From a957b0b1925da8113dcaeadf0aa4e8126efb24cd Mon Sep 17 00:00:00 2001
From: husein zolkepli <husein.zol05@gmail.com>
Date: Tue, 25 Dec 2018 23:23:01 +0800
Subject: [PATCH] fixing docs

---
 docs/load-topic-modelling.rst    | 174 +++++++++++++++----------------
 docs/load-topics-influencers.rst |  60 ++++++-----
 docs/load-toxic.rst              |  95 +++++++++--------
 docs/load-word2vec.rst           |  25 +++--
 docs/text-clustering.rst         |  26 +++--
 5 files changed, 186 insertions(+), 194 deletions(-)

diff --git a/docs/load-topic-modelling.rst b/docs/load-topic-modelling.rst
index 25ad60e7..951ebcef 100644
--- a/docs/load-topic-modelling.rst
+++ b/docs/load-topic-modelling.rst
@@ -1,10 +1,10 @@
 
-.. code:: ipython3
+.. code:: python
 
     import pandas as pd
     import malaya
 
-.. code:: ipython3
+.. code:: python
 
     df = pd.read_csv('tests/02032018.csv',sep=';')
     df = df.iloc[3:,1:]
@@ -14,7 +14,7 @@
 Train LDA2Vec model
 -------------------
 
-.. code:: ipython3
+.. code:: python
 
     lda2vec = malaya.topic_model.lda2vec(corpus, 10)
 
@@ -36,33 +36,33 @@ Train LDA2Vec model
 Print topics
 ^^^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     lda2vec.print_topics(5)
 
 
 .. parsed-literal::
 
-    topic 0       topic 1       topic 2       topic 3       topic 4       
-    --------      --------      --------      --------      --------      
-    jahat         fakta         perinci       selaras       timbal        
-    tabur         intelek       intelek       tiket         merosak       
-    fakta         pajak         fakta         intelek       perinci       
-    seru          mewah         puji          digital       samping       
-    melenyapka    minimum       maruah        kelas         intelek       
-    maruah        kelas         asa           pajak         mewah         
-    indah         butir         suai          jarang        minimum       
-    anggap        atus          tadbir        ketua         labur         
-    misi          ogos          jelang        celik         catat         
-    banding       pesawat       liow          rancak        giat          
-    
-    
+    topic 0       topic 1       topic 2       topic 3       topic 4
+    --------      --------      --------      --------      --------
+    jahat         fakta         perinci       selaras       timbal
+    tabur         intelek       intelek       tiket         merosak
+    fakta         pajak         fakta         intelek       perinci
+    seru          mewah         puji          digital       samping
+    melenyapka    minimum       maruah        kelas         intelek
+    maruah        kelas         asa           pajak         mewah
+    indah         butir         suai          jarang        minimum
+    anggap        atus          tadbir        ketua         labur
+    misi          ogos          jelang        celik         catat
+    banding       pesawat       liow          rancak        giat
+
+
 
 
 Important sentences based on topics
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     lda2vec.get_sentences(5)
 
@@ -82,7 +82,7 @@ Important sentences based on topics
 Get topics
 ^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     lda2vec.get_topics(10)
 
@@ -108,7 +108,7 @@ Get topics
 Visualize topics
 ^^^^^^^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     lda2vec.visualize_topics(notebook_mode = True)
 
@@ -117,15 +117,15 @@ Visualize topics
 
 .. raw:: html
 
-    
+
     <link rel="stylesheet" type="text/css" href="https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css">
-    
-    
+
+
     <div id="ldavis_el35103499035552013519612"></div>
     <script type="text/javascript">
-    
+
     var ldavis_el35103499035552013519612_data = {"mdsDat": {"x": [6.138338538543649e-05, 0.00010683588380374251, 6.0571953412114225e-05, -8.016633454865896e-06, 9.569359122329906e-05, -5.8746842795627114e-05, -1.0617715530318644e-05, -5.757316494000778e-05, -7.131496800808359e-05, -0.00011821548909568914], "y": [1.5670863505427678e-05, -3.956077663673784e-06, -4.217283064737652e-05, 1.8220817945128012e-05, 5.726917939727906e-05, -2.1492447355468255e-05, -0.00011981292071325229, 0.00010171369842256702, -1.3665344825200246e-05, 8.22506193456928e-06], "topics": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "cluster": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], "Freq": [12.10607624053955, 10.752044677734375, 10.70901870727539, 10.363924026489258, 10.322507858276367, 10.302469253540039, 10.045714378356934, 9.869651794433594, 9.040380477905273, 6.488214492797852]}, "tinfo": {"Category": ["Default", "Default", "Default", "Default", "Default", "Default", "Default", "Default", "Default", "Default", "Default", 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-    
+
     function LDAvis_load_lib(url, callback){
       var s = document.createElement('script');
       s.src = url;
@@ -134,7 +134,7 @@ Visualize topics
       s.onerror = function(){console.warn("failed to load library " + url);};
       document.getElementsByTagName("head")[0].appendChild(s);
     }
-    
+
     if(typeof(LDAvis) !== "undefined"){
        // already loaded: just create the visualization
        !function(LDAvis){
@@ -164,7 +164,7 @@ Visualize topics
 Train LDA model
 ---------------
 
-.. code:: ipython3
+.. code:: python
 
     lda = malaya.topic_model.lda(corpus,10,stemming=False)
 
@@ -178,33 +178,33 @@ Train LDA model
 Print topics
 ^^^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     lda.print_topics(5)
 
 
 .. parsed-literal::
 
-    topic 0       topic 1       topic 2       topic 3       topic 4       
-    --------      --------      --------      --------      --------      
-    malaysia      menteri       laku          projek        bahasa        
-    ambil         tumpu         wujud         terima        raja          
-    negara        perdana       projek        tingkat       mudah         
-    tindak        najib         malaysia      jual          ajar          
-    wang          islam         salah         undi          inggeris      
-    putus         kuok          bukti         syarikat      rumah         
-    isu           nyata         berita        raja          bayar         
-    punca         pekan         hadap         bangun        laku          
-    raja          isu           nyata         beli          sewa          
-    hak           amanah        rakyat        indonesia     masuk         
-    
-    
+    topic 0       topic 1       topic 2       topic 3       topic 4
+    --------      --------      --------      --------      --------
+    malaysia      menteri       laku          projek        bahasa
+    ambil         tumpu         wujud         terima        raja
+    negara        perdana       projek        tingkat       mudah
+    tindak        najib         malaysia      jual          ajar
+    wang          islam         salah         undi          inggeris
+    putus         kuok          bukti         syarikat      rumah
+    isu           nyata         berita        raja          bayar
+    punca         pekan         hadap         bangun        laku
+    raja          isu           nyata         beli          sewa
+    hak           amanah        rakyat        indonesia     masuk
+
+
 
 
 Important sentences based on topics
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     lda.get_sentences(5)
 
@@ -224,7 +224,7 @@ Important sentences based on topics
 Get topics
 ^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     lda.get_topics(10)
 
@@ -249,7 +249,7 @@ Get topics
 Visualize topics
 ^^^^^^^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     lda.visualize_topics(notebook_mode = True)
 
@@ -258,15 +258,15 @@ Visualize topics
 
 .. raw:: html
 
-    
+
     <link rel="stylesheet" type="text/css" href="https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css">
-    
-    
+
+
     <div id="ldavis_el3510350419294569931983172"></div>
     <script type="text/javascript">
-    
+
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"kampung", "trx", "huni", "duduk", "raja", "urus", "milik", "proses", "tingkat", "jalan", "laku", "didik", "sedia", "niaga", "jual", "umno", "jemaah", "raya", "nik", "cakap", "status", "nampak", "april", "cawangan", "mesyuarat", "fasal", "guru", "allahyarham", "aziz", "pilih", "konon", "pelabur", "razak", "buah", "majlis", "lantan", "nama", "lancong", "keras", "pulang", "lapang", "muka", "pengaruh", "sepenuh", "insya", "selesai", "kaya", "ubah", "menteri", "isu", "lembaga", "kelulus", "seri", "tangguh", "amanah", "terima", "perdana", "laku", "parti", "putus", "sokong", "ahli", "jalan", "salah", "wujud", "berita", "laku", "bimbang", "bukti", "tolak", "silap", "ulang", "tn", "pelan", "palsu", "serah", "hadap", "serang", "mahukan", "komprehensif", "mab", "tangan", "resolusi", "peribadi", "ganas", "belia", "lampau", "berfikiran", "hilang", "aspirasi", "najib", "pekan", "henti", "projek", "nyata", "didik", "malaysia", "rakyat", "dasar", "lihat", "mdb", "sabah", "pelihara", "negara", 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"cabar", "cadang", "cadang", "cakap", "cari", "cari", "cawangan", "cina", "cina", "cina", "cina", "daftar", "daftar", "daging", "dakwa", "dakwa", "dakwa", "dakwa", "dana", "dana", "dar", "dar", "dasar", "dasar", "dasar", "dasar", "demokrasi", "demokrasi", "didik", "didik", "didik", "didik", "didik", "diplomasi", "duduk", "duduk", "duduk", "duduk", "dunia", "ekonomi", "ekonomi", "ekonomi", "faham", "faham", "faktor", "faktor", "fasal", "fungsi", "fungsi", "gabung", "gabung", "gagal", "gagal", "gaji", "galak", "galak", "galak", "gambar", "gambar", "ganas", "ganas", "ganti", "gantung", "gantung", "gantung", "gembira", "gembira", "giat", "giat", "global", "guru", "hadap", "hadap", "hadap", "hadap", "hak", "hak", "hapus", "harga", "harga", "harga", "hasil", "hasil", "hati", "hati", "hebat", "henti", "henti", "hidup", "hidup", "hilang", "hilang", "huni", "huni", "hutang", "hutang", "hutang", "idea", "ikan", "ilmu", "ilmu", "imbang", "imbang", "imej", "imej", "indonesia", "indonesia", "inggeris", "insya", "insya", "isi", "islam", "islam", "islam", "isu", "isu", "isu", "isu", "isu", "jabat", "jabat", "jaga", "jaga", "jakoa", "jalan", "jalan", "jalan", "jalan", "jangka", "jangka", "jangka", "jawat", "jawat", "jejas", "jemaah", "jho", "jho", "jho", "jppm", "jual", "jual", "jual", "jual", "juang", "kait", "kait", "kait", "kampung", "kampung", "kapal", "kapal", "kapal", "kawasan", "kawasan", "kaya", "kaya", "kelulus", "kelulus", "kelulus", "kembang", "kembang", "kempen", "kempen", "kena", "kena", "kena", "kena", "kena", "kena", "kena", "kenal", "keras", "keras", "kerja", "kerja", "kerjasama", "kerjasama", "ketua", "khidmat", "khidmat", "khusus", "khusus", "khusus", "khusus", "komprehensif", "komprehensif", "kongsi", "kongsi", "konon", "kuasa", "kuasa", "kumpul", "kumpul", "kuok", "kuok", "laksana", "laksana", "laksana", "laksana", "laksana", "laksana", "laku", "laku", "laku", "laku", "lampau", "lampau", "lancar", "lancar", "lancong", "lancong", "langgar", "langgar", "langkah", "langkah", "langkah", "lantan", "lantan", "lantik", "lantik", "lapang", "lapang", "lapor", "lapor", "layan", "layan", "lembaga", "lembaga", "lembaga", "lihat", "lihat", "lihat", "lihat", "lihat", "lihat", "low", "low", "low", "low", "ltat", "ltat", "luas", "luas", "lupa", "lupa", "mab", "mab", "mahir", "mahkamah", "mahkamah", "mahkamah", "mahukan", "mahukan", "main", "main", "majlis", "majlis", "majlis", "maju", "maju", "maju", "makan", "makan", "maklum", "malaysia", "malaysia", "malaysia", "malaysia", "malaysia", "malaysia", "malu", "malu", "malu", "manfaat", "manusia", "masuk", "masuk", "masuk", "masyarakat", "masyarakat", "masyarakat", "matlamat", "mca", "mdb", "mdb", "mdb", "mdb", "melayu", "melayu", "melayu", "menang", "menteri", "menteri", "menteri", "menteri", "menteri", "menteri", "mesyuarat", "mesyuarat", "milik", "milik", "milik", "minimum", "minimum", "mtcp", "mudah", "mudah", "muka", "muka", "murah", "murah", "musnah", "musuh", "musuh", "musuh", "najib", "najib", "najib", "nama", "nama", "nampak", "negara", "negara", "negara", "negara", "negara", "negara", "negeri", "negeri", "negeri", "niaga", "niaga", "niaga", "niaga", "niaga", "nik", "nilai", "nilai", "nilai", "nyata", "nyata", "nyata", "nyata", "palsu", "palsu", "palsu", "pandang", "pandang", "pandang", "parti", "parti", "parti", "pas", "pas", "pas", "pas", "pas", "pasar", "pegang", "pegang", "pekan", "pekan", "pekan", "pelabur", "pelabur", "pelan", "pelan", "pelihara", "pelihara", "pelopor", "penambahbaikan", "pendek", "pengaruh", "pengaruh", "penuh", "peran", "peran", "perdana", "perdana", "perdana", "perdana", "peribadi", "peribadi", "peringkat", "peringkat", "pesara", "pesara", "pesawat", "pesawat", "ph", "ph", "ph", "pikir", "pikir", "pilih", "pilih", "pilih", "pilihanraya", "pimpin", "pimpin", "pimpin", "politik", "politik", "politik", "politik", "politik", "projek", "projek", "projek", "promosi", "promosi", "promosi", "proses", "proses", "pru", "pru", "pru", "pru", "pulang", "pulang", "punca", "putus", "putus", "putus", "putus", "raja", "raja", "raja", "raja", "raja", "raja", "rak", "rak", "rakyat", "rakyat", "rakyat", "rana", "rana", "rana", "rana", "rana", "rana", "rana", "rangka", "rasmi", "raya", "raya", "razak", "razak", "rendah", "resolusi", "resolusi", "robert", "robert", "ros", "ros", "rumah", "rumah", "rupa", "sabah", "sabah", "sabah", "sah", "salah", "salah", "sasar", "sebahagian", "sebarang", "sebarang", "sebarang", "sedia", "sedia", "sedia", "selamat", "selamat", "selamat", "selatan", "selesa", "selesa", "selesa", "selesai", "selesai", "selesai", "seleweng", "semak", "semak", "seni", "seni", "sepenuh", "sepenuh", "serah", "serah", "serah", "serang", "serang", "seri", "seri", "seri", "seri", "sesuai", "sewa", "sewa", "sidang", "silap", "silap", "simpul", "simpul", "sokong", "sokong", "sokong", "sosial", "sosial", "status", "strategi", "strategi", "struktur", "suasana", "suasana", "suci", "suci", "sukan", "sukan", "sumber", "sumber", "swasta", "swasta", "syarikat", "syarikat", "syarikat", "syarikat", "tahap", "tahap", "takut", "tanah", "tanah", "tanah", "tanding", "tanding", "tangan", "tangan", "tangguh", "tangguh", "tangguh", "tanggung", "tani", "tawar", "tawar", "teknikal", "temiar", "tempoh", "tempoh", "tempoh", "tentera", "tentera", "terbang", "terbang", "terima", "terima", "terima", "terima", "terima", "terjemah", "teruk", "teruk", "timbang", "timbang", "tindak", "tindak", "tinggal", "tinggal", "tinggal", "tingkat", "tingkat", "tingkat", "tingkat", "tn", "tn", "tolak", "tolak", "trx", "trx", "tuju", "tuju", "tumbuh", "tumbuh", "tumbuh", "tumbuh", "tumpu", "tunggu", "tunggu", "tutup", "tutup", "ubah", "ubah", "uji", "ulang", "ulang", "umno", "undang", "undang", "undi", "undi", "undi", "undi", "untung", "untung", "untung", "urus", "urus", "urus", "urus", "urus", "usaha", "usaha", "usaha", "wajar", "wajar", "wang", "wang", "wang", "wang", "wujud", "wujud", "wujud"]}, "R": 30, "lambda.step": 0.01, "plot.opts": {"xlab": "PC1", "ylab": "PC2"}, "topic.order": [6, 4, 9, 7, 1, 5, 10, 3, 8, 2]};
-    
+
     function LDAvis_load_lib(url, callback){
       var s = document.createElement('script');
       s.src = url;
@@ -275,7 +275,7 @@ Visualize topics
       s.onerror = function(){console.warn("failed to load library " + url);};
       document.getElementsByTagName("head")[0].appendChild(s);
     }
-    
+
     if(typeof(LDAvis) !== "undefined"){
        // already loaded: just create the visualization
        !function(LDAvis){
@@ -305,7 +305,7 @@ Visualize topics
 Train NMF model
 ---------------
 
-.. code:: ipython3
+.. code:: python
 
     nmf = malaya.topic_model.nmf(corpus,10)
     nmf.print_topics(5)
@@ -313,23 +313,23 @@ Train NMF model
 
 .. parsed-literal::
 
-    topic 0       topic 1       topic 2       topic 3       topic 4       
-    --------      --------      --------      --------      --------      
-    bangun        projek        parti         mdb           menteri       
-    negara        sewa          umno          niaga         perdana       
-    malaysia      syarikat      tangguh       urus          asli          
-    kongsi        jual          pilih         doj           seri          
-    alam          tanah         putus         low           terima        
-    sedia         swasta        jalan         jho           najib         
-    selatan       indonesia     lembaga       tuntut        razak         
-    program       kena          ros           sivil         jemaah        
-    kerjasama     rana          ambil         dakwa         kuok          
-    sosial        rumah         politik       sebarang      lantik        
-    
-    
+    topic 0       topic 1       topic 2       topic 3       topic 4
+    --------      --------      --------      --------      --------
+    bangun        projek        parti         mdb           menteri
+    negara        sewa          umno          niaga         perdana
+    malaysia      syarikat      tangguh       urus          asli
+    kongsi        jual          pilih         doj           seri
+    alam          tanah         putus         low           terima
+    sedia         swasta        jalan         jho           najib
+    selatan       indonesia     lembaga       tuntut        razak
+    program       kena          ros           sivil         jemaah
+    kerjasama     rana          ambil         dakwa         kuok
+    sosial        rumah         politik       sebarang      lantik
+
 
 
-.. code:: ipython3
+
+.. code:: python
 
     nmf.get_sentences(5)
 
@@ -346,7 +346,7 @@ Train NMF model
 
 
 
-.. code:: ipython3
+.. code:: python
 
     nmf.get_topics(10)
 
@@ -372,7 +372,7 @@ Train NMF model
 Train LSA model
 ---------------
 
-.. code:: ipython3
+.. code:: python
 
     lsa = malaya.topic_model.lsa(corpus,10)
     lsa.print_topics(5)
@@ -380,23 +380,23 @@ Train LSA model
 
 .. parsed-literal::
 
-    topic 0       topic 1       topic 2       topic 3       topic 4       
-    --------      --------      --------      --------      --------      
-    negara        menteri       mdb           projek        projek        
-    malaysia      projek        niaga         jual          parti         
-    bangun        mdb           projek        menteri       jual          
-    kongsi        parti         wang          tingkat       sewa          
-    alam          raja          hutang        sewa          tangguh       
-    sedia         rana          doj           asli          syarikat      
-    selatan       perdana       urus          syarikat      pilih         
-    ekonomi       tingkat       jho           tanah         umno          
-    rakyat        kena          low           perdana       rana          
-    teknikal      laku          tuntut        swasta        tanah         
-    
-    
+    topic 0       topic 1       topic 2       topic 3       topic 4
+    --------      --------      --------      --------      --------
+    negara        menteri       mdb           projek        projek
+    malaysia      projek        niaga         jual          parti
+    bangun        mdb           projek        menteri       jual
+    kongsi        parti         wang          tingkat       sewa
+    alam          raja          hutang        sewa          tangguh
+    sedia         rana          doj           asli          syarikat
+    selatan       perdana       urus          syarikat      pilih
+    ekonomi       tingkat       jho           tanah         umno
+    rakyat        kena          low           perdana       rana
+    teknikal      laku          tuntut        swasta        tanah
+
+
 
 
-.. code:: ipython3
+.. code:: python
 
     lsa.get_sentences(5)
 
@@ -413,7 +413,7 @@ Train LSA model
 
 
 
-.. code:: ipython3
+.. code:: python
 
     lsa.get_topics(10)
 
@@ -434,5 +434,3 @@ Train LSA model
      (7, 'ajar tingkat raja maju bidang didik mudah ahli proses laksana'),
      (8, 'hutang asli mdb wang masyarakat malaysia selesai bangun tingkat jakoa'),
      (9, 'tingkat harga malaysia airasia jual pilih tangguh umno lihat niaga')]
-
-
diff --git a/docs/load-topics-influencers.rst b/docs/load-topics-influencers.rst
index 4e1b13c3..e55fdf24 100644
--- a/docs/load-topics-influencers.rst
+++ b/docs/load-topics-influencers.rst
@@ -1,9 +1,9 @@
 
-.. code:: ipython3
+.. code:: python
 
     import malaya
 
-.. code:: ipython3
+.. code:: python
 
     news = 'najib razak dan mahathir mengalami masalah air di kemamam terengganu'
     second_news = 'ikat penyedia perkhidmatan jalur lebar Telekom Malaysia (TM) perlu mencari jalan penyelesaian bagi meningkatkan akses capaian Internet ke seluruh negara, kata Menteri Komunikasi dan Multimedia, Gobind Singh Deo. Beliau berkata menjadi dasar kerajaan untuk membekalkan akses Internet jalur lebar kepada semua dan memberi penekanan kepada kualiti perkhidmatan yang terbaik. "Dasar kerajaan untuk bekalkan akses kepada semua bukan sekadar pembekalan sahaja tetapi beri penekanan kepada kualiti perkhidmatan yang baik dan dapat bersaing dengan negara lain pada tahap antarabangsa," kata Gobind Singh menerusi catatan di laman rasmi Twitter beliau, malam tadi. Beliau berkata demikian sebagai respons terhadap aduan beberapa pengguna Twitter berhubung akses Internet yang masih tidak stabil serta harga yang tidak berpatutan di beberapa lokasi di seluruh negara.'
@@ -11,7 +11,7 @@
 Using fuzzy for topics
 ----------------------
 
-.. code:: ipython3
+.. code:: python
 
     malaya.topic_influencer.fuzzy_topic(news)
 
@@ -24,7 +24,7 @@ Using fuzzy for topics
 
 
 
-.. code:: ipython3
+.. code:: python
 
     malaya.topic_influencer.fuzzy_topic(second_news)
 
@@ -49,7 +49,7 @@ Using fuzzy for topics
 Using fuzzy for influencers
 ---------------------------
 
-.. code:: ipython3
+.. code:: python
 
     malaya.topic_influencer.fuzzy_influencer(news)
 
@@ -62,7 +62,7 @@ Using fuzzy for influencers
 
 
 
-.. code:: ipython3
+.. code:: python
 
     malaya.topic_influencer.fuzzy_influencer(second_news)
 
@@ -78,7 +78,7 @@ Using fuzzy for influencers
 Using fuzzy for location
 ------------------------
 
-.. code:: ipython3
+.. code:: python
 
     malaya.topic_influencer.fuzzy_location('saya suka makan sate di sungai petani')
 
@@ -94,7 +94,7 @@ Using fuzzy for location
 Check location from a string
 ----------------------------
 
-.. code:: ipython3
+.. code:: python
 
     malaya.topic_influencer.is_location('sungai petani')
 
@@ -110,11 +110,11 @@ Check location from a string
 Train TF-IDF for topics analysis
 --------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     topics_similarity = malaya.topic_influencer.fast_topic()
 
-.. code:: ipython3
+.. code:: python
 
     topics_similarity.get_similarity(news)
 
@@ -133,11 +133,11 @@ Train TF-IDF for topics analysis
 Train TF-IDF for influencers analysis
 -------------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     influencers_similarity = malaya.topic_influencer.fast_influencer()
 
-.. code:: ipython3
+.. code:: python
 
     influencers_similarity.get_similarity(news)
 
@@ -153,7 +153,7 @@ Train TF-IDF for influencers analysis
 
 
 
-.. code:: ipython3
+.. code:: python
 
     influencers_similarity.get_similarity(second_news)
 
@@ -176,7 +176,7 @@ Train TF-IDF for influencers analysis
 Train skip-thought model for topics analysis
 --------------------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     deep_topic = malaya.topic_influencer.skipthought_topic()
 
@@ -190,7 +190,7 @@ Train skip-thought model for topics analysis
     minibatch loop: 100%|██████████| 157/157 [01:44<00:00,  1.70it/s, cost=0.00152]
 
 
-.. code:: ipython3
+.. code:: python
 
     deep_topic.get_similarity(news, anchor = 0.5)
 
@@ -211,7 +211,7 @@ Train skip-thought model for topics analysis
 
 
 
-.. code:: ipython3
+.. code:: python
 
     deep_topic.get_similarity(second_news, anchor = 0.5)
 
@@ -237,7 +237,7 @@ Train skip-thought model for topics analysis
 Train skip-thought model for influencers analysis
 -------------------------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     deep_influencer = malaya.topic_influencer.skipthought_influencer()
 
@@ -256,7 +256,7 @@ Train skip-thought model for influencers analysis
     minibatch loop: 100%|██████████| 20/20 [00:12<00:00,  1.62it/s, cost=0.219]
 
 
-.. code:: ipython3
+.. code:: python
 
     deep_influencer.get_similarity(news, anchor = 0.5)
 
@@ -269,7 +269,7 @@ Train skip-thought model for influencers analysis
 
 
 
-.. code:: ipython3
+.. code:: python
 
     deep_influencer.get_similarity(second_news, anchor = 0.5)
 
@@ -285,7 +285,7 @@ Train skip-thought model for influencers analysis
 Train siamese network for topics analysis
 -----------------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     deep_topic = malaya.topic_influencer.siamese_topic()
     print(deep_topic.get_similarity(news, anchor = 0.5))
@@ -294,11 +294,11 @@ Train siamese network for topics analysis
 
 .. parsed-literal::
 
-    minibatch loop: 100%|██████████| 157/157 [01:50<00:00,  1.67it/s, accuracy=1, cost=0.114]    
-    minibatch loop: 100%|██████████| 157/157 [01:49<00:00,  1.69it/s, accuracy=1, cost=0.0739]   
-    minibatch loop: 100%|██████████| 157/157 [01:49<00:00,  1.66it/s, accuracy=1, cost=0.0686]    
-    minibatch loop: 100%|██████████| 157/157 [01:50<00:00,  1.68it/s, accuracy=1, cost=0.0279]    
-    minibatch loop: 100%|██████████| 157/157 [01:49<00:00,  1.70it/s, accuracy=1, cost=0.0193]    
+    minibatch loop: 100%|██████████| 157/157 [01:50<00:00,  1.67it/s, accuracy=1, cost=0.114]
+    minibatch loop: 100%|██████████| 157/157 [01:49<00:00,  1.69it/s, accuracy=1, cost=0.0739]
+    minibatch loop: 100%|██████████| 157/157 [01:49<00:00,  1.66it/s, accuracy=1, cost=0.0686]
+    minibatch loop: 100%|██████████| 157/157 [01:50<00:00,  1.68it/s, accuracy=1, cost=0.0279]
+    minibatch loop: 100%|██████████| 157/157 [01:49<00:00,  1.70it/s, accuracy=1, cost=0.0193]
 
 
 .. parsed-literal::
@@ -307,7 +307,7 @@ Train siamese network for topics analysis
     ['politik', 'kkmm', 'bumiputra', 'malaysia-indonesia', 'menteri pertahanan', 'motogp', 'programming language', 'twitter', 'lgbt', 'gaji menteri', 'singapura']
 
 
-.. code:: ipython3
+.. code:: python
 
     print(deep_topic.get_similarity(news, anchor = 0.7))
     print(deep_topic.get_similarity(second_news, anchor = 0.7))
@@ -322,7 +322,7 @@ Train siamese network for topics analysis
 Train siamese network for influencers analysis
 ----------------------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     deep_influencer = malaya.topic_influencer.siamese_influencer()
 
@@ -336,7 +336,7 @@ Train siamese network for influencers analysis
     minibatch loop: 100%|██████████| 20/20 [00:14<00:00,  1.47it/s, accuracy=0.875, cost=0.0637]
 
 
-.. code:: ipython3
+.. code:: python
 
     deep_influencer.get_similarity(news, anchor = 0.5)
 
@@ -349,7 +349,7 @@ Train siamese network for influencers analysis
 
 
 
-.. code:: ipython3
+.. code:: python
 
     deep_influencer.get_similarity(second_news, anchor = 0.5)
 
@@ -359,5 +359,3 @@ Train siamese network for influencers analysis
 .. parsed-literal::
 
     ['gobind singh deo']
-
-
diff --git a/docs/load-toxic.rst b/docs/load-toxic.rst
index d6686e6a..d93af752 100644
--- a/docs/load-toxic.rst
+++ b/docs/load-toxic.rst
@@ -1,5 +1,5 @@
 
-.. code:: ipython3
+.. code:: python
 
     import malaya
     print(malaya.version, malaya.bump_version)
@@ -10,7 +10,7 @@
     1.0 1.0.0.0
 
 
-.. code:: ipython3
+.. code:: python
 
     string = 'Benda yg SALAH ni, jgn lah didebatkan. Yg SALAH xkan jadi betul. Ingat tu. Mcm mana kesat sekalipun org sampaikan mesej, dan memang benda tu salah, diam je. Xyah nk tunjuk kau open sangat nk tegur cara org lain berdakwah. '
     another_string = 'bodoh, dah la gay, sokong lgbt lagi, memang tak guna'
@@ -18,7 +18,7 @@
 Load multinomial model
 ----------------------
 
-.. code:: ipython3
+.. code:: python
 
     model = malaya.toxic.multinomial()
 
@@ -30,7 +30,7 @@ Load multinomial model
 
 .. parsed-literal::
 
-    11.0MB [00:04, 1.98MB/s]                          
+    11.0MB [00:04, 1.98MB/s]
       0%|          | 0.00/19.3 [00:00<?, ?MB/s]
 
 .. parsed-literal::
@@ -40,10 +40,10 @@ Load multinomial model
 
 .. parsed-literal::
 
-    20.0MB [00:08, 3.55MB/s]                          
+    20.0MB [00:08, 3.55MB/s]
 
 
-.. code:: ipython3
+.. code:: python
 
     model.predict(string)
 
@@ -61,7 +61,7 @@ Load multinomial model
 
 
 
-.. code:: ipython3
+.. code:: python
 
     model.predict(string,get_proba=True)
 
@@ -79,7 +79,7 @@ Load multinomial model
 
 
 
-.. code:: ipython3
+.. code:: python
 
     model.predict(another_string)
 
@@ -97,7 +97,7 @@ Load multinomial model
 
 
 
-.. code:: ipython3
+.. code:: python
 
     model.predict(another_string,get_proba=True)
 
@@ -115,7 +115,7 @@ Load multinomial model
 
 
 
-.. code:: ipython3
+.. code:: python
 
     model.predict_batch([string,another_string])
 
@@ -133,7 +133,7 @@ Load multinomial model
 
 
 
-.. code:: ipython3
+.. code:: python
 
     model.predict_batch([string,another_string],get_proba=True)
 
@@ -154,7 +154,7 @@ Load multinomial model
 Load logistics model
 --------------------
 
-.. code:: ipython3
+.. code:: python
 
     model = malaya.toxic.logistic()
 
@@ -166,7 +166,7 @@ Load logistics model
 
 .. parsed-literal::
 
-    3.00MB [00:00, 2.99MB/s]                          
+    3.00MB [00:00, 2.99MB/s]
       0%|          | 0.00/19.3 [00:00<?, ?MB/s]
 
 .. parsed-literal::
@@ -176,10 +176,10 @@ Load logistics model
 
 .. parsed-literal::
 
-    20.0MB [00:06, 4.17MB/s]                          
+    20.0MB [00:06, 4.17MB/s]
 
 
-.. code:: ipython3
+.. code:: python
 
     model.predict(string)
 
@@ -197,7 +197,7 @@ Load logistics model
 
 
 
-.. code:: ipython3
+.. code:: python
 
     model.predict_batch([string,another_string],get_proba=True)
 
@@ -218,7 +218,7 @@ Load logistics model
 List available deep learning models
 -----------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     malaya.toxic.available_deep_model()
 
@@ -234,13 +234,13 @@ List available deep learning models
 Load deep learning model
 ------------------------
 
-.. code:: ipython3
+.. code:: python
 
     for model in malaya.toxic.available_deep_model():
         deep_model = malaya.toxic.deep_model(model = model)
         print(deep_model.predict(string))
         print(deep_model.predict_batch([string, another_string]),'\n')
-        
+
 
 
 .. parsed-literal::
@@ -254,7 +254,7 @@ Load deep learning model
 
 .. parsed-literal::
 
-    61.0MB [00:40, 1.02s/MB]                          
+    61.0MB [00:40, 1.02s/MB]
 
 
 .. parsed-literal::
@@ -264,7 +264,7 @@ Load deep learning model
 
 .. parsed-literal::
 
-    2.00MB [00:01, 1.28MB/s]                          
+    2.00MB [00:01, 1.28MB/s]
 
 
 .. parsed-literal::
@@ -278,14 +278,14 @@ Load deep learning model
 
 .. parsed-literal::
 
-    {'toxic': [0.0016551929, 0.8803142], 'severe_toxic': [1.36922545e-05, 0.003729248], 'obscene': [0.00017486684, 0.06859021], 'threat': [1.4183259e-05, 0.00055752473], 'insult': [7.17542e-05, 0.1324517], 'identity_hate': [1.6397036e-05, 0.1142907]} 
-    
+    {'toxic': [0.0016551929, 0.8803142], 'severe_toxic': [1.36922545e-05, 0.003729248], 'obscene': [0.00017486684, 0.06859021], 'threat': [1.4183259e-05, 0.00055752473], 'insult': [7.17542e-05, 0.1324517], 'identity_hate': [1.6397036e-05, 0.1142907]}
+
     downloading frozen /Users/huseinzol/Malaya/toxic/hierarchical model
 
 
 .. parsed-literal::
 
-    66.0MB [00:28, 3.40MB/s]                          
+    66.0MB [00:28, 3.40MB/s]
       0%|          | 0.00/1.98 [00:00<?, ?MB/s]
 
 .. parsed-literal::
@@ -295,7 +295,7 @@ Load deep learning model
 
 .. parsed-literal::
 
-    2.00MB [00:00, 3.60MB/s]                          
+    2.00MB [00:00, 3.60MB/s]
 
 
 .. parsed-literal::
@@ -309,14 +309,14 @@ Load deep learning model
 
 .. parsed-literal::
 
-    {'toxic': [0.019022115, 0.7026508], 'severe_toxic': [0.00018498747, 0.002541811], 'obscene': [0.0040143826, 0.04182527], 'threat': [0.00040674658, 0.0038593104], 'insult': [0.0021619846, 0.23215641], 'identity_hate': [0.0008833514, 0.056888826]} 
-    
+    {'toxic': [0.019022115, 0.7026508], 'severe_toxic': [0.00018498747, 0.002541811], 'obscene': [0.0040143826, 0.04182527], 'threat': [0.00040674658, 0.0038593104], 'insult': [0.0021619846, 0.23215641], 'identity_hate': [0.0008833514, 0.056888826]}
+
     downloading frozen /Users/huseinzol/Malaya/toxic/luong model
 
 
 .. parsed-literal::
 
-    61.0MB [00:29, 2.90MB/s]                          
+    61.0MB [00:29, 2.90MB/s]
       0%|          | 0.00/1.98 [00:00<?, ?MB/s]
 
 .. parsed-literal::
@@ -326,20 +326,20 @@ Load deep learning model
 
 .. parsed-literal::
 
-    2.00MB [00:00, 3.72MB/s]                          
+    2.00MB [00:00, 3.72MB/s]
 
 
 .. parsed-literal::
 
     {'toxic': 0.0014804129, 'severe_toxic': 0.00017674293, 'obscene': 0.0008130327, 'threat': 0.00028337093, 'insult': 0.00023613627, 'identity_hate': 0.0007284258, 'attention': [['benda', 0.0014139642], ['yg', 0.0016312348], ['salah', 0.007912597], ['ni', 0.0016698316], ['jgn', 0.001369154], ['didebatkan', 0.0012927211], ['yg', 0.0016312348], ['salah', 0.007912597], ['jadi', 0.0013706309], ['betul', 0.0016492187], ['ingat', 0.0013966222], ['tu', 0.0013238997], ['mcm', 0.001623619], ['mana', 0.0014395164], ['kesat', 0.0076838294], ['sekalipun', 0.013703095], ['org', 0.0030888263], ['sampaikan', 0.0023580098], ['mesej', 0.0012328016], ['memang', 0.0013224662], ['benda', 0.0014139642], ['tu', 0.0013238997], ['salah', 0.007912597], ['diam', 0.0012353956], ['je', 0.0013446732], ['xyah', 0.001369154], ['nk', 0.0032641657], ['tunjuk', 0.0015030154], ['kau', 0.011394377], ['sangat', 0.0017340722], ['nk', 0.0032641657], ['tegur', 0.0034078276], ['cara', 0.8561393], ['org', 0.0030888263], ['lain', 0.0037536188], ['berdakwah', 0.034825023]]}
-    {'toxic': [0.0050339997, 0.97731346], 'severe_toxic': [0.0012615193, 0.015941802], 'obscene': [0.0023579854, 0.25056282], 'threat': [0.0025420662, 0.0076949443], 'insult': [0.00090396986, 0.36417997], 'identity_hate': [0.002062297, 0.11571509]} 
-    
+    {'toxic': [0.0050339997, 0.97731346], 'severe_toxic': [0.0012615193, 0.015941802], 'obscene': [0.0023579854, 0.25056282], 'threat': [0.0025420662, 0.0076949443], 'insult': [0.00090396986, 0.36417997], 'identity_hate': [0.002062297, 0.11571509]}
+
     downloading frozen /Users/huseinzol/Malaya/toxic/fast-text model
 
 
 .. parsed-literal::
 
-    258MB [01:52, 2.37MB/s]                          
+    258MB [01:52, 2.37MB/s]
 
 
 .. parsed-literal::
@@ -349,7 +349,7 @@ Load deep learning model
 
 .. parsed-literal::
 
-    2.00MB [00:00, 3.69MB/s]                          
+    2.00MB [00:00, 3.69MB/s]
 
 
 .. parsed-literal::
@@ -359,20 +359,20 @@ Load deep learning model
 
 .. parsed-literal::
 
-    8.00MB [00:02, 2.97MB/s]                          
+    8.00MB [00:02, 2.97MB/s]
 
 
 .. parsed-literal::
 
     {'toxic': 0.0020534173, 'severe_toxic': 0.0050337594, 'obscene': 3.7653503e-05, 'threat': 0.7628687, 'insult': 9.012385e-05, 'identity_hate': 0.22991635}
-    {'toxic': [4.6989637e-08, 0.07565687], 'severe_toxic': [2.8443527e-08, 0.005023106], 'obscene': [4.1618722e-10, 0.0053009894], 'threat': [3.280739e-06, 0.0040464187], 'insult': [7.941728e-10, 0.043121953], 'identity_hate': [8.946894e-07, 0.016103525]} 
-    
+    {'toxic': [4.6989637e-08, 0.07565687], 'severe_toxic': [2.8443527e-08, 0.005023106], 'obscene': [4.1618722e-10, 0.0053009894], 'threat': [3.280739e-06, 0.0040464187], 'insult': [7.941728e-10, 0.043121953], 'identity_hate': [8.946894e-07, 0.016103525]}
+
     downloading frozen /Users/huseinzol/Malaya/toxic/entity-network model
 
 
 .. parsed-literal::
 
-    56.0MB [00:22, 1.92MB/s]                          
+    56.0MB [00:22, 1.92MB/s]
       0%|          | 0.00/1.98 [00:00<?, ?MB/s]
 
 .. parsed-literal::
@@ -382,20 +382,20 @@ Load deep learning model
 
 .. parsed-literal::
 
-    2.00MB [00:00, 2.25MB/s]                          
+    2.00MB [00:00, 2.25MB/s]
 
 
 .. parsed-literal::
 
     {'toxic': 0.501814, 'severe_toxic': 0.03271238, 'obscene': 0.15100613, 'threat': 0.028492289, 'insult': 0.24221319, 'identity_hate': 0.043762065}
-    {'toxic': [0.7704032, 0.23564923], 'severe_toxic': [0.1794783, 0.009002773], 'obscene': [0.50242037, 0.14901799], 'threat': [0.16002978, 0.030735493], 'insult': [0.61826205, 0.12641545], 'identity_hate': [0.2263789, 0.019457512]} 
-    
+    {'toxic': [0.7704032, 0.23564923], 'severe_toxic': [0.1794783, 0.009002773], 'obscene': [0.50242037, 0.14901799], 'threat': [0.16002978, 0.030735493], 'insult': [0.61826205, 0.12641545], 'identity_hate': [0.2263789, 0.019457512]}
+
 
 
 Unsupervised important words learning
 -------------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     import matplotlib.pyplot as plt
     import seaborn as sns
@@ -404,11 +404,11 @@ Unsupervised important words learning
 Visualizing bahdanau model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     model = malaya.toxic.deep_model('bahdanau')
     result = model.predict(another_string)['attention']
-    
+
     plt.figure(figsize = (15, 7))
     labels = [r[0] for r in result]
     val = [r[1] for r in result]
@@ -425,11 +425,11 @@ Visualizing bahdanau model
 Visualizing luong model
 ^^^^^^^^^^^^^^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     model = malaya.toxic.deep_model('luong')
     result = model.predict(another_string)['attention']
-    
+
     plt.figure(figsize = (15, 7))
     labels = [r[0] for r in result]
     val = [r[1] for r in result]
@@ -446,11 +446,11 @@ Visualizing luong model
 Visualizing hierarchical model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. code:: ipython3
+.. code:: python
 
     model = malaya.toxic.deep_model('hierarchical')
     result = model.predict(another_string)['attention']
-    
+
     plt.figure(figsize = (15, 7))
     labels = [r[0] for r in result]
     val = [r[1] for r in result]
@@ -462,4 +462,3 @@ Visualizing hierarchical model
 
 
 .. image:: load-toxic_files/load-toxic_25_0.png
-
diff --git a/docs/load-word2vec.rst b/docs/load-word2vec.rst
index a3c987c8..209bdf97 100644
--- a/docs/load-word2vec.rst
+++ b/docs/load-word2vec.rst
@@ -1,5 +1,5 @@
 
-.. code:: ipython3
+.. code:: python
 
     import malaya
     %matplotlib inline
@@ -7,21 +7,21 @@
 Load malaya news word2vec
 -------------------------
 
-.. code:: ipython3
+.. code:: python
 
     embedded = malaya.word2vec.load(256)
 
 Load word2vec model
 -------------------
 
-.. code:: ipython3
+.. code:: python
 
     word_vector = malaya.word2vec.word2vec(embedded['nce_weights'], embedded['dictionary'])
 
 Check top-k similar semantics based on a word
 ---------------------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     word = 'anwar'
     print("Embedding layer: 8 closest words to: '%s'"%(word))
@@ -37,7 +37,7 @@ Check top-k similar semantics based on a word
 Calculate vb - va + vc
 ----------------------
 
-.. code:: ipython3
+.. code:: python
 
     print(word_vector.analogy('anwar', 'penjara', 'kerajaan', 5))
 
@@ -52,7 +52,7 @@ Word2vec calculator
 
 You can put any equation you wanted.
 
-.. code:: ipython3
+.. code:: python
 
     word_vector.calculator('anwar + amerika + mahathir', num_closest=8, metric='cosine',
                           return_similarity=False)
@@ -74,7 +74,7 @@ You can put any equation you wanted.
 
 
 
-.. code:: ipython3
+.. code:: python
 
     word_vector.calculator('anwar * amerika', num_closest=8, metric='cosine',
                           return_similarity=False)
@@ -99,7 +99,7 @@ You can put any equation you wanted.
 Visualize scatter-plot
 ----------------------
 
-.. code:: ipython3
+.. code:: python
 
     word = 'anwar'
     result = word_vector.n_closest(word=word, num_closest=8, metric='cosine')
@@ -110,7 +110,7 @@ Visualize scatter-plot
 .. image:: load-word2vec_files/load-word2vec_13_0.png
 
 
-.. code:: ipython3
+.. code:: python
 
     result = word_vector.calculator('anwar * amerika', num_closest=8, metric='cosine',
                           return_similarity=True)
@@ -124,7 +124,7 @@ Visualize scatter-plot
 Visualize tree-plot
 -------------------
 
-.. code:: ipython3
+.. code:: python
 
     word = 'anwar'
     result = word_vector.n_closest(word=word, num_closest=8, metric='cosine')
@@ -147,7 +147,7 @@ Get embedding from a word
 If a word not found in the vocabulary, it will throw an exception with
 top-5 nearest words
 
-.. code:: ipython3
+.. code:: python
 
     word_vector.get_vector_by_name('husein-comel')
 
@@ -161,7 +161,7 @@ top-5 nearest words
 
     <ipython-input-11-a2cb3bf2a088> in <module>
     ----> 1 word_vector.get_vector_by_name('husein-comel')
-    
+
 
     ~/Documents/Malaya/malaya/word2vec.py in get_vector_by_name(self, word)
         109             raise Exception(
@@ -172,4 +172,3 @@ top-5 nearest words
 
 
     Exception: input not found in dictionary, here top-5 nearest words [income, husein, incomes, hussein, husseiny]
-
diff --git a/docs/text-clustering.rst b/docs/text-clustering.rst
index 1f43f67e..79b8ecc3 100644
--- a/docs/text-clustering.rst
+++ b/docs/text-clustering.rst
@@ -1,30 +1,30 @@
 
-.. code:: ipython3
+.. code:: python
 
     import malaya
 
-.. code:: ipython3
+.. code:: python
 
     string = 'KUALA LUMPUR: Sempena sambutan Aidilfitri minggu depan, Perdana Menteri Tun Dr Mahathir Mohamad dan Menteri Pengangkutan Anthony Loke Siew Fook menitipkan pesanan khas kepada orang ramai yang mahu pulang ke kampung halaman masing-masing. Dalam video pendek terbitan Jabatan Keselamatan Jalan Raya (JKJR) itu, Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar  sekiranya mengantuk ketika memandu.'
 
 Cluster same word structure based on POS and Entities
 -----------------------------------------------------
 
-.. code:: ipython3
+.. code:: python
 
     bahdanau_entities = malaya.entity.deep_model('bahdanau')
     bahdanau_pos = malaya.pos.deep_model('bahdanau')
 
-.. code:: ipython3
+.. code:: python
 
     result_entities = bahdanau_entities.predict(string)
     result_pos = bahdanau_pos.predict(string)
 
-.. code:: ipython3
+.. code:: python
 
     from malaya.cluster import cluster_words, pos_entities_ngram
 
-.. code:: ipython3
+.. code:: python
 
     generated_grams = pos_entities_ngram(
         result_pos,
@@ -165,7 +165,7 @@ Cluster same word structure based on POS and Entities
 
 
 
-.. code:: ipython3
+.. code:: python
 
     cluster_words(generated_grams)
 
@@ -221,11 +221,11 @@ Cluster same word structure based on POS and Entities
 Cluster POS and Entities
 ------------------------
 
-.. code:: ipython3
+.. code:: python
 
     from malaya.cluster import cluster_pos, cluster_entities
 
-.. code:: ipython3
+.. code:: python
 
     cluster_pos(result_pos)
 
@@ -265,7 +265,7 @@ Cluster POS and Entities
 
 
 
-.. code:: ipython3
+.. code:: python
 
     cluster_entities(result_entities)
 
@@ -298,11 +298,11 @@ Cluster POS and Entities
 Generate ngrams
 ---------------
 
-.. code:: ipython3
+.. code:: python
 
     from malaya.cluster import sentence_ngram
 
-.. code:: ipython3
+.. code:: python
 
     sentence_ngram(string, ngram = (3, 5))
 
@@ -473,5 +473,3 @@ Generate ngrams
      'sambutan Aidilfitri minggu depan,',
      'masing-masing. Dalam video',
      'depan, Perdana Menteri Tun']
-
-