Update spacy pipeline to 3.4.0
Browse files- README.md +27 -27
- config.cfg +3 -3
- hu_core_news_lg-any-py3-none-any.whl +2 -2
- lemmatizer/model +1 -1
- meta.json +189 -189
- morphologizer/model +1 -1
- ner/model +1 -1
- parser/model +1 -1
- senter/model +1 -1
- tagger/model +1 -1
- tok2vec/model +1 -1
- vocab/strings.json +2 -2
README.md
CHANGED
@@ -14,70 +14,70 @@ model-index:
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metrics:
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- name: NER Precision
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type: precision
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-
value: 0.
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- name: NER Recall
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type: recall
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value: 0.
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- name: NER F Score
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type: f_score
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-
value: 0.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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---
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Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
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| Feature | Description |
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| --- | --- |
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| **Name** | `hu_core_news_lg` |
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-
| **Version** | `3.
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| **spaCy** | `>=3.
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| **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
|
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| **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
|
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| **Vectors** | -1 keys, 200000 unique vectors (300 dimensions) |
|
@@ -108,18 +108,18 @@ Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morpholog
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| `TOKEN_P` | 99.86 |
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| `TOKEN_R` | 99.93 |
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| `TOKEN_F` | 99.89 |
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-
| `SENTS_P` |
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| `SENTS_R` |
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| `SENTS_F` |
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114 |
-
| `TAG_ACC` | 96.
|
115 |
-
| `POS_ACC` | 96.
|
116 |
-
| `MORPH_ACC` |
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117 |
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| `MORPH_MICRO_P` | 96.
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118 |
-
| `MORPH_MICRO_R` |
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-
| `MORPH_MICRO_F` |
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-
| `LEMMA_ACC` | 97.
|
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-
| `DEP_UAS` |
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| `DEP_LAS` |
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-
| `ENTS_P` |
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| `ENTS_R` |
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| `ENTS_F` | 85.
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metrics:
|
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- name: NER Precision
|
16 |
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:
|
28 |
- name: TAG (XPOS) Accuracy
|
29 |
type: accuracy
|
30 |
+
value: 0.9651179482
|
31 |
- task:
|
32 |
name: POS
|
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type: token-classification
|
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metrics:
|
35 |
- name: POS (UPOS) Accuracy
|
36 |
type: accuracy
|
37 |
+
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:
|
42 |
- name: Morph (UFeats) Accuracy
|
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type: accuracy
|
44 |
+
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
|
55 |
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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---
|
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Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner
|
75 |
|
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| Feature | Description |
|
77 |
| --- | --- |
|
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| **Name** | `hu_core_news_lg` |
|
79 |
+
| **Version** | `3.4.0` |
|
80 |
+
| **spaCy** | `>=3.4.1,<3.5.0` |
|
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| **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
|
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| **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `lemmatizer`, `lemma_smoother`, `parser`, `ner` |
|
83 |
| **Vectors** | -1 keys, 200000 unique vectors (300 dimensions) |
|
|
|
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| `TOKEN_P` | 99.86 |
|
109 |
| `TOKEN_R` | 99.93 |
|
110 |
| `TOKEN_F` | 99.89 |
|
111 |
+
| `SENTS_P` | 98.44 |
|
112 |
+
| `SENTS_R` | 98.22 |
|
113 |
+
| `SENTS_F` | 98.33 |
|
114 |
+
| `TAG_ACC` | 96.50 |
|
115 |
+
| `POS_ACC` | 96.45 |
|
116 |
+
| `MORPH_ACC` | 93.44 |
|
117 |
+
| `MORPH_MICRO_P` | 96.85 |
|
118 |
+
| `MORPH_MICRO_R` | 96.14 |
|
119 |
+
| `MORPH_MICRO_F` | 96.49 |
|
120 |
+
| `LEMMA_ACC` | 97.05 |
|
121 |
+
| `DEP_UAS` | 79.50 |
|
122 |
+
| `DEP_LAS` | 72.47 |
|
123 |
+
| `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.
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ner_model = "models/hu_core_news_lg-ner-3.
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lemmatizer_lookups = "models/hu_core_news_lg-lookup-lemmatizer-3.
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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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train = null
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dev = null
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vectors = null
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hu_core_news_lg-any-py3-none-any.whl
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lemmatizer/model
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meta.json
CHANGED
@@ -1,13 +1,13 @@
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{
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"lang":"hu",
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"name":"core_news_lg",
|
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-
"version":"3.
|
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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":"gyorgy@orosz.link",
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"url":"https://github.com/huspacy/huspacy",
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