Update spacy pipeline to 3.5.0

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Files changed (13) hide show

README.md +28 -28
config.cfg +5 -4
hu_core_news_md-any-py3-none-any.whl +2 -2
lemmatizer/model +1 -1
meta.json +185 -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 +1 -1
vocab/strings.json +2 -2

README.md CHANGED Viewed

@@ -14,70 +14,70 @@ model-index:
     metrics:
     - name: NER Precision
       type: precision
-      value: 0.8538804639
     - name: NER Recall
       type: recall
-      value: 0.8414205345
     - name: NER F Score
       type: f_score
-      value: 0.8476047109
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
-      value: 0.9619102306
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
-      value: 0.9619102306
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
-      value: 0.9279356876
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
-      value: 0.9716773514
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
-      value: 0.8106528823
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
-      value: 0.7407573885
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
-      value: 0.9719416386
 ---
 Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
 | Feature | Description |
 | --- | --- |
 | **Name** | `hu_core_news_md` |
-| **Version** | `3.4.2` |
-| **spaCy** | `>=3.4.0,<3.5.0` |
 | **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) |
@@ -104,22 +104,22 @@ Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morpholog
 | Type | Score |
 | --- | --- |
-| `TOKEN_ACC` | 100.00 |
 | `TOKEN_P` | 99.86 |
 | `TOKEN_R` | 99.93 |
 | `TOKEN_F` | 99.89 |
-| `SENTS_P` | 97.96 |
-| `SENTS_R` | 96.44 |
-| `SENTS_F` | 97.19 |
-| `TAG_ACC` | 96.19 |
-| `POS_ACC` | 96.19 |
-| `MORPH_ACC` | 92.79 |
-| `MORPH_MICRO_P` | 96.70 |
-| `MORPH_MICRO_R` | 95.63 |
-| `MORPH_MICRO_F` | 96.16 |
-| `LEMMA_ACC` | 97.17 |
-| `DEP_UAS` | 81.07 |
-| `DEP_LAS` | 74.08 |
-| `ENTS_P` | 85.39 |
-| `ENTS_R` | 84.14 |
-| `ENTS_F` | 84.76 |

     metrics:
     - name: NER Precision
       type: precision
+      value: 0.8581105169
     - name: NER Recall
       type: recall
+      value: 0.8463431786
     - name: NER F Score
       type: f_score
+      value: 0.8521862277
   - task:
       name: TAG
       type: token-classification
     metrics:
     - name: TAG (XPOS) Accuracy
       type: accuracy
+      value: 0.9649265515
   - task:
       name: POS
       type: token-classification
     metrics:
     - name: POS (UPOS) Accuracy
       type: accuracy
+      value: 0.9634910761
   - task:
       name: MORPH
       type: token-classification
     metrics:
     - name: Morph (UFeats) Accuracy
       type: accuracy
+      value: 0.9308067758
   - task:
       name: LEMMA
       type: token-classification
     metrics:
     - name: Lemma Accuracy
       type: accuracy
+      value: 0.9738780978
   - task:
       name: UNLABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Unlabeled Attachment Score (UAS)
       type: f_score
+      value: 0.8116901329
   - task:
       name: LABELED_DEPENDENCIES
       type: token-classification
     metrics:
     - name: Labeled Attachment Score (LAS)
       type: f_score
+      value: 0.7417545434
   - task:
       name: SENTS
       type: token-classification
     metrics:
     - name: Sentences F-Score
       type: f_score
+      value: 0.9710467706
 ---
 Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
 | Feature | Description |
 | --- | --- |
 | **Name** | `hu_core_news_md` |
+| **Version** | `3.5.0` |
+| **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) |
 | Type | Score |
 | --- | --- |
+| `TOKEN_ACC` | 99.99 |
 | `TOKEN_P` | 99.86 |
 | `TOKEN_R` | 99.93 |
 | `TOKEN_F` | 99.89 |
+| `SENTS_P` | 97.10 |
+| `SENTS_R` | 97.10 |
+| `SENTS_F` | 97.10 |
+| `TAG_ACC` | 96.49 |
+| `POS_ACC` | 96.35 |
+| `MORPH_ACC` | 93.08 |
+| `MORPH_MICRO_P` | 96.72 |
+| `MORPH_MICRO_R` | 95.96 |
+| `MORPH_MICRO_F` | 96.34 |
+| `LEMMA_ACC` | 97.39 |
+| `DEP_UAS` | 81.17 |
+| `DEP_LAS` | 74.18 |
+| `ENTS_P` | 85.81 |
+| `ENTS_R` | 84.63 |
+| `ENTS_F` | 85.22 |

config.cfg CHANGED Viewed

@@ -1,8 +1,8 @@
 [paths]
-parser_model = "models/hu_core_news_md-parser-3.4.2/model-best"
-ner_model = "models/hu_core_news_md-ner-3.4.2/model-best"
-lemmatizer_lookups = "models/hu_core_news_md-lookup-lemmatizer-3.4.2"
-tagger_model = "models/hu_core_news_md-tagger-3.4.2/model-best"
 train = null
 dev = null
 vectors = null
@@ -216,6 +216,7 @@ annotating_components = []
 dev_corpus = "corpora.dev"
 train_corpus = "corpora.train"
 before_to_disk = null
 [training.batcher]
 @batchers = "spacy.batch_by_words.v1"

 [paths]
+parser_model = "models/hu_core_news_md-parser-3.5.0/model-best"
+ner_model = "models/hu_core_news_md-ner-3.5.0/model-best"
+lemmatizer_lookups = "models/hu_core_news_md-lookup-lemmatizer-3.5.0"
+tagger_model = "models/hu_core_news_md-tagger-3.5.0/model-best"
 train = null
 dev = null
 vectors = null
 dev_corpus = "corpora.dev"
 train_corpus = "corpora.train"
 before_to_disk = null
+before_update = null
 [training.batcher]
 @batchers = "spacy.batch_by_words.v1"

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meta.json CHANGED Viewed

@@ -1,13 +1,13 @@
 {
   "lang":"hu",
   "name":"core_news_md",
-  "version":"3.4.2",
   "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",
   "license":"cc-by-sa-4.0",
-  "spacy_version":">=3.4.0,<3.5.0",
   "spacy_git_version":"Unknown",
   "vectors":{
     "width":100,
@@ -1269,84 +1269,84 @@
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 {
   "lang":"hu",
   "name":"core_news_md",
+  "version":"3.5.0",
   "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",
   "license":"cc-by-sa-4.0",
+  "spacy_version":">=3.5.0,<3.6.0",
   "spacy_git_version":"Unknown",
   "vectors":{
     "width":100,
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