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 @@
  "f": 0.0
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  "fixed": {
- "p": 0.4736842105,
- "r": 0.5094339623,
- "f": 0.4909090909
- },
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@@ -303,36 +303,36 @@
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- "speed": 8876.6554314359
 }

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

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