szA1 = size(A1,1); szA2 = size(A2,1); [m,idx] = max([szA1 szA2]) m = 7 idx = 2 Pad the shorter vector to match the length of the longer vector. B1 = paddata(A1,m) B1 =7×12 8 3 5 0 0 0 You can concatenate vectors of the same length. Create a matrix using th...
GPT2Tokenizer, Trainer, TrainingArguments import torch import numpy as np # Function to shift the labels for language modeling def shift_labels(examples): examples["labels"] = examples["input_ids"].copy() return examples # Load the dataset dataset = load_dataset("wikipedia", "20220301.simple"...
"model_max_length": 16384, "pad_token": { "__type": "AddedToken", "content": "<|end▁of▁sentence|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false } zhzhang commented May 29, 2024 Related question, how does the FIM model learn to stop generatio...
str_repeat($pad_string, $length); } elseif ($pad_type == STR_PAD_BOTH) { // STR_PAD_BOTH == 2 $str = str_repeat($pad_string, floor($length/2)); $str .= $input; $str .= str_repeat($pad_string, ceil($length/2)); } else { // defaults to STR_PAD_LEFT == 0...
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szA1 = size(A1,1); szA2 = size(A2,1); [m,idx] = max([szA1 szA2]) m = 7 idx = 2 Pad the shorter vector to match the length of the longer vector. Get B1 = paddata(A1,m) B1 = 7×1 2 8 3 5 0 0 0 You can concatenate vectors of the same length. Create a ...
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encode_plus(sentence, max_length=MAX_LEN, truncation=True, padding='max_length', add_special_tokens=True, return_attention_mask=True, return_token_type_ids=False, return_tensors='tf') return tokens['input_ids'], tokens['attention_mask'] # initialize two arrays for input tensors Xids =...