Update spacy pipeline to 3.3.0
Browse files- 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
@@ -14,73 +14,73 @@ model-index:
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14 |
metrics:
|
15 |
- name: NER Precision
|
16 |
type: precision
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17 |
-
value: 0.
|
18 |
- name: NER Recall
|
19 |
type: recall
|
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-
value: 0.
|
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- name: NER F Score
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type: f_score
|
23 |
-
value: 0.
|
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- task:
|
25 |
name: TAG
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26 |
type: token-classification
|
27 |
metrics:
|
28 |
- name: TAG (XPOS) Accuracy
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29 |
type: accuracy
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30 |
-
value: 0.
|
31 |
- task:
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32 |
name: POS
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33 |
type: token-classification
|
34 |
metrics:
|
35 |
- name: POS (UPOS) Accuracy
|
36 |
type: accuracy
|
37 |
-
value: 0.
|
38 |
- task:
|
39 |
name: MORPH
|
40 |
type: token-classification
|
41 |
metrics:
|
42 |
- name: Morph (UFeats) Accuracy
|
43 |
type: accuracy
|
44 |
-
value: 0.
|
45 |
- task:
|
46 |
name: LEMMA
|
47 |
type: token-classification
|
48 |
metrics:
|
49 |
- name: Lemma Accuracy
|
50 |
type: accuracy
|
51 |
-
value: 0.
|
52 |
- task:
|
53 |
name: UNLABELED_DEPENDENCIES
|
54 |
type: token-classification
|
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metrics:
|
56 |
- name: Unlabeled Attachment Score (UAS)
|
57 |
type: f_score
|
58 |
-
value: 0.
|
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- task:
|
60 |
name: LABELED_DEPENDENCIES
|
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type: token-classification
|
62 |
metrics:
|
63 |
- name: Labeled Attachment Score (LAS)
|
64 |
type: f_score
|
65 |
-
value: 0.
|
66 |
- task:
|
67 |
name: SENTS
|
68 |
type: token-classification
|
69 |
metrics:
|
70 |
- name: Sentences F-Score
|
71 |
type: f_score
|
72 |
-
value: 0.
|
73 |
---
|
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.
|
80 |
-
| **spaCy** | `>=3.
|
81 |
| **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner` |
|
82 |
| **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner` |
|
83 |
-
| **Vectors** |
|
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))<br />[Webcorpuswiki word2vec model](https://github.com/oroszgy/hunlp-resources/releases/tag/webcorpuswiki_word2vec_v0.1) (György Orosz) |
|
85 |
| **License** | `cc-by-sa-4.0` |
|
86 |
| **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) |
|
@@ -108,18 +108,18 @@ Core Hungarian model for HuSpaCy. Components: tok2vec, senter, tagger, morpholog
|
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| `TOKEN_P` | 99.86 |
|
109 |
| `TOKEN_R` | 99.93 |
|
110 |
| `TOKEN_F` | 99.89 |
|
111 |
-
| `SENTS_P` |
|
112 |
-
| `SENTS_R` |
|
113 |
-
| `SENTS_F` |
|
114 |
-
| `TAG_ACC` | 96.
|
115 |
-
| `POS_ACC` | 96.
|
116 |
-
| `MORPH_ACC` |
|
117 |
-
| `MORPH_MICRO_P` | 96.
|
118 |
| `MORPH_MICRO_R` | 95.85 |
|
119 |
-
| `MORPH_MICRO_F` | 96.
|
120 |
-
| `LEMMA_ACC` | 96.
|
121 |
-
| `DEP_UAS` |
|
122 |
-
| `DEP_LAS` |
|
123 |
-
| `ENTS_P` |
|
124 |
-
| `ENTS_R` | 85.
|
125 |
-
| `ENTS_F` | 85.
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|
|
14 |
metrics:
|
15 |
- name: NER Precision
|
16 |
type: precision
|
17 |
+
value: 0.8607281725
|
18 |
- name: NER Recall
|
19 |
type: recall
|
20 |
+
value: 0.8561884669
|
21 |
- name: NER F Score
|
22 |
type: f_score
|
23 |
+
value: 0.858452318
|
24 |
- task:
|
25 |
name: TAG
|
26 |
type: token-classification
|
27 |
metrics:
|
28 |
- name: TAG (XPOS) Accuracy
|
29 |
type: accuracy
|
30 |
+
value: 0.9638738696
|
31 |
- task:
|
32 |
name: POS
|
33 |
type: token-classification
|
34 |
metrics:
|
35 |
- name: POS (UPOS) Accuracy
|
36 |
type: accuracy
|
37 |
+
value: 0.9649265515
|
38 |
- task:
|
39 |
name: MORPH
|
40 |
type: token-classification
|
41 |
metrics:
|
42 |
- name: Morph (UFeats) Accuracy
|
43 |
type: accuracy
|
44 |
+
value: 0.9324337257
|
45 |
- task:
|
46 |
name: LEMMA
|
47 |
type: token-classification
|
48 |
metrics:
|
49 |
- name: Lemma Accuracy
|
50 |
type: accuracy
|
51 |
+
value: 0.9681370204
|
52 |
- task:
|
53 |
name: UNLABELED_DEPENDENCIES
|
54 |
type: token-classification
|
55 |
metrics:
|
56 |
- name: Unlabeled Attachment Score (UAS)
|
57 |
type: f_score
|
58 |
+
value: 0.8211578415
|
59 |
- task:
|
60 |
name: LABELED_DEPENDENCIES
|
61 |
type: token-classification
|
62 |
metrics:
|
63 |
- name: Labeled Attachment Score (LAS)
|
64 |
type: f_score
|
65 |
+
value: 0.7476698484
|
66 |
- task:
|
67 |
name: SENTS
|
68 |
type: token-classification
|
69 |
metrics:
|
70 |
- name: Sentences F-Score
|
71 |
type: f_score
|
72 |
+
value: 0.9788182832
|
73 |
---
|
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** | `3.3.0` |
|
80 |
+
| **spaCy** | `>=3.3.0,<3.4.0` |
|
81 |
| **Default Pipeline** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner` |
|
82 |
| **Components** | `tok2vec`, `senter`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner` |
|
83 |
+
| **Vectors** | -1 keys, 200000 unique vectors (300 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))<br />[Webcorpuswiki word2vec model](https://github.com/oroszgy/hunlp-resources/releases/tag/webcorpuswiki_word2vec_v0.1) (György Orosz) |
|
85 |
| **License** | `cc-by-sa-4.0` |
|
86 |
| **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) |
|
|
|
108 |
| `TOKEN_P` | 99.86 |
|
109 |
| `TOKEN_R` | 99.93 |
|
110 |
| `TOKEN_F` | 99.89 |
|
111 |
+
| `SENTS_P` | 97.99 |
|
112 |
+
| `SENTS_R` | 97.77 |
|
113 |
+
| `SENTS_F` | 97.88 |
|
114 |
+
| `TAG_ACC` | 96.39 |
|
115 |
+
| `POS_ACC` | 96.49 |
|
116 |
+
| `MORPH_ACC` | 93.24 |
|
117 |
+
| `MORPH_MICRO_P` | 96.86 |
|
118 |
| `MORPH_MICRO_R` | 95.85 |
|
119 |
+
| `MORPH_MICRO_F` | 96.35 |
|
120 |
+
| `LEMMA_ACC` | 96.81 |
|
121 |
+
| `DEP_UAS` | 82.12 |
|
122 |
+
| `DEP_LAS` | 74.77 |
|
123 |
+
| `ENTS_P` | 86.07 |
|
124 |
+
| `ENTS_R` | 85.62 |
|
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+
| `ENTS_F` | 85.85 |
|
config.cfg
CHANGED
@@ -1,7 +1,6 @@
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[paths]
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-
parser_model = "models/hu_core_news_lg-parser-3.
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-
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-
ner_model = "models/hu_core_news_lg-ner-3.2.3/model-best"
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train = null
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dev = null
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vectors = null
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@@ -24,8 +23,33 @@ batch_size = 1000
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[components]
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[components.lemmatizer]
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-
factory = "
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scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
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[components.morphologizer]
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factory = "morphologizer"
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@@ -211,14 +235,15 @@ learn_rate = 0.001
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sents_f = 0.0
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sents_p = null
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sents_r = null
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-
tag_acc = 0.
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-
pos_acc = 0.
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-
morph_acc = 0.
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morph_per_feat = null
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-
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-
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dep_las_per_type = null
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-
ents_f = 0.
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ents_p = 0.0
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ents_r = 0.0
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ents_per_type = null
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@@ -235,7 +260,4 @@ after_init = null
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[initialize.components]
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-
[initialize.components.lemmatizer]
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-
model_path = ${paths.lemmy_model}
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-
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[initialize.tokenizer]
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[paths]
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+
parser_model = "models/hu_core_news_lg-parser-3.3.0/model-best"
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+
ner_model = "models/hu_core_news_lg-ner-3.3.0/model-best"
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train = null
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dev = null
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vectors = null
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[components]
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[components.lemmatizer]
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+
factory = "trainable_lemmatizer"
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+
backoff = "orth"
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+
min_tree_freq = 1
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+
overwrite = false
|
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scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
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+
top_k = 3
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+
|
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+
[components.lemmatizer.model]
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+
@architectures = "spacy.Tagger.v1"
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+
nO = null
|
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+
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+
[components.lemmatizer.model.tok2vec]
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+
@architectures = "spacy.Tok2Vec.v2"
|
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+
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+
[components.lemmatizer.model.tok2vec.embed]
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+
@architectures = "spacy.MultiHashEmbed.v2"
|
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+
width = 300
|
43 |
+
attrs = ["LOWER","PREFIX","SUFFIX","SHAPE"]
|
44 |
+
rows = [5000,2500,2500,2500]
|
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+
include_static_vectors = true
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+
|
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+
[components.lemmatizer.model.tok2vec.encode]
|
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+
@architectures = "spacy.MaxoutWindowEncoder.v2"
|
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+
width = 300
|
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+
depth = 4
|
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+
window_size = 2
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+
maxout_pieces = 5
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[components.morphologizer]
|
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factory = "morphologizer"
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|
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sents_f = 0.0
|
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sents_p = null
|
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sents_r = null
|
238 |
+
tag_acc = 0.2
|
239 |
+
pos_acc = 0.1
|
240 |
+
morph_acc = 0.1
|
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morph_per_feat = null
|
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+
lemma_acc = 0.2
|
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+
dep_uas = 0.1
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+
dep_las = 0.1
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dep_las_per_type = null
|
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+
ents_f = 0.2
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ents_p = 0.0
|
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ents_r = 0.0
|
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ents_per_type = null
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260 |
|
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[initialize.components]
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|
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[initialize.tokenizer]
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hu_core_news_lg-any-py3-none-any.whl
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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-
size
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1 |
version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:6823f3f9d1f5036dcff94d1a5ba074155da18ba6f1a4e8dc6c5c124d9daff3d2
|
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+
size 401139364
|
lemmatizer/cfg
ADDED
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942 |
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946 |
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947 |
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948 |
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951 |
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952 |
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953 |
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954 |
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955 |
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962 |
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963 |
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964 |
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965 |
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966 |
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|
967 |
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|
968 |
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969 |
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970 |
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971 |
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|
972 |
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|
973 |
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|
974 |
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|
975 |
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|
976 |
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|
977 |
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|
978 |
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|
979 |
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|
980 |
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|
981 |
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|
982 |
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|
983 |
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|
984 |
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|
985 |
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|
986 |
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|
987 |
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1949,
|
988 |
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1950,
|
989 |
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1951,
|
990 |
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1953,
|
991 |
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1954,
|
992 |
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1955,
|
993 |
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1957,
|
994 |
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1958,
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995 |
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1960,
|
996 |
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1961,
|
997 |
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|
998 |
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1964,
|
999 |
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1965,
|
1000 |
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|
1001 |
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1969,
|
1002 |
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|
1003 |
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|
1004 |
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1974,
|
1005 |
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|
1006 |
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1978,
|
1007 |
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|
1008 |
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1982,
|
1009 |
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1984,
|
1010 |
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|
1011 |
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1987,
|
1012 |
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1989,
|
1013 |
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1991,
|
1014 |
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1993,
|
1015 |
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1995,
|
1016 |
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1997,
|
1017 |
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1998,
|
1018 |
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2000,
|
1019 |
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2003,
|
1020 |
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2005,
|
1021 |
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|
1022 |
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|
1023 |
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2011,
|
1024 |
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|
1025 |
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|
1026 |
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|
1027 |
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1028 |
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|
lemmatizer/model
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@@ -1,3 +1,3 @@
|
|
1 |
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|
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|
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size
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lemmatizer/trees
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meta.json
CHANGED
@@ -1,18 +1,18 @@
|
|
1 |
{
|
2 |
"lang":"hu",
|
3 |
"name":"core_news_lg",
|
4 |
-
"version":"3.
|
5 |
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|
6 |
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|
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|
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|
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|
15 |
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"keys"
|
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|
17 |
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|
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|
@@ -1188,9 +1188,6 @@
|
|
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|
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|
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|
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|
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|
@@ -1270,80 +1267,80 @@
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1 |
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2 |
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