Update spaCy pipeline

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Files changed (14) hide show

README.md +28 -28
accuracy.json +188 -188
config.cfg +5 -4
hr_core_news_md-any-py3-none-any.whl +2 -2
lemmatizer/model +0 -0
meta.json +191 -191
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: 0.8247048138
     - name: NER Recall
       type: recall
-      value: 0.8107142857
     - name: NER F Score
       type: f_score
-      value: 0.8176497073
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9161831256
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9730540586
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.9225301272
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9279823473
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8633758668
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.8006200962
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9494584838
 ---
 ### Details: https://spacy.io/models/hr#hr_core_news_md
@@ -78,8 +78,8 @@ Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
 | Feature | Description |
 | --- | --- |
 | **Name** | `hr_core_news_md` |
-| **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 (50000, 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` | 91.62 |
-| `POS_ACC` | 97.31 |
-| `MORPH_ACC` | 92.25 |
-| `MORPH_MICRO_P` | 95.94 |
-| `MORPH_MICRO_R` | 95.52 |
-| `MORPH_MICRO_F` | 95.73 |
-| `SENTS_P` | 95.29 |
-| `SENTS_R` | 94.60 |
-| `SENTS_F` | 94.95 |
-| `DEP_UAS` | 86.34 |
-| `DEP_LAS` | 80.06 |
-| `LEMMA_ACC` | 92.80 |
-| `ENTS_P` | 82.47 |
-| `ENTS_R` | 81.07 |
-| `ENTS_F` | 81.76 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.8244343891
     - name: NER Recall
       type: recall
+      value: 0.8133928571
     - name: NER F Score
       type: f_score
+      value: 0.8188764045
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9168748959
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9732718382
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.9231452688
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9281478417
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.8645157205
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.800506249
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.9425287356
 ---
 ### Details: https://spacy.io/models/hr#hr_core_news_md
 | Feature | Description |
 | --- | --- |
 | **Name** | `hr_core_news_md` |
+| **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 (50000, 300) |
 | Type | Score |
 | --- | --- |
+| `TOKEN_ACC` | 99.89 |
 | `TOKEN_P` | 97.28 |
 | `TOKEN_R` | 98.71 |
 | `TOKEN_F` | 97.99 |
+| `TAG_ACC` | 91.69 |
+| `POS_ACC` | 97.33 |
+| `MORPH_ACC` | 92.31 |
+| `MORPH_MICRO_P` | 95.98 |
+| `MORPH_MICRO_R` | 95.56 |
+| `MORPH_MICRO_F` | 95.77 |
+| `SENTS_P` | 95.12 |
+| `SENTS_R` | 93.41 |
+| `SENTS_F` | 94.25 |
+| `DEP_UAS` | 86.45 |
+| `DEP_LAS` | 80.05 |
+| `LEMMA_ACC` | 92.81 |
+| `ENTS_P` | 82.44 |
+| `ENTS_R` | 81.34 |
+| `ENTS_F` | 81.89 |

accuracy.json CHANGED Viewed

@@ -1,266 +1,261 @@
 {
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   "token_r": 0.9870598689,
   "token_f": 0.9798765024,
-  "tag_acc": 0.9161831256,
-  "pos_acc": 0.9730540586,
-  "morph_acc": 0.9225301272,
-  "morph_micro_p": 0.9593737572,
-  "morph_micro_r": 0.95517536,
-  "morph_micro_f": 0.9572699553,
   "morph_per_feat": {
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@@ -268,19 +263,24 @@
       "f": 0.0
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@@ -303,36 +303,36 @@
       "f": 0.0
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-  "speed": 9126.0180589508
 }

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 [components.ner.model.tok2vec.embed]
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vocab/strings.json CHANGED Viewed

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