Update spacy pipeline to 3.3.0

Browse files

Files changed (15) hide show

README.md +27 -27
config.cfg +35 -13
hu_core_news_lg-any-py3-none-any.whl +2 -2
lemmatizer/cfg +6027 -0
lemmatizer/model +2 -2
lemmatizer/trees +0 -0
meta.json +182 -185
morphologizer/model +1 -1
ner/model +1 -1
parser/model +1 -1
senter/model +1 -1
tagger/model +1 -1
tok2vec/model +1 -1
tokenizer +0 -0
vocab/strings.json +2 -2

README.md CHANGED Viewed

@@ -14,73 +14,73 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.8669194655
     - name: NER Recall
       type: recall
-      value: 0.8440576653
     - name: NER F Score
       type: f_score
-      value: 0.8553358275
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9645437581
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9641609646
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.9311895875
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9638312123
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8157554644
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.7455189077
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9776785714
 ---
 Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
 | Feature | Description |
 | --- | --- |
 | **Name** | `hu_core_news_lg` |
-| **Version** | `3.2.3` |
-| **spaCy** | `>=3.2.4,<3.3.0` |
 | **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner` |
 | **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner` |
-| **Vectors** | 0 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` |
 | **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) |
@@ -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` | 0.00 |
-| `SENTS_R` | 0.00 |
-| `SENTS_F` | 0.00 |
-| `TAG_ACC` | 96.17 |
-| `POS_ACC` | 96.21 |
-| `MORPH_ACC` | 92.98 |
-| `MORPH_MICRO_P` | 96.80 |
 | `MORPH_MICRO_R` | 95.85 |
-| `MORPH_MICRO_F` | 96.32 |
-| `LEMMA_ACC` | 96.01 |
-| `DEP_UAS` | 75.78 |
-| `DEP_LAS` | 68.82 |
-| `ENTS_P` | 85.95 |
-| `ENTS_R` | 85.53 |
-| `ENTS_F` | 85.74 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.8607281725
     - name: NER Recall
       type: recall
+      value: 0.8561884669
     - name: NER F Score
       type: f_score
+      value: 0.858452318
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9638738696
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9649265515
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.9324337257
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9681370204
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.8211578415
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.7476698484
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.9788182832
 ---
 Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
 | Feature | Description |
 | --- | --- |
 | **Name** | `hu_core_news_lg` |
+| **Version** | `3.3.0` |
+| **spaCy** | `>=3.3.0,<3.4.0` |
 | **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner` |
 | **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `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` |
 | **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) |
 | `TOKEN_P` | 99.86 |
 | `TOKEN_R` | 99.93 |
 | `TOKEN_F` | 99.89 |
+| `SENTS_P` | 97.99 |
+| `SENTS_R` | 97.77 |
+| `SENTS_F` | 97.88 |
+| `TAG_ACC` | 96.39 |
+| `POS_ACC` | 96.49 |
+| `MORPH_ACC` | 93.24 |
+| `MORPH_MICRO_P` | 96.86 |
 | `MORPH_MICRO_R` | 95.85 |
+| `MORPH_MICRO_F` | 96.35 |
+| `LEMMA_ACC` | 96.81 |
+| `DEP_UAS` | 82.12 |
+| `DEP_LAS` | 74.77 |
+| `ENTS_P` | 86.07 |
+| `ENTS_R` | 85.62 |
+| `ENTS_F` | 85.85 |

config.cfg CHANGED Viewed

@@ -1,7 +1,6 @@
 [paths]
-parser_model = "models/hu_core_news_lg-parser-3.2.3/model-best"
-lemmy_model = "models/lemmy-3.2.3.bin"
-ner_model = "models/hu_core_news_lg-ner-3.2.3/model-best"
 train = null
 dev = null
 vectors = null
@@ -24,8 +23,33 @@ batch_size = 1000
 [components]
 [components.lemmatizer]
-factory = "hu.lemmatizer"
 scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
 [components.morphologizer]
 factory = "morphologizer"
@@ -211,14 +235,15 @@ learn_rate = 0.001
 sents_f = 0.0
 sents_p = null
 sents_r = null
-tag_acc = 0.26
-pos_acc = 0.12
-morph_acc = 0.12
 morph_per_feat = null
-dep_uas = 0.12
-dep_las = 0.12
 dep_las_per_type = null
-ents_f = 0.26
 ents_p = 0.0
 ents_r = 0.0
 ents_per_type = null
@@ -235,7 +260,4 @@ after_init = null
 [initialize.components]
-[initialize.components.lemmatizer]
-model_path = ${paths.lemmy_model}
 [initialize.tokenizer]

 [paths]
+parser_model = "models/hu_core_news_lg-parser-3.3.0/model-best"
+ner_model = "models/hu_core_news_lg-ner-3.3.0/model-best"
 train = null
 dev = null
 vectors = null
 [components]
 [components.lemmatizer]
+factory = "trainable_lemmatizer"
+backoff = "orth"
+min_tree_freq = 1
+overwrite = false
 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.morphologizer]
 factory = "morphologizer"
 sents_f = 0.0
 sents_p = null
 sents_r = null
+tag_acc = 0.2
+pos_acc = 0.1
+morph_acc = 0.1
 morph_per_feat = null
+lemma_acc = 0.2
+dep_uas = 0.1
+dep_las = 0.1
 dep_las_per_type = null
+ents_f = 0.2
 ents_p = 0.0
 ents_r = 0.0
 ents_per_type = null
 [initialize.components]
 [initialize.tokenizer]

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 {
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   "description":"Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner",
   "author":"SzegedAI, MILAB",
   "email":"[email protected]",
   "url":"https://github.com/huspacy/huspacy",
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