33 25.05 45.61 1.0-0.1
line-corporation/japanese-large-lm-1.7b-instruction-sft 31.51 50.50 26.32 13.32 23.58 43.86 1.0-0.5
google/gemma-1.1-7b-it 31.36 36.68 28.95 17.09 26.74 47.37 1.0-0.2
sbintuitions/tiny-lm-chat 31.20 46.74 26.32 13.82 25.26 43.86 default
karakuri-ai/karakuri-lm-7b-apm-v0.2 31.10 35.95 36.84 18.84 25.26 38.60 1.0-0.2
stockmark/gpt-neox-japanese-1.4b 31.07 51.10 26.32 15.83 25.26 36.84 1.0-0.6
llm-jp/llm-jp-13b-instruct-full-dolly_en-dolly_ja-ichikara_003_001-oasst_en-oasst_ja-v1.1 30.87 42.11 23.68 18.09 24.84 45.61 1.0-0.4
Qwen/Qwen1.5-0.5B 30.82 50.40 21.05 15.58 26.74 40.35 1.0-0.6
cyberagent/open-calm-3b 30.76 37.49 26.32 15.33 23.79 50.88 1.0-0.1
stabilityai/japanese-stablelm-instruct-alpha-7b-v2 30.55 35.95 26.32 17.09 22.53 50.88 1.0-0.2.1
cyberagent/open-calm-1b 30.46 30.08 28.95 16.83 23.79 52.63 1.0-0.1
sbintuitions/tiny-lm 30.30 40.42 21.05 19.60 24.84 45.61 1.0-0.1.2
abeja/gpt-neox-japanese-2.7b 30.17 40.43 31.58 14.07 24.42 40.35 1.0-0.1.2
stabilityai/japanese-stablelm-base-alpha-7b 30.16 35.95 31.58 16.33 24.84 42.11 default
Qwen/Qwen1.5-0.5B-Chat 29.98 36.69 34.21 15.33 25.05 38.60 1.0-0.1
line-corporation/japanese-large-lm-3.6b-instruction-sft 29.54 35.95 26.32 14.07 24.00 47.37 1.0-0.2.1
line-corporation/japanese-large-lm-3.6b 29.54 35.95 26.32 14.07 24.00 47.37 1.0-0.1
Qwen/Qwen2-0.5B 29.49 35.98 28.95 17.34 24.84 40.35 1.0-0.2
cyberagent/open-calm-small 29.48 35.95 23.68 18.59 23.58 45.61 1.0-0.6
cyberagent/open-calm-7b 28.80 37.83 28.95 13.07 23.79 40.35 1.0-0.4

Note: Prompt selection is not performed only for Open AI models. For Open AI models, results are counted as wrong when the content filter is applied.

Recently, we updated the evaluation policy. Please refer to the UPDATE.md for more details.

How to evaluate your model

  1. git clone this repository
  2. Install the requirements 
    poetry install
  3. Choose your prompt template based on docs/prompt_templates.md and num_fewshots (In this official leaderboard, we use prompt template peforming the best score.)
  4. Replace TEMPLATE to the version and change MODEL_PATH . And, save the script as harness.sh 
    MODEL_ARGS="pretrained=MODEL_PATH,other_options"
TASK="chabsa-1.0-TEMPLATE,cma_basics-1.0-TEMPLATE,cpa_audit-1.0-TEMPLATE,security_sales_1-1.0-0.2,fp2-1.0-TEMPLATE"
python main.py --model hf --model_args $MODEL_ARGS --tasks $TASK --num_fewshot 0 --output_path "result.json"
  5. Run the script 
    poetry run bash harness.sh

vllm is also supported. Please refer to model examples and lm_eval official pages.

Model Regulation

  • Training/Tuning data of the model must not include this evaluation dataset
    • Japanese annual reports included in chabsa are allowed to be used only if chabsa's sentiment data is not used for training/tuning.
  • No license violation or concerns are argued for the model (e.g. using ShareGPT or Alpaca for training corpus)

Citation

If you use this repository, please cite the following paper:

@preprint{Hirano2023-pre-finllm,
  title={{金融分野における言語モデル性能評価のための日本語金融ベンチマーク構築}},
  author={平野, 正徳},
  doi={10.51094/jxiv.564},
  year={2023}
}
@inproceedings{Hirano2023-finnlpkdf,
  title={{Construction of a Japanese Financial Benchmark for Large Language Models}},
  author={Masanori Hirano},
  booktitle={Joint Workshop of the 7th Financial Technology and Natural Language Processing (FinNLP), the 5th Knowledge Discovery from Unstructured Data in Financial Services (KDF), and The 4th Workshop on Economics and Natural Language Processing (ECONLP)},
  pages={1-9},
  doi={10.2139/ssrn.4769124},
  url={https://aclanthology.org/2024.finnlp-1.1},
  archivePrefix={arXiv},
  arxivId={2403.15062},
  year={2024}
}

Or cite directory this repository:

@misc{Hirano2023-jlfh
    title={{Japanese Language Model Financial Evaluation Harness}},
    author={Masanori Hirano},
    year={2023},
    url = {https://github.com/pfnet-research/japanese-lm-fin-harness}
}

Note:

cpa_audit data comes from an existing collection of Japanese CPA Audit exam questions and answers [1]. In addition, this dataset was built using data from the Institute of Certified Public Accountants and Auditing Oversight Board Web site and is subject to a CC-BY 4.0 license. We got special permission to include this data directly for this evaluation. Thanks to their contribution.

[1] Tatsuki Masuda, Kei Nakagawa, Takahiro Hoshino, Can ChatGPT pass the JCPA exam?: Challenge for the short-answer method test on Auditing, JSAI Technical Report, Type 2 SIG, 2023, Volume 2023, Issue FIN-031, Pages 81-88, Released on J-STAGE October 12, 2023, Online ISSN 2436-5556, https://doi.org/10.11517/jsaisigtwo.2023.FIN-031_81

Contribution

This project is owned by Preferred Networks and maintained by Masanori Hirano.

If you want to add models or evaluation dataset, please let me know via issues or pull requests.

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Japanese Language Model Financial Evaluation Harness

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