04 语音NLP论文阅读 CLIP (1/3):Learning Transferable Visual Models From Natural Language 55:00 语音NLP论文阅读 Token-level Sequence Labeling for SLU using Compositional E2E Models 55:06 语音文本技术论文阅读 Joint Unsupervised and Supervised Training for Multilingual ASR 18:35 解锁天顶星科技ChatGPT 1...
CV论文阅读OpenAI CLIP(2/3):Learning Transferable Visual Models From Natural Language 1388 -- 57:31 App [Long Review] Cascaded Diffusion Models for High Fidelity Image Generation 289 -- 52:27 App [Long Review] Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using 1273 61 38:10...
定义编码器层和编码器:CLIPEncoderLayer 和CLIPEncoder 类定义了 CLIP 模型的编码器层和编码器结构,用于处理嵌入后的输入数据。 定义模型:CLIPModel, CLIPTextModel, CLIPVisionModel, CLIPTextModelWithProjection,和 CLIPVisionModelWithProjection 类定义了 CLIP 模型的主体结构,包括如何处理文本和图像输入,以及如何将它...
Large-scale pre-trained multi-modal models (e.g., CLIP) demonstrate strong zero-shot transfer ca...
[:, 0, :]) if self.proj is not None: x = x @ self.proj return x class CLIP(nn.Module): def __init__(self, embed_dim: int, # vision image_resolution: int, vision_layers: Union[Tuple[int, int, int, int], int], vision_width: int, vision_patch_size: int, # text context...
Given a compressed input video, we extract a sequence of spacetime patches which act as transformer tokens. This scheme works for images too since images are just videos with a single frame. Our patch-based representation enables Sora to train on videos and images of variable resolutions, duratio...
Self Attention: attends to different parts of the input sequence itself, rather than another sequence or modality. Captures long-range dependencies and contextual information. Used in transformer models. Multi-head Self-Attention: performs self-attention multiple times in parallel, allowing the model to...
However, this is sometimes unreliable, text extraction mistakes are common (especially with scientific terms or nucleotide sequences), and errors are frequent with complex multi-panel figures. Even at their current level of accuracy, the multimodal capabilities of these models are enabling novel uses...
Transformer是ChatGPT语言模型的核心技术,是一种用于序列到序列(Sequence-to-Sequence)任务的神经网络模型,例如机器翻译,语音识别和生成对话等,它使用了注意力机制来计算输入序列和输出序列之间的关系。如下图所示 Transformer的主要优点是它可以并行地处理输入序列中的所有信息,因此在训练和推理时都有很高效率。
For a codex embedding, does one just train it more on code, and then it is able to distinguish more sequence semantics. Does that then matter 0% for a single token, though? Or what fine-tune works to make a “doc” model compare big to small? One might extract 50k single-token...