Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +36 -0
- components/finetuning_validator/fingerprints-for-validation.json +5 -0
- components/train_CountVectorsFeaturizer3/oov_words.json +1 -0
- components/train_CountVectorsFeaturizer3/vocabularies.json +2387 -0
- components/train_DIETClassifier4/DIETClassifier.data_example.st +0 -0
- components/train_DIETClassifier4/DIETClassifier.data_example_metadata.json +179 -0
- components/train_DIETClassifier4/DIETClassifier.entity_tag_specs.json +28 -0
- components/train_DIETClassifier4/DIETClassifier.index_label_id_mapping.json +8 -0
- components/train_DIETClassifier4/DIETClassifier.label_data.st +0 -0
- components/train_DIETClassifier4/DIETClassifier.label_data_metadata.json +107 -0
- components/train_DIETClassifier4/DIETClassifier.sparse_feature_sizes.json +11 -0
- components/train_DIETClassifier4/DIETClassifier.tf_model.data-00000-of-00001 +3 -0
- components/train_DIETClassifier4/DIETClassifier.tf_model.index +0 -0
- components/train_DIETClassifier4/checkpoint +2 -0
- components/train_LexicalSyntacticFeaturizer2/feature_to_idx_dict.json +50 -0
- components/train_RegexFeaturizer1/patterns.json +1 -0
- metadata.json +308 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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components/train_DIETClassifier4/DIETClassifier.tf_model.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
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README.md
ADDED
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# Email Processing DIET Model
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DIET (Dual Intent and Entity Transformer) model trained for email processing.
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## Model Capabilities
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This model can extract:
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- Email addresses
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- Subject lines
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And classify intents such as:
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- provide_email
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- provide_subject
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- greeting
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- thank_you
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- goodbye
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## Usage with Rasa
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```python
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from rasa.core.agent import Agent
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# Load the model
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agent = Agent.load('path/to/model')
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# Use the model
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async def test():
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result = await agent.parse_message('Email is john.doe@example.com')
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print(result)
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```
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## Model Information
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- Model source: nlu-20250424-041833-grave-grouse.tar.gz
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- Published date: 2025-04-24
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- Framework: Rasa NLU with DIET architecture
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components/finetuning_validator/fingerprints-for-validation.json
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{
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"rasa-version": "3.6.21",
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"fingerprint-config": "5978f89b8dc39ff9a62218d79e405651",
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"fingerprint-nlu": "eef5ec8a1cd035288f5cef2f5d5dcd16"
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}
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components/train_CountVectorsFeaturizer3/oov_words.json
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[]
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components/train_CountVectorsFeaturizer3/vocabularies.json
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| 1 |
+
{
|
| 2 |
+
"text": {
|
| 3 |
+
" ": 0,
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| 4 |
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"g": 998,
|
| 5 |
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"r": 1772,
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| 6 |
+
"e": 769,
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| 7 |
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"t": 2074,
|
| 8 |
+
"i": 1103,
|
| 9 |
+
"n": 1384,
|
| 10 |
+
"s": 1942,
|
| 11 |
+
" g": 84,
|
| 12 |
+
"gr": 1031,
|
| 13 |
+
"re": 1812,
|
| 14 |
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"ee": 823,
|
| 15 |
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"et": 931,
|
| 16 |
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"ti": 2137,
|
| 17 |
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"in": 1149,
|
| 18 |
+
"ng": 1449,
|
| 19 |
+
"gs": 1034,
|
| 20 |
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"s ": 1943,
|
| 21 |
+
" gr": 87,
|
| 22 |
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"gre": 1032,
|
| 23 |
+
"ree": 1827,
|
| 24 |
+
"eet": 832,
|
| 25 |
+
"eti": 937,
|
| 26 |
+
"tin": 2144,
|
| 27 |
+
"ing": 1165,
|
| 28 |
+
"ngs": 1460,
|
| 29 |
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"gs ": 1035,
|
| 30 |
+
" gre": 88,
|
| 31 |
+
"gree": 1033,
|
| 32 |
+
"reet": 1828,
|
| 33 |
+
"eeti": 833,
|
| 34 |
+
"etin": 938,
|
| 35 |
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"ting": 2145,
|
| 36 |
+
"ings": 1169,
|
| 37 |
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"ngs ": 1461,
|
| 38 |
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"h": 1041,
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| 39 |
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"l": 1251,
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| 40 |
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"o": 1528,
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| 41 |
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" h": 89,
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| 42 |
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"he": 1068,
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| 43 |
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"el": 844,
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| 44 |
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"ll": 1294,
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| 45 |
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"lo": 1301,
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| 46 |
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"o ": 1529,
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| 47 |
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" he": 94,
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| 48 |
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"hel": 1075,
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| 49 |
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"ell": 846,
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| 50 |
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"llo": 1298,
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| 51 |
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"lo ": 1302,
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| 52 |
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" hel": 96,
|
| 53 |
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| 1720 |
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| 1721 |
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| 1722 |
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| 1723 |
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| 1724 |
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| 1771 |
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| 1772 |
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| 1773 |
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| 1774 |
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| 1784 |
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| 1788 |
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| 1789 |
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| 1790 |
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| 1791 |
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| 1792 |
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| 1793 |
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| 1794 |
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| 1795 |
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| 1797 |
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| 1798 |
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| 1799 |
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| 1800 |
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| 1801 |
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| 1802 |
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| 1803 |
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| 1804 |
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| 1805 |
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| 1806 |
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| 1807 |
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| 1808 |
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| 1809 |
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| 1810 |
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| 1811 |
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| 1812 |
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| 1813 |
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| 1814 |
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| 1815 |
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| 1816 |
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| 1817 |
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| 1818 |
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| 1819 |
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| 1820 |
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| 1821 |
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| 1822 |
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| 1823 |
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| 1824 |
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| 1825 |
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| 1826 |
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| 1827 |
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| 1828 |
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| 1829 |
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| 1830 |
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| 1831 |
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| 1832 |
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| 1833 |
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| 1834 |
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| 1835 |
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| 1836 |
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| 1837 |
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| 1838 |
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| 1839 |
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| 1840 |
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| 1841 |
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| 1842 |
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| 1843 |
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| 1844 |
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| 1845 |
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| 1846 |
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| 1847 |
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| 1848 |
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| 1849 |
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| 1850 |
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| 1851 |
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| 1852 |
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| 1853 |
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| 1854 |
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| 1855 |
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| 1856 |
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| 1857 |
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| 1858 |
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| 1859 |
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| 1860 |
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| 1861 |
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| 1862 |
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| 1863 |
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| 1864 |
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| 1865 |
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| 1866 |
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| 1867 |
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| 1868 |
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| 1869 |
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| 1870 |
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| 1871 |
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| 1872 |
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| 1873 |
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| 1874 |
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| 1875 |
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| 1876 |
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| 1877 |
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| 1878 |
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| 1879 |
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| 1880 |
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| 1881 |
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| 1882 |
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| 1883 |
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| 1884 |
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| 1885 |
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| 1886 |
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| 1887 |
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| 1888 |
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| 1889 |
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|
| 1890 |
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| 1891 |
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| 1892 |
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| 1893 |
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| 1894 |
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| 1895 |
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| 1896 |
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| 1897 |
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| 1898 |
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| 1899 |
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| 1900 |
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| 1901 |
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| 1902 |
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| 1903 |
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| 1904 |
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| 1905 |
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| 1906 |
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| 1907 |
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| 1908 |
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| 1909 |
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| 1910 |
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| 1911 |
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| 1912 |
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| 1913 |
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| 1914 |
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| 1915 |
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| 1916 |
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| 1917 |
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| 1918 |
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| 1919 |
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|
| 1920 |
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| 1921 |
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| 1922 |
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| 1923 |
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| 1924 |
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|
| 1925 |
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| 1926 |
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|
| 1927 |
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|
| 1928 |
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|
| 1929 |
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|
| 1930 |
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|
| 1931 |
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|
| 1932 |
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|
| 1933 |
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|
| 1934 |
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|
| 1935 |
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|
| 1936 |
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|
| 1937 |
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|
| 1938 |
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|
| 1939 |
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|
| 1940 |
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|
| 1941 |
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|
| 1942 |
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|
| 1943 |
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|
| 1944 |
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| 1945 |
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|
| 1946 |
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|
| 1947 |
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|
| 1948 |
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|
| 1949 |
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|
| 1950 |
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|
| 1951 |
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| 1952 |
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|
| 1953 |
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| 1954 |
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|
| 1955 |
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|
| 1956 |
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| 1957 |
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|
| 1958 |
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| 1959 |
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| 1960 |
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| 1961 |
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| 1962 |
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|
| 1963 |
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|
| 1964 |
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|
| 1965 |
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|
| 1966 |
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|
| 1967 |
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|
| 1968 |
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|
| 1969 |
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|
| 1970 |
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|
| 1971 |
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|
| 1972 |
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|
| 1973 |
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|
| 1974 |
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|
| 1975 |
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|
| 1976 |
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|
| 1977 |
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|
| 1978 |
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|
| 1979 |
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|
| 1980 |
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|
| 1981 |
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|
| 1982 |
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|
| 1983 |
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|
| 1984 |
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|
| 1985 |
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|
| 1986 |
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|
| 1987 |
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|
| 1988 |
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|
| 1989 |
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|
| 1990 |
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|
| 1991 |
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|
| 1992 |
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|
| 1993 |
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|
| 1994 |
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|
| 1995 |
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|
| 1996 |
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| 1997 |
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|
| 1998 |
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|
| 1999 |
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|
| 2000 |
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|
| 2001 |
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|
| 2002 |
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|
| 2003 |
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|
| 2004 |
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|
| 2005 |
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|
| 2006 |
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| 2007 |
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| 2008 |
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| 2009 |
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| 2010 |
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| 2011 |
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| 2012 |
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| 2013 |
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| 2014 |
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| 2015 |
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| 2017 |
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| 2019 |
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| 2020 |
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| 2021 |
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| 2022 |
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| 2023 |
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| 2025 |
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| 2027 |
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| 2028 |
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| 2029 |
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| 2030 |
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| 2031 |
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| 2032 |
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|
components/train_DIETClassifier4/DIETClassifier.data_example.st
ADDED
|
Binary file (2.44 kB). View file
|
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|
components/train_DIETClassifier4/DIETClassifier.data_example_metadata.json
ADDED
|
@@ -0,0 +1,179 @@
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| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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{
|
| 72 |
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|
| 73 |
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|
| 74 |
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{
|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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]
|
| 81 |
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}
|
| 82 |
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]
|
| 83 |
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},
|
| 84 |
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{
|
| 85 |
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"key": "sentence",
|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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{
|
| 92 |
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|
| 93 |
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|
| 94 |
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{
|
| 95 |
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|
| 96 |
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"key": "component_label_sentence_0_0_0_list"
|
| 97 |
+
}
|
| 98 |
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]
|
| 99 |
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}
|
| 100 |
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]
|
| 101 |
+
}
|
| 102 |
+
]
|
| 103 |
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}
|
| 104 |
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]
|
| 105 |
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},
|
| 106 |
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{
|
| 107 |
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"key": "text",
|
| 108 |
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|
| 109 |
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{
|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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{
|
| 117 |
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|
| 118 |
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"subcomponents": [
|
| 119 |
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{
|
| 120 |
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"type": "list",
|
| 121 |
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"key": "component_text_mask_0_0_0_list"
|
| 122 |
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}
|
| 123 |
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|
| 124 |
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|
| 125 |
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]
|
| 126 |
+
}
|
| 127 |
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]
|
| 128 |
+
},
|
| 129 |
+
{
|
| 130 |
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"key": "sentence",
|
| 131 |
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"number_of_dimensions": 3,
|
| 132 |
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|
| 133 |
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{
|
| 134 |
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"type": "group",
|
| 135 |
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|
| 136 |
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|
| 137 |
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"type": "sparse",
|
| 138 |
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"key": "component_text_sentence_0_0",
|
| 139 |
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"shape": [
|
| 140 |
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1,
|
| 141 |
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2381
|
| 142 |
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|
| 143 |
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}
|
| 144 |
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]
|
| 145 |
+
}
|
| 146 |
+
]
|
| 147 |
+
},
|
| 148 |
+
{
|
| 149 |
+
"key": "sequence",
|
| 150 |
+
"number_of_dimensions": 3,
|
| 151 |
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"features": [
|
| 152 |
+
{
|
| 153 |
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"type": "group",
|
| 154 |
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"subcomponents": [
|
| 155 |
+
{
|
| 156 |
+
"type": "sparse",
|
| 157 |
+
"key": "component_text_sequence_0_0",
|
| 158 |
+
"shape": [
|
| 159 |
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1,
|
| 160 |
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2405
|
| 161 |
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]
|
| 162 |
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}
|
| 163 |
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]
|
| 164 |
+
}
|
| 165 |
+
]
|
| 166 |
+
},
|
| 167 |
+
{
|
| 168 |
+
"key": "sequence_lengths",
|
| 169 |
+
"number_of_dimensions": 1,
|
| 170 |
+
"features": [
|
| 171 |
+
{
|
| 172 |
+
"type": "list",
|
| 173 |
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"key": "component_text_sequence_lengths_0_list"
|
| 174 |
+
}
|
| 175 |
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]
|
| 176 |
+
}
|
| 177 |
+
]
|
| 178 |
+
}
|
| 179 |
+
]
|
components/train_DIETClassifier4/DIETClassifier.entity_tag_specs.json
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"tag_name": "entity",
|
| 4 |
+
"ids_to_tags": {
|
| 5 |
+
"1": "B-email",
|
| 6 |
+
"2": "I-email",
|
| 7 |
+
"3": "L-email",
|
| 8 |
+
"4": "U-email",
|
| 9 |
+
"5": "B-subject",
|
| 10 |
+
"6": "I-subject",
|
| 11 |
+
"7": "L-subject",
|
| 12 |
+
"8": "U-subject",
|
| 13 |
+
"0": "O"
|
| 14 |
+
},
|
| 15 |
+
"tags_to_ids": {
|
| 16 |
+
"B-email": 1,
|
| 17 |
+
"I-email": 2,
|
| 18 |
+
"L-email": 3,
|
| 19 |
+
"U-email": 4,
|
| 20 |
+
"B-subject": 5,
|
| 21 |
+
"I-subject": 6,
|
| 22 |
+
"L-subject": 7,
|
| 23 |
+
"U-subject": 8,
|
| 24 |
+
"O": 0
|
| 25 |
+
},
|
| 26 |
+
"num_tags": 9
|
| 27 |
+
}
|
| 28 |
+
]
|
components/train_DIETClassifier4/DIETClassifier.index_label_id_mapping.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"0": "confirm",
|
| 3 |
+
"1": "goodbye",
|
| 4 |
+
"2": "greeting",
|
| 5 |
+
"3": "provide_email",
|
| 6 |
+
"4": "provide_subject",
|
| 7 |
+
"5": "thank_you"
|
| 8 |
+
}
|
components/train_DIETClassifier4/DIETClassifier.label_data.st
ADDED
|
Binary file (1.23 kB). View file
|
|
|
components/train_DIETClassifier4/DIETClassifier.label_data_metadata.json
ADDED
|
@@ -0,0 +1,107 @@
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
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|
|
|
|
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|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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[
|
| 2 |
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{
|
| 3 |
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"key": "label",
|
| 4 |
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|
| 5 |
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|
| 6 |
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|
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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"key": "component_label_sentence_0_1_0_list"
|
| 27 |
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}
|
| 28 |
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|
| 29 |
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|
| 30 |
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{
|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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{
|
| 40 |
+
"type": "group",
|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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"key": "component_label_sentence_0_3_0_list"
|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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{
|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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"key": "component_label_sentence_0_4_0_list"
|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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{
|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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},
|
| 70 |
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{
|
| 71 |
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"key": "ids",
|
| 72 |
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|
| 73 |
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|
| 74 |
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{
|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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{
|
| 82 |
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"type": "list",
|
| 83 |
+
"key": "component_label_ids_0_1_list"
|
| 84 |
+
},
|
| 85 |
+
{
|
| 86 |
+
"type": "list",
|
| 87 |
+
"key": "component_label_ids_0_2_list"
|
| 88 |
+
},
|
| 89 |
+
{
|
| 90 |
+
"type": "list",
|
| 91 |
+
"key": "component_label_ids_0_3_list"
|
| 92 |
+
},
|
| 93 |
+
{
|
| 94 |
+
"type": "list",
|
| 95 |
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"key": "component_label_ids_0_4_list"
|
| 96 |
+
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|
| 97 |
+
{
|
| 98 |
+
"type": "list",
|
| 99 |
+
"key": "component_label_ids_0_5_list"
|
| 100 |
+
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|
| 101 |
+
]
|
| 102 |
+
}
|
| 103 |
+
]
|
| 104 |
+
}
|
| 105 |
+
]
|
| 106 |
+
}
|
| 107 |
+
]
|
components/train_DIETClassifier4/DIETClassifier.sparse_feature_sizes.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"text": {
|
| 3 |
+
"sequence": [
|
| 4 |
+
24,
|
| 5 |
+
2381
|
| 6 |
+
],
|
| 7 |
+
"sentence": [
|
| 8 |
+
2381
|
| 9 |
+
]
|
| 10 |
+
}
|
| 11 |
+
}
|
components/train_DIETClassifier4/DIETClassifier.tf_model.data-00000-of-00001
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:681f3f21d4ff9a579deac2433b591bd011716b3c105582b50044426374c16e34
|
| 3 |
+
size 33243823
|
components/train_DIETClassifier4/DIETClassifier.tf_model.index
ADDED
|
Binary file (10.5 kB). View file
|
|
|
components/train_DIETClassifier4/checkpoint
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model_checkpoint_path: "DIETClassifier.tf_model"
|
| 2 |
+
all_model_checkpoint_paths: "DIETClassifier.tf_model"
|
components/train_LexicalSyntacticFeaturizer2/feature_to_idx_dict.json
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"0###low": {
|
| 3 |
+
"False": 0,
|
| 4 |
+
"True": 1
|
| 5 |
+
},
|
| 6 |
+
"0###title": {
|
| 7 |
+
"False": 2,
|
| 8 |
+
"True": 3
|
| 9 |
+
},
|
| 10 |
+
"0###upper": {
|
| 11 |
+
"False": 4,
|
| 12 |
+
"True": 5
|
| 13 |
+
},
|
| 14 |
+
"1###BOS": {
|
| 15 |
+
"False": 6,
|
| 16 |
+
"True": 7
|
| 17 |
+
},
|
| 18 |
+
"1###EOS": {
|
| 19 |
+
"False": 8,
|
| 20 |
+
"True": 9
|
| 21 |
+
},
|
| 22 |
+
"1###digit": {
|
| 23 |
+
"False": 10,
|
| 24 |
+
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|
| 25 |
+
},
|
| 26 |
+
"1###low": {
|
| 27 |
+
"False": 12,
|
| 28 |
+
"True": 13
|
| 29 |
+
},
|
| 30 |
+
"1###title": {
|
| 31 |
+
"False": 14,
|
| 32 |
+
"True": 15
|
| 33 |
+
},
|
| 34 |
+
"1###upper": {
|
| 35 |
+
"False": 16,
|
| 36 |
+
"True": 17
|
| 37 |
+
},
|
| 38 |
+
"2###low": {
|
| 39 |
+
"False": 18,
|
| 40 |
+
"True": 19
|
| 41 |
+
},
|
| 42 |
+
"2###title": {
|
| 43 |
+
"False": 20,
|
| 44 |
+
"True": 21
|
| 45 |
+
},
|
| 46 |
+
"2###upper": {
|
| 47 |
+
"False": 22,
|
| 48 |
+
"True": 23
|
| 49 |
+
}
|
| 50 |
+
}
|
components/train_RegexFeaturizer1/patterns.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
[]
|
metadata.json
ADDED
|
@@ -0,0 +1,308 @@
|
|
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|
|
|
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| 1 |
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| 21 |
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| 32 |
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| 33 |
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| 34 |
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| 38 |
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| 47 |
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| 49 |
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| 50 |
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| 63 |
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| 66 |
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| 67 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 85 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 101 |
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| 102 |
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| 103 |
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| 104 |
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| 105 |
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| 106 |
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| 107 |
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| 108 |
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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| 123 |
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| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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| 148 |
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| 149 |
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| 150 |
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| 151 |
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| 152 |
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| 153 |
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| 154 |
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| 156 |
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| 159 |
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| 160 |
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| 161 |
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| 162 |
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| 163 |
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| 164 |
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| 165 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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| 187 |
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| 188 |
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| 189 |
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| 190 |
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| 191 |
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| 192 |
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| 193 |
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| 194 |
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| 195 |
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| 196 |
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| 197 |
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| 202 |
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| 205 |
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| 217 |
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| 218 |
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| 231 |
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| 233 |
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| 234 |
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| 247 |
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| 249 |
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| 250 |
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