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  1. (.venv_py3) pegasus@pegasus-v2-backend-3:~/app/entity-v2$ python
  2. Python 3.7.5 (default, Dec 16 2019, 13:06:40)
  3. [GCC 5.4.0 20160609] on linux
  4. Type "help", "copyright", "credits" or "license" for more information.
  5. >>> import stanfordnlp
  6. >>> nlp = stanfordnlp.Pipeline(lang='id')
  7. Use device: cpu
  8. ---
  9. Loading: tokenize
  10. With settings:
  11. {'model_path': '/home/pegasus/stanfordnlp_resources/id_gsd_models/id_gsd_tokenizer.pt', 'lang': 'id', 'shorthand': 'id_gsd', 'mode': 'predict'}
  12. ---
  13. Loading: pos
  14. With settings:
  15. {'model_path': '/home/pegasus/stanfordnlp_resources/id_gsd_models/id_gsd_tagger.pt', 'pretrain_path': '/home/pegasus/stanfordnlp_resources/id_gsd_models/id_gsd.pretrain.pt', 'lang': 'id', 'shorthand': 'id_gsd', 'mode': 'predict'}
  16. ---
  17. Loading: lemma
  18. With settings:
  19. {'model_path': '/home/pegasus/stanfordnlp_resources/id_gsd_models/id_gsd_lemmatizer.pt', 'lang': 'id', 'shorthand': 'id_gsd', 'mode': 'predict'}
  20. Building an attentional Seq2Seq model...
  21. Using a Bi-LSTM encoder
  22. Using soft attention for LSTM.
  23. Finetune all embeddings.
  24. [Running seq2seq lemmatizer with edit classifier]
  25. ---
  26. Loading: depparse
  27. With settings:
  28. {'model_path': '/home/pegasus/stanfordnlp_resources/id_gsd_models/id_gsd_parser.pt', 'pretrain_path': '/home/pegasus/stanfordnlp_resources/id_gsd_models/id_gsd.pretrain.pt', 'lang': 'id', 'shorthand': 'id_gsd', 'mode': 'predict'}
  29. Done loading processors!
  30. ---
  31. >>> text = """
  32. ... Rencana pembangunan pembangkit megaproyek 35 ribu megawatt masih terkendala oleh permasalahan pembangunan transmisi listrik. Hal tersebut sempat diungkapkan oleh Pelaksana Tugas (Plt) Direktur Utama PLN Sripeni Inten Cahyani kepada anggota Dewan Perwakilan Rakyat (DPR). Saat dikonfirmasi, Vice Presiden Public Relation PLN Dwi Suryo Abdullah menuturkan kendala dalam pembangunan transmisi ini terjadi lantaran masih ada sebagian lahan yang belum bisa bebas sesuai target waktu.
  33. ... """
  34. >>> doc = nlp(text)
  35. /pytorch/aten/src/ATen/native/LegacyDefinitions.cpp:19: UserWarning: masked_fill_ received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead.
  36. >>> print(*[f'word: {word.text+" "}\tupos: {word.upos}\txpos: {word.xpos}' for sent in doc.sentences for word in sent.words], sep='\n')
  37. word: Rencana upos: PROPN xpos: NSD
  38. word: pembangunan upos: NOUN xpos: NSD
  39. word: pembangkit upos: NOUN xpos: NSD
  40. word: megaproyek upos: NOUN xpos: X--
  41. word: 35 upos: NUM xpos: CC-
  42. word: ribu upos: NOUN xpos: CC-
  43. word: megawatt upos: NOUN xpos: NSD
  44. word: masih upos: ADV xpos: D--
  45. word: terkendala upos: VERB xpos: VSP
  46. word: oleh upos: ADP xpos: R--
  47. word: permasalahan upos: NOUN xpos: NSD
  48. word: pembangunan upos: NOUN xpos: NSD
  49. word: transmisi upos: NOUN xpos: NSD
  50. word: listrik upos: NOUN xpos: NSD
  51. word: . upos: PUNCT xpos: Z--
  52. word: Hal upos: NOUN xpos: NSD
  53. word: tersebut upos: DET xpos: B--
  54. word: sempat upos: ADV xpos: D--
  55. word: diungkapkan upos: VERB xpos: VSP
  56. word: oleh upos: ADP xpos: R--
  57. word: Pelaksana upos: PROPN xpos: NSD
  58. word: Tugas upos: PROPN xpos: NSD
  59. word: ( upos: PUNCT xpos: Z--
  60. word: Plt upos: PROPN xpos: X--
  61. word: ) upos: PUNCT xpos: Z--
  62. word: Direktur upos: PROPN xpos: NSD
  63. word: Utama upos: PROPN xpos: ASP
  64. word: PLN upos: PROPN xpos: F--
  65. word: Sripeni upos: PROPN xpos: X--
  66. word: Inten upos: PROPN xpos: X--
  67. word: Cahyani upos: PROPN xpos: X--
  68. word: kepada upos: ADP xpos: R--
  69. word: anggota upos: NOUN xpos: NSD
  70. word: Dewan upos: PROPN xpos: NSD
  71. word: Perwakilan upos: PROPN xpos: NSD
  72. word: Rakyat upos: PROPN xpos: NSD
  73. word: ( upos: PUNCT xpos: Z--
  74. word: DPR upos: PROPN xpos: X--
  75. word: ) upos: PUNCT xpos: Z--
  76. word: . upos: PUNCT xpos: Z--
  77. word: Saat upos: SCONJ xpos: NSD
  78. word: dikonfirmasi upos: VERB xpos: VSP
  79. word: , upos: PUNCT xpos: Z--
  80. word: Vice upos: PROPN xpos: F--
  81. word: Presiden upos: PROPN xpos: NSD
  82. word: Public upos: PROPN xpos: F--
  83. word: Relation upos: PROPN xpos: F--
  84. word: PLN upos: PROPN xpos: F--
  85. word: Dwi upos: PROPN xpos: F--
  86. word: Suryo upos: PROPN xpos: F--
  87. word: Abdullah upos: PROPN xpos: F--
  88. word: menuturkan upos: VERB xpos: VSA
  89. word: kendala upos: NOUN xpos: NSD
  90. word: dalam upos: ADP xpos: ASP
  91. word: pembangunan upos: NOUN xpos: NSD
  92. word: transmisi upos: NOUN xpos: NSD
  93. word: ini upos: DET xpos: B--
  94. word: terjadi upos: VERB xpos: VSP
  95. word: lantaran upos: SCONJ xpos: S--
  96. word: masih upos: ADV xpos: D--
  97. word: ada upos: VERB xpos: ASP
  98. word: sebagian upos: DET xpos: ASP
  99. word: lahan upos: NOUN xpos: NSD
  100. word: yang upos: PRON xpos: S--
  101. word: belum upos: ADV xpos: G--
  102. word: bisa upos: ADV xpos: M--
  103. word: bebas upos: ADJ xpos: ASP
  104. word: sesuai upos: ADP xpos: ASP
  105. word: target upos: NOUN xpos: NSD
  106. word: waktu upos: NOUN xpos: NSD
  107. word: . upos: PUNCT xpos: Z--
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