基于此,文中提出了任务驱动的语言建模(Task-driven Language Modeling,简称TLM),以求改进预训练->微调的训练范式,首先基于通用语料库用任务文本构造检索利用BM25检索文本构造小型语料库,然后将小型语料库预训练目标和任务目标同时优化,最后微调,发现计算量减少两个数量级的同时,效果不弱于甚至强于传统预训练->微调范式。 传统训练范式先在大
Language modeling refers to the process of constructing a probability distribution function that assigns probabilities to strings or sentences in a language. It is a fundamental task in Natural Language Processing (NLP) and is used in various applications such as machine translation, speech recognition...
1. Task1: Language modeling 首先回想一下,我们可以使用链式法则将联合概率分解为每个词的条件概率的乘积: p(x_{1:L}) = p(x_1)p(x_2|x_1)p(x_3|x_1,x_2) \dots p(x_L|x_{1:L-1})=\prod_{i=1}^{L} p(x_i|x_{1:i-1}).\\ 1.1 Perplexity (困惑度) 序列的联合概率依赖于...
Language modeling Language modeling is the task of predicting the next word or character in a document. * indicates models using dynamic evaluation; where, at test time, models may adapt to seen tokens in order to improve performance on following tokens. (Mikolov et al., (2010), Krause et ...
New data science techniques, such as fine-tuning and transfer learning, have become essential in language modeling. Rather than training a model from scratch, fine-tuning lets developers take a pre-trained language model and adapt it to a task or domain. This approach has reduced the amount of...
「定义:」Language Modeling就是预测下一个出现的词的概率的任务。(Language Modeling is the task of predicting what word comes next.) 即: P(xt+1|xt,xt−1...x1) 1.1 统计学方法:n-gram language model 简化:一个词出现的概率只和它前面的n-1个词有关系,这就是"n-gram"的含义。因此有: ...
language_modeling import LanguageModelingModel import logging logging.basicConfig(level=logging.INFO) transformers_logger = logging.getLogger("transformers") transformers_logger.setLevel(logging.WARNING) train_args = { "reprocess_input_data": True, "overwrite_output_dir": True, "vocab_size": 52000, }...
language_modeling import LanguageModelingModel import logging logging.basicConfig(level=logging.INFO) transformers_logger = logging.getLogger("transformers") transformers_logger.setLevel(logging.WARNING) train_args = { "reprocess_input_data": True, "overwrite_output_dir": True, "vocab_size": 52000, }...
We baseline our performance with a bidirectional LSTM model trained using the same language modeling task on the same training dataset, where validation performance plateaus at 28% pseudo-accuracy and 15% absolute accuracy (Supplementary Fig. 2 and Supplementary Table 2, note that biLSTM is smaller...
With rich and structured information such as task input/output format, TEP reduces interference among tasks, allowing the model to focus on their shared structure. With a single model, Musketeer achieves results comparable to or better than strong baselines trained on single tasks, almost uniformly ...