Update spacy pipeline to 3.5.2

Browse files

Files changed (14) hide show

README.md +28 -28
config.cfg +35 -38
hu_core_news_lg-any-py3-none-any.whl +2 -2
meta.json +183 -188
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

@@ -14,72 +14,72 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.861328125
     - name: NER Recall
       type: recall
-      value: 0.8528481013
     - name: NER F Score
       type: f_score
-      value: 0.8570671378
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9680845973
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9686587875
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.9363127422
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9747392594
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8158633861
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.7489046175
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.983277592
 ---
 Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
 | Feature | Description |
 | --- | --- |
 | **Name** | `hu_core_news_lg` |
-| **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 (300 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))<br />[Webcorpuswiki word2vec model](https://github.com/oroszgy/hunlp-resources/releases/tag/webcorpuswiki_word2vec_v0.1) (György Orosz) |
 | **License** | `cc-by-sa-4.0` |
@@ -108,18 +108,18 @@ Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morpholog
 | `TOKEN_P` | 99.86 |
 | `TOKEN_R` | 99.93 |
 | `TOKEN_F` | 99.89 |
-| `SENTS_P` | 98.44 |
-| `SENTS_R` | 98.22 |
-| `SENTS_F` | 98.33 |
-| `TAG_ACC` | 96.81 |
-| `POS_ACC` | 96.87 |
-| `MORPH_ACC` | 93.63 |
-| `MORPH_MICRO_P` | 96.93 |
-| `MORPH_MICRO_R` | 96.36 |
-| `MORPH_MICRO_F` | 96.65 |
-| `LEMMA_ACC` | 97.47 |
-| `DEP_UAS` | 81.59 |
-| `DEP_LAS` | 74.89 |
-| `ENTS_P` | 86.13 |
-| `ENTS_R` | 85.28 |
-| `ENTS_F` | 85.71 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.8625558534
     - name: NER Recall
       type: recall
+      value: 0.8484528833
     - name: NER F Score
       type: f_score
+      value: 0.8554462466
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9631543688
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9651641305
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.9281270935
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9750263133
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.8058213942
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.7387315968
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.9753914989
 ---
 Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
 | Feature | Description |
 | --- | --- |
 | **Name** | `hu_core_news_lg` |
+| **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 (300 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))<br />[Webcorpuswiki word2vec model](https://github.com/oroszgy/hunlp-resources/releases/tag/webcorpuswiki_word2vec_v0.1) (György Orosz) |
 | **License** | `cc-by-sa-4.0` |
 | `TOKEN_P` | 99.86 |
 | `TOKEN_R` | 99.93 |
 | `TOKEN_F` | 99.89 |
+| `SENTS_P` | 97.98 |
+| `SENTS_R` | 97.10 |
+| `SENTS_F` | 97.54 |
+| `TAG_ACC` | 96.32 |
+| `POS_ACC` | 96.52 |
+| `MORPH_ACC` | 92.81 |
+| `MORPH_MICRO_P` | 96.62 |
+| `MORPH_MICRO_R` | 95.86 |
+| `MORPH_MICRO_F` | 96.24 |
+| `LEMMA_ACC` | 97.50 |
+| `DEP_UAS` | 80.58 |
+| `DEP_LAS` | 73.87 |
+| `ENTS_P` | 86.26 |
+| `ENTS_R` | 84.85 |
+| `ENTS_F` | 85.54 |

config.cfg CHANGED Viewed

@@ -1,8 +1,8 @@
 [paths]
-parser_model = "models/hu_core_news_lg-parser-3.5.1/model-best"
-ner_model = "models/hu_core_news_lg-ner-3.5.1/model-best"
-lemmatizer_lookups = "models/hu_core_news_lg-lookup-lemmatizer-3.5.1"
-tagger_model = "models/hu_core_news_lg-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 = 300
-attrs = ["LOWER","PREFIX","SUFFIX","SHAPE"]
-rows = [5000,2500,2500,2500]
-include_static_vectors = true
-[components.lemmatizer.model.tok2vec.encode]
-@architectures = "spacy.MaxoutWindowEncoder.v2"
-width = 300
-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_lg-parser-3.5.2/model-best"
+ner_model = "models/hu_core_news_lg-ner-3.5.2/model-best"
+lemmatizer_lookups = "models/hu_core_news_lg-lookup-lemmatizer-3.5.2"
+tagger_model = "models/hu_core_news_lg-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 = 300
+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 = 300
+depth = 4
+window_size = 2
+maxout_pieces = 5
 [corpora]
 [corpora.dev]

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

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meta.json CHANGED Viewed

@@ -1,7 +1,7 @@
 {
   "lang":"hu",
   "name":"core_news_lg",
-  "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"
   ],
@@ -1273,210 +1268,210 @@
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-    "sents_p":0.984375,
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-    "tag_acc":0.9680845973,
-    "pos_acc":0.9686587875,
-    "morph_acc":0.9363127422,
-    "morph_micro_p":0.9693092418,
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@@ -1484,64 +1479,64 @@
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@@ -1559,9 +1554,9 @@
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@@ -1590,8 +1585,8 @@
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@@ -1599,32 +1594,32 @@
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