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| a | b/QueryExtraction/output.txt | ||
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| 1 | Extraction: |
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| 2 | SpaCy TextRank: |
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| 3 | Captured 4 |
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| 4 | longer sequences |
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| 5 | long input sentences |
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| 6 | high bleu score |
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| 7 | the complete input sequence |
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| 8 | vector |
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| 9 | the input sequence |
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| 10 | information |
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| 11 | long dependencies |
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| 12 | a candidate translation |
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| 13 | simpler words |
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| 14 | the entire sequence |
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| 15 | fixed length |
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| 16 | valuable parts |
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| 17 | parallel processing |
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| 18 | one or more |
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| 19 | reference translations |
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| 20 | Seq2Seq Models |
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| 21 | some input words |
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| 22 | encodes |
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| 23 | the input |
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| 24 | sequence |
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| 25 | hidden state |
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| 26 | Vaswani et al |
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| 27 | a context vector |
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| 28 | this context vector |
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| 29 | this fixed-length context vector design |
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| 30 | the |
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| 31 | complete sequence |
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| 32 | an output word |
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| 33 | image captioning |
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| 34 | various problems |
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| 35 | Attention |
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| 36 | attention |
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| 37 | ------------------------------ |
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| 38 | Gensim TextRank: |
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| 39 | Captured 4 |
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| 40 | learning |
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| 41 | learn |
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| 42 | attention |
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| 43 | words |
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| 44 | word |
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| 45 | encoder |
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| 46 | encodes |
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| 47 | translation |
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| 48 | translated |
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| 49 | translations |
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| 50 | translate |
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| 51 | decoder |
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| 52 | paper |
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| 53 | input sequence |
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| 54 | sequences |
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| 55 | vector |
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| 56 | bleu |
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| 57 | sentence |
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| 58 | sentences |
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| 59 | unit |
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| 60 | ideas |
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| 61 | idea |
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| 62 | parts |
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| 63 | models |
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| 64 | model |
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| 65 | like image |
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| 66 | length |
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| 67 | designed |
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| 68 | design |
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| 69 | long |
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| 70 | context |
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| 71 | works |
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| 72 | work |
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| 73 | processes |
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| 74 | processed |
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| 75 | processing |
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| 76 | hidden |
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| 77 | evaluation |
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| 78 | information |
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| 79 | ------------------------------ |
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| 80 | Rake: |
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| 81 | Captured 1 |
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| 82 | 2014 paper “ neural machine translation |
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| 83 | neural machine translation using seq2seq models |
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| 84 | various problems like image captioning |
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| 85 | “ thought vector ”) |
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| 86 | every encoder hidden state |
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| 87 | vaswani et al ., |
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| 88 | decoder unit works well |
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| 89 | high bleu score ). |
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| 90 | length context vector design |
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| 91 | retain longer sequences |
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| 92 | bilingual evaluation understudy |
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| 93 | decoder unit fails |
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| 94 | whole long sentence |
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| 95 | famous paper attention |
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| 96 | deep learning community |
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| 97 | deep learning arena |
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| 98 | long input sentences |
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| 99 | complete input sequence |
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| 100 | candidate translation |
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| 101 | et al |
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| 102 | seq2seq model |
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| 103 | context vector |
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| 104 | paper laid |
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| 105 | complete sequence |
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| 106 | long dependencies |
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| 107 | jointly learning |
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| 108 | shorter sentences |
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| 109 | fixed length |
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| 110 | input sequence |
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| 111 | decoder architecture |
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| 112 | ------------------------------ |
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| 113 | Rakun: |
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| 114 | 01-Mar-21 20:38:03 - Initiated a keyword detector instance. |
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| 115 | 01-Mar-21 20:38:03 - Number of nodes reduced from 128 to 119 |
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| 116 | Captured 5 |
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| 117 | attention |
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| 118 | sequence |
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| 119 | input sequence attention |
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| 120 | model |
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| 121 | paper |
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| 122 | using |
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| 123 | encoder |
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| 124 | seq2seq |
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| 125 | encoder-decoder |
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| 126 | vector |
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| 127 | information |
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| 128 | architecture |
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| 129 | learning |
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| 130 | decoder |
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| 131 | ideas |
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| 132 | prominent |
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| 133 | translation |
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| 134 | hence |
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| 135 | processes |
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| 136 | previous |
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| 137 | extension |
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| 138 | natural |
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| 139 | reads |
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| 140 | translate |
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| 141 | align |
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| 142 | candidate |
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| 143 | comparing |
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| 144 | score |
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| 145 | instead |
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| 146 | concentrated |
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| 147 | ------------------------------ |
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| 148 | Yake: |
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| 149 | Captured 5 |
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| 150 | neural machine translation |
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| 151 | input sequence |
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| 152 | deep learning community |
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| 153 | context vector |
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| 154 | sequence |
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| 155 | input |
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| 156 | neural machine |
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| 157 | machine translation |
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| 158 | attention |
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| 159 | model |
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| 160 | complete input sequence |
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| 161 | learning community |
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| 162 | deep learning |
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| 163 | context |
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| 164 | vector |
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| 165 | bilingual evaluation understudy |
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| 166 | encoder-decoder |
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| 167 | translation |
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| 168 | learning |
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| 169 | prominent ideas |
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| 170 | fixed-length context vector |
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| 171 | context vector design |
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| 172 | long input sentences |
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| 173 | machine |
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| 174 | encoder-decoder unit |
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| 175 | neural |
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| 176 | encoder-decoder model |
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| 177 | words |
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| 178 | encoder |
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| 179 | complete sequence |
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| 180 | ------------------------------ |
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| 181 | KeyBERT: |
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| 182 | 01-Mar-21 20:38:03 - Load pretrained SentenceTransformer: distilbert-base-nli-mean-tokens |
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| 183 | 01-Mar-21 20:38:03 - Did not find folder distilbert-base-nli-mean-tokens |
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| 184 | 01-Mar-21 20:38:03 - Try to download model from server: https://sbert.net/models/distilbert-base-nli-mean-tokens.zip |
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| 185 | 01-Mar-21 20:38:03 - Load SentenceTransformer from folder: /Users/irene/.cache/torch/sentence_transformers/sbert.net_models_distilbert-base-nli-mean-tokens |
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| 186 | 01-Mar-21 20:38:04 - Use pytorch device: cpu |
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| 187 | Batches: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 9.71it/s] |
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| 188 | Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:01<00:00, 6.87it/s] |
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| 189 | Captured 1 |
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| 190 | deep learning |
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| 191 | neural machine |
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| 192 | encoder decoder |
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| 193 | learning arena |
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| 194 | ideas deep |
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| 195 | context neural |
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| 196 | decoder architecture |
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| 197 | machine translation |
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| 198 | transformers revolutionized |
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| 199 | graph encoder |
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| 200 | memorize long |
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| 201 | architecture encoder |
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| 202 | learning community |
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| 203 | decoder initialized |
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| 204 | prominent ideas |
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| 205 | reflected graph |
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| 206 | revolutionized deep |
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| 207 | translations graph |
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| 208 | valuable parts |
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| 209 | connecting encoder |
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| 210 | famous paper |
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| 211 | focus valuable |
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| 212 | candidate translation |
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| 213 | paper neural |
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| 214 | composed encoder |
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| 215 | arena concept |
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| 216 | helps model |
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| 217 | output critical |
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| 218 | memorize |
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| 219 | foundation famous |