Update spaCy pipeline

Browse files

Files changed (14) hide show

README.md +28 -28
accuracy.json +182 -182
config.cfg +5 -4
hr_core_news_sm-any-py3-none-any.whl +2 -2
lemmatizer/model +0 -0
meta.json +185 -185
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: 0.763016158
     - name: NER Recall
       type: recall
-      value: 0.7589285714
     - name: NER F Score
       type: f_score
-      value: 0.7609668756
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9010995113
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9664962774
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.9082224876
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9187698249
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8468673822
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.7749240891
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9411764706
 ---
 ### Details: https://spacy.io/models/hr#hr_core_news_sm
@@ -78,8 +78,8 @@ Croatian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
 | Feature | Description |
 | --- | --- |
 | **Name** | `hr_core_news_sm` |
-| **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** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -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` | 90.11 |
-| `POS_ACC` | 96.65 |
-| `MORPH_ACC` | 90.82 |
-| `MORPH_MICRO_P` | 94.83 |
-| `MORPH_MICRO_R` | 94.32 |
-| `MORPH_MICRO_F` | 94.57 |
-| `SENTS_P` | 94.23 |
-| `SENTS_R` | 94.00 |
-| `SENTS_F` | 94.12 |
-| `DEP_UAS` | 84.69 |
-| `DEP_LAS` | 77.49 |
-| `LEMMA_ACC` | 91.88 |
-| `ENTS_P` | 76.30 |
-| `ENTS_R` | 75.89 |
-| `ENTS_F` | 76.10 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.7738095238
     - name: NER Recall
       type: recall
+      value: 0.7544642857
     - name: NER F Score
       type: f_score
+      value: 0.7640144665
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9002581684
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9673834528
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.9062135599
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9194318025
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.8446878561
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.776466867
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.9483793517
 ---
 ### Details: https://spacy.io/models/hr#hr_core_news_sm
 | Feature | Description |
 | --- | --- |
 | **Name** | `hr_core_news_sm` |
+| **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** | 0 keys, 0 unique vectors (0 dimensions) |
 | Type | Score |
 | --- | --- |
+| `TOKEN_ACC` | 99.89 |
 | `TOKEN_P` | 97.28 |
 | `TOKEN_R` | 98.71 |
 | `TOKEN_F` | 97.99 |
+| `TAG_ACC` | 90.03 |
+| `POS_ACC` | 96.74 |
+| `MORPH_ACC` | 90.62 |
+| `MORPH_MICRO_P` | 94.88 |
+| `MORPH_MICRO_R` | 94.34 |
+| `MORPH_MICRO_F` | 94.61 |
+| `SENTS_P` | 94.95 |
+| `SENTS_R` | 94.72 |
+| `SENTS_F` | 94.84 |
+| `DEP_UAS` | 84.47 |
+| `DEP_LAS` | 77.65 |
+| `LEMMA_ACC` | 91.94 |
+| `ENTS_P` | 77.38 |
+| `ENTS_R` | 75.45 |
+| `ENTS_F` | 76.40 |

accuracy.json CHANGED Viewed

@@ -1,261 +1,261 @@
 {
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   "token_r": 0.9870598689,
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-  "tag_acc": 0.9010995113,
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-  "morph_acc": 0.9082224876,
-  "morph_micro_p": 0.9482693597,
-  "morph_micro_r": 0.9431512701,
-  "morph_micro_f": 0.9457033903,
   "morph_per_feat": {
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     "compound": {
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@@ -263,9 +263,9 @@
       "f": 0.0
     },
     "fixed": {
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@@ -273,14 +273,14 @@
       "f": 0.0
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     "expl": {
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@@ -303,36 +303,36 @@
       "f": 0.0
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   },
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-  "speed": 8900.8287549458
 }

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config.cfg CHANGED Viewed

@@ -81,8 +81,8 @@ nO = null
 [components.ner.model.tok2vec.embed]
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 width = 96
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 [components.tok2vec.model.embed]
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 before_to_disk = null
 annotating_components = []
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 @batchers = "spacy.batch_by_words.v1"

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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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