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[] Udemy - Natural Language Processing With Transformers in Python

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文件列表

  1. 7. Long Text Classification With BERT/1. Classification of Long Text Using Windows.mp4 116MB
  2. 8. Named Entity Recognition (NER)/9. NER With Sentiment.mp4 100MB
  3. 8. Named Entity Recognition (NER)/5. Pulling Data With The Reddit API.mp4 89MB
  4. 7. Long Text Classification With BERT/2. Window Method in PyTorch.mp4 85MB
  5. 14. Fine-Tuning Transformer Models/5. The Logic of MLM.mp4 79MB
  6. 14. Fine-Tuning Transformer Models/10. Fine-tuning with NSP - Data Preparation.mp4 78MB
  7. 6. [Project] Sentiment Model With TensorFlow and Transformers/6. Build and Save.mp4 77MB
  8. 14. Fine-Tuning Transformer Models/6. Fine-tuning with MLM - Data Preparation.mp4 77MB
  9. 11. Reader-Retriever QA With Haystack/13. Retriever-Reader Stack.mp4 75MB
  10. 14. Fine-Tuning Transformer Models/7. Fine-tuning with MLM - Training.mp4 70MB
  11. 11. Reader-Retriever QA With Haystack/10. FAISS in Haystack.mp4 68MB
  12. 6. [Project] Sentiment Model With TensorFlow and Transformers/3. Preprocessing.mp4 62MB
  13. 8. Named Entity Recognition (NER)/10. NER With roBERTa.mp4 59MB
  14. 6. [Project] Sentiment Model With TensorFlow and Transformers/7. Loading and Prediction.mp4 57MB
  15. 12. [Project] Open-Domain QA/3. Building the Haystack Pipeline.mp4 56MB
  16. 2. NLP and Transformers/9. Positional Encoding.mp4 56MB
  17. 5. Language Classification/4. Tokenization And Special Tokens For BERT.mp4 55MB
  18. 8. Named Entity Recognition (NER)/1. Introduction to spaCy.mp4 52MB
  19. 4. Attention/2. Alignment With Dot-Product.mp4 49MB
  20. 14. Fine-Tuning Transformer Models/3. BERT Pretraining - Masked-Language Modeling (MLM).mp4 47MB
  21. 9. Question and Answering/7. Our First Q&A Model.mp4 46MB
  22. 14. Fine-Tuning Transformer Models/14. Fine-tuning with MLM and NSP - Data Preparation.mp4 44MB
  23. 11. Reader-Retriever QA With Haystack/9. What is FAISS.mp4 43MB
  24. 12. [Project] Open-Domain QA/2. Creating the Database.mp4 42MB
  25. 14. Fine-Tuning Transformer Models/4. BERT Pretraining - Next Sentence Prediction (NSP).mp4 42MB
  26. 2. NLP and Transformers/10. Transformer Heads.mp4 40MB
  27. 11. Reader-Retriever QA With Haystack/5. Elasticsearch in Haystack.mp4 39MB
  28. 9. Question and Answering/4. Processing SQuAD Training Data.mp4 38MB
  29. 5. Language Classification/1. Introduction to Sentiment Analysis.mp4 38MB
  30. 1. Introduction/3. Environment Setup.mp4 37MB
  31. 8. Named Entity Recognition (NER)/4. Authenticating With The Reddit API.mp4 36MB
  32. 6. [Project] Sentiment Model With TensorFlow and Transformers/2. Getting the Data (Kaggle API).mp4 35MB
  33. 1. Introduction/2. Course Overview.mp4 34MB
  34. 10. Metrics For Language/3. Applying ROUGE to Q&A.mp4 34MB
  35. 13. Similarity/4. Using Cosine Similarity.mp4 34MB
  36. 4. Attention/6. Multi-head and Scaled Dot-Product Attention.mp4 34MB
  37. 8. Named Entity Recognition (NER)/2. Extracting Entities.mp4 34MB
  38. 2. NLP and Transformers/2. Pros and Cons of Neural AI.mp4 33MB
  39. 13. Similarity/3. Sentence Vectors With Mean Pooling.mp4 32MB
  40. 5. Language Classification/2. Prebuilt Flair Models.mp4 31MB
  41. 3. Preprocessing for NLP/9. Unicode Normalization - NFKD and NFKC.mp4 30MB
  42. 6. [Project] Sentiment Model With TensorFlow and Transformers/5. Dataset Shuffle, Batch, Split, and Save.mp4 30MB
  43. 9. Question and Answering/5. (Optional) Processing SQuAD Training Data with Match-Case.mp4 30MB
  44. 13. Similarity/2. Extracting The Last Hidden State Tensor.mp4 30MB
  45. 11. Reader-Retriever QA With Haystack/11. What is DPR.mp4 30MB
  46. 14. Fine-Tuning Transformer Models/2. Introduction to BERT For Pretraining Code.mp4 29MB
  47. 4. Attention/3. Dot-Product Attention.mp4 29MB
  48. 9. Question and Answering/2. Retrievers, Readers, and Generators.mp4 29MB
  49. 14. Fine-Tuning Transformer Models/1. Visual Guide to BERT Pretraining.mp4 29MB
  50. 4. Attention/4. Self Attention.mp4 28MB
  51. 13. Similarity/1. Introduction to Similarity.mp4 28MB
  52. 8. Named Entity Recognition (NER)/6. Extracting ORGs From Reddit Data.mp4 28MB
  53. 5. Language Classification/3. Introduction to Sentiment Models With Transformers.mp4 27MB
  54. 11. Reader-Retriever QA With Haystack/7. Cleaning the Index.mp4 26MB
  55. 14. Fine-Tuning Transformer Models/13. The Logic of MLM and NSP.mp4 26MB
  56. 5. Language Classification/5. Making Predictions.mp4 26MB
  57. 9. Question and Answering/3. Intro to SQuAD 2.0.mp4 25MB
  58. 2. NLP and Transformers/6. Encoder-Decoder Attention.mp4 25MB
  59. 3. Preprocessing for NLP/2. Tokens Introduction.mp4 24MB
  60. 1. Introduction/4. CUDA Setup.mp4 24MB
  61. 11. Reader-Retriever QA With Haystack/2. What is Elasticsearch.mp4 24MB
  62. 3. Preprocessing for NLP/1. Stopwords.mp4 23MB
  63. 13. Similarity/5. Similarity With Sentence-Transformers.mp4 23MB
  64. 6. [Project] Sentiment Model With TensorFlow and Transformers/4. Building a Dataset.mp4 23MB
  65. 2. NLP and Transformers/1. The Three Eras of AI.mp4 22MB
  66. 2. NLP and Transformers/3. Word Vectors.mp4 22MB
  67. 10. Metrics For Language/2. ROUGE in Python.mp4 22MB
  68. 10. Metrics For Language/4. Recall, Precision and F1.mp4 21MB
  69. 11. Reader-Retriever QA With Haystack/3. Elasticsearch Setup (Windows).mp4 21MB
  70. 14. Fine-Tuning Transformer Models/9. The Logic of NSP.mp4 21MB
  71. 2. NLP and Transformers/7. Self-Attention.mp4 21MB
  72. 11. Reader-Retriever QA With Haystack/6. Sparse Retrievers.mp4 20MB
  73. 3. Preprocessing for NLP/7. Unicode Normalization - Composition and Decomposition.mp4 20MB
  74. 11. Reader-Retriever QA With Haystack/4. Elasticsearch Setup (Linux).mp4 20MB
  75. 8. Named Entity Recognition (NER)/8. Entity Blacklist.mp4 20MB
  76. 3. Preprocessing for NLP/8. Unicode Normalization - NFD and NFC.mp4 20MB
  77. 14. Fine-Tuning Transformer Models/8. Fine-tuning with MLM - Training with Trainer.mp4 20MB
  78. 3. Preprocessing for NLP/3. Model-Specific Special Tokens.mp4 19MB
  79. 10. Metrics For Language/6. Q&A Performance With ROUGE.mp4 19MB
  80. 8. Named Entity Recognition (NER)/7. Getting Entity Frequency.mp4 18MB
  81. 10. Metrics For Language/1. Q&A Performance With Exact Match (EM).mp4 18MB
  82. 3. Preprocessing for NLP/4. Stemming.mp4 17MB
  83. 2. NLP and Transformers/4. Recurrent Neural Networks.mp4 17MB
  84. 3. Preprocessing for NLP/6. Unicode Normalization - Canonical and Compatibility Equivalence.mp4 17MB
  85. 9. Question and Answering/1. Open Domain and Reading Comprehension.mp4 16MB
  86. 4. Attention/1. Attention Introduction.mp4 16MB
  87. 10. Metrics For Language/5. Longest Common Subsequence (LCS).mp4 15MB
  88. 11. Reader-Retriever QA With Haystack/12. The DPR Architecture.mp4 14MB
  89. 14. Fine-Tuning Transformer Models/11. Fine-tuning with NSP - DataLoader.mp4 14MB
  90. 11. Reader-Retriever QA With Haystack/1. Intro to Retriever-Reader and Haystack.mp4 14MB
  91. 2. NLP and Transformers/8. Multi-head Attention.mp4 13MB
  92. 11. Reader-Retriever QA With Haystack/8. Implementing a BM25 Retriever.mp4 13MB
  93. 6. [Project] Sentiment Model With TensorFlow and Transformers/1. Project Overview.mp4 13MB
  94. 4. Attention/5. Bidirectional Attention.mp4 11MB
  95. 3. Preprocessing for NLP/5. Lemmatization.mp4 11MB
  96. 1. Introduction/1. Introduction.mp4 9MB
  97. 2. NLP and Transformers/5. Long Short-Term Memory.mp4 6MB
  98. 12. [Project] Open-Domain QA/1. ODQA Stack Structure.mp4 6MB
  99. 7. Long Text Classification With BERT/1. Classification of Long Text Using Windows.srt 24KB
  100. 8. Named Entity Recognition (NER)/9. NER With Sentiment.srt 20KB
  101. 7. Long Text Classification With BERT/2. Window Method in PyTorch.srt 16KB
  102. 6. [Project] Sentiment Model With TensorFlow and Transformers/3. Preprocessing.srt 15KB
  103. 14. Fine-Tuning Transformer Models/10. Fine-tuning with NSP - Data Preparation.srt 15KB
  104. 6. [Project] Sentiment Model With TensorFlow and Transformers/6. Build and Save.srt 14KB
  105. 4. Attention/2. Alignment With Dot-Product.srt 14KB
  106. 14. Fine-Tuning Transformer Models/7. Fine-tuning with MLM - Training.srt 14KB
  107. 14. Fine-Tuning Transformer Models/6. Fine-tuning with MLM - Data Preparation.srt 13KB
  108. 11. Reader-Retriever QA With Haystack/10. FAISS in Haystack.srt 13KB
  109. 14. Fine-Tuning Transformer Models/5. The Logic of MLM.srt 13KB
  110. 8. Named Entity Recognition (NER)/5. Pulling Data With The Reddit API.srt 13KB
  111. 6. [Project] Sentiment Model With TensorFlow and Transformers/7. Loading and Prediction.srt 12KB
  112. 11. Reader-Retriever QA With Haystack/13. Retriever-Reader Stack.srt 11KB
  113. 2. NLP and Transformers/10. Transformer Heads.srt 11KB
  114. 8. Named Entity Recognition (NER)/10. NER With roBERTa.srt 10KB
  115. 5. Language Classification/1. Introduction to Sentiment Analysis.srt 10KB
  116. 11. Reader-Retriever QA With Haystack/9. What is FAISS.srt 10KB
  117. 14. Fine-Tuning Transformer Models/1. Visual Guide to BERT Pretraining.srt 10KB
  118. 2. NLP and Transformers/9. Positional Encoding.srt 10KB
  119. 8. Named Entity Recognition (NER)/1. Introduction to spaCy.srt 9KB
  120. 5. Language Classification/2. Prebuilt Flair Models.srt 9KB
  121. 14. Fine-Tuning Transformer Models/3. BERT Pretraining - Masked-Language Modeling (MLM).srt 9KB
  122. 9. Question and Answering/7. Our First Q&A Model.srt 9KB
  123. 12. [Project] Open-Domain QA/3. Building the Haystack Pipeline.srt 9KB
  124. 14. Fine-Tuning Transformer Models/14. Fine-tuning with MLM and NSP - Data Preparation.srt 9KB
  125. 11. Reader-Retriever QA With Haystack/5. Elasticsearch in Haystack.srt 9KB
  126. 3. Preprocessing for NLP/9. Unicode Normalization - NFKD and NFKC.srt 9KB
  127. 10. Metrics For Language/3. Applying ROUGE to Q&A.srt 9KB
  128. 11. Reader-Retriever QA With Haystack/11. What is DPR.srt 8KB
  129. 3. Preprocessing for NLP/2. Tokens Introduction.srt 8KB
  130. 5. Language Classification/4. Tokenization And Special Tokens For BERT.srt 8KB
  131. 6. [Project] Sentiment Model With TensorFlow and Transformers/2. Getting the Data (Kaggle API).srt 8KB
  132. 1. Introduction/2. Course Overview.srt 8KB
  133. 13. Similarity/3. Sentence Vectors With Mean Pooling.srt 8KB
  134. 13. Similarity/1. Introduction to Similarity.srt 8KB
  135. 8. Named Entity Recognition (NER)/4. Authenticating With The Reddit API.srt 8KB
  136. 12. [Project] Open-Domain QA/2. Creating the Database.srt 8KB
  137. 2. NLP and Transformers/1. The Three Eras of AI.srt 8KB
  138. 6. [Project] Sentiment Model With TensorFlow and Transformers/5. Dataset Shuffle, Batch, Split, and Save.srt 8KB
  139. 1. Introduction/3. Environment Setup.srt 7KB
  140. 11. Reader-Retriever QA With Haystack/2. What is Elasticsearch.srt 7KB
  141. 4. Attention/6. Multi-head and Scaled Dot-Product Attention.srt 7KB
  142. 3. Preprocessing for NLP/3. Model-Specific Special Tokens.srt 7KB
  143. 5. Language Classification/3. Introduction to Sentiment Models With Transformers.srt 7KB
  144. 9. Question and Answering/2. Retrievers, Readers, and Generators.srt 7KB
  145. 9. Question and Answering/4. Processing SQuAD Training Data.srt 7KB
  146. 14. Fine-Tuning Transformer Models/4. BERT Pretraining - Next Sentence Prediction (NSP).srt 7KB
  147. 5. Language Classification/5. Making Predictions.srt 7KB
  148. 8. Named Entity Recognition (NER)/2. Extracting Entities.srt 7KB
  149. 8. Named Entity Recognition (NER)/6. Extracting ORGs From Reddit Data.srt 7KB
  150. 9. Question and Answering/3. Intro to SQuAD 2.0.srt 7KB
  151. 3. Preprocessing for NLP/6. Unicode Normalization - Canonical and Compatibility Equivalence.srt 6KB
  152. 3. Preprocessing for NLP/4. Stemming.srt 6KB
  153. 3. Preprocessing for NLP/1. Stopwords.srt 6KB
  154. 3. Preprocessing for NLP/8. Unicode Normalization - NFD and NFC.srt 6KB
  155. 4. Attention/4. Self Attention.srt 6KB
  156. 2. NLP and Transformers/6. Encoder-Decoder Attention.srt 6KB
  157. 6. [Project] Sentiment Model With TensorFlow and Transformers/4. Building a Dataset.srt 6KB
  158. 13. Similarity/4. Using Cosine Similarity.srt 6KB
  159. 3. Preprocessing for NLP/7. Unicode Normalization - Composition and Decomposition.srt 6KB
  160. 13. Similarity/2. Extracting The Last Hidden State Tensor.srt 6KB
  161. 10. Metrics For Language/1. Q&A Performance With Exact Match (EM).srt 6KB
  162. 4. Attention/3. Dot-Product Attention.srt 6KB
  163. 14. Fine-Tuning Transformer Models/13. The Logic of MLM and NSP.srt 5KB
  164. 10. Metrics For Language/4. Recall, Precision and F1.srt 5KB
  165. 2. NLP and Transformers/2. Pros and Cons of Neural AI.srt 5KB
  166. 11. Reader-Retriever QA With Haystack/7. Cleaning the Index.srt 5KB
  167. 14. Fine-Tuning Transformer Models/2. Introduction to BERT For Pretraining Code.srt 5KB
  168. 2. NLP and Transformers/3. Word Vectors.srt 5KB
  169. 9. Question and Answering/5. (Optional) Processing SQuAD Training Data with Match-Case.srt 5KB
  170. 2. NLP and Transformers/7. Self-Attention.srt 5KB
  171. 14. Fine-Tuning Transformer Models/9. The Logic of NSP.srt 5KB
  172. 10. Metrics For Language/2. ROUGE in Python.srt 4KB
  173. 2. NLP and Transformers/4. Recurrent Neural Networks.srt 4KB
  174. 3. Preprocessing for NLP/5. Lemmatization.srt 4KB
  175. 11. Reader-Retriever QA With Haystack/6. Sparse Retrievers.srt 4KB
  176. 10. Metrics For Language/6. Q&A Performance With ROUGE.srt 4KB
  177. 13. Similarity/5. Similarity With Sentence-Transformers.srt 4KB
  178. 8. Named Entity Recognition (NER)/8. Entity Blacklist.srt 4KB
  179. 8. Named Entity Recognition (NER)/7. Getting Entity Frequency.srt 4KB
  180. 11. Reader-Retriever QA With Haystack/1. Intro to Retriever-Reader and Haystack.srt 4KB
  181. 9. Question and Answering/1. Open Domain and Reading Comprehension.srt 4KB
  182. 1. Introduction/4. CUDA Setup.srt 4KB
  183. 6. [Project] Sentiment Model With TensorFlow and Transformers/1. Project Overview.srt 3KB
  184. 14. Fine-Tuning Transformer Models/8. Fine-tuning with MLM - Training with Trainer.srt 3KB
  185. 14. Fine-Tuning Transformer Models/11. Fine-tuning with NSP - DataLoader.srt 3KB
  186. 2. NLP and Transformers/8. Multi-head Attention.srt 3KB
  187. 1. Introduction/1. Introduction.srt 3KB
  188. 10. Metrics For Language/5. Longest Common Subsequence (LCS).srt 3KB
  189. 4. Attention/5. Bidirectional Attention.srt 3KB
  190. 4. Attention/1. Attention Introduction.srt 3KB
  191. 11. Reader-Retriever QA With Haystack/8. Implementing a BM25 Retriever.srt 2KB
  192. 11. Reader-Retriever QA With Haystack/12. The DPR Architecture.srt 2KB
  193. 2. NLP and Transformers/5. Long Short-Term Memory.srt 2KB
  194. 11. Reader-Retriever QA With Haystack/3. Elasticsearch Setup (Windows).srt 2KB
  195. 11. Reader-Retriever QA With Haystack/4. Elasticsearch Setup (Linux).srt 2KB
  196. 12. [Project] Open-Domain QA/1. ODQA Stack Structure.srt 2KB
  197. 11. Reader-Retriever QA With Haystack/2.1 Elasticsearch (Cloud) Introduction Article.html 195B
  198. 11. Reader-Retriever QA With Haystack/11.1 Article.html 189B
  199. 11. Reader-Retriever QA With Haystack/12.1 Article.html 189B
  200. 7. Long Text Classification With BERT/1.1 Article.html 188B
  201. 9. Question and Answering/5.2 Pattern Matching Article.html 184B
  202. 5. Language Classification/3.1 Notebook.html 181B
  203. 5. Language Classification/4.1 Notebook.html 181B
  204. 5. Language Classification/5.1 Notebook.html 181B
  205. 12. [Project] Open-Domain QA/3.1 Notebook.html 180B
  206. 6. [Project] Sentiment Model With TensorFlow and Transformers/7.1 Notebook.html 179B
  207. 5. Language Classification/1.1 Notebook.html 178B
  208. 6. [Project] Sentiment Model With TensorFlow and Transformers/6.1 Notebook.html 178B
  209. 7. Long Text Classification With BERT/2.1 Notebook.html 178B
  210. 6. [Project] Sentiment Model With TensorFlow and Transformers/4.1 Notebook.html 177B
  211. 6. [Project] Sentiment Model With TensorFlow and Transformers/5.1 Notebook.html 177B
  212. 8. Named Entity Recognition (NER)/10.1 Notebook.html 177B
  213. 6. [Project] Sentiment Model With TensorFlow and Transformers/2.1 Notebook.html 176B
  214. 6. [Project] Sentiment Model With TensorFlow and Transformers/3.1 Notebook.html 176B
  215. 8. Named Entity Recognition (NER)/6.2 Notebook.html 176B
  216. 8. Named Entity Recognition (NER)/7.1 Notebook.html 176B
  217. 8. Named Entity Recognition (NER)/8.1 Notebook.html 176B
  218. 9. Question and Answering/4.1 Notebook.html 175B
  219. 12. [Project] Open-Domain QA/2.2 Notebook.html 174B
  220. 5. Language Classification/2.1 Notebook.html 174B
  221. 8. Named Entity Recognition (NER)/4.1 Notebook.html 174B
  222. 8. Named Entity Recognition (NER)/5.1 Notebook.html 174B
  223. 7. Long Text Classification With BERT/1.2 Notebook.html 173B
  224. 8. Named Entity Recognition (NER)/9.1 Notebook.html 172B
  225. 8. Named Entity Recognition (NER)/6.1 Data.html 171B
  226. 11. Reader-Retriever QA With Haystack/9.1 Article.html 170B
  227. 8. Named Entity Recognition (NER)/1.1 Notebook.html 169B
  228. 8. Named Entity Recognition (NER)/2.1 Notebook.html 169B
  229. 11. Reader-Retriever QA With Haystack/5.1 Notebook.html 168B
  230. 11. Reader-Retriever QA With Haystack/6.1 Notebook.html 168B
  231. 11. Reader-Retriever QA With Haystack/7.1 Notebook.html 168B
  232. 11. Reader-Retriever QA With Haystack/8.1 Notebook.html 168B
  233. 9. Question and Answering/5.1 Notebook.html 167B
  234. 11. Reader-Retriever QA With Haystack/10.1 Notebook.html 166B
  235. 2. NLP and Transformers/2.3 Self-Driving Limitations.html 163B
  236. 4. Attention/5.1 Notebook.html 163B
  237. 9. Question and Answering/7.1 Notebook.html 163B
  238. 10. Metrics For Language/3.1 Notebook.html 162B
  239. 11. Reader-Retriever QA With Haystack/9.2 Notebook.html 162B
  240. 9. Question and Answering/3.1 Notebook.html 162B
  241. 4. Attention/2.1 Notebook.html 161B
  242. 4. Attention/3.1 Notebook.html 161B
  243. 10. Metrics For Language/1.1 Notebook.html 160B
  244. 11. Reader-Retriever QA With Haystack/11.2 Notebook.html 160B
  245. 11. Reader-Retriever QA With Haystack/12.2 Notebook.html 160B
  246. 9. Question and Answering/1.1 Notebook.html 160B
  247. 9. Question and Answering/2.1 Notebook.html 160B
  248. 14. Fine-Tuning Transformer Models/14.1 Notebook.html 159B
  249. 14. Fine-Tuning Transformer Models/8.1 Notebook.html 159B
  250. 2. NLP and Transformers/2.1 2010 Flash Crash.html 159B
  251. 4. Attention/6.1 Notebook.html 159B
  252. 11. Reader-Retriever QA With Haystack/1.1 Notebook.html 157B
  253. 3. Preprocessing for NLP/5.1 Notebook.html 157B
  254. 3. Preprocessing for NLP/6.1 Notebook.html 157B
  255. 3. Preprocessing for NLP/7.1 Notebook.html 157B
  256. 3. Preprocessing for NLP/8.1 Notebook.html 157B
  257. 3. Preprocessing for NLP/9.1 Notebook.html 157B
  258. 14. Fine-Tuning Transformer Models/13.1 Notebook.html 156B
  259. 11. Reader-Retriever QA With Haystack/13.1 Notebook.html 155B
  260. 10. Metrics For Language/2.1 Notebook.html 154B
  261. 10. Metrics For Language/4.1 Notebook.html 154B
  262. 10. Metrics For Language/5.1 Notebook.html 154B
  263. 10. Metrics For Language/6.1 Notebook.html 154B
  264. 14. Fine-Tuning Transformer Models/5.1 Notebook.html 154B
  265. 14. Fine-Tuning Transformer Models/9.1 Notebook.html 154B
  266. 4. Attention/4.1 Notebook.html 154B
  267. 3. Preprocessing for NLP/1.1 Notebook.html 153B
  268. 3. Preprocessing for NLP/4.1 Notebook.html 152B
  269. 14. Fine-Tuning Transformer Models/10.1 Notebook.html 151B
  270. 14. Fine-Tuning Transformer Models/11.1 Notebook.html 151B
  271. 14. Fine-Tuning Transformer Models/6.1 Notebook.html 151B
  272. 14. Fine-Tuning Transformer Models/7.1 Notebook.html 151B
  273. 3. Preprocessing for NLP/2.1 Notebook.html 150B
  274. 3. Preprocessing for NLP/3.1 Notebook.html 150B
  275. 4. Attention/1.1 Notebook.html 147B
  276. 12. [Project] Open-Domain QA/2.1 Data.html 145B
  277. 2. NLP and Transformers/2.2 Amazon AI Recruitment Bias.html 144B
  278. 14. Fine-Tuning Transformer Models/2.1 Notebook.html 143B
  279. 14. Fine-Tuning Transformer Models/3.1 Notebook.html 143B
  280. 14. Fine-Tuning Transformer Models/4.1 Notebook.html 143B
  281. 14. Fine-Tuning Transformer Models/12. Setup the NSP Fine-tuning Training Loop.html 136B
  282. 14. Fine-Tuning Transformer Models/15. Setup DataLoader and Model Fine-tuning For MLM and NSP.html 136B
  283. 8. Named Entity Recognition (NER)/3. NER Walkthrough.html 136B
  284. 9. Question and Answering/6. Processing SQuAD Dev Data.html 136B
  285. 0. Websites you may like/[FCS Forum].url 133B
  286. 1. Introduction/3.1 Installation Instructions.html 129B
  287. 1. Introduction/4.1 Installation Instructions.html 129B
  288. 0. Websites you may like/[FreeCourseSite.com].url 127B
  289. 0. Websites you may like/[CourseClub.ME].url 122B
  290. 1. Introduction/2.1 GitHub Repo.html 103B
  291. 8. Named Entity Recognition (NER)/1.2 spaCy Model Docs.html 84B