Update spaCy pipeline

Browse files

Files changed (14) hide show

README.md +27 -27
accuracy.json +95 -95
config.cfg +5 -4
lemmatizer/model +1 -1
lt_core_news_md-any-py3-none-any.whl +2 -2
meta.json +98 -98
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: 0.7452830189
     - name: NER Recall
       type: recall
-      value: 0.8299149575
     - name: NER F Score
       type: f_score
-      value: 0.7853254438
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.8592067131
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9480968858
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.8679326902
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.8527440211
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.7355533274
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.6620580475
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.8243785084
 ---
 ### Details: https://spacy.io/models/lt#lt_core_news_md
@@ -78,8 +78,8 @@ Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagge
 | Feature | Description |
 | --- | --- |
 | **Name** | `lt_core_news_md` |
-| **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** | 500000 keys, 20000 unique vectors (300 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` | 94.81 |
-| `MORPH_ACC` | 86.79 |
-| `MORPH_MICRO_P` | 91.38 |
-| `MORPH_MICRO_R` | 90.49 |
 | `MORPH_MICRO_F` | 90.93 |
-| `TAG_ACC` | 85.92 |
-| `SENTS_P` | 81.59 |
-| `SENTS_R` | 83.31 |
-| `SENTS_F` | 82.44 |
-| `DEP_UAS` | 73.56 |
-| `DEP_LAS` | 66.21 |
-| `LEMMA_ACC` | 85.27 |
-| `ENTS_P` | 74.53 |
-| `ENTS_R` | 82.99 |
-| `ENTS_F` | 78.53 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.7436594203
     - name: NER Recall
       type: recall
+      value: 0.8214107054
     - name: NER F Score
       type: f_score
+      value: 0.7806037556
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.8587619501
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9465761128
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.8683652723
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.8558578039
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.7410926366
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.6668074101
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.8121990369
 ---
 ### Details: https://spacy.io/models/lt#lt_core_news_md
 | Feature | Description |
 | --- | --- |
 | **Name** | `lt_core_news_md` |
+| **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** | 500000 keys, 20000 unique vectors (300 dimensions) |
 | Type | Score |
 | --- | --- |
+| `TOKEN_ACC` | 99.97 |
 | `TOKEN_P` | 99.84 |
 | `TOKEN_R` | 99.89 |
 | `TOKEN_F` | 99.86 |
+| `POS_ACC` | 94.66 |
+| `MORPH_ACC` | 86.84 |
+| `MORPH_MICRO_P` | 91.25 |
+| `MORPH_MICRO_R` | 90.60 |
 | `MORPH_MICRO_F` | 90.93 |
+| `TAG_ACC` | 85.88 |
+| `SENTS_P` | 80.45 |
+| `SENTS_R` | 82.01 |
+| `SENTS_F` | 81.22 |
+| `DEP_UAS` | 74.11 |
+| `DEP_LAS` | 66.68 |
+| `LEMMA_ACC` | 85.59 |
+| `ENTS_P` | 74.37 |
+| `ENTS_R` | 82.14 |
+| `ENTS_F` | 78.06 |

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": 0.9480968858,
-  "morph_acc": 0.8679326902,
-  "morph_micro_p": 0.9138175262,
-  "morph_micro_r": 0.9049030859,
-  "morph_micro_f": 0.9093384591,
   "morph_per_feat": {
     "Case": {
-      "p": 0.9220990874,
-      "r": 0.9272369715,
-      "f": 0.9246608923
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     "Degree": {
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     "Polarity": {
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     "Aspect": {
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-  "tag_acc": 0.8592067131,
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-  "dep_uas": 0.7355533274,
-  "dep_las": 0.6620580475,
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-  "lemma_acc": 0.8527440211,
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-      "r": 0.9679012346,
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   },
-  "speed": 10668.5945482026
 }

 {
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   "token_p": 0.998357254,
   "token_r": 0.9988754325,
   "token_f": 0.9986162761,
+  "pos_acc": 0.9465761128,
+  "morph_acc": 0.8683652723,
+  "morph_micro_p": 0.9125060202,
+  "morph_micro_r": 0.9060188727,
+  "morph_micro_f": 0.9092508758,
   "morph_per_feat": {
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   },
+  "speed": 10971.329748042
 }

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 = true
 [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 = true
 [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 = true
 [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 = true
 [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_md-any-py3-none-any.whl CHANGED Viewed

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meta.json CHANGED Viewed

@@ -1,14 +1,14 @@
 {
   "lang":"lt",
   "name":"core_news_md",
-  "version":"3.4.0",
   "description":"Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.",
   "author":"Explosion",
   "email":"contact@explosion.ai",
   "url":"https://explosion.ai",
   "license":"CC BY-SA 4.0",
-  "spacy_version":">=3.4.0,<3.5.0",
-  "spacy_git_version":"dd038b536",
   "vectors":{
     "width":300,
     "vectors":20000,
@@ -1723,163 +1723,163 @@
     "senter"
   ],
   "performance":{
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