Update spaCy pipeline

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Files changed (14) hide show

README.md +28 -28
accuracy.json +180 -180
config.cfg +5 -4
hr_core_news_lg-any-py3-none-any.whl +2 -2
lemmatizer/model +0 -0
meta.json +183 -183
morphologizer/model +0 -0
ner/model +2 -2
parser/model +0 -0
senter/model +0 -0
tagger/model +0 -0
tok2vec/model +2 -2
tokenizer +1 -1
vocab/strings.json +0 -0

README.md CHANGED Viewed

@@ -14,62 +14,62 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.8288770053
     - name: NER Recall
       type: recall
-      value: 0.8303571429
     - name: NER F Score
       type: f_score
-      value: 0.8296164139
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9226241706
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9745769776
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.9283393953
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9291408082
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8678779438
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.803833502
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9401088929
 ---
 ### Details: https://spacy.io/models/hr#hr_core_news_lg
@@ -78,8 +78,8 @@ Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
 | Feature | Description |
 | --- | --- |
 | **Name** | `hr_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) |
@@ -106,22 +106,22 @@ Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
 | Type | Score |
 | --- | --- |
-| `TOKEN_ACC` | 99.94 |
 | `TOKEN_P` | 97.28 |
 | `TOKEN_R` | 98.71 |
 | `TOKEN_F` | 97.99 |
-| `TAG_ACC` | 92.26 |
-| `POS_ACC` | 97.46 |
-| `MORPH_ACC` | 92.83 |
-| `MORPH_MICRO_P` | 96.18 |
-| `MORPH_MICRO_R` | 95.99 |
-| `MORPH_MICRO_F` | 96.08 |
-| `SENTS_P` | 94.87 |
-| `SENTS_R` | 93.17 |
-| `SENTS_F` | 94.01 |
-| `DEP_UAS` | 86.79 |
-| `DEP_LAS` | 80.38 |
-| `LEMMA_ACC` | 92.91 |
-| `ENTS_P` | 82.89 |
-| `ENTS_R` | 83.04 |
-| `ENTS_F` | 82.96 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.8309481216
     - name: NER Recall
       type: recall
+      value: 0.8294642857
     - name: NER F Score
       type: f_score
+      value: 0.8302055407
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9217400961
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9759146596
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.9267547757
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9287546545
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.8661791243
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.8015566665
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.9454545455
 ---
 ### Details: https://spacy.io/models/hr#hr_core_news_lg
 | Feature | Description |
 | --- | --- |
 | **Name** | `hr_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) |
 | Type | Score |
 | --- | --- |
+| `TOKEN_ACC` | 99.89 |
 | `TOKEN_P` | 97.28 |
 | `TOKEN_R` | 98.71 |
 | `TOKEN_F` | 97.99 |
+| `TAG_ACC` | 92.17 |
+| `POS_ACC` | 97.59 |
+| `MORPH_ACC` | 92.68 |
+| `MORPH_MICRO_P` | 96.20 |
+| `MORPH_MICRO_R` | 95.88 |
+| `MORPH_MICRO_F` | 96.04 |
+| `SENTS_P` | 95.59 |
+| `SENTS_R` | 93.53 |
+| `SENTS_F` | 94.55 |
+| `DEP_UAS` | 86.62 |
+| `DEP_LAS` | 80.16 |
+| `LEMMA_ACC` | 92.88 |
+| `ENTS_P` | 83.09 |
+| `ENTS_R` | 82.95 |
+| `ENTS_F` | 83.02 |

accuracy.json CHANGED Viewed

@@ -1,261 +1,261 @@
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@@ -263,35 +263,35 @@
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@@ -303,36 +303,36 @@
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 }

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config.cfg CHANGED Viewed

@@ -81,8 +81,8 @@ nO = null
 [components.ner.model.tok2vec.embed]
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 before_to_disk = null
 annotating_components = []
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 [training.batcher]
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vocab/strings.json CHANGED Viewed

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