for i, batch in enumerate(dataloader): # 标签形状为 (batch_size, 1) label = batch["label"] text = batch["text"] # tokenized_text 包括 input_ids, token_type_ids, attention_mask tokenized_text = tokenizer(text, max_length=100, add_special_tokens=True, truncation=True, padding=True, ...
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for batch_idx, (inputs, labels, descriptions) in enumerate(loader): with autocast(): optimizer.zero_grad() class_tokens = clip.tokenize(descriptions, context_length=77, truncate=True) inputs, class_tokens = inputs.to(device), class_tokens.to(device) logits_per_image, logits_per_text =...
for i, batch in enumerate(dataloader): # 标签形状为 (batch_size, 1) label = batch["label"] text = batch["text"] # tokenized_text 包括 input_ids, token_type_ids, attention_mask tokenized_text = tokenizer(text, max_length=100, add_special_tokens=True, truncation=True, padding=True, r...
I found the problem. After I turned off the function switch of r1, it was all normal. Thank you guys Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment Assignees No one assigned Labels 🌠 Feature Request Projects None yet Milestone ...
# 存储每一个batch的lossloss_collect = []foriintrange(10, desc='Epoch'):forstep, batch_datainenumerate( tqdm_notebook(train_dataloder, desc='Iteration')): batch_data =tuple(t.to(device)fortinbatch_data) batch_seqs, batch_seq_masks, batch_seq_segments, batch_labels = batch_data# 对...
6. Post Processing predicted_labels: extracting amounts, aggregating the amounts mentioned in the messages based on their predicted categories. # Post-processing: Aggregate amounts for 'credited' and 'debited' credited_amounts = 0 debited_amounts = 0 for i, label in enumerate(predicted_labels)...
Leaflet|©OpenStreetMapcontributors Here we create a map in the'map'div, addtiles of our choice, and then add a marker with some text in a popup: varmap = L.map('map').setView([51.505, -0.09],13); L.tileLayer('https://tile.openstreetmap.org/{z}/{x}/{y}.png', { attribut...
for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration") # 就是加了个进度条 for step, batch in enumerate(epoch_iterator): self.model.train() batch = tuple(t.to(self.device) for t in batch) # GPU or CPU inputs = {'input_ids': batch[0], 'attention_mas...
tolist() sep_index = [i for i, num in enumerate(doc_list) if num == self.word_embedding.stoi['[SEP]']] sentence_list = [] if sep_index: pre = 0 for cur in sep_index: sentence_list.append(doc_list[pre:cur]) pre = cur sentence_list.append(doc_list[cur:]) else: sentence...