import numpy as np tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext") model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext") query = "cardiopathy" query_toks = tokenizer.batch_encode_plus([query], padding="max_length", max_...
layer=None, heads=None): inputs = tokenizer.encode_plus(sentence_a, sentence_b, return_ten...
self.df=light_load_csv(data_path, [xcol,ycol],nrows=nrows)self.xcol=xcolself.ycol=ycolself.xmax=xmaxself.ymax=ymaxself.tokenizer=tokenizerdefencode_str(self,s,lim):returnself.tokenizer.encode_plus(s,max_length=lim,truncation=True,padding='max_length',return_tensors='pt')def__len__...
The bug Some special tokens have ids that are out of the vocab size in transformers, this can happen with fine-tuned models with extra added special tokens to the original tokenizer. It causes the Tokenizer object failing to initialise a...
tokenizer_encode = encode_str, accelerate_kwargs = dict( cpu = True ) ) trainer(overwrite_checkpoints = True) # checkpoints after each finetuning stage will be saved to ./checkpoints SPIN can be trained as follows - it can also be added to the fine-tuning pipeline as shown in the fi...
幸运的是,KerasNLP 使用 keras_nlp.tokenizers.compute_word_piece_vocabulary 实用程序在语料库上训练 WordPiece 变得非常简单。 注意:FNet 的官方实现使用 SentencePiece Tokenizer。 def train_word_piece(ds, vocab_size, reserved_tokens): word_piece_ds = ds.unbatch().map(lambda x, y: x) vocab = ...