Update spaCy pipeline

Browse files

Files changed (12) hide show

README.md +27 -27
accuracy.json +166 -166
attribute_ruler/patterns +0 -0
config.cfg +5 -4
es_core_news_md-any-py3-none-any.whl +2 -2
meta.json +169 -169
morphologizer/model +1 -1
ner/model +2 -2
parser/model +1 -1
senter/model +1 -1
tok2vec/model +2 -2
tokenizer +1 -1

README.md CHANGED Viewed

@@ -14,62 +14,62 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.8941678521
     - name: NER Recall
       type: recall
-      value: 0.8955509423
     - name: NER F Score
       type: f_score
-      value: 0.8948588628
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9604424465
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9841558915
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.9797944943
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9643011165
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.9137247211
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.8824529289
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9721724219
 ---
 ### Details: https://spacy.io/models/es#es_core_news_md
@@ -78,8 +78,8 @@ Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser,
 | Feature | Description |
 | --- | --- |
 | **Name** | `es_core_news_md` |
-| **Version** | `3.4.0` |
-| **spaCy** | `>=3.4.0,<3.5.0` |
 | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
 | **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
 | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
@@ -109,18 +109,18 @@ Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser,
 | `TOKEN_P` | 99.89 |
 | `TOKEN_R` | 99.95 |
 | `TOKEN_F` | 99.92 |
-| `POS_ACC` | 98.42 |
-| `MORPH_ACC` | 97.98 |
-| `MORPH_MICRO_P` | 99.44 |
-| `MORPH_MICRO_R` | 98.81 |
-| `MORPH_MICRO_F` | 99.12 |
-| `SENTS_P` | 96.28 |
-| `SENTS_R` | 98.17 |
-| `SENTS_F` | 97.22 |
-| `DEP_UAS` | 91.37 |
-| `DEP_LAS` | 88.25 |
-| `TAG_ACC` | 96.04 |
-| `LEMMA_ACC` | 96.43 |
-| `ENTS_P` | 89.42 |
-| `ENTS_R` | 89.56 |
-| `ENTS_F` | 89.49 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.8925237438
     - name: NER Recall
       type: recall
+      value: 0.8951031872
     - name: NER F Score
       type: f_score
+      value: 0.8938116045
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9610850187
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9848174063
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.9802342746
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9645886243
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.9125758682
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.8800383563
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.9880453102
 ---
 ### Details: https://spacy.io/models/es#es_core_news_md
 | Feature | Description |
 | --- | --- |
 | **Name** | `es_core_news_md` |
+| **Version** | `3.5.0` |
+| **spaCy** | `>=3.5.0,<3.6.0` |
 | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
 | **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
 | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
 | `TOKEN_P` | 99.89 |
 | `TOKEN_R` | 99.95 |
 | `TOKEN_F` | 99.92 |
+| `POS_ACC` | 98.48 |
+| `MORPH_ACC` | 98.02 |
+| `MORPH_MICRO_P` | 99.43 |
+| `MORPH_MICRO_R` | 98.85 |
+| `MORPH_MICRO_F` | 99.14 |
+| `SENTS_P` | 98.40 |
+| `SENTS_R` | 99.21 |
+| `SENTS_F` | 98.80 |
+| `DEP_UAS` | 91.26 |
+| `DEP_LAS` | 88.00 |
+| `TAG_ACC` | 96.11 |
+| `LEMMA_ACC` | 96.46 |
+| `ENTS_P` | 89.25 |
+| `ENTS_R` | 89.51 |
+| `ENTS_F` | 89.38 |

accuracy.json CHANGED Viewed

@@ -1,33 +1,33 @@
 {
-  "token_acc": 0.9999808396,
   "token_p": 0.9989078786,
   "token_r": 0.9995398685,
   "token_f": 0.9992237736,
-  "pos_acc": 0.9841558915,
-  "morph_acc": 0.9797944943,
-  "morph_micro_p": 0.9943683537,
-  "morph_micro_r": 0.9881093812,
-  "morph_micro_f": 0.9912289872,
   "morph_per_feat": {
     "Definite": {
-      "p": 0.998716486,
-      "r": 0.9990014265,
-      "f": 0.998858936
     },
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-      "r": 0.9866472697,
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-      "r": 0.9931232092,
-      "f": 0.9952620244
     },
     "PunctType": {
       "p": 0.9995216074,
@@ -35,39 +35,39 @@
       "f": 0.9993622449
     },
     "VerbForm": {
-      "p": 0.9905985584,
-      "r": 0.9878125,
-      "f": 0.9892035675
     },
     "Mood": {
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-      "r": 0.9753914989,
-      "f": 0.9781877103
     },
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       "r": 0.9846823325,
-      "f": 0.9879497031
     },
     "Tense": {
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-      "r": 0.9792616226,
-      "f": 0.9811622331
     },
     "NumForm": {
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-      "r": 0.9724473258,
-      "f": 0.9795918367
     },
     "NumType": {
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-      "r": 0.9629941127,
-      "f": 0.9728122345
     },
     "Poss": {
       "p": 1.0,
-      "r": 0.9981718464,
-      "f": 0.9990850869
     },
     "PunctSide": {
       "p": 1.0,
@@ -75,24 +75,24 @@
       "f": 0.998998999
     },
     "Case": {
-      "p": 0.9399477807,
-      "r": 0.9342560554,
-      "f": 0.9370932755
     },
     "PrepCase": {
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-      "r": 0.9855072464,
-      "f": 0.9921834289
     },
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-      "r": 0.9919614148,
-      "f": 0.9927594529
     },
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-      "r": 0.9537366548,
-      "f": 0.9745454545
     },
     "Polarity": {
       "p": 1.0,
@@ -100,9 +100,9 @@
       "f": 0.996742671
     },
     "AdvType": {
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-      "r": 0.9255319149,
-      "f": 0.9380053908
     },
     "Number[psor]": {
       "p": 1.0,
@@ -115,167 +115,167 @@
       "f": 1.0
     }
   },
-  "sents_p": 0.9627914158,
-  "sents_r": 0.9817380353,
-  "sents_f": 0.9721724219,
-  "dep_uas": 0.9137247211,
-  "dep_las": 0.8824529289,
   "dep_las_per_type": {
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-    },
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     "parataxis": {
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@@ -292,32 +292,32 @@
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   },
-  "speed": 10222.9086606079
 }

 {
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 before_to_disk = null
 annotating_components = []
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 [training.batcher]
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