Adding Evaluation Results

This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

README.md

@@ -1,11 +1,114 @@
 ---
-license: apache-2.0
 language:
 - en
 - zh
+license: apache-2.0
 tags:
 - finance
 - invest
+model-index:
+- name: deepmoney-34b-200k-base
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 63.99
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TriadParty/deepmoney-34b-200k-base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 83.87
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TriadParty/deepmoney-34b-200k-base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 74.04
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TriadParty/deepmoney-34b-200k-base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 45.93
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TriadParty/deepmoney-34b-200k-base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 81.45
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TriadParty/deepmoney-34b-200k-base
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 0.0
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TriadParty/deepmoney-34b-200k-base
+      name: Open LLM Leaderboard
 ---
 # **Deepmoney**
 
@@ -295,4 +398,17 @@ raw text，全参数训练。基座采用了长上下文的yi-34b-200k。这对
 2， 请你设计一套定量方法研究以上新闻对____业产生的影响。并据此说明具体需要使用哪些数据。
 3， 请根据以下数据，_____设计一套具体的定量方法定量分析以上新闻对____业产生的影响。
 
-其中，第一个问题是主观判断，提取新闻影响的标的。这更多的依赖模型的主观分析能力。然后从第一个回答中提取行业名称（这对于熟悉大模型的人来说，设计一套自动化流程易如反掌），填入第二个问题中，目的是获取定量分析的数据。之所以先问定量方法再数据，是COT的魔法。最后一个问题的回答才是我们真正需要的，这个问题上下文中给了足够多的信息，需要它回复一个确切且具体的定量方法。结合代码编写的模型和函数调用的模型，如果你有一个数据字典完善的宏微观数据库的话，这是完全可以实现的。以上是deepmoney和gpt4的这三步回答，该新闻是20240115北京时间早晨9:35刚刚发生的新闻。
+其中，第一个问题是主观判断，提取新闻影响的标的。这更多的依赖模型的主观分析能力。然后从第一个回答中提取行业名称（这对于熟悉大模型的人来说，设计一套自动化流程易如反掌），填入第二个问题中，目的是获取定量分析的数据。之所以先问定量方法再数据，是COT的魔法。最后一个问题的回答才是我们真正需要的，这个问题上下文中给了足够多的信息，需要它回复一个确切且具体的定量方法。结合代码编写的模型和函数调用的模型，如果你有一个数据字典完善的宏微观数据库的话，这是完全可以实现的。以上是deepmoney和gpt4的这三步回答，该新闻是20240115北京时间早晨9:35刚刚发生的新闻。
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TriadParty__deepmoney-34b-200k-base)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |58.21|
+|AI2 Reasoning Challenge (25-Shot)|63.99|
+|HellaSwag (10-Shot)              |83.87|
+|MMLU (5-Shot)                    |74.04|
+|TruthfulQA (0-shot)              |45.93|
+|Winogrande (5-shot)              |81.45|
+|GSM8k (5-shot)                   | 0.00|
+