原文链接:huggingface.co/docs/transformers/v4.37.2/en/tasks_explained 在🤗 Transformers 能做什么中,您了解了自然语言处理(NLP)、语音和音频、计算机视觉任务以及它们的一些重要应用。本页将仔细研究模型如何解决这些任务,并解释发生在幕后的情况。解决给定任务的方法有很多种,一些模型可能会实现特定的技术,甚至从新...
1.1 RNNs explained Recurrent Neural Networks (RNNs) have been key to many early advances in NLP. They were designed with a unique concept: to keep a form of memory. RNNs process sequences step by step, maintaining a hidden state from previous steps to inform the current output. This sequ...
def get_position_angle_vec(i): return [i / np.power(10000, 2 * (j // 2) / token_len) for j in range(token_len)] sinusoid_table = np.array([get_position_angle_vec(i) for i in range(num_tokens)]) sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) sinusoid_...
In line with the philosophy of the Transformers package Transformers Interpret allows any transformers model to be explained in just two lines. Explainers are available for both text and computer vision models. Visualizations are also available in notebooks and as savable png and html files Keywords...
ChatGPT, GPT-4, BERT, Deep Learning, Machine Learning & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch 热门课程 评分:4.5,满分 5 分4.5(2422 个评分) 7,423 个学生 创建者Lazy Programmer Team,Lazy Programmer Inc. 上次更新时间:9/2024 ...
As previously explained, the data loader class takes care of preparing the data from end to end, and that is by: Retrieving the similarity scores from the dataset. Dividing each score by 5.0 in order to normalize it. Assembling the pairs of sentences into theconcatenated_sentencesattribute, for...
Transformers in Action adds the revolutionary transformers architecture to your AI toolkit. You’ll dive into the essential details of the model’s architecture, with all complex concepts explained through easy-to-understand examples and clever analogies—from sock sorting to skiing! Even complex founda...
In the past, the LSTM and GRU architecture(as explained here in my pastposton NLP) along with attention mechanism used to be the State of the Art Approach for Language modeling problems (put very simply, predict the next word) and Translation systems. But, the main problem with these archi...
Transformers, Explained: Understand the Model Behind GPT-3, BERT, and T5 Dale Markowitz· Follow Published in Towards Data Science · 9 min read ·May 6, 2021 -- 6 You know that expression, When you have a hammer, everything looks like a nail? Well, in machine learning, it seems...
Swin / ViT Vision transformer explained in intuitive detail. Summary of recent advances in computer vision models. Transformers versus CNNs