91精品国产综合久久四虎久久_国产成人午夜高潮毛片_99er视频精品免费观看_2020亚洲熟女在线观看_日本女优人体写真_国内黄色毛片_年轻的老师中文版在线_丰满女邻居做爰_久久久久久精品成人免费图片

 
Transformers自然語言處理（修訂版，影印版）
Transformers自然語言處理（修訂版，影印版）
Lewis Tunstall, Leandro von Werra, Thomas Wolf 著
出版時間：2023年03月
頁數(shù)：383
“transformers相關書籍的杰作 —— 清晰易懂！”
——Jeremy Howard
fast.ai聯(lián)合創(chuàng)始人，昆士蘭大學教授
“清晰精辟的現(xiàn)代自然語言處理指南。推薦！”
——Christopher Manning
斯坦福大學機器學習
Thomas M. Siebel教授

自2017年推出以來，transformers已迅速成為在各種自然語言處理任務中實現(xiàn)最佳結果的主導架構。如果你是一名數(shù)據(jù)科學家或程序員，這本實踐用書將向你展示如何使用Hugging FaceTransformers（基于Python的深度學習庫）訓練和擴展這些大型模型。
transformers已經被用來撰寫真實的新聞故事、改進Google搜索查詢，甚至創(chuàng)建會講老套笑話的聊天機器人。在這本指南中，作者Lewis Tunstall、Leandro von Werra、Thomas Wolf（Hugging Face Transformers的創(chuàng)建者）通過實踐方法來教你如何使用transformers以及如何將它集成到你的應用中。你將快速學習可以由transformers幫助解決的各種任務。
● 為核心NLP任務構建、調試和優(yōu)化transformers模型，例如文本分類、命名實體識別和問答
● 學習如何使用transformers進行跨語言遷移學習
● 在缺乏標記數(shù)據(jù)的實際場景中應用transformers
● 使用提取、修剪和量化等技術高效部署transformers模型
● 從頭開始訓練transformers并學習如何擴展到多個GPU和分布式環(huán)境
  1. Foreword
  2. Preface
  3. 1. Hello Transformers
  4. The Encoder-Decoder Framework
  5. Attention Mechanisms
  6. Transfer Learning in NLP
  7. Hugging Face Transformers: Bridging the Gap
  8. A Tour of Transformer Applications
  9. The Hugging Face Ecosystem
  10. Main Challenges with Transformers
  11. Conclusion
  12. 2. Text Classification
  13. The Dataset
  14. From Text to Tokens
  15. Training a Text Classifier
  16. Conclusion
  17. 3. Transformer Anatomy
  18. The Transformer Architecture
  19. The Encoder
  20. The Decoder
  21. Meet the Transformers
  22. Conclusion
  23. 4. Multilingual Named Entity Recognition
  24. The Dataset
  25. Multilingual Transformers
  26. A Closer Look at Tokenization
  27. Transformers for Named Entity Recognition
  28. The Anatomy of the Transformers Model Class
  29. Tokenizing Texts for NER
  30. Performance Measures
  31. Fine-Tuning XLM-RoBERTa
  32. Error Analysis
  33. Cross-Lingual Transfer
  34. Interacting with Model Widgets
  35. Conclusion
  36. 5. Text Generation
  37. The Challenge with Generating Coherent Text
  38. Greedy Search Decoding
  39. Beam Search Decoding
  40. Sampling Methods
  41. Top-k and Nucleus Sampling
  42. Which Decoding Method Is Best?
  43. Conclusion
  44. 6. Summarization
  45. The CNN/DailyMail Dataset
  46. Text Summarization Pipelines
  47. Comparing Different Summaries
  48. Measuring the Quality of Generated Text
  49. Evaluating PEGASUS on the CNN/DailyMail Dataset
  50. Training a Summarization Model
  51. Conclusion
  52. 7. Question Answering
  53. Building a Review-Based QA System
  54. Improving Our QA Pipeline
  55. Going Beyond Extractive QA
  56. Conclusion
  57. 8. Making Transformers Efficient in Production
  58. Intent Detection as a Case Study
  59. Creating a Performance Benchmark
  60. Making Models Smaller via Knowledge Distillation
  61. Making Models Faster with Quantization
  62. Benchmarking Our Quantized Model
  63. Optimizing Inference with ONNX and the ONNX Runtime
  64. Making Models Sparser with Weight Pruning
  65. Conclusion
  66. 9. Dealing with Few to No Labels
  67. Building a GitHub Issues Tagger
  68. Implementing a Naive Bayesline
  69. Working with No Labeled Data
  70. Working with a Few Labels
  71. Leveraging Unlabeled Data
  72. Conclusion
  73. 10. Training Transformers from Scratch
  74. Large Datasets and Where to Find Them
  75. Building a Tokenizer
  76. Training a Model from Scratch
  77. Results and Analysis
  78. Conclusion
  79. 11. Future Directions
  80. Scaling Transformers
  81. Going Beyond Text
  82. Multimodal Transformers
  83. Where to from Here?
  84. Index
書名：Transformers自然語言處理（修訂版，影印版）
國內出版社：東南大學出版社
出版時間：2023年03月
頁數(shù)：383
書號：978-7-5766-0589-1
原版書書名：Natural Language Processing with Transformers
原版書出版商：O'Reilly Media
Lewis Tunstall
 
Lewis Tunstall是Hugging Face機器學習工程師，致力于為NLP社區(qū)開發(fā)實用工具，并幫助人們更好地使用這些工具。
 
 
Leandro von Werra
 
Leandro von Werra是Hugging Face機器學習工程師，致力于代碼生成模型的研究與社區(qū)推廣工作。
 
 
Thomas Wolf
 
Thomas Wolf是Hugging Face首席科學官兼聯(lián)合創(chuàng)始人，他的團隊肩負著促進AI研究和普及的使命。
 
 
The bird on the cover of Natural Language Processing with Transformers is a coconut lorikeet (Trichoglossus haematodus), a relative of parakeets and parrots. It is also known as the green-naped lorikeet and is native to Oceania.
The plumage of coconut lorikeets blends into their colorful tropical and subtropical surroundings; their green nape meets a yellow collar beneath a deep dark blue head, which ends in an orange-red bill. Their eyes are orange and the breast feathers are red. Coconut lorikeets have one of the longest, pointed tails of the seven species of lorikeet, which is green from above and yellow underneath. These birds measure 10 to 12 inches long and weigh 3.8 to 4.8 ounces.
Coconut lorikeets have one monogamous partner and lay two matte white eggs at a time. They build nests over 80 feet high in eucalyptus trees and live 15 to 20 years in the wild. This species suffers from habitat loss and capture for the pet trade.
購買選項
定價：119.00元
書號：978-7-5766-0589-1
出版社：東南大學出版社