lizgzil commited on
Commit
ce96244
1 Parent(s): e995418

Update spaCy pipeline

Browse files
README.md CHANGED
@@ -14,25 +14,25 @@ model-index:
14
  metrics:
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  - name: NER Precision
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  type: precision
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- value: 0.5991309071
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  - name: NER Recall
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  type: recall
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- value: 0.5768828452
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  - name: NER F Score
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  type: f_score
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- value: 0.5877964295
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  ---
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  A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT from job adverts.
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  | Feature | Description |
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  | --- | --- |
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  | **Name** | `en_skillner` |
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- | **Version** | `3.5.0` |
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- | **spaCy** | `>=3.5.3,<3.6.0` |
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  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
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  | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
34
  | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
35
- | **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) |
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  | **License** | `MIT` |
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  | **Author** | [nestauk](https://explosion.ai) |
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@@ -52,15 +52,15 @@ A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT
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  | Type | Score |
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  | --- | --- |
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- | `ENTS_P` | 59.91 |
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- | `ENTS_R` | 57.69 |
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- | `ENTS_F` | 58.78 |
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- | `SKILL_P` | 72.77 |
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- | `SKILL_R` | 72.38 |
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- | `SKILL_F` | 72.57 |
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- | `EXPERIENCE_P` | 56.00 |
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- | `EXPERIENCE_R` | 47.73 |
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- | `EXPERIENCE_F` | 51.53 |
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- | `BENEFIT_P` | 77.42 |
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- | `BENEFIT_R` | 35.82 |
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- | `BENEFIT_F` | 48.98 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.5919354839
18
  - name: NER Recall
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  type: recall
20
+ value: 0.5758368201
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  - name: NER F Score
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  type: f_score
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+ value: 0.5837751856
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  ---
25
  A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT from job adverts.
26
 
27
  | Feature | Description |
28
  | --- | --- |
29
  | **Name** | `en_skillner` |
30
+ | **Version** | `3.7.1` |
31
+ | **spaCy** | `>=3.7.4,<3.8.0` |
32
  | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
33
  | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
34
  | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
35
+ | **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) |
36
  | **License** | `MIT` |
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  | **Author** | [nestauk](https://explosion.ai) |
38
 
 
52
 
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  | Type | Score |
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  | --- | --- |
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+ | `ENTS_P` | 59.19 |
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+ | `ENTS_R` | 57.58 |
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+ | `ENTS_F` | 58.38 |
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+ | `SKILL_P` | 72.19 |
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+ | `SKILL_R` | 72.62 |
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+ | `SKILL_F` | 72.40 |
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+ | `EXPERIENCE_P` | 52.14 |
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+ | `EXPERIENCE_R` | 41.48 |
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+ | `EXPERIENCE_F` | 46.20 |
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+ | `BENEFIT_P` | 75.61 |
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+ | `BENEFIT_R` | 46.27 |
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+ | `BENEFIT_F` | 57.41 |
attribute_ruler/patterns CHANGED
Binary files a/attribute_ruler/patterns and b/attribute_ruler/patterns differ
 
config.cfg CHANGED
@@ -17,6 +17,7 @@ after_creation = null
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  after_pipeline_creation = null
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  batch_size = 256
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  tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
 
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  [components]
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@@ -116,6 +117,7 @@ maxout_pieces = 2
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  [components.tagger]
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  factory = "tagger"
 
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  neg_prefix = "!"
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  overwrite = false
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  scorer = {"@scorers":"spacy.tagger_scorer.v1"}
 
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  after_pipeline_creation = null
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  batch_size = 256
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  tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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+ vectors = {"@vectors":"spacy.Vectors.v1"}
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  [components]
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  [components.tagger]
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  factory = "tagger"
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+ label_smoothing = 0.0
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  neg_prefix = "!"
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  overwrite = false
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  scorer = {"@scorers":"spacy.tagger_scorer.v1"}
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meta.json CHANGED
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  {
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  "author":"nestauk",
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  "email":"[email protected]",
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@@ -43,54 +43,54 @@
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