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eval_charm.py
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from mmengine.config import read_base
with read_base():
from .datasets.CHARM.charm_reason_gen_f8fca2 import charm_reason_datasets as datasets
from .models.hf_internlm.lmdeploy_internlm2_chat_7b import models as lmdeploy_7b_chat_model
# from models.openai.gpt_3_5_turbo_1106 import models as gpt_3_5_turbo_1106_model
# from models.openai.gpt_4_1106_preview import models as gpt_4_1106_preview_model
# from .models.chatglm.hf_chatglm3_6b_32k import models as chatglm3_6b_32k_model
# from .models.yi.hf_yi_6b_chat import models as yi_6b_chat_model
# from .models.hf_internlm.hf_internlm2_chat_7b import models as hf_internlm2_chat_7b_model
# from .models.deepseek.hf_deepseek_7b_chat import models as deepseek_7b_chat_model
# from .models.baichuan.hf_baichuan2_7b_chat import models as baichuan2_7b_chat_model # need torch 2.1
# from .models.hf_llama.hf_llama2_7b_chat import models as llama2_7b_chat_model
# from .models.vicuna.hf_vicuna_7b_v15_16k import models as vicuna_7b_v15_16k_model
# from .models.baichuan.hf_baichuan2_13b_chat import models as baichuan2_13b_chat_model # need torch 2.1
# from .models.hf_llama.hf_llama2_13b_chat import models as llama2_13b_chat_model
# from .models.vicuna.hf_vicuna_13b_v15_16k import models as vicuna_13b_v15_16k_model
# from .models.hf_internlm.hf_internlm2_chat_20b import models as hf_internlm2_chat_20b_model
# from .models.yi.hf_yi_34b_chat import models as yi_34b_chat_model
# from .models.deepseek.hf_deepseek_67b_chat import models as deepseek_67b_chat_model
# from .models.hf_llama.hf_llama2_70b_chat import models as llama2_70b_chat_model
# from .models.hf_llama.hf_llama3_8b_instruct import models as llama3_8b_instruct_model
# from .models.hf_llama.hf_llama3_70b_instruct import models as llama3_70b_instruct_model
from .summarizers.charm_rea import summarizer
models = sum([v for k, v in locals().items() if k.endswith('_model')], [])
work_dir = './outputs/CHARM/chat/'
# dataset version metric mode internlm2-chat-7b-turbomind
# ------------------------------------------------------------- --------- ------------- ------ -----------------------------
# charm-reason-Direct - naive_average gen 49.51
# charm-reason-ZH-CoT - naive_average gen 61.33
# charm-reason-EN-CoT - naive_average gen 54.55
# charm-reason-XLT - naive_average gen 58.46
# charm-reason-Translate-EN - naive_average gen 56.15
# - - - -
# charm-reason-Chinese_Direct - naive_average gen 47.14
# charm-reason-Chinese_ZH-CoT - naive_average gen 58.40
# charm-reason-Chinese_EN-CoT - naive_average gen 48.31
# charm-reason-Chinese_XLT - naive_average gen 53.57
# charm-reason-Chinese_Translate-EN - naive_average gen 48.21
# charm-reason-Global_Direct - naive_average gen 51.88
# charm-reason-Global_ZH-CoT - naive_average gen 64.26
# charm-reason-Global_EN-CoT - naive_average gen 60.79
# charm-reason-Global_XLT - naive_average gen 63.36
# charm-reason-Global_Translate-EN - naive_average gen 64.10