Update spacy pipeline to 3.7.0
Browse files- README.md +30 -30
- config.cfg +7 -4
- experimental_arc_labeler/model +1 -1
- experimental_arc_predicter/model +1 -1
- hu_core_news_trf-any-py3-none-any.whl +2 -2
- meta.json +190 -195
- morphologizer/cfg +1 -0
- morphologizer/model +1 -1
- ner/model +1 -1
- senter/model +1 -1
- tagger/cfg +1 -0
- tagger/model +1 -1
- trainable_lemmatizer/model +1 -1
- transformer/model +1 -1
- vocab/strings.json +2 -2
README.md
CHANGED
@@ -14,74 +14,74 @@ 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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---
|
74 |
Hungarian transformer pipeline (huBERT) for HuSpaCy. Components: transformer, senter, tagger, morphologizer, lemmatizer, parser, ner
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75 |
|
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| Feature | Description |
|
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| --- | --- |
|
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| **Name** | `hu_core_news_trf` |
|
79 |
-
| **Version** | `3.
|
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-
| **spaCy** | `>=3.
|
81 |
| **Default Pipeline** | `transformer`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `experimental_arc_predicter`, `experimental_arc_labeler`, `ner` |
|
82 |
| **Components** | `transformer`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `experimental_arc_predicter`, `experimental_arc_labeler`, `ner` |
|
83 |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
|
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-
| **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
|
85 |
| **License** | `cc-by-sa-4.0` |
|
86 |
| **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) |
|
87 |
|
@@ -108,20 +108,20 @@ Hungarian transformer pipeline (huBERT) for HuSpaCy. Components: transformer, se
|
|
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| `TOKEN_P` | 99.86 |
|
109 |
| `TOKEN_R` | 99.93 |
|
110 |
| `TOKEN_F` | 99.89 |
|
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-
| `SENTS_P` |
|
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-
| `SENTS_R` | 99.
|
113 |
-
| `SENTS_F` | 99.
|
114 |
-
| `TAG_ACC` | 98.
|
115 |
-
| `POS_ACC` | 98.
|
116 |
-
| `MORPH_ACC` | 96.
|
117 |
-
| `MORPH_MICRO_P` | 98.
|
118 |
-
| `MORPH_MICRO_R` | 98.
|
119 |
-
| `MORPH_MICRO_F` | 98.
|
120 |
-
| `LEMMA_ACC` | 98.
|
121 |
-
| `BOUND_DEP_LAS` |
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-
| `BOUND_DEP_UAS` | 90.
|
123 |
-
| `DEP_UAS` | 90.
|
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-
| `DEP_LAS` |
|
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-
| `ENTS_P` |
|
126 |
-
| `ENTS_R` |
|
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-
| `ENTS_F` | 91.
|
|
|
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metrics:
|
15 |
- name: NER Precision
|
16 |
type: precision
|
17 |
+
value: 0.9119332986
|
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- name: NER Recall
|
19 |
type: recall
|
20 |
+
value: 0.9229957806
|
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- name: NER F Score
|
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type: f_score
|
23 |
+
value: 0.9174311927
|
24 |
- task:
|
25 |
name: TAG
|
26 |
type: token-classification
|
27 |
metrics:
|
28 |
- name: TAG (XPOS) Accuracy
|
29 |
type: accuracy
|
30 |
+
value: 0.9823906594
|
31 |
- task:
|
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name: POS
|
33 |
type: token-classification
|
34 |
metrics:
|
35 |
- name: POS (UPOS) Accuracy
|
36 |
type: accuracy
|
37 |
+
value: 0.9820078476
|
38 |
- task:
|
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name: MORPH
|
40 |
type: token-classification
|
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metrics:
|
42 |
- name: Morph (UFeats) Accuracy
|
43 |
type: accuracy
|
44 |
+
value: 0.9658340511
|
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- task:
|
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name: LEMMA
|
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type: token-classification
|
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metrics:
|
49 |
- name: Lemma Accuracy
|
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type: accuracy
|
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+
value: 0.9861257296
|
52 |
- task:
|
53 |
name: UNLABELED_DEPENDENCIES
|
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type: token-classification
|
55 |
metrics:
|
56 |
- name: Unlabeled Attachment Score (UAS)
|
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type: f_score
|
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+
value: 0.9000861326
|
59 |
- task:
|
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name: LABELED_DEPENDENCIES
|
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type: token-classification
|
62 |
metrics:
|
63 |
- name: Labeled Attachment Score (LAS)
|
64 |
type: f_score
|
65 |
+
value: 0.8568421053
|
66 |
- task:
|
67 |
name: SENTS
|
68 |
type: token-classification
|
69 |
metrics:
|
70 |
- name: Sentences F-Score
|
71 |
type: f_score
|
72 |
+
value: 0.9899888765
|
73 |
---
|
74 |
Hungarian transformer pipeline (huBERT) for HuSpaCy. Components: transformer, senter, tagger, morphologizer, lemmatizer, parser, ner
|
75 |
|
76 |
| Feature | Description |
|
77 |
| --- | --- |
|
78 |
| **Name** | `hu_core_news_trf` |
|
79 |
+
| **Version** | `3.7.0` |
|
80 |
+
| **spaCy** | `>=3.7.0,<3.8.0` |
|
81 |
| **Default Pipeline** | `transformer`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `experimental_arc_predicter`, `experimental_arc_labeler`, `ner` |
|
82 |
| **Components** | `transformer`, `senter`, `tagger`, `morphologizer`, `lookup_lemmatizer`, `trainable_lemmatizer`, `experimental_arc_predicter`, `experimental_arc_labeler`, `ner` |
|
83 |
| **Vectors** | 0 keys, 0 unique vectors (0 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>[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>[huBERT base model (cased)](https://huggingface.co/SZTAKI-HLT/hubert-base-cc) (Dávid Márk Nemeskey (SZTAKI-HLT)) |
|
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| **License** | `cc-by-sa-4.0` |
|
86 |
| **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) |
|
87 |
|
|
|
108 |
| `TOKEN_P` | 99.86 |
|
109 |
| `TOKEN_R` | 99.93 |
|
110 |
| `TOKEN_F` | 99.89 |
|
111 |
+
| `SENTS_P` | 98.89 |
|
112 |
+
| `SENTS_R` | 99.11 |
|
113 |
+
| `SENTS_F` | 99.00 |
|
114 |
+
| `TAG_ACC` | 98.24 |
|
115 |
+
| `POS_ACC` | 98.20 |
|
116 |
+
| `MORPH_ACC` | 96.58 |
|
117 |
+
| `MORPH_MICRO_P` | 98.81 |
|
118 |
+
| `MORPH_MICRO_R` | 98.40 |
|
119 |
+
| `MORPH_MICRO_F` | 98.60 |
|
120 |
+
| `LEMMA_ACC` | 98.61 |
|
121 |
+
| `BOUND_DEP_LAS` | 85.75 |
|
122 |
+
| `BOUND_DEP_UAS` | 90.08 |
|
123 |
+
| `DEP_UAS` | 90.01 |
|
124 |
+
| `DEP_LAS` | 85.68 |
|
125 |
+
| `ENTS_P` | 91.19 |
|
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+
| `ENTS_R` | 92.30 |
|
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+
| `ENTS_F` | 91.74 |
|
config.cfg
CHANGED
@@ -1,8 +1,8 @@
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[paths]
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-
tagger_model = "models/hu_core_news_trf-tagger-3.
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-
parser_model = "models/hu_core_news_trf-parser-3.
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-
ner_model = "models/hu_core_news_trf-ner-3.
|
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-
lemmatizer_lookups = "models/hu_core_news_trf-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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@@ -21,6 +21,7 @@ before_creation = null
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after_creation = null
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after_pipeline_creation = null
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batch_size = 1000
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[components]
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@@ -68,6 +69,7 @@ source = ${paths.lemmatizer_lookups}
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[components.morphologizer]
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factory = "morphologizer"
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extend = false
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overwrite = true
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scorer = {"@scorers":"spacy.morphologizer_scorer.v1"}
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@@ -137,6 +139,7 @@ pooling = {"@layers":"reduce_mean.v1"}
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[components.tagger]
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factory = "tagger"
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neg_prefix = "!"
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overwrite = false
|
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scorer = {"@scorers":"spacy.tagger_scorer.v1"}
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[paths]
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+
tagger_model = "models/hu_core_news_trf-tagger-3.7.0/model-best"
|
3 |
+
parser_model = "models/hu_core_news_trf-parser-3.7.0/model-best"
|
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+
ner_model = "models/hu_core_news_trf-ner-3.7.0/model-best"
|
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+
lemmatizer_lookups = "models/hu_core_news_trf-lookup-lemmatizer-3.7.0"
|
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train = null
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dev = null
|
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vectors = null
|
|
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after_creation = null
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after_pipeline_creation = null
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batch_size = 1000
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+
vectors = {"@vectors":"spacy.Vectors.v1"}
|
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[components]
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|
|
|
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[components.morphologizer]
|
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factory = "morphologizer"
|
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extend = false
|
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+
label_smoothing = 0.0
|
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overwrite = true
|
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scorer = {"@scorers":"spacy.morphologizer_scorer.v1"}
|
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|
|
|
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[components.tagger]
|
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factory = "tagger"
|
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+
label_smoothing = 0.0
|
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neg_prefix = "!"
|
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overwrite = false
|
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scorer = {"@scorers":"spacy.tagger_scorer.v1"}
|
experimental_arc_labeler/model
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experimental_arc_predicter/model
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hu_core_news_trf-any-py3-none-any.whl
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meta.json
CHANGED
@@ -1,14 +1,14 @@
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{
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"lang":"hu",
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"name":"core_news_trf",
|
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-
"version":"3.
|
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"description":"Hungarian transformer pipeline (huBERT) for HuSpaCy. Components: transformer, senter, tagger, morphologizer, lemmatizer, parser, ner",
|
6 |
"author":"SzegedAI, MILAB",
|
7 |
"email":"gyorgy@orosz.link",
|
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"url":"https://github.com/huspacy/huspacy",
|
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"license":"cc-by-sa-4.0",
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"spacy_version":">=3.
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"spacy_git_version":"
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"vectors":{
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