Update spacy pipeline to 3.5.2

Browse files

Files changed (14) hide show

README.md +24 -24
config.cfg +35 -38
hu_core_news_md-any-py3-none-any.whl +2 -2
meta.json +192 -197
morphologizer/model +1 -1
ner/model +1 -1
parser/model +1 -1
senter/model +1 -1
tagger/model +1 -1
tok2vec/model +1 -1
trainable_lemmatizer/cfg +4021 -0
trainable_lemmatizer/model +3 -0
trainable_lemmatizer/trees +0 -0
vocab/strings.json +2 -2

README.md CHANGED Viewed

@@ -14,55 +14,55 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.8572441922
     - name: NER Recall
       type: recall
-      value: 0.849859353
     - name: NER F Score
       type: f_score
-      value: 0.8535357994
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9624844483
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9631543688
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.928892717
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9728255669
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8127597439
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.743681905
   - task:
       name: SENTS
       type: token-classification
@@ -76,10 +76,10 @@ Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morpholog
 | Feature | Description |
 | --- | --- |
 | **Name** | `hu_core_news_md` |
-| **Version** | `3.5.1` |
 | **spaCy** | `>=3.5.0,<3.6.0` |
-| **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
-| **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
 | **Vectors** | -1 keys, 200000 unique vectors (100 dimensions) |
 | **Sources** | [UD Hungarian Szeged](https://universaldependencies.org/treebanks/hu_szeged/index.html) (Richárd Farkas, Katalin Simkó, Zsolt Szántó, Viktor Varga, Veronika Vincze (MTA-SZTE Research Group on Artificial Intelligence))<br />[NYTK-NerKor Corpus](https://github.com/nytud/NYTK-NerKor) (Eszter Simon, Noémi Vadász (Department of Language Technology and Applied Linguistics))<br />[hunNERwiki](http://hlt.sztaki.hu/resources/hunnerwiki.html) (Eszter Simon, Dávid Márk Nemeskey (HLT Group, Budapest University of Technology and Economics))<br />[Szeged NER Corpus](https://rgai.inf.u-szeged.hu/node/130) (György Szarvas, Richárd Farkas, László Felföldi, András Kocsor, János Csirik (MTA-SZTE Research Group on Artificial Intelligence)) |
 | **License** | `cc-by-sa-4.0` |
@@ -111,15 +111,15 @@ Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morpholog
 | `SENTS_P` | 98.21 |
 | `SENTS_R` | 97.55 |
 | `SENTS_F` | 97.88 |
-| `TAG_ACC` | 96.25 |
-| `POS_ACC` | 96.32 |
-| `MORPH_ACC` | 92.89 |
-| `MORPH_MICRO_P` | 96.49 |
-| `MORPH_MICRO_R` | 95.78 |
-| `MORPH_MICRO_F` | 96.14 |
-| `LEMMA_ACC` | 97.28 |
-| `DEP_UAS` | 81.28 |
-| `DEP_LAS` | 74.37 |
-| `ENTS_P` | 85.72 |
-| `ENTS_R` | 84.99 |
-| `ENTS_F` | 85.35 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.8557640751
     - name: NER Recall
       type: recall
+      value: 0.8417721519
     - name: NER F Score
       type: f_score
+      value: 0.8487104493
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9633953778
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.964398507
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.9338692698
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9724428284
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.7978436658
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.7223314055
   - task:
       name: SENTS
       type: token-classification
 | Feature | Description |
 | --- | --- |
 | **Name** | `hu_core_news_md` |
+| **Version** | `3.5.2` |
 | **spaCy** | `>=3.5.0,<3.6.0` |
+| **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `parser`, `ner` |
+| **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `parser`, `ner` |
 | **Vectors** | -1 keys, 200000 unique vectors (100 dimensions) |
 | **Sources** | [UD Hungarian Szeged](https://universaldependencies.org/treebanks/hu_szeged/index.html) (Richárd Farkas, Katalin Simkó, Zsolt Szántó, Viktor Varga, Veronika Vincze (MTA-SZTE Research Group on Artificial Intelligence))<br />[NYTK-NerKor Corpus](https://github.com/nytud/NYTK-NerKor) (Eszter Simon, Noémi Vadász (Department of Language Technology and Applied Linguistics))<br />[hunNERwiki](http://hlt.sztaki.hu/resources/hunnerwiki.html) (Eszter Simon, Dávid Márk Nemeskey (HLT Group, Budapest University of Technology and Economics))<br />[Szeged NER Corpus](https://rgai.inf.u-szeged.hu/node/130) (György Szarvas, Richárd Farkas, László Felföldi, András Kocsor, János Csirik (MTA-SZTE Research Group on Artificial Intelligence)) |
 | **License** | `cc-by-sa-4.0` |
 | `SENTS_P` | 98.21 |
 | `SENTS_R` | 97.55 |
 | `SENTS_F` | 97.88 |
+| `TAG_ACC` | 96.34 |
+| `POS_ACC` | 96.44 |
+| `MORPH_ACC` | 93.39 |
+| `MORPH_MICRO_P` | 96.76 |
+| `MORPH_MICRO_R` | 95.92 |
+| `MORPH_MICRO_F` | 96.34 |
+| `LEMMA_ACC` | 97.24 |
+| `DEP_UAS` | 79.78 |
+| `DEP_LAS` | 72.23 |
+| `ENTS_P` | 85.58 |
+| `ENTS_R` | 84.18 |
+| `ENTS_F` | 84.87 |

config.cfg CHANGED Viewed

@@ -1,8 +1,8 @@
 [paths]
-parser_model = "models/hu_core_news_md-parser-3.5.1/model-best"
-ner_model = "models/hu_core_news_md-ner-3.5.1/model-best"
-lemmatizer_lookups = "models/hu_core_news_md-lookup-lemmatizer-3.5.1"
-tagger_model = "models/hu_core_news_md-tagger-3.5.1/model-best"
 train = null
 dev = null
 vectors = null
@@ -14,7 +14,7 @@ gpu_allocator = null
 [nlp]
 lang = "hu"
-pipeline = ["tok2vec","senter","tagger","morphologizer","lookup_lemmatizer","lemmatizer","lemma_smoother","parser","ner"]
 tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
 disabled = []
 before_creation = null
@@ -24,39 +24,6 @@ batch_size = 1000
 [components]
-[components.lemma_smoother]
-factory = "hu.lemma_smoother"
-[components.lemmatizer]
-factory = "trainable_lemmatizer_v2"
-backoff = "orth"
-min_tree_freq = 1
-overwrite = false
-overwrite_labels = true
-scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
-top_k = 3
-[components.lemmatizer.model]
-@architectures = "spacy.Tagger.v1"
-nO = null
-[components.lemmatizer.model.tok2vec]
-@architectures = "spacy.Tok2Vec.v2"
-[components.lemmatizer.model.tok2vec.embed]
-@architectures = "spacy.MultiHashEmbed.v2"
-width = 100
-attrs = ["LOWER","PREFIX","SUFFIX","SHAPE"]
-rows = [5000,2500,2500,2500]
-include_static_vectors = true
-[components.lemmatizer.model.tok2vec.encode]
-@architectures = "spacy.MaxoutWindowEncoder.v2"
-width = 100
-depth = 4
-window_size = 2
-maxout_pieces = 5
 [components.lookup_lemmatizer]
 factory = "hu.lookup_lemmatizer"
 scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
@@ -184,6 +151,36 @@ depth = 4
 window_size = 2
 maxout_pieces = 5
 [corpora]
 [corpora.dev]

 [paths]
+parser_model = "models/hu_core_news_md-parser-3.5.2/model-best"
+ner_model = "models/hu_core_news_md-ner-3.5.2/model-best"
+lemmatizer_lookups = "models/hu_core_news_md-lookup-lemmatizer-3.5.2"
+tagger_model = "models/hu_core_news_md-tagger-3.5.2/model-best"
 train = null
 dev = null
 vectors = null
 [nlp]
 lang = "hu"
+pipeline = ["tok2vec","senter","tagger","morphologizer","lookup_lemmatizer","trainable_lemmatizer","parser","ner"]
 tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
 disabled = []
 before_creation = null
 [components]
 [components.lookup_lemmatizer]
 factory = "hu.lookup_lemmatizer"
 scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
 window_size = 2
 maxout_pieces = 5
+[components.trainable_lemmatizer]
+factory = "trainable_lemmatizer_v2"
+backoff = "orth"
+min_tree_freq = 1
+overwrite = false
+overwrite_labels = true
+scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
+top_k = 3
+[components.trainable_lemmatizer.model]
+@architectures = "spacy.Tagger.v1"
+nO = null
+[components.trainable_lemmatizer.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+[components.trainable_lemmatizer.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 100
+attrs = ["LOWER","PREFIX","SUFFIX","SHAPE"]
+rows = [5000,2500,2500,2500]
+include_static_vectors = true
+[components.trainable_lemmatizer.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 100
+depth = 4
+window_size = 2
+maxout_pieces = 5
 [corpora]
 [corpora.dev]

hu_core_news_md-any-py3-none-any.whl CHANGED Viewed

@@ -1,3 +1,3 @@
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meta.json CHANGED Viewed

@@ -1,7 +1,7 @@
 {
   "lang":"hu",
   "name":"core_news_md",
-  "version":"3.5.1",
   "description":"Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner",
   "author":"SzegedAI, MILAB",
   "email":"gyorgy@orosz.link",
@@ -1187,9 +1187,6 @@
     ],
     "lookup_lemmatizer":[
-    ],
-    "lemma_smoother":[
     ],
     "parser":[
       "ROOT",
@@ -1249,8 +1246,7 @@
     "tagger",
     "morphologizer",
     "lookup_lemmatizer",
-    "lemmatizer",
-    "lemma_smoother",
     "parser",
     "ner"
   ],
@@ -1260,8 +1256,7 @@
     "tagger",
     "morphologizer",
     "lookup_lemmatizer",
-    "lemmatizer",
-    "lemma_smoother",
     "parser",
     "ner"
   ],
@@ -1276,87 +1271,82 @@
     "sents_p":0.9820627803,
     "sents_r":0.9755011136,
     "sents_f":0.9787709497,
-    "tag_acc":0.9624844483,
-    "pos_acc":0.9631543688,
-    "morph_acc":0.928892717,
-    "morph_micro_p":0.9648917749,
-    "morph_micro_r":0.9578427159,
-    "morph_micro_f":0.9613543239,
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@@ -1367,116 +1357,121 @@
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-    "lemma_acc":0.9728255669,
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@@ -1489,74 +1484,74 @@
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@@ -1564,9 +1559,9 @@
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   "sources":[
     {

 {
   "lang":"hu",
   "name":"core_news_md",
+  "version":"3.5.2",
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