etikaj-digital commited on
Commit
ab1b310
1 Parent(s): 50a7d4b

Update spaCy pipeline

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README.md ADDED
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+ ---
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+ tags:
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+ - spacy
4
+ - token-classification
5
+ language:
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+ - en
7
+ license: mit
8
+ model-index:
9
+ - name: en_statistics
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+ results:
11
+ - task:
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+ name: NER
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+ type: token-classification
14
+ metrics:
15
+ - name: NER Precision
16
+ type: precision
17
+ value: 0.853733758
18
+ - name: NER Recall
19
+ type: recall
20
+ value: 0.8456530449
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+ - name: NER F Score
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+ type: f_score
23
+ value: 0.8496741892
24
+ - task:
25
+ name: TAG
26
+ type: token-classification
27
+ metrics:
28
+ - name: TAG (XPOS) Accuracy
29
+ type: accuracy
30
+ value: 0.9727831973
31
+ - task:
32
+ name: UNLABELED_DEPENDENCIES
33
+ type: token-classification
34
+ metrics:
35
+ - name: Unlabeled Attachment Score (UAS)
36
+ type: f_score
37
+ value: 0.9186878782
38
+ - task:
39
+ name: LABELED_DEPENDENCIES
40
+ type: token-classification
41
+ metrics:
42
+ - name: Labeled Attachment Score (LAS)
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+ type: f_score
44
+ value: 0.9005160534
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+ - task:
46
+ name: SENTS
47
+ type: token-classification
48
+ metrics:
49
+ - name: Sentences F-Score
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+ type: f_score
51
+ value: 0.8923519379
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+ ---
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+ English pipeline that provides statistics, readability and formality scores.
54
+
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+ | Feature | Description |
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+ | --- | --- |
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+ | **Name** | `en_statistics` |
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+ | **Version** | `0.0.1` |
59
+ | **spaCy** | `>=3.1.1,<3.2.0` |
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+ | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`, `syllables`, `formality`, `readability` |
61
+ | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner`, `syllables`, `formality`, `readability` |
62
+ | **Vectors** | 684830 keys, 20000 unique vectors (300 dimensions) |
63
+ | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br />[GloVe Common Crawl](https://nlp.stanford.edu/projects/glove/) (Jeffrey Pennington, Richard Socher, and Christopher D. Manning) |
64
+ | **License** | `MIT` |
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+ | **Author** | [Contentologie]() |
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+
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+ ### Label Scheme
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+
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+ <details>
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+
71
+ <summary>View label scheme (114 labels for 4 components)</summary>
72
+
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+ | Component | Labels |
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+ | --- | --- |
75
+ | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
76
+ | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
77
+ | **`senter`** | `I`, `S` |
78
+ | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
79
+
80
+ </details>
81
+
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+ ### Accuracy
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+
84
+ | Type | Score |
85
+ | --- | --- |
86
+ | `TOKEN_ACC` | 99.93 |
87
+ | `TAG_ACC` | 97.28 |
88
+ | `DEP_UAS` | 91.87 |
89
+ | `DEP_LAS` | 90.05 |
90
+ | `ENTS_P` | 85.37 |
91
+ | `ENTS_R` | 84.57 |
92
+ | `ENTS_F` | 84.97 |
93
+ | `SENTS_P` | 90.49 |
94
+ | `SENTS_R` | 88.01 |
95
+ | `SENTS_F` | 89.24 |
attribute_ruler/patterns ADDED
Binary file (16 kB). View file
config.cfg ADDED
@@ -0,0 +1,274 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [paths]
2
+ train = "corpus/en-core-web/train.spacy"
3
+ dev = "corpus/en-core-web/dev.spacy"
4
+ vectors = "corpus/en_vectors"
5
+ raw = null
6
+ init_tok2vec = null
7
+ vocab_data = null
8
+
9
+ [system]
10
+ gpu_allocator = null
11
+ seed = 0
12
+
13
+ [nlp]
14
+ lang = "en"
15
+ pipeline = ["tok2vec","tagger","parser","senter","attribute_ruler","lemmatizer","ner","syllables","formality","readability"]
16
+ disabled = ["senter"]
17
+ before_creation = null
18
+ after_creation = null
19
+ after_pipeline_creation = null
20
+ batch_size = 256
21
+ tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
22
+
23
+ [components]
24
+
25
+ [components.attribute_ruler]
26
+ factory = "attribute_ruler"
27
+ validate = false
28
+
29
+ [components.formality]
30
+ factory = "formality"
31
+
32
+ [components.lemmatizer]
33
+ factory = "lemmatizer"
34
+ mode = "rule"
35
+ model = null
36
+ overwrite = false
37
+
38
+ [components.ner]
39
+ factory = "ner"
40
+ incorrect_spans_key = null
41
+ moves = null
42
+ update_with_oracle_cut_size = 100
43
+
44
+ [components.ner.model]
45
+ @architectures = "spacy.TransitionBasedParser.v2"
46
+ state_type = "ner"
47
+ extra_state_tokens = false
48
+ hidden_width = 64
49
+ maxout_pieces = 2
50
+ use_upper = true
51
+ nO = null
52
+
53
+ [components.ner.model.tok2vec]
54
+ @architectures = "spacy.Tok2Vec.v2"
55
+
56
+ [components.ner.model.tok2vec.embed]
57
+ @architectures = "spacy.MultiHashEmbed.v2"
58
+ width = 96
59
+ attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
60
+ rows = [5000,2500,2500,2500]
61
+ include_static_vectors = true
62
+
63
+ [components.ner.model.tok2vec.encode]
64
+ @architectures = "spacy.MaxoutWindowEncoder.v2"
65
+ width = 96
66
+ depth = 4
67
+ window_size = 1
68
+ maxout_pieces = 3
69
+
70
+ [components.parser]
71
+ factory = "parser"
72
+ learn_tokens = false
73
+ min_action_freq = 30
74
+ moves = null
75
+ update_with_oracle_cut_size = 100
76
+
77
+ [components.parser.model]
78
+ @architectures = "spacy.TransitionBasedParser.v2"
79
+ state_type = "parser"
80
+ extra_state_tokens = false
81
+ hidden_width = 64
82
+ maxout_pieces = 2
83
+ use_upper = true
84
+ nO = null
85
+
86
+ [components.parser.model.tok2vec]
87
+ @architectures = "spacy.Tok2VecListener.v1"
88
+ width = ${components.tok2vec.model.encode:width}
89
+ upstream = "tok2vec"
90
+
91
+ [components.readability]
92
+ factory = "readability"
93
+
94
+ [components.senter]
95
+ factory = "senter"
96
+
97
+ [components.senter.model]
98
+ @architectures = "spacy.Tagger.v1"
99
+ nO = null
100
+
101
+ [components.senter.model.tok2vec]
102
+ @architectures = "spacy.Tok2Vec.v2"
103
+
104
+ [components.senter.model.tok2vec.embed]
105
+ @architectures = "spacy.MultiHashEmbed.v2"
106
+ width = 16
107
+ attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
108
+ rows = [1000,500,500,500]
109
+ include_static_vectors = true
110
+
111
+ [components.senter.model.tok2vec.encode]
112
+ @architectures = "spacy.MaxoutWindowEncoder.v2"
113
+ width = 16
114
+ depth = 2
115
+ window_size = 1
116
+ maxout_pieces = 2
117
+
118
+ [components.syllables]
119
+ factory = "syllables"
120
+
121
+ [components.tagger]
122
+ factory = "tagger"
123
+
124
+ [components.tagger.model]
125
+ @architectures = "spacy.Tagger.v1"
126
+ nO = null
127
+
128
+ [components.tagger.model.tok2vec]
129
+ @architectures = "spacy.Tok2VecListener.v1"
130
+ width = ${components.tok2vec.model.encode:width}
131
+ upstream = "tok2vec"
132
+
133
+ [components.tok2vec]
134
+ factory = "tok2vec"
135
+
136
+ [components.tok2vec.model]
137
+ @architectures = "spacy.Tok2Vec.v2"
138
+
139
+ [components.tok2vec.model.embed]
140
+ @architectures = "spacy.MultiHashEmbed.v2"
141
+ width = ${components.tok2vec.model.encode:width}
142
+ attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
143
+ rows = [5000,2500,2500,2500]
144
+ include_static_vectors = true
145
+
146
+ [components.tok2vec.model.encode]
147
+ @architectures = "spacy.MaxoutWindowEncoder.v2"
148
+ width = 96
149
+ depth = 4
150
+ window_size = 1
151
+ maxout_pieces = 3
152
+
153
+ [corpora]
154
+
155
+ [corpora.dev]
156
+ @readers = "spacy.Corpus.v1"
157
+ limit = 0
158
+ max_length = 0
159
+ path = ${paths:dev}
160
+ gold_preproc = false
161
+ augmenter = null
162
+
163
+ [corpora.train]
164
+ @readers = "spacy.Corpus.v1"
165
+ path = ${paths:train}
166
+ max_length = 5000
167
+ gold_preproc = false
168
+ limit = 0
169
+
170
+ [corpora.train.augmenter]
171
+ @augmenters = "spacy.orth_variants.v1"
172
+ level = 0.2
173
+ lower = 0.5
174
+
175
+ [corpora.train.augmenter.orth_variants]
176
+ @readers = "srsly.read_json.v1"
177
+ path = "assets/orth_variants.json"
178
+
179
+ [training]
180
+ train_corpus = "corpora.train"
181
+ dev_corpus = "corpora.dev"
182
+ seed = ${system:seed}
183
+ gpu_allocator = ${system:gpu_allocator}
184
+ dropout = 0.1
185
+ accumulate_gradient = 1
186
+ patience = 5000
187
+ max_epochs = 0
188
+ max_steps = 0
189
+ eval_frequency = 1000
190
+ frozen_components = []
191
+ before_to_disk = null
192
+ annotating_components = []
193
+
194
+ [training.batcher]
195
+ @batchers = "spacy.batch_by_words.v1"
196
+ discard_oversize = false
197
+ tolerance = 0.2
198
+ get_length = null
199
+
200
+ [training.batcher.size]
201
+ @schedules = "compounding.v1"
202
+ start = 100
203
+ stop = 1000
204
+ compound = 1.001
205
+ t = 0.0
206
+
207
+ [training.logger]
208
+ @loggers = "spacy.WandbLogger.v1"
209
+ project_name = "spacy-v3.0.0a2"
210
+ remove_config_values = []
211
+
212
+ [training.optimizer]
213
+ @optimizers = "Adam.v1"
214
+ beta1 = 0.9
215
+ beta2 = 0.999
216
+ L2_is_weight_decay = true
217
+ L2 = 0.01
218
+ grad_clip = 1.0
219
+ use_averages = true
220
+ eps = 0.00000001
221
+ learn_rate = 0.001
222
+
223
+ [training.score_weights]
224
+ tag_acc = 0.16
225
+ dep_uas = 0.0
226
+ dep_las = 0.16
227
+ dep_las_per_type = null
228
+ sents_p = null
229
+ sents_r = null
230
+ sents_f = 0.02
231
+ lemma_acc = 0.33
232
+ ents_f = 0.33
233
+ ents_p = 0.0
234
+ ents_r = 0.0
235
+ ents_per_type = null
236
+
237
+ [pretraining]
238
+
239
+ [initialize]
240
+ vocab_data = ${paths.vocab_data}
241
+ vectors = ${paths.vectors}
242
+ init_tok2vec = ${paths.init_tok2vec}
243
+ before_init = null
244
+ after_init = null
245
+
246
+ [initialize.components]
247
+
248
+ [initialize.components.ner]
249
+
250
+ [initialize.components.ner.labels]
251
+ @readers = "spacy.read_labels.v1"
252
+ path = "corpus/labels/ner.json"
253
+ require = false
254
+
255
+ [initialize.components.parser]
256
+
257
+ [initialize.components.parser.labels]
258
+ @readers = "spacy.read_labels.v1"
259
+ path = "corpus/labels/parser.json"
260
+ require = false
261
+
262
+ [initialize.components.tagger]
263
+
264
+ [initialize.components.tagger.labels]
265
+ @readers = "spacy.read_labels.v1"
266
+ path = "corpus/labels/tagger.json"
267
+ require = false
268
+
269
+ [initialize.lookups]
270
+ @misc = "spacy.LookupsDataLoader.v1"
271
+ lang = ${nlp.lang}
272
+ tables = ["lexeme_norm"]
273
+
274
+ [initialize.tokenizer]
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1
+ {
2
+ "lang":"en",
3
+ "name":"statistics",
4
+ "version":"0.0.1",
5
+ "description":"English pipeline that provides statistics, readability and formality scores.",
6
+ "author":"Contentologie",
7
+ "email":"",
8
+ "url":"",
9
+ "license":"MIT",
10
+ "spacy_version":">=3.1.1,<3.2.0",
11
+ "spacy_git_version":"caba63b74",
12
+ "vectors":{
13
+ "width":300,
14
+ "vectors":20000,
15
+ "keys":684830,
16
+ "name":"en_vectors"
17
+ },
18
+ "labels":{
19
+ "tok2vec":[
20
+
21
+ ],
22
+ "tagger":[
23
+ "$",
24
+ "''",
25
+ ",",
26
+ "-LRB-",
27
+ "-RRB-",
28
+ ".",
29
+ ":",
30
+ "ADD",
31
+ "AFX",
32
+ "CC",
33
+ "CD",
34
+ "DT",
35
+ "EX",
36
+ "FW",
37
+ "HYPH",
38
+ "IN",
39
+ "JJ",
40
+ "JJR",
41
+ "JJS",
42
+ "LS",
43
+ "MD",
44
+ "NFP",
45
+ "NN",
46
+ "NNP",
47
+ "NNPS",
48
+ "NNS",
49
+ "PDT",
50
+ "POS",
51
+ "PRP",
52
+ "PRP$",
53
+ "RB",
54
+ "RBR",
55
+ "RBS",
56
+ "RP",
57
+ "SYM",
58
+ "TO",
59
+ "UH",
60
+ "VB",
61
+ "VBD",
62
+ "VBG",
63
+ "VBN",
64
+ "VBP",
65
+ "VBZ",
66
+ "WDT",
67
+ "WP",
68
+ "WP$",
69
+ "WRB",
70
+ "XX",
71
+ "``"
72
+ ],
73
+ "parser":[
74
+ "ROOT",
75
+ "acl",
76
+ "acomp",
77
+ "advcl",
78
+ "advmod",
79
+ "agent",
80
+ "amod",
81
+ "appos",
82
+ "attr",
83
+ "aux",
84
+ "auxpass",
85
+ "case",
86
+ "cc",
87
+ "ccomp",
88
+ "compound",
89
+ "conj",
90
+ "csubj",
91
+ "csubjpass",
92
+ "dative",
93
+ "dep",
94
+ "det",
95
+ "dobj",
96
+ "expl",
97
+ "intj",
98
+ "mark",
99
+ "meta",
100
+ "neg",
101
+ "nmod",
102
+ "npadvmod",
103
+ "nsubj",
104
+ "nsubjpass",
105
+ "nummod",
106
+ "oprd",
107
+ "parataxis",
108
+ "pcomp",
109
+ "pobj",
110
+ "poss",
111
+ "preconj",
112
+ "predet",
113
+ "prep",
114
+ "prt",
115
+ "punct",
116
+ "quantmod",
117
+ "relcl",
118
+ "xcomp"
119
+ ],
120
+ "senter":[
121
+ "I",
122
+ "S"
123
+ ],
124
+ "attribute_ruler":[
125
+
126
+ ],
127
+ "lemmatizer":[
128
+
129
+ ],
130
+ "ner":[
131
+ "CARDINAL",
132
+ "DATE",
133
+ "EVENT",
134
+ "FAC",
135
+ "GPE",
136
+ "LANGUAGE",
137
+ "LAW",
138
+ "LOC",
139
+ "MONEY",
140
+ "NORP",
141
+ "ORDINAL",
142
+ "ORG",
143
+ "PERCENT",
144
+ "PERSON",
145
+ "PRODUCT",
146
+ "QUANTITY",
147
+ "TIME",
148
+ "WORK_OF_ART"
149
+ ]
150
+ },
151
+ "pipeline":[
152
+ "tok2vec",
153
+ "tagger",
154
+ "parser",
155
+ "attribute_ruler",
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