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README.md
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@@ -75,10 +75,10 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
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| MVBench | 55.1 | 37.0 | 60.2 | 57.9 |
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| MMBench-Video<sub>8f</sub> | - | 0.99 | 0.97 | 0.95 |
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| MMBench-Video<sub>16f</sub> | - | 1.04 | 1.03 | 0.98 |
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| Video-MME<br>
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- We evaluate our models on MVBench by extracting 16 frames from each video, and each frame was resized to a 448x448 image.
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Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
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@@ -462,10 +462,10 @@ InternVL 2.0 是一个多模态大语言模型系列,包含各种规模的模
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| MVBench | 55.1 | 37.0 | 60.2 | 57.9 |
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| MMBench-Video<sub>8f</sub> | - | 0.99 | 0.97 | 0.95 |
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| MMBench-Video<sub>16f</sub> | - | 1.04 | 1.03 | 0.98 |
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| Video-MME<br>
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- 我们通过从每个视频中提取16帧来评估我们的模型在MVBench上的性能,每个视频帧被调整为448x448的图像。
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限制:尽管在训练过程中我们非常注重模型的安全性,尽力促使模型输出符合伦理和法律要求的文本,但受限于模型大小以及概率生成范式,模型可能会产生各种不符合预期的输出,例如回复内容包含偏见、歧视等有害内容,请勿传播这些内容。由于传播不良信息导致的任何后果,本项目不承担责任。
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| MVBench | 55.1 | 37.0 | 60.2 | 57.9 |
|
76 |
| MMBench-Video<sub>8f</sub> | - | 0.99 | 0.97 | 0.95 |
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| MMBench-Video<sub>16f</sub> | - | 1.04 | 1.03 | 0.98 |
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| Video-MME<br>w/o subs | - | 42.9 | 45.0 | 42.6 |
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| Video-MME<br>w subs | - | 44.7 | 47.3 | 44.7 |
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- We evaluate our models on MVBench and Video-MME by extracting 16 frames from each video, and each frame was resized to a 448x448 image.
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82 |
|
83 |
Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
|
84 |
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|
|
462 |
| MVBench | 55.1 | 37.0 | 60.2 | 57.9 |
|
463 |
| MMBench-Video<sub>8f</sub> | - | 0.99 | 0.97 | 0.95 |
|
464 |
| MMBench-Video<sub>16f</sub> | - | 1.04 | 1.03 | 0.98 |
|
465 |
+
| Video-MME<br>w/o subs | - | 42.9 | 45.0 | 42.6 |
|
466 |
+
| Video-MME<br>w subs | - | 44.7 | 47.3 | 44.7 |
|
467 |
|
468 |
+
- 我们通过从每个视频中提取 16 帧来评估我们的模型在 MVBench 和 Video-MME 上的性能,每个视频帧被调整为 448x448 的图像。
|
469 |
|
470 |
限制:尽管在训练过程中我们非常注重模型的安全性,尽力促使模型输出符合伦理和法律要求的文本,但受限于模型大小以及概率生成范式,模型可能会产生各种不符合预期的输出,例如回复内容包含偏见、歧视等有害内容,请勿传播这些内容。由于传播不良信息导致的任何后果,本项目不承担责任。
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