self, input_ids, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, lm_labels=None ):returnself.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decode
具体如下:input_ids(batch_size, seq_len, hidden_size) salut comment ça - va </s> => label tokens20239 1670 3664 18 900 1 => labels---DECODER ---0 20239 1670
dummy_inputs属性: 这个属性定义了一个示例输入,它是一个包含输入input_ids和decoder_input_ids(解码器输入)的字典。这个属性用于在加载模型时检查模型的输入尺寸是否匹配。 _init_weights方法: 这个方法用于初始化模型权重。根据模型组件的类型,它采用不同的方式初始化权重。对于T5中的不同组件,使用了不同的初始化...
decoder_input_ids = tensor([[0,250099], [0,250099]]) 接下来进入t5block网络层之中 layer_outputs=layer_module(hidden_states,attention_mask=extended_attention_mask,position_bias=position_bias,encoder_hidden_states=encoder_hidden_states,encoder_attention_mask=encoder_extended_attention_mask,encoder_decod...
ids = data['source_ids'].to(device, dtype = torch.long) mask = data['source_mask'].to(device, dtype = torch.long) outputs = model(input_ids = ids, attention_mask = mask, decoder_input_ids=y_ids, labels=lm_labels) loss = outputs[0] step = (epoch * len(loader)) + _ layer...
outputs = model(input_ids = ids, attention_mask = mask, decoder_input_ids=y_ids, labels=lm_labels) loss = outputs[0] step = (epoch * len(loader)) + _ layer.log({"loss": float(loss)}, step) optimizer.zero_grad() loss.backward() ...
ids = data['source_ids'].to(device, dtype = torch.long) mask = data['source_mask'].to(device, dtype = torch.long) outputs = model(input_ids = ids, attention_mask = mask, decoder_input_ids=y_ids, labels=lm_labels) loss = outputs[0] ...
long) outputs = model(input_ids = ids, attention_mask = mask, decoder_input_ids=y_ids, labels=lm_labels) loss = outputs[0] step = (epoch * len(loader)) + _ layer.log({"loss": float(loss)}, step) optimizer.zero_grad() loss.backward() optimizer.step() 在这里,我们使用三个单独...
outputs = model(input_ids = ids, attention_mask = mask, decoder_input_ids=y_ids, labels=lm_labels) loss = outputs[0] step = (epoch * len(loader)) + _ layer.log({"loss": float(loss)}, step) optimizer.zero_grad() loss.backward() ...
outputs = model(input_ids = ids, attention_mask = mask, decoder_input_ids=y_ids, labels=lm_labels) loss = outputs[0] step = (epoch * len(loader)) + _ layer.log({"loss": float(loss)}, step) optimizer.zero_grad() loss.backward() ...