输出包含作为 0 向量的 padding tokens 的输出，我们可以使用 binary mask 将其删除。 这是前向传播的...
# Returns `torch.Tensor` The bag-of-words representations for the input sequence, shape `(batch_size, vocab_size)` """ bag_of_words_vectors = [] mask = get_text_field_mask({"tokens": {"tokens": inputs}}) if self._ignore_oov: # also mask out positions corresponding to oov mask...
nan_mask = torch.isnan(att_loss) if sum(nan_mask) > 0: if sum(~nan_mask) == 0: print("!"*20,"cross_entopy_loss all is nan","!"*20) att_loss = torch.tensor(0.0,requires_grad=True) else: print("!"*20,"cross_entopy_loss has nan","!"*20) att_loss = att_loss.mask...
masked_fill_(unk_drop_mask, unk_idx) # drop to random_mask if rand_drop_prob: rand_drop_mask = (noise > 1 - rand_drop_prob) & token_mask rand_tokens = torch.randint_like(x, len(vocab)) rand_tokens.masked_fill_(1 - rand_drop_mask, 0) x2.masked_fill_(rand_drop_mask, 0)...
[SEP] Jim Henson was a puppeteer [SEP]"tokenized_text = enc.tokenize(text)# 输入标记之一进行掩码masked_index =8tokenized_text[masked_index] ='[MASK]'indexed_tokens = enc.convert_tokens_to_ids(tokenized_text) segments_ids = [0,0,0,0,0,0,0,1,1,1,1,1,1,1]# 创建虚拟输入tokens_...
(*args, **kwargs) File "/opt/conda/lib/python3.8/site-packages/transformers/generation/utils.py", line 1437, in generate return self.sample( File "/opt/conda/lib/python3.8/site-packages/transformers/generation/utils.py", line 2479, in sample next_tokens = torch.multinomial(probs, num_...
tokens.append(i[1]) # 存储的是向量 tsne_model = TSNE(perplexity=20, n_components=2, init='pca', n_iter=2500, random_state=23) # perplexity: 默认为30,数据集越大,需要参数值越大,建议值位5-50 , n_components=2 默认为2,嵌入空间的维度(嵌入空间的意思就是结果空间),别的参数估计不重要 ...
tokens masked_index = 8 tokenized_text[masked_index] = '[MASK]' indexed_tokens = script_tokenizer.convert_tokens_to_ids(tokenized_text) segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1] # Creating a dummy input tokens_tensor = torch.tensor([indexed_tokens]) ...
def npu_prompt_flash_attention_forward(query, key, value, *, padding_mask=None, atten_mask=None, pse_shift=None, actual_seq_lengths=None, deq_scale1=None, quant_scale1=None, deq_scale2=None, quant_scale2=None, quant_offset2=None, num_heads=1, scale_value=1.0, pre_tokens=2147...
input_ids = tokenizer.convert_tokens_to_ids(tokenized_text) attention_mask = [1] * len(input_ids) train_input_ids.append(input_ids) train_attention_masks.append(attention_mask) train_labels.append([label2id[label] for label in token_labels]) ...