Update spaCy pipeline

Browse files

Files changed (14) hide show

README.md +27 -27
accuracy.json +73 -73
attribute_ruler/patterns +0 -0
config.cfg +5 -4
el_core_news_sm-any-py3-none-any.whl +2 -2
lemmatizer/model +1 -1
meta.json +76 -76
morphologizer/model +1 -1
ner/model +2 -2
parser/model +1 -1
senter/model +1 -1
tok2vec/model +2 -2
tokenizer +1 -1
vocab/strings.json +2 -2

README.md CHANGED Viewed

@@ -14,62 +14,62 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.7117117117
     - name: NER Recall
       type: recall
-      value: 0.6638655462
     - name: NER F Score
       type: f_score
-      value: 0.6869565217
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9136715476
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9436473894
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.8869496623
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.8857664054
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8457438039
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.8013408804
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9287469287
 ---
 ### Details: https://spacy.io/models/el#el_core_news_sm
@@ -78,8 +78,8 @@ Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, le
 | Feature | Description |
 | --- | --- |
 | **Name** | `el_core_news_sm` |
-| **Version** | `3.4.0` |
-| **spaCy** | `>=3.4.0,<3.5.0` |
 | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
 | **Components** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
 | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -109,18 +109,18 @@ Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, le
 | `TOKEN_P` | 99.90 |
 | `TOKEN_R` | 99.95 |
 | `TOKEN_F` | 99.93 |
-| `POS_ACC` | 94.36 |
-| `MORPH_ACC` | 88.69 |
-| `MORPH_MICRO_P` | 94.65 |
-| `MORPH_MICRO_R` | 94.41 |
-| `MORPH_MICRO_F` | 94.53 |
-| `SENTS_P` | 91.97 |
-| `SENTS_R` | 93.80 |
-| `SENTS_F` | 92.87 |
-| `DEP_UAS` | 84.57 |
-| `DEP_LAS` | 80.13 |
-| `LEMMA_ACC` | 88.58 |
-| `TAG_ACC` | 91.37 |
-| `ENTS_P` | 71.17 |
-| `ENTS_R` | 66.39 |
-| `ENTS_F` | 68.70 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.742081448
     - name: NER Recall
       type: recall
+      value: 0.6890756303
     - name: NER F Score
       type: f_score
+      value: 0.7145969499
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9127841049
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9427599468
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.8905980378
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.8865552433
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.8463356974
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.8032327231
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.9372693727
 ---
 ### Details: https://spacy.io/models/el#el_core_news_sm
 | Feature | Description |
 | --- | --- |
 | **Name** | `el_core_news_sm` |
+| **Version** | `3.5.0` |
+| **spaCy** | `>=3.5.0,<3.6.0` |
 | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
 | **Components** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
 | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
 | `TOKEN_P` | 99.90 |
 | `TOKEN_R` | 99.95 |
 | `TOKEN_F` | 99.93 |
+| `POS_ACC` | 94.28 |
+| `MORPH_ACC` | 89.06 |
+| `MORPH_MICRO_P` | 94.85 |
+| `MORPH_MICRO_R` | 94.52 |
+| `MORPH_MICRO_F` | 94.69 |
+| `SENTS_P` | 92.93 |
+| `SENTS_R` | 94.54 |
+| `SENTS_F` | 93.73 |
+| `DEP_UAS` | 84.63 |
+| `DEP_LAS` | 80.32 |
+| `LEMMA_ACC` | 88.66 |
+| `TAG_ACC` | 91.28 |
+| `ENTS_P` | 74.21 |
+| `ENTS_R` | 68.91 |
+| `ENTS_F` | 71.46 |

accuracy.json CHANGED Viewed

@@ -3,124 +3,124 @@
   "token_p": 0.9990295973,
   "token_r": 0.9995068547,
   "token_f": 0.9992604644,
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-  "morph_acc": 0.8869496623,
-  "morph_micro_p": 0.9464935242,
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-  "morph_micro_f": 0.9452724495,
   "morph_per_feat": {
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-      "f": 0.8823529412
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-      "f": 0.9251814163
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-  "dep_uas": 0.8457438039,
-  "dep_las": 0.8013408804,
   "dep_las_per_type": {},
-  "lemma_acc": 0.8857664054,
-  "tag_acc": 0.9136715476,
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-  "ents_r": 0.6638655462,
-  "ents_f": 0.6869565217,
   "ents_per_type": {
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     "EVENT": {
       "p": 1.0,
@@ -133,5 +133,5 @@
       "f": 0.0
     }
   },
-  "speed": 10345.4245329374
 }

   "token_p": 0.9990295973,
   "token_r": 0.9995068547,
   "token_f": 0.9992604644,
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+  "morph_acc": 0.8905980378,
+  "morph_micro_p": 0.9485199047,
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   "morph_per_feat": {
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+  "dep_uas": 0.8463356974,
+  "dep_las": 0.8032327231,
   "dep_las_per_type": {},
+  "lemma_acc": 0.8865552433,
+  "tag_acc": 0.9127841049,
+  "ents_p": 0.742081448,
+  "ents_r": 0.6890756303,
+  "ents_f": 0.7145969499,
   "ents_per_type": {
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     "GPE": {
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     "ORG": {
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+      "f": 0.4
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     "EVENT": {
       "p": 1.0,
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 }

attribute_ruler/patterns CHANGED Viewed

Binary files a/attribute_ruler/patterns and b/attribute_ruler/patterns differ

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 = false
 [components.ner.model.tok2vec.encode]
@@ -150,8 +150,8 @@ 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 = false
 [components.tok2vec.model.encode]
@@ -193,6 +193,7 @@ 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 = false
 [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 = false
 [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":"el",
   "name":"core_news_sm",
-  "version":"3.4.0",
   "description":"Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.",
   "author":"Explosion",
   "email":"contact@explosion.ai",
   "url":"https://explosion.ai",
   "license":"CC BY-NC-SA 3.0",
-  "spacy_version":">=3.4.0,<3.5.0",
-  "spacy_git_version":"dd038b536",
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@@ -581,7 +581,7 @@
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     {

 {
   "lang":"el",
   "name":"core_news_sm",
+  "version":"3.5.0",
   "description":"Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.",
   "author":"Explosion",
   "email":"contact@explosion.ai",
   "url":"https://explosion.ai",
   "license":"CC BY-NC-SA 3.0",
+  "spacy_version":">=3.5.0,<3.6.0",
+  "spacy_git_version":"9e0322de1",
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     "vectors":0,
     "token_p":0.9990295973,
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+    "morph_acc":0.8905980378,
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     "morph_per_feat":{
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