diyclassics commited on
Commit
c74d2a2
1 Parent(s): 74f9469

Update spaCy pipeline

Browse files
README.md CHANGED
@@ -14,72 +14,72 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.9546354339
18
  - name: NER Recall
19
  type: recall
20
- value: 0.9408226305
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.947678703
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9649296122
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9829896358
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.9568359544
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9525518858
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8847980068
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.844676641
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.9438152831
73
  ---
74
  | Feature | Description |
75
  | --- | --- |
76
  | **Name** | `la_core_web_trf` |
77
- | **Version** | `3.7.6` |
78
- | **spaCy** | `>=3.7.4,<3.8.0` |
79
- | **Default Pipeline** | `transformer`, `normer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `lookup_lemmatizer`, `ner` |
80
- | **Components** | `transformer`, `normer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `lookup_lemmatizer`, `ner` |
81
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
82
- | **Sources** | UD_Latin-Perseus<br>UD_Latin-PROIEL<br>UD_Latin-ITTB<br>UD_Latin-LLCT<br>UD_Latin-UDante |
83
  | **License** | `MIT` |
84
  | **Author** | [Patrick J. Burns; with Nora Bernhardt [ner], Tim Geelhaar [tagger, morphologizer, parser, ner], Vincent Koch [ner]](https://diyclassics.github.io/) |
85
 
@@ -102,21 +102,21 @@ model-index:
102
 
103
  | Type | Score |
104
  | --- | --- |
105
- | `ENTS_F` | 94.98 |
106
- | `ENTS_P` | 94.10 |
107
- | `ENTS_R` | 95.89 |
108
- | `TRANSFORMER_LOSS` | 3954242.59 |
109
- | `NER_LOSS` | 8199.98 |
110
- | `TAG_ACC` | 96.49 |
111
- | `POS_ACC` | 98.30 |
112
- | `MORPH_ACC` | 95.68 |
113
- | `LEMMA_ACC` | 95.26 |
114
- | `DEP_UAS` | 88.48 |
115
- | `DEP_LAS` | 84.47 |
116
- | `SENTS_P` | 94.66 |
117
- | `SENTS_R` | 94.10 |
118
- | `SENTS_F` | 94.38 |
119
- | `TAGGER_LOSS` | 41020.70 |
120
- | `MORPHOLOGIZER_LOSS` | 348603.00 |
121
- | `TRAINABLE_LEMMATIZER_LOSS` | 349936.53 |
122
- | `PARSER_LOSS` | 2759001.09 |
 
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.9631931948
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.9570871261
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.9601304525
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9640958514
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9831838987
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9581374663
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9531809911
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.8882308136
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.8492401865
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.9959496442
73
  ---
74
  | Feature | Description |
75
  | --- | --- |
76
  | **Name** | `la_core_web_trf` |
77
+ | **Version** | `3.7.7` |
78
+ | **spaCy** | `>=3.7.5,<3.8.0` |
79
+ | **Default Pipeline** | `senter`, `transformer`, `normer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `lookup_lemmatizer`, `ner` |
80
+ | **Components** | `senter`, `transformer`, `normer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `lookup_lemmatizer`, `ner` |
81
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
82
+ | **Sources** | UD_Latin-Perseus (via Gamba/Zeman 2023)<br>UD_Latin-PROIEL (via Gamba/Zeman 2023)<br>UD_Latin-ITTB (via Gamba/Zeman 2023)<br>UD_Latin-LLCT (via Gamba/Zeman 2023<br>UD_Latin-UDante (via Gamba/Zeman 2023)<br>CIRCSE/LASLA: LASLA Corpus |
83
  | **License** | `MIT` |
84
  | **Author** | [Patrick J. Burns; with Nora Bernhardt [ner], Tim Geelhaar [tagger, morphologizer, parser, ner], Vincent Koch [ner]](https://diyclassics.github.io/) |
85
 
 
102
 
103
  | Type | Score |
104
  | --- | --- |
105
+ | `ENTS_F` | 95.43 |
106
+ | `ENTS_P` | 94.87 |
107
+ | `ENTS_R` | 95.99 |
108
+ | `TRANSFORMER_LOSS` | 3054585.96 |
109
+ | `NER_LOSS` | 9051.00 |
110
+ | `TAG_ACC` | 96.33 |
111
+ | `POS_ACC` | 98.31 |
112
+ | `MORPH_ACC` | 95.75 |
113
+ | `LEMMA_ACC` | 95.27 |
114
+ | `DEP_UAS` | 88.61 |
115
+ | `DEP_LAS` | 84.72 |
116
+ | `SENTS_P` | 94.93 |
117
+ | `SENTS_R` | 94.41 |
118
+ | `SENTS_F` | 94.67 |
119
+ | `TAGGER_LOSS` | 28864.85 |
120
+ | `MORPHOLOGIZER_LOSS` | 246784.11 |
121
+ | `TRAINABLE_LEMMATIZER_LOSS` | 242230.83 |
122
+ | `PARSER_LOSS` | 2414158.66 |
config.cfg CHANGED
@@ -10,7 +10,7 @@ seed = 0
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11
  [nlp]
12
  lang = "la"
13
- pipeline = ["transformer","normer","tagger","morphologizer","trainable_lemmatizer","parser","lookup_lemmatizer","ner"]
14
  tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
15
  disabled = []
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  before_creation = null
@@ -129,6 +129,26 @@ use_fast = true
129
 
130
  [components.parser.model.tok2vec.transformer_config]
131
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
132
  [components.tagger]
133
  factory = "tagger"
134
  label_smoothing = 0.0
@@ -251,6 +271,9 @@ eps = 0.00000001
251
  learn_rate = 0.001
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253
  [training.score_weights]
 
 
 
254
  tag_acc = 0.2
255
  pos_acc = 0.1
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  morph_acc = 0.1
@@ -259,9 +282,6 @@ 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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- sents_p = null
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- sents_r = null
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- sents_f = 0.0
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  ents_f = 0.2
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  ents_p = 0.0
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  ents_r = 0.0
 
10
 
11
  [nlp]
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  lang = "la"
13
+ pipeline = ["senter","transformer","normer","tagger","morphologizer","trainable_lemmatizer","parser","lookup_lemmatizer","ner"]
14
  tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
15
  disabled = []
16
  before_creation = null
 
129
 
130
  [components.parser.model.tok2vec.transformer_config]
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132
+ [components.senter]
133
+ factory = "senter"
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+ overwrite = false
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+ scorer = {"@scorers":"spacy.senter_scorer.v1"}
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+
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+ [components.senter.model]
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+ @architectures = "spacy.Tagger.v2"
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+ nO = null
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+ normalize = false
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+
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+ [components.senter.model.tok2vec]
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+ @architectures = "spacy.HashEmbedCNN.v2"
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+ pretrained_vectors = null
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+ width = 12
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+ depth = 1
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+ embed_size = 2000
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+ window_size = 1
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+ maxout_pieces = 2
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+ subword_features = true
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+
152
  [components.tagger]
153
  factory = "tagger"
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  label_smoothing = 0.0
 
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  learn_rate = 0.001
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273
  [training.score_weights]
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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 = 0.2
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  pos_acc = 0.1
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  morph_acc = 0.1
 
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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
 
 
 
285
  ents_f = 0.2
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  ents_p = 0.0
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  ents_r = 0.0
functions.py CHANGED
@@ -200,11 +200,8 @@ import string
200
  blank_nlp = spacy.blank("la")
201
  lookups = Lookups()
202
 
203
- try:
204
- lookups_data = load_lookups(lang=blank_nlp.vocab.lang, tables=["lemma_lookup"])
205
- except:
206
- lookups_data = lookups.from_disk("scripts/lemmatizer_lookups")
207
 
 
208
  LOOKUPS = lookups_data.get_table("lemma_lookup")
209
 
210
  predicted_lemma_getter = lambda token: token.lemma_
 
200
  blank_nlp = spacy.blank("la")
201
  lookups = Lookups()
202
 
 
 
 
 
203
 
204
+ lookups_data = load_lookups(lang=blank_nlp.vocab.lang, tables=["lemma_lookup"])
205
  LOOKUPS = lookups_data.get_table("lemma_lookup")
206
 
207
  predicted_lemma_getter = lambda token: token.lemma_
la_core_web_trf-any-py3-none-any.whl CHANGED
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meta.json CHANGED
@@ -1,14 +1,14 @@
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  {
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  "lang":"la",
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  "name":"core_web_trf",
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- "version":"3.7.6",
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  "description":"",
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  "author":"Patrick J. Burns; with Nora Bernhardt [ner], Tim Geelhaar [tagger, morphologizer, parser, ner], Vincent Koch [ner]",
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  "email":"pjb311@nyu.edu",
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  "url":"https://diyclassics.github.io/",
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  "license":"MIT",
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@@ -811,6 +811,7 @@
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