Update spaCy pipeline

README.md

@@ -14,13 +14,13 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.0
+      value: 0.4605714286
     - name: NER Recall
       type: recall
-      value: 0.0
+      value: 0.4574347333
     - name: NER F Score
       type: f_score
-      value: 0
+      value: 0.4589977221
 ---
 A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT from job adverts.
 
@@ -34,7 +34,7 @@ A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT
 | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
 | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br>[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br>[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br>[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) |
 | **License** | `MIT` |
-| **Author** | [Explosion](https://explosion.ai) |
+| **Author** | [nestauk](https://explosion.ai) |
 
 ### Label Scheme
 
@@ -52,6 +52,6 @@ A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT
 
 | Type | Score |
 | --- | --- |
-| `ENTS_P` | 0.00 |
-| `ENTS_R` | 0.00 |
-| `ENTS_F` | 0.00 |
+| `ENTS_P` | 46.06 |
+| `ENTS_R` | 45.74 |
+| `ENTS_F` | 45.90 |

en_skillner-any-py3-none-any.whl

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meta.json

@@ -3,7 +3,7 @@
   "name":"skillner",
   "version":"3.7.1",
   "description":"A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT from job adverts.",
-  "author":"Explosion",
+  "author":"nestauk",
   "email":"[email protected]",
   "url":"https://explosion.ai",
   "license":"MIT",
@@ -43,159 +43,45 @@
     "senter"
   ],
   "performance":{
-    "ents_p":0.0,
-    "ents_r":0.0,
-    "ents_f":0,
+    "ents_p":0.4605714286,
+    "ents_r":0.4574347333,
+    "ents_f":0.4589977221,
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