oroszgy commited on
Commit
e921a77
1 Parent(s): d32f26e

Update spacy pipeline to 3.5.2

Browse files
README.md CHANGED
@@ -14,55 +14,55 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.8572441922
18
  - name: NER Recall
19
  type: recall
20
- value: 0.849859353
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.8535357994
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9624844483
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9631543688
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.928892717
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9728255669
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8127597439
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.743681905
66
  - task:
67
  name: SENTS
68
  type: token-classification
@@ -76,10 +76,10 @@ Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morpholog
76
  | Feature | Description |
77
  | --- | --- |
78
  | **Name** | `hu_core_news_md` |
79
- | **Version** | `3.5.1` |
80
  | **spaCy** | `>=3.5.0,<3.6.0` |
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 (100 dimensions) |
84
  | **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)) |
85
  | **License** | `cc-by-sa-4.0` |
@@ -111,15 +111,15 @@ Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morpholog
111
  | `SENTS_P` | 98.21 |
112
  | `SENTS_R` | 97.55 |
113
  | `SENTS_F` | 97.88 |
114
- | `TAG_ACC` | 96.25 |
115
- | `POS_ACC` | 96.32 |
116
- | `MORPH_ACC` | 92.89 |
117
- | `MORPH_MICRO_P` | 96.49 |
118
- | `MORPH_MICRO_R` | 95.78 |
119
- | `MORPH_MICRO_F` | 96.14 |
120
- | `LEMMA_ACC` | 97.28 |
121
- | `DEP_UAS` | 81.28 |
122
- | `DEP_LAS` | 74.37 |
123
- | `ENTS_P` | 85.72 |
124
- | `ENTS_R` | 84.99 |
125
- | `ENTS_F` | 85.35 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.8557640751
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.8417721519
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.8487104493
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9633953778
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.964398507
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9338692698
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9724428284
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.7978436658
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.7223314055
66
  - task:
67
  name: SENTS
68
  type: token-classification
 
76
  | Feature | Description |
77
  | --- | --- |
78
  | **Name** | `hu_core_news_md` |
79
+ | **Version** | `3.5.2` |
80
  | **spaCy** | `>=3.5.0,<3.6.0` |
81
+ | **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `parser`, `ner` |
82
+ | **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `parser`, `ner` |
83
  | **Vectors** | -1 keys, 200000 unique vectors (100 dimensions) |
84
  | **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)) |
85
  | **License** | `cc-by-sa-4.0` |
 
111
  | `SENTS_P` | 98.21 |
112
  | `SENTS_R` | 97.55 |
113
  | `SENTS_F` | 97.88 |
114
+ | `TAG_ACC` | 96.34 |
115
+ | `POS_ACC` | 96.44 |
116
+ | `MORPH_ACC` | 93.39 |
117
+ | `MORPH_MICRO_P` | 96.76 |
118
+ | `MORPH_MICRO_R` | 95.92 |
119
+ | `MORPH_MICRO_F` | 96.34 |
120
+ | `LEMMA_ACC` | 97.24 |
121
+ | `DEP_UAS` | 79.78 |
122
+ | `DEP_LAS` | 72.23 |
123
+ | `ENTS_P` | 85.58 |
124
+ | `ENTS_R` | 84.18 |
125
+ | `ENTS_F` | 84.87 |
config.cfg CHANGED
@@ -1,8 +1,8 @@
1
  [paths]
2
- parser_model = "models/hu_core_news_md-parser-3.5.1/model-best"
3
- ner_model = "models/hu_core_news_md-ner-3.5.1/model-best"
4
- lemmatizer_lookups = "models/hu_core_news_md-lookup-lemmatizer-3.5.1"
5
- tagger_model = "models/hu_core_news_md-tagger-3.5.1/model-best"
6
  train = null
7
  dev = null
8
  vectors = null
@@ -14,7 +14,7 @@ gpu_allocator = null
14
 
15
  [nlp]
16
  lang = "hu"
17
- pipeline = ["tok2vec","senter","tagger","morphologizer","lookup_lemmatizer","lemmatizer","lemma_smoother","parser","ner"]
18
  tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
19
  disabled = []
20
  before_creation = null
@@ -24,39 +24,6 @@ batch_size = 1000
24
 
25
  [components]
26
 
27
- [components.lemma_smoother]
28
- factory = "hu.lemma_smoother"
29
-
30
- [components.lemmatizer]
31
- factory = "trainable_lemmatizer_v2"
32
- backoff = "orth"
33
- min_tree_freq = 1
34
- overwrite = false
35
- overwrite_labels = true
36
- scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
37
- top_k = 3
38
-
39
- [components.lemmatizer.model]
40
- @architectures = "spacy.Tagger.v1"
41
- nO = null
42
-
43
- [components.lemmatizer.model.tok2vec]
44
- @architectures = "spacy.Tok2Vec.v2"
45
-
46
- [components.lemmatizer.model.tok2vec.embed]
47
- @architectures = "spacy.MultiHashEmbed.v2"
48
- width = 100
49
- attrs = ["LOWER","PREFIX","SUFFIX","SHAPE"]
50
- rows = [5000,2500,2500,2500]
51
- include_static_vectors = true
52
-
53
- [components.lemmatizer.model.tok2vec.encode]
54
- @architectures = "spacy.MaxoutWindowEncoder.v2"
55
- width = 100
56
- depth = 4
57
- window_size = 2
58
- maxout_pieces = 5
59
-
60
  [components.lookup_lemmatizer]
61
  factory = "hu.lookup_lemmatizer"
62
  scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
@@ -184,6 +151,36 @@ depth = 4
184
  window_size = 2
185
  maxout_pieces = 5
186
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
187
  [corpora]
188
 
189
  [corpora.dev]
 
1
  [paths]
2
+ parser_model = "models/hu_core_news_md-parser-3.5.2/model-best"
3
+ ner_model = "models/hu_core_news_md-ner-3.5.2/model-best"
4
+ lemmatizer_lookups = "models/hu_core_news_md-lookup-lemmatizer-3.5.2"
5
+ tagger_model = "models/hu_core_news_md-tagger-3.5.2/model-best"
6
  train = null
7
  dev = null
8
  vectors = null
 
14
 
15
  [nlp]
16
  lang = "hu"
17
+ pipeline = ["tok2vec","senter","tagger","morphologizer","lookup_lemmatizer","trainable_lemmatizer","parser","ner"]
18
  tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
19
  disabled = []
20
  before_creation = null
 
24
 
25
  [components]
26
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
27
  [components.lookup_lemmatizer]
28
  factory = "hu.lookup_lemmatizer"
29
  scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
 
151
  window_size = 2
152
  maxout_pieces = 5
153
 
154
+ [components.trainable_lemmatizer]
155
+ factory = "trainable_lemmatizer_v2"
156
+ backoff = "orth"
157
+ min_tree_freq = 1
158
+ overwrite = false
159
+ overwrite_labels = true
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+ scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
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+ top_k = 3
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+
163
+ [components.trainable_lemmatizer.model]
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+ @architectures = "spacy.Tagger.v1"
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+ nO = null
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+
167
+ [components.trainable_lemmatizer.model.tok2vec]
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+ @architectures = "spacy.Tok2Vec.v2"
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+
170
+ [components.trainable_lemmatizer.model.tok2vec.embed]
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+ @architectures = "spacy.MultiHashEmbed.v2"
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+ width = 100
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+ attrs = ["LOWER","PREFIX","SUFFIX","SHAPE"]
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+ rows = [5000,2500,2500,2500]
175
+ include_static_vectors = true
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+
177
+ [components.trainable_lemmatizer.model.tok2vec.encode]
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+ @architectures = "spacy.MaxoutWindowEncoder.v2"
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+ width = 100
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+ depth = 4
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+ window_size = 2
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+ maxout_pieces = 5
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+
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  [corpora]
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186
  [corpora.dev]
hu_core_news_md-any-py3-none-any.whl CHANGED
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meta.json CHANGED
@@ -1,7 +1,7 @@
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  {
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  "lang":"hu",
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  "name":"core_news_md",
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- "version":"3.5.1",
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  "description":"Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morphologizer, lemmatizer, parser, ner",
6
  "author":"SzegedAI, MILAB",
7
  "email":"gyorgy@orosz.link",
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@@ -1249,8 +1246,7 @@
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