Update spaCy pipeline

Browse files

Files changed (14) hide show

README.md +28 -28
accuracy.json +98 -98
config.cfg +5 -4
lemmatizer/model +1 -1
lt_core_news_sm-any-py3-none-any.whl +2 -2
meta.json +101 -101
morphologizer/model +1 -1
ner/model +2 -2
parser/model +1 -1
senter/model +1 -1
tagger/model +1 -1
tok2vec/model +2 -2
tokenizer +1 -1
vocab/strings.json +2 -2

README.md CHANGED Viewed

@@ -14,62 +14,62 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.7138927098
     - name: NER Recall
       type: recall
-      value: 0.7788894447
     - name: NER F Score
       type: f_score
-      value: 0.7449760766
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.8234632522
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9061729463
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.8351790967
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.8414133114
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.6821852732
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.589179203
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.7898089172
 ---
 ### Details: https://spacy.io/models/lt#lt_core_news_sm
@@ -78,8 +78,8 @@ Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagge
 | Feature | Description |
 | --- | --- |
 | **Name** | `lt_core_news_sm` |
-| **Version** | `3.4.0` |
-| **spaCy** | `>=3.4.0,<3.5.0` |
 | **Default Pipeline** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
 | **Components** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
 | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
@@ -106,22 +106,22 @@ Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagge
 | Type | Score |
 | --- | --- |
-| `TOKEN_ACC` | 99.99 |
 | `TOKEN_P` | 99.84 |
 | `TOKEN_R` | 99.89 |
 | `TOKEN_F` | 99.86 |
-| `POS_ACC` | 90.62 |
-| `MORPH_ACC` | 83.52 |
-| `MORPH_MICRO_P` | 87.74 |
-| `MORPH_MICRO_R` | 86.39 |
-| `MORPH_MICRO_F` | 87.06 |
-| `TAG_ACC` | 82.35 |
-| `SENTS_P` | 77.62 |
-| `SENTS_R` | 80.39 |
-| `SENTS_F` | 78.98 |
-| `DEP_UAS` | 68.22 |
-| `DEP_LAS` | 58.92 |
-| `LEMMA_ACC` | 84.14 |
-| `ENTS_P` | 71.39 |
-| `ENTS_R` | 77.89 |
-| `ENTS_F` | 74.50 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.7163836622
     - name: NER Recall
       type: recall
+      value: 0.7808904452
     - name: NER F Score
       type: f_score
+      value: 0.7472474868
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.8210407925
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9029718389
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.8305935283
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.8348397699
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.6802828795
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.5857519789
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.8089171975
 ---
 ### Details: https://spacy.io/models/lt#lt_core_news_sm
 | Feature | Description |
 | --- | --- |
 | **Name** | `lt_core_news_sm` |
+| **Version** | `3.5.0` |
+| **spaCy** | `>=3.5.0,<3.6.0` |
 | **Default Pipeline** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
 | **Components** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
 | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
 | Type | Score |
 | --- | --- |
+| `TOKEN_ACC` | 99.97 |
 | `TOKEN_P` | 99.84 |
 | `TOKEN_R` | 99.89 |
 | `TOKEN_F` | 99.86 |
+| `POS_ACC` | 90.30 |
+| `MORPH_ACC` | 83.06 |
+| `MORPH_MICRO_P` | 87.17 |
+| `MORPH_MICRO_R` | 85.77 |
+| `MORPH_MICRO_F` | 86.46 |
+| `TAG_ACC` | 82.10 |
+| `SENTS_P` | 79.50 |
+| `SENTS_R` | 82.33 |
+| `SENTS_F` | 80.89 |
+| `DEP_UAS` | 68.03 |
+| `DEP_LAS` | 58.58 |
+| `LEMMA_ACC` | 83.48 |
+| `ENTS_P` | 71.64 |
+| `ENTS_R` | 78.09 |
+| `ENTS_F` | 74.72 |

accuracy.json CHANGED Viewed

@@ -1,157 +1,157 @@
 {
-  "token_acc": 0.9998702927,
   "token_p": 0.998357254,
   "token_r": 0.9988754325,
   "token_f": 0.9986162761,
-  "pos_acc": 0.9061729463,
-  "morph_acc": 0.8351790967,
-  "morph_micro_p": 0.8773594069,
-  "morph_micro_r": 0.8639058914,
-  "morph_micro_f": 0.8705806763,
   "morph_per_feat": {
     "Case": {
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-      "r": 0.9013438217,
-      "f": 0.8968609865
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     "Definite": {
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     "Degree": {
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     "Polarity": {
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     "VerbForm": {
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     },
     "Voice": {
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     "PronType": {
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     "Aspect": {
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     "Hyph": {
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     "Foreign": {
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-      "r": 0.1875,
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     }
   },
-  "tag_acc": 0.8234632522,
-  "sents_p": 0.7762128326,
-  "sents_r": 0.8038897893,
-  "sents_f": 0.7898089172,
-  "dep_uas": 0.6821852732,
-  "dep_las": 0.589179203,
   "dep_las_per_type": {},
-  "lemma_acc": 0.8414133114,
-  "ents_p": 0.7138927098,
-  "ents_r": 0.7788894447,
-  "ents_f": 0.7449760766,
   "ents_per_type": {
     "ORG": {
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     "GPE": {
-      "p": 0.75,
-      "r": 0.9555555556,
-      "f": 0.8403908795
     }
   },
-  "speed": 11636.1364727645
 }

 {
+  "token_acc": 0.9997406191,
   "token_p": 0.998357254,
   "token_r": 0.9988754325,
   "token_f": 0.9986162761,
+  "pos_acc": 0.9029718389,
+  "morph_acc": 0.8305935283,
+  "morph_micro_p": 0.8717234229,
+  "morph_micro_r": 0.857689365,
+  "morph_micro_f": 0.8646494512,
   "morph_per_feat": {
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     "Gender": {
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     "Foreign": {
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   },
+  "speed": 11368.988225792
 }

config.cfg CHANGED Viewed

@@ -81,8 +81,8 @@ nO = null
 [components.ner.model.tok2vec.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = 96
-attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
-rows = [5000,1000,2500,2500,50]
 include_static_vectors = false
 [components.ner.model.tok2vec.encode]
@@ -166,8 +166,8 @@ factory = "tok2vec"
 [components.tok2vec.model.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = ${components.tok2vec.model.encode:width}
-attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
-rows = [5000,1000,2500,2500,50]
 include_static_vectors = false
 [components.tok2vec.model.encode]
@@ -209,6 +209,7 @@ eval_frequency = 1000
 frozen_components = []
 before_to_disk = null
 annotating_components = []
 [training.batcher]
 @batchers = "spacy.batch_by_words.v1"

 [components.ner.model.tok2vec.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = 96
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
+rows = [5000,1000,2500,2500]
 include_static_vectors = false
 [components.ner.model.tok2vec.encode]
 [components.tok2vec.model.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = ${components.tok2vec.model.encode:width}
+attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY","IS_SPACE"]
+rows = [5000,1000,2500,2500,50,50]
 include_static_vectors = false
 [components.tok2vec.model.encode]
 frozen_components = []
 before_to_disk = null
 annotating_components = []
+before_update = null
 [training.batcher]
 @batchers = "spacy.batch_by_words.v1"

lemmatizer/model CHANGED Viewed

@@ -1,3 +1,3 @@
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lt_core_news_sm-any-py3-none-any.whl CHANGED Viewed

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meta.json CHANGED Viewed

@@ -1,14 +1,14 @@
 {
   "lang":"lt",
   "name":"core_news_sm",
-  "version":"3.4.0",
   "description":"Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.",
   "author":"Explosion",
   "email":"[email protected]",
   "url":"https://explosion.ai",
   "license":"CC BY-SA 4.0",
-  "spacy_version":">=3.4.0,<3.5.0",
-  "spacy_git_version":"dd038b536",
   "vectors":{
     "width":0,
     "vectors":0,
@@ -1723,163 +1723,163 @@
     "senter"
   ],
   "performance":{
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     "token_p":0.998357254,
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   "author":"Explosion",
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