Update spaCy pipeline

Browse files

Files changed (14) hide show

README.md +22 -22
accuracy.json +112 -112
config.cfg +5 -4
ko_core_news_lg-any-py3-none-any.whl +2 -2
lemmatizer/model +1 -1
meta.json +115 -115
morphologizer/model +1 -1
ner/model +2 -2
parser/model +1 -1
senter/model +1 -1
tagger/model +1 -1
tok2vec/model +2 -2
tokenizer +1 -1
vocab/strings.json +2 -2

README.md CHANGED Viewed

@@ -14,55 +14,55 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.8651890864
     - name: NER Recall
       type: recall
-      value: 0.835605793
     - name: NER F Score
       type: f_score
-      value: 0.8501401567
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.8436480152
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9500375806
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9020748037
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8407495711
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.812317565
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9992737836
 ---
 ### Details: https://spacy.io/models/ko#ko_core_news_lg
@@ -71,8 +71,8 @@ Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, p
 | Feature | Description |
 | --- | --- |
 | **Name** | `ko_core_news_lg` |
-| **Version** | `3.4.0` |
-| **spaCy** | `>=3.4.0,<3.5.0` |
 | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
 | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
 | **Vectors** | floret (200000, 300) |
@@ -103,14 +103,14 @@ Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, p
 | `TOKEN_P` | 100.00 |
 | `TOKEN_R` | 100.00 |
 | `TOKEN_F` | 100.00 |
-| `TAG_ACC` | 84.36 |
-| `POS_ACC` | 95.00 |
-| `SENTS_P` | 99.95 |
-| `SENTS_R` | 99.90 |
-| `SENTS_F` | 99.93 |
-| `DEP_UAS` | 84.07 |
-| `DEP_LAS` | 81.23 |
-| `LEMMA_ACC` | 90.21 |
-| `ENTS_P` | 86.52 |
-| `ENTS_R` | 83.56 |
-| `ENTS_F` | 85.01 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.8669446273
     - name: NER Recall
       type: recall
+      value: 0.837301307
     - name: NER F Score
       type: f_score
+      value: 0.8518651621
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.8400253175
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9487717077
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9009276291
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.8416620252
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.8140177338
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 1.0
 ---
 ### Details: https://spacy.io/models/ko#ko_core_news_lg
 | Feature | Description |
 | --- | --- |
 | **Name** | `ko_core_news_lg` |
+| **Version** | `3.5.0` |
+| **spaCy** | `>=3.5.0,<3.6.0` |
 | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
 | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
 | **Vectors** | floret (200000, 300) |
 | `TOKEN_P` | 100.00 |
 | `TOKEN_R` | 100.00 |
 | `TOKEN_F` | 100.00 |
+| `TAG_ACC` | 84.00 |
+| `POS_ACC` | 94.88 |
+| `SENTS_P` | 100.00 |
+| `SENTS_R` | 100.00 |
+| `SENTS_F` | 100.00 |
+| `DEP_UAS` | 84.17 |
+| `DEP_LAS` | 81.40 |
+| `LEMMA_ACC` | 90.09 |
+| `ENTS_P` | 86.69 |
+| `ENTS_R` | 83.73 |
+| `ENTS_F` | 85.19 |

accuracy.json CHANGED Viewed

@@ -3,113 +3,113 @@
   "token_p": 1.0,
   "token_r": 1.0,
   "token_f": 1.0,
-  "tag_acc": 0.8436480152,
-  "pos_acc": 0.9500375806,
-  "sents_p": 0.9995157385,
-  "sents_r": 0.9990319458,
-  "sents_f": 0.9992737836,
-  "dep_uas": 0.8407495711,
-  "dep_las": 0.812317565,
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@@ -117,81 +117,81 @@
       "f": 0.0
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     }
   }
 }

   "token_p": 1.0,
   "token_r": 1.0,
   "token_f": 1.0,
+  "tag_acc": 0.8400253175,
+  "pos_acc": 0.9487717077,
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 }

config.cfg CHANGED Viewed

@@ -81,8 +81,8 @@ nO = null
 [components.ner.model.tok2vec.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = 96
-attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
-rows = [5000,1000,2500,2500,50]
 include_static_vectors = true
 [components.ner.model.tok2vec.encode]
@@ -166,8 +166,8 @@ factory = "tok2vec"
 [components.tok2vec.model.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = ${components.tok2vec.model.encode:width}
-attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
-rows = [5000,1000,2500,2500,50]
 include_static_vectors = true
 [components.tok2vec.model.encode]
@@ -209,6 +209,7 @@ eval_frequency = 1000
 frozen_components = []
 before_to_disk = null
 annotating_components = []
 [training.batcher]
 @batchers = "spacy.batch_by_words.v1"

 [components.ner.model.tok2vec.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = 96
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
+rows = [5000,1000,2500,2500]
 include_static_vectors = true
 [components.ner.model.tok2vec.encode]
 [components.tok2vec.model.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = ${components.tok2vec.model.encode:width}
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY","IS_SPACE"]
+rows = [5000,1000,2500,2500,50,50]
 include_static_vectors = true
 [components.tok2vec.model.encode]
 frozen_components = []
 before_to_disk = null
 annotating_components = []
+before_update = null
 [training.batcher]
 @batchers = "spacy.batch_by_words.v1"

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meta.json CHANGED Viewed

@@ -1,14 +1,14 @@
 {
   "lang":"ko",
   "name":"core_news_lg",
-  "version":"3.4.0",
   "description":"Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.",
   "author":"Explosion",
   "email":"contact@explosion.ai",
   "url":"https://explosion.ai",
   "license":"CC BY-SA 4.0",
-  "spacy_version":">=3.4.0,<3.5.0",
-  "spacy_git_version":"dd038b536",
   "vectors":{
     "width":300,
     "vectors":200000,
@@ -2086,113 +2086,113 @@
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