Update spaCy pipeline

Browse files

Files changed (14) hide show

README.md +25 -25
accuracy.json +187 -192
config.cfg +5 -4
lemmatizer/model +1 -1
meta.json +190 -195
morphologizer/model +1 -1
ner/model +2 -2
parser/model +1 -1
senter/model +1 -1
sv_core_news_lg-any-py3-none-any.whl +2 -2
tagger/model +1 -1
tok2vec/model +2 -2
tokenizer +1 -1
vocab/strings.json +2 -2

README.md CHANGED Viewed

@@ -14,20 +14,20 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.8592572659
     - name: NER Recall
       type: recall
-      value: 0.7768856448
     - name: NER F Score
       type: f_score
-      value: 0.8159979555
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9490660406
   - task:
       name: POS
       type: token-classification
@@ -41,35 +41,35 @@ model-index:
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.9566193733
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.95447586
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8286636101
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.7788532292
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9269717624
 ---
 ### Details: https://spacy.io/models/sv#sv_core_news_lg
@@ -78,8 +78,8 @@ Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
 | Feature | Description |
 | --- | --- |
 | **Name** | `sv_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) |
@@ -110,18 +110,18 @@ Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer,
 | `TOKEN_P` | 99.95 |
 | `TOKEN_R` | 99.96 |
 | `TOKEN_F` | 99.95 |
-| `TAG_ACC` | 94.91 |
 | `POS_ACC` | 96.38 |
-| `MORPH_ACC` | 95.66 |
-| `MORPH_MICRO_P` | 97.58 |
-| `MORPH_MICRO_R` | 97.31 |
-| `MORPH_MICRO_F` | 97.45 |
-| `SENTS_P` | 91.01 |
-| `SENTS_R` | 94.44 |
-| `SENTS_F` | 92.70 |
-| `DEP_UAS` | 82.87 |
-| `DEP_LAS` | 77.89 |
-| `LEMMA_ACC` | 95.45 |
-| `ENTS_P` | 85.93 |
-| `ENTS_R` | 77.69 |
-| `ENTS_F` | 81.60 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.8602032409
     - name: NER Recall
       type: recall
+      value: 0.7620437956
     - name: NER F Score
       type: f_score
+      value: 0.8081537866
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9509033378
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.9584566704
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9555986526
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.8362874455
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.7879268362
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.9364613881
 ---
 ### Details: https://spacy.io/models/sv#sv_core_news_lg
 | Feature | Description |
 | --- | --- |
 | **Name** | `sv_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) |
 | `TOKEN_P` | 99.95 |
 | `TOKEN_R` | 99.96 |
 | `TOKEN_F` | 99.95 |
+| `TAG_ACC` | 95.09 |
 | `POS_ACC` | 96.38 |
+| `MORPH_ACC` | 95.85 |
+| `MORPH_MICRO_P` | 97.77 |
+| `MORPH_MICRO_R` | 97.39 |
+| `MORPH_MICRO_F` | 97.58 |
+| `SENTS_P` | 92.29 |
+| `SENTS_R` | 95.04 |
+| `SENTS_F` | 93.65 |
+| `DEP_UAS` | 83.63 |
+| `DEP_LAS` | 78.79 |
+| `LEMMA_ACC` | 95.56 |
+| `ENTS_P` | 86.02 |
+| `ENTS_R` | 76.20 |
+| `ENTS_F` | 80.82 |

accuracy.json CHANGED Viewed

@@ -1,271 +1,266 @@
 {
-  "token_acc": 0.9999489666,
   "token_p": 0.9994896918,
   "token_r": 0.9995917117,
   "token_f": 0.9995406992,
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   "pos_acc": 0.9637644177,
-  "morph_acc": 0.9566193733,
-  "morph_micro_p": 0.9758341895,
-  "morph_micro_r": 0.9730957684,
-  "morph_micro_f": 0.9744630551,
   "morph_per_feat": {
     "Case": {
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     },
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-      "f": 0.5744680851
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-    "dep": {
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     "ccomp": {
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@@ -288,51 +283,51 @@
       "f": 0.0
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   },
-  "lemma_acc": 0.95447586,
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   },
-  "speed": 9402.4258502395
 }

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