A class that keeps all conversation history.

CARDINAL类

中文数字/数位字符 每个字符除繁简体外还有一个额外的大写字符 e.g. '陆' 和 '陸'

Rescales the input PIL.Image to the given 'size'. 'size' will be the size of the smaller edge. For example, if height > width, then image wil

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

DIGIT类

FRACTION类

Fake file-like stream object that redirects writes to a logger instance.

TELEPHONE类

中文数字字符

Convolutional 2D subsampling (to 1/4 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_ra

Convolutional 2D subsampling (to 1/4 length). Args: idim (int): Input dimension. odim (int): Output dimension. dropout_ra

Collate examples for supervised fine-tuning.

DATE类

Vision Transformer with support for patch or hybrid CNN input stage

Convert any spelled-out numbers into arabic numbers, while handling: - remove any commas - keep the suffixes such as: `1960s`, `274th`,

Applies British-American spelling mappings as listed in [1]. [1] https://www.tysto.com/uk-us-spelling-list.html

A generic evaluation-time tokenizer, which leverages built-in tokenizers in sacreBLEU (https://github.com/mjpost/sacrebleu). It additionally provi

Implement the scaled dot product attention with softmax. Arguments --------- softmax_scale: The temperature to use for the softmax att

Multi-headed attention from 'Attention Is All You Need' paper

Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`InternEncoderLayer`]. Args:

E2E module. Args: idim (int): dimension of inputs odim (int): dimension of outputs args (namespace): argument Namespace c

Dataset for supervised fine-tuning.

r""" Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while keeping a bit of rand

This implementation fine-tunes 20 LoRA modules based on the InternVL-Chat-V1-5 model. The fine-tuned LoRA modules are then merged with the In

用于中文数字系统的数学符号 (繁/简体), e.g. positive = ['正', '正'] negative = ['负', '負'] point = ['点', '點']

MONEY类

Multi-Head Attention layer. :param int n_head: the number of head s :param int n_feat: the number of features :param float dropout_rate:

Multi-Head Attention layer. :param int n_head: the number of head s :param int n_feat: the number of features :param float dropout_rate:

Multi-input multi-output torch.nn.Sequential.

Multi-input multi-output torch.nn.Sequential.

中文数字系统

https://arxiv.org/abs/2212.00794

Image to Patch Embedding

PERCENTAGE类