mirror of https://github.com/InternLM/InternLM
update 7b evaluation results
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@ -49,18 +49,19 @@ InternLM は、70 億のパラメータを持つベースモデルと、実用
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オープンソースの評価ツール [OpenCompass](https://github.com/internLM/OpenCompass/) を用いて、InternLM の総合的な評価を行った。この評価では、分野別能力、言語能力、知識能力、推論能力、理解能力の 5 つの次元をカバーしました。以下は評価結果の一部であり、その他の評価結果については [OpenCompass leaderboard](https://opencompass.org.cn/rank) をご覧ください。
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| データセット\モデル | **InternLM-7B** | LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
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| ---------------- | --------------------- | -------- | ----------- | ----------- | --------- | --------- |
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| C-Eval(Val) | 53.4 | 24.2 | 42.7 | 50.9 | 28.9 | 31.2 |
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| MMLU | 51.0 | 35.2* | 41.5 | 46.0 | 39.7 | 47.3 |
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| AGIEval | 37.6 | 20.8 | 24.6 | 39.0 | 24.1 | 26.4 |
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| CommonSenseQA | 59.5 | 65.0 | 58.8 | 60.0 | 68.7 | 66.7 |
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| BUSTM | 50.6 | 48.5 | 51.3 | 55.0 | 48.8 | 62.5 |
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| CLUEWSC | 59.1 | 50.3 | 52.8 | 59.8 | 50.3 | 52.2 |
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| MATH | 7.1 | 2.8 | 3.0 | 6.6 | 2.2 | 2.8 |
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| GSM8K | 31.2 | 10.1 | 9.7 | 29.2 | 6.0 | 15.3 |
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| HumanEval | 10.4 | 14.0 | 9.2 | 9.2 | 9.2 | 11.0 |
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| RACE(High) | 57.4 | 46.9* | 28.1 | 66.3 | 40.7 | 54.0 |
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| データセット\モデル | **InternLM-Chat-7B** | **InternLM-7B** | LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
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| --------------- | -------------------------- | --------------------- | -------- | ----------- | ----------- | --------- | --------- |
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| C-Eval(Val) | 52.0 | 53.4 | 24.2 | 42.7 | 50.9 | 28.9 | 31.2 |
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| MMLU | 52.6 | 51.0 | 35.2* | 41.5 | 46.0 | 39.7 | 47.3 |
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| AGIEval | 46.4 | 37.6 | 20.8 | 24.6 | 39.0 | 24.1 | 26.4 |
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| CommonSenseQA | 80.8 | 59.5 | 65.0 | 58.8 | 60.0 | 68.7 | 66.7 |
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| BUSTM | 80.6 | 50.6 | 48.5 | 51.3 | 55.0 | 48.8 | 62.5 |
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| CLUEWSC | 81.8 | 59.1 | 50.3 | 52.8 | 59.8 | 50.3 | 52.2 |
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| MATH | 5.0 | 7.1 | 2.8 | 3.0 | 6.6 | 2.2 | 2.8 |
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| GSM8K | 36.2 | 31.2 | 10.1 | 9.7 | 29.2 | 6.0 | 15.3 |
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| HumanEval | 15.9 | 10.4 | 14.0 | 9.2 | 9.2 | 9.2 | 11.0 |
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| RACE(High) | 80.3 | 57.4 | 46.9* | 28.1 | 66.3 | 40.7 | 54.0 |
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- 評価結果は [OpenCompass 20230706](https://github.com/internLM/OpenCompass/) (*印のあるデータは原著論文からの引用を意味する)から取得したもので、評価設定は [OpenCompass](https://github.com/internLM/OpenCompass/) が提供する設定ファイルに記載されています。
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- 評価データは、[OpenCompass](https://github.com/internLM/OpenCompass/) のバージョンアップにより数値的な差異が生じる可能性がありますので、[OpenCompass](https://github.com/internLM/OpenCompass/) の最新の評価結果をご参照ください。
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@ -131,18 +131,18 @@ InternLM-7B 包含了一个拥有70亿参数的基础模型和一个为实际场
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我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 从学科综合能力、语言能力、知识能力、推理能力、理解能力五大能力维度对InternLM开展全面评测，部分评测结果如下表所示，欢迎访问[OpenCompass 榜单](https://opencompass.org.cn/rank)获取更多的评测结果。
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| 数据集\模型 | **InternLM-7B** | LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
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| -------------------- | ---------------- | --------- | --------- | ------------ | --------- | ---------- |
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| C-Eval(Val) | 53.4 | 24.2 | 42.7 | 50.9 | 28.9 | 31.2 |
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| MMLU | 51.0 | 35.2* | 41.5 | 46.0 | 39.7 | 47.3 |
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| AGIEval | 37.6 | 20.8 | 24.6 | 39.0 | 24.1 | 26.4 |
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| CommonSenseQA | 59.5 | 65.0 | 58.8 | 60.0 | 68.7 | 66.7 |
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| BUSTM | 50.6 | 48.5 | 51.3 | 55.0 | 48.8 | 62.5 |
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| CLUEWSC | 59.1 | 50.3 | 52.8 | 59.8 | 50.3 | 52.2 |
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| MATH | 7.1 | 2.8 | 3.0 | 6.6 | 2.2 | 2.8 |
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| GSM8K | 31.2 | 10.1 | 9.7 | 29.2 | 6.0 | 15.3 |
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| HumanEval | 10.4 | 14.0 | 9.2 | 9.2 | 9.2 | 11.0 |
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| RACE(High) | 57.4 | 46.9* | 28.1 | 66.3 | 40.7 | 54.0 |
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| 数据集\模型 | **InternLM-Chat-7B** | **InternLM-7B** | LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
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| --------------- | -------------------------- | --------------------- | -------- | ----------- | ----------- | --------- | --------- |
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| C-Eval(Val) | 52.0 | 53.4 | 24.2 | 42.7 | 50.9 | 28.9 | 31.2 |
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| MMLU | 52.6 | 51.0 | 35.2* | 41.5 | 46.0 | 39.7 | 47.3 |
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| AGIEval | 46.4 | 37.6 | 20.8 | 24.6 | 39.0 | 24.1 | 26.4 |
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| CommonSenseQA | 80.8 | 59.5 | 65.0 | 58.8 | 60.0 | 68.7 | 66.7 |
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| BUSTM | 80.6 | 50.6 | 48.5 | 51.3 | 55.0 | 48.8 | 62.5 |
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| CLUEWSC | 81.8 | 59.1 | 50.3 | 52.8 | 59.8 | 50.3 | 52.2 |
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| MATH | 5.0 | 7.1 | 2.8 | 3.0 | 6.6 | 2.2 | 2.8 |
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| GSM8K | 36.2 | 31.2 | 10.1 | 9.7 | 29.2 | 6.0 | 15.3 |
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| HumanEval | 15.9 | 10.4 | 14.0 | 9.2 | 9.2 | 9.2 | 11.0 |
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| RACE(High) | 80.3 | 57.4 | 46.9* | 28.1 | 66.3 | 40.7 | 54.0 |
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- 以上评测结果基于 [OpenCompass 20230706](https://github.com/internLM/OpenCompass/) 获得（部分数据标注`*`代表数据来自原始论文），具体测试细节可参见 [OpenCompass](https://github.com/internLM/OpenCompass/) 中提供的配置文件。
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- 评测数据会因 [OpenCompass](https://github.com/internLM/OpenCompass/) 的版本迭代而存在数值差异，请以 [OpenCompass](https://github.com/internLM/OpenCompass/) 最新版的评测结果为主。
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24
README.md
24
README.md
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@ -130,18 +130,18 @@ InternLM-7B contains a 7 billion parameter base model and a chat model tailored
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We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool [OpenCompass](https://github.com/internLM/OpenCompass/). The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the [OpenCompass leaderboard](https://opencompass.org.cn/rank) for more evaluation results.
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| Datasets\Models | **InternLM-7B** | LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
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| --------------- | --------------------- | -------- | ----------- | ----------- | --------- | --------- |
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| C-Eval(Val) | 53.4 | 24.2 | 42.7 | 50.9 | 28.9 | 31.2 |
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| MMLU | 51.0 | 35.2* | 41.5 | 46.0 | 39.7 | 47.3 |
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| AGIEval | 37.6 | 20.8 | 24.6 | 39.0 | 24.1 | 26.4 |
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| CommonSenseQA | 59.5 | 65.0 | 58.8 | 60.0 | 68.7 | 66.7 |
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| BUSTM | 50.6 | 48.5 | 51.3 | 55.0 | 48.8 | 62.5 |
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| CLUEWSC | 59.1 | 50.3 | 52.8 | 59.8 | 50.3 | 52.2 |
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| MATH | 7.1 | 2.8 | 3.0 | 6.6 | 2.2 | 2.8 |
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| GSM8K | 31.2 | 10.1 | 9.7 | 29.2 | 6.0 | 15.3 |
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| HumanEval | 10.4 | 14.0 | 9.2 | 9.2 | 9.2 | 11.0 |
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| RACE(High) | 57.4 | 46.9* | 28.1 | 66.3 | 40.7 | 54.0 |
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| Datasets\Models | **InternLM-Chat-7B** | **InternLM-7B** | LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
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| --------------- | -------------------------- | --------------------- | -------- | ----------- | ----------- | --------- | --------- |
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| C-Eval(Val) | 52.0 | 53.4 | 24.2 | 42.7 | 50.9 | 28.9 | 31.2 |
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| MMLU | 52.6 | 51.0 | 35.2* | 41.5 | 46.0 | 39.7 | 47.3 |
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| AGIEval | 46.4 | 37.6 | 20.8 | 24.6 | 39.0 | 24.1 | 26.4 |
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| CommonSenseQA | 80.8 | 59.5 | 65.0 | 58.8 | 60.0 | 68.7 | 66.7 |
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| BUSTM | 80.6 | 50.6 | 48.5 | 51.3 | 55.0 | 48.8 | 62.5 |
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| CLUEWSC | 81.8 | 59.1 | 50.3 | 52.8 | 59.8 | 50.3 | 52.2 |
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| MATH | 5.0 | 7.1 | 2.8 | 3.0 | 6.6 | 2.2 | 2.8 |
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| GSM8K | 36.2 | 31.2 | 10.1 | 9.7 | 29.2 | 6.0 | 15.3 |
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| HumanEval | 15.9 | 10.4 | 14.0 | 9.2 | 9.2 | 9.2 | 11.0 |
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| RACE(High) | 80.3 | 57.4 | 46.9* | 28.1 | 66.3 | 40.7 | 54.0 |
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- The evaluation results were obtained from [OpenCompass 20230706](https://github.com/internLM/OpenCompass/) (some data marked with *, which means come from the original papers), and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/internLM/OpenCompass/).
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- The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/internLM/OpenCompass/), so please refer to the latest evaluation results of [OpenCompass](https://github.com/internLM/OpenCompass/).
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