oroszgy commited on
Commit
231bb3f
1 Parent(s): d3769bc

Update spacy pipeline to 3.4.0

Browse files
README.md CHANGED
@@ -14,70 +14,70 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.847826087
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  - name: NER Recall
19
  type: recall
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- value: 0.8570675105
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  - name: NER F Score
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  type: f_score
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- value: 0.8524217521
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  - task:
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  name: TAG
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  type: token-classification
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  metrics:
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  - name: TAG (XPOS) Accuracy
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  type: accuracy
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- value: 0.9677018039
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  - task:
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  name: POS
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  type: token-classification
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  metrics:
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  - name: POS (UPOS) Accuracy
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  type: accuracy
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- value: 0.9675104072
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  - task:
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  name: MORPH
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  type: token-classification
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  metrics:
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  - name: Morph (UFeats) Accuracy
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  type: accuracy
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- value: 0.9386544167
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  - task:
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  name: LEMMA
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  type: token-classification
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  metrics:
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  - name: Lemma Accuracy
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  type: accuracy
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- value: 0.9716773514
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  - task:
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  name: UNLABELED_DEPENDENCIES
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  type: token-classification
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  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
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  type: f_score
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- value: 0.8069939475
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  - task:
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  name: LABELED_DEPENDENCIES
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  type: token-classification
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  metrics:
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  - name: Labeled Attachment Score (LAS)
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  type: f_score
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- value: 0.736004483
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  - task:
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  name: SENTS
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  type: token-classification
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  metrics:
70
  - name: Sentences F-Score
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  type: f_score
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- value: 0.9821029083
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  ---
74
  Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
75
 
76
  | Feature | Description |
77
  | --- | --- |
78
  | **Name** | `hu_core_news_lg` |
79
- | **Version** | `3.3.1` |
80
- | **spaCy** | `>=3.3.0,<3.4.0` |
81
  | **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
82
  | **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
83
  | **Vectors** | -1 keys, 200000 unique vectors (300 dimensions) |
@@ -108,18 +108,18 @@ Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morpholog
108
  | `TOKEN_P` | 99.86 |
109
  | `TOKEN_R` | 99.93 |
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  | `TOKEN_F` | 99.89 |
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- | `SENTS_P` | 97.77 |
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- | `SENTS_R` | 97.55 |
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- | `SENTS_F` | 97.66 |
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- | `TAG_ACC` | 96.31 |
115
- | `POS_ACC` | 96.34 |
116
- | `MORPH_ACC` | 92.89 |
117
- | `MORPH_MICRO_P` | 96.28 |
118
- | `MORPH_MICRO_R` | 95.58 |
119
- | `MORPH_MICRO_F` | 95.93 |
120
- | `LEMMA_ACC` | 97.25 |
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- | `DEP_UAS` | 81.13 |
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- | `DEP_LAS` | 74.49 |
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- | `ENTS_P` | 87.15 |
124
- | `ENTS_R` | 83.72 |
125
- | `ENTS_F` | 85.40 |
 
14
  metrics:
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  - name: NER Precision
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  type: precision
17
+ value: 0.8710737765
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  - name: NER Recall
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  type: recall
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+ value: 0.8386075949
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  - name: NER F Score
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  type: f_score
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+ value: 0.8545324257
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  - task:
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  name: TAG
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  type: token-classification
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  metrics:
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  - name: TAG (XPOS) Accuracy
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  type: accuracy
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+ value: 0.9651179482
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  - task:
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  name: POS
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  type: token-classification
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  metrics:
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  - name: POS (UPOS) Accuracy
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  type: accuracy
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+ value: 0.9639695679
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  - task:
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  name: MORPH
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  type: token-classification
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  metrics:
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  - name: Morph (UFeats) Accuracy
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  type: accuracy
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+ value: 0.9321466169
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  - task:
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  name: LEMMA
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  type: token-classification
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  metrics:
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  - name: Lemma Accuracy
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  type: accuracy
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+ value: 0.9711032437
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  - task:
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  name: UNLABELED_DEPENDENCIES
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  type: token-classification
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  metrics:
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  - name: Unlabeled Attachment Score (UAS)
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  type: f_score
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+ value: 0.8218578007
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  - task:
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  name: LABELED_DEPENDENCIES
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  type: token-classification
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  metrics:
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  - name: Labeled Attachment Score (LAS)
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  type: f_score
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+ value: 0.7535662136
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  - task:
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  name: SENTS
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  type: token-classification
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  metrics:
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  - name: Sentences F-Score
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  type: f_score
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+ value: 0.9732739421
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  ---
74
  Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
75
 
76
  | Feature | Description |
77
  | --- | --- |
78
  | **Name** | `hu_core_news_lg` |
79
+ | **Version** | `3.4.0` |
80
+ | **spaCy** | `>=3.4.1,<3.5.0` |
81
  | **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
82
  | **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
83
  | **Vectors** | -1 keys, 200000 unique vectors (300 dimensions) |
 
108
  | `TOKEN_P` | 99.86 |
109
  | `TOKEN_R` | 99.93 |
110
  | `TOKEN_F` | 99.89 |
111
+ | `SENTS_P` | 98.44 |
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+ | `SENTS_R` | 98.22 |
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+ | `SENTS_F` | 98.33 |
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+ | `TAG_ACC` | 96.50 |
115
+ | `POS_ACC` | 96.45 |
116
+ | `MORPH_ACC` | 93.44 |
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+ | `MORPH_MICRO_P` | 96.85 |
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+ | `MORPH_MICRO_R` | 96.14 |
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+ | `MORPH_MICRO_F` | 96.49 |
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+ | `LEMMA_ACC` | 97.05 |
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+ | `DEP_UAS` | 79.50 |
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+ | `DEP_LAS` | 72.47 |
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+ | `ENTS_P` | 86.06 |
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+ | `ENTS_R` | 85.28 |
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+ | `ENTS_F` | 85.67 |
config.cfg CHANGED
@@ -1,7 +1,7 @@
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  [paths]
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- parser_model = "models/hu_core_news_lg-parser-3.3.1/model-best"
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- ner_model = "models/hu_core_news_lg-ner-3.3.1/model-best"
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- lemmatizer_lookups = "models/hu_core_news_lg-lookup-lemmatizer-3.3.1"
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  train = null
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  dev = null
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  vectors = null
 
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  [paths]
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+ parser_model = "models/hu_core_news_lg-parser-3.4.0/model-best"
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+ ner_model = "models/hu_core_news_lg-ner-3.4.0/model-best"
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+ lemmatizer_lookups = "models/hu_core_news_lg-lookup-lemmatizer-3.4.0"
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meta.json CHANGED
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  {
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  "lang":"hu",
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  "name":"core_news_lg",
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- "version":"3.3.1",
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  "description":"Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner",
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  "author":"SzegedAI, MILAB",
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  "email":"[email protected]",
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