adrianeboyd
commited on
Commit
•
4752428
1
Parent(s):
6185031
Update spaCy pipeline
Browse files- README.md +28 -28
- accuracy.json +168 -168
- attribute_ruler/patterns +0 -0
- config.cfg +5 -4
- fr_core_news_sm-any-py3-none-any.whl +2 -2
- meta.json +171 -171
- morphologizer/model +1 -1
- ner/model +2 -2
- parser/model +1 -1
- senter/model +1 -1
- tok2vec/model +2 -2
- tokenizer +2 -2
README.md
CHANGED
@@ -14,62 +14,62 @@ 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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---
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### Details: https://spacy.io/models/fr#fr_core_news_sm
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|
@@ -78,8 +78,8 @@ French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, s
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| Feature | Description |
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| --- | --- |
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| **Name** | `fr_core_news_sm` |
|
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-
| **Version** | `3.
|
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-
| **spaCy** | `>=3.
|
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| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
|
84 |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
|
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
|
@@ -105,22 +105,22 @@ French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, s
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| Type | Score |
|
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| --- | --- |
|
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-
| `TOKEN_ACC` | 99.
|
109 |
| `TOKEN_P` | 98.44 |
|
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| `TOKEN_R` | 98.96 |
|
111 |
| `TOKEN_F` | 98.70 |
|
112 |
-
| `POS_ACC` | 96.
|
113 |
-
| `MORPH_ACC` | 95.
|
114 |
-
| `MORPH_MICRO_P` | 97.
|
115 |
-
| `MORPH_MICRO_R` | 96.
|
116 |
-
| `MORPH_MICRO_F` | 97.
|
117 |
-
| `SENTS_P` |
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-
| `SENTS_R` |
|
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-
| `SENTS_F` |
|
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-
| `DEP_UAS` | 87.
|
121 |
-
| `DEP_LAS` |
|
122 |
-
| `TAG_ACC` | 93.
|
123 |
-
| `LEMMA_ACC` | 90.
|
124 |
-
| `ENTS_P` | 81.
|
125 |
-
| `ENTS_R` | 81.
|
126 |
-
| `ENTS_F` | 81.
|
|
|
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metrics:
|
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- name: NER Precision
|
16 |
type: precision
|
17 |
+
value: 0.8148438757
|
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- name: NER Recall
|
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type: recall
|
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+
value: 0.8106360834
|
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- name: NER F Score
|
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type: f_score
|
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+
value: 0.8127345333
|
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- task:
|
25 |
name: TAG
|
26 |
type: token-classification
|
27 |
metrics:
|
28 |
- name: TAG (XPOS) Accuracy
|
29 |
type: accuracy
|
30 |
+
value: 0.933216531
|
31 |
- task:
|
32 |
name: POS
|
33 |
type: token-classification
|
34 |
metrics:
|
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- name: POS (UPOS) Accuracy
|
36 |
type: accuracy
|
37 |
+
value: 0.9617644028
|
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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: 0.9529502705
|
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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
|
50 |
type: accuracy
|
51 |
+
value: 0.9084463625
|
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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: 0.8781984485
|
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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
|
65 |
+
value: 0.8347514036
|
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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: 0.861278649
|
73 |
---
|
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### Details: https://spacy.io/models/fr#fr_core_news_sm
|
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|
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|
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| Feature | Description |
|
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| --- | --- |
|
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| **Name** | `fr_core_news_sm` |
|
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+
| **Version** | `3.5.0` |
|
82 |
+
| **spaCy** | `>=3.5.0,<3.6.0` |
|
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| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
|
84 |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
|
85 |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
|
|
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|
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| Type | Score |
|
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| --- | --- |
|
108 |
+
| `TOKEN_ACC` | 99.80 |
|
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| `TOKEN_P` | 98.44 |
|
110 |
| `TOKEN_R` | 98.96 |
|
111 |
| `TOKEN_F` | 98.70 |
|
112 |
+
| `POS_ACC` | 96.18 |
|
113 |
+
| `MORPH_ACC` | 95.30 |
|
114 |
+
| `MORPH_MICRO_P` | 97.96 |
|
115 |
+
| `MORPH_MICRO_R` | 96.64 |
|
116 |
+
| `MORPH_MICRO_F` | 97.29 |
|
117 |
+
| `SENTS_P` | 85.61 |
|
118 |
+
| `SENTS_R` | 86.65 |
|
119 |
+
| `SENTS_F` | 86.13 |
|
120 |
+
| `DEP_UAS` | 87.82 |
|
121 |
+
| `DEP_LAS` | 83.48 |
|
122 |
+
| `TAG_ACC` | 93.32 |
|
123 |
+
| `LEMMA_ACC` | 90.84 |
|
124 |
+
| `ENTS_P` | 81.48 |
|
125 |
+
| `ENTS_R` | 81.06 |
|
126 |
+
| `ENTS_F` | 81.27 |
|
accuracy.json
CHANGED
@@ -1,68 +1,68 @@
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{
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@@ -70,181 +70,181 @@
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@@ -267,9 +267,9 @@
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@@ -277,9 +277,9 @@
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@@ -297,32 +297,32 @@
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attribute_ruler/patterns
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|
config.cfg
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@@ -70,8 +70,8 @@ nO = null
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|
70 |
[components.ner.model.tok2vec.embed]
|
71 |
@architectures = "spacy.MultiHashEmbed.v2"
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72 |
width = 96
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE"
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74 |
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rows = [5000,1000,2500,2500
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75 |
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76 |
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77 |
[components.ner.model.tok2vec.encode]
|
@@ -139,8 +139,8 @@ factory = "tok2vec"
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|
139 |
[components.tok2vec.model.embed]
|
140 |
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141 |
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142 |
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE","SPACY"]
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143 |
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rows = [5000,1000,2500,2500,50]
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144 |
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145 |
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146 |
[components.tok2vec.model.encode]
|
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|
182 |
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183 |
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184 |
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185 |
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186 |
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|
187 |
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|
|
70 |
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|
71 |
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72 |
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73 |
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rows = [5000,1000,2500,2500]
|
75 |
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|
76 |
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77 |
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146 |
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|
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before_update = null
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186 |
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187 |
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|
188 |
@batchers = "spacy.batch_by_words.v1"
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fr_core_news_sm-any-py3-none-any.whl
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