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fr_core_news_md

Token Classification spaCy French Eval Results
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adrianeboyd commited on
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a012f8c
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1 Parent(s): 545dda3

Update spaCy pipeline

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Files changed (12) hide show
  1. README.md +28 -28
  2. accuracy.json +158 -158
  3. attribute_ruler/patterns +0 -0
  4. config.cfg +5 -4
  5. fr_core_news_md-any-py3-none-any.whl +2 -2
  6. meta.json +161 -161
  7. morphologizer/model +1 -1
  8. ner/model +2 -2
  9. parser/model +1 -1
  10. senter/model +1 -1
  11. tok2vec/model +2 -2
  12. tokenizer +2 -2
README.md CHANGED
@@ -14,62 +14,62 @@ model-index:
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
- value: 0.8336070499
18
  - name: NER Recall
19
  type: recall
20
- value: 0.8330731356
21
  - name: NER F Score
22
  type: f_score
23
- value: 0.8333400072
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
- value: 0.9432561975
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
- value: 0.9720146369
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
- value: 0.9609157471
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
- value: 0.9076243321
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
- value: 0.8998842593
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
- value: 0.8596552123
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
- value: 0.9097472924
73
  ---
74
  ### Details: https://spacy.io/models/fr#fr_core_news_md
75
 
@@ -78,8 +78,8 @@ French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, s
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `fr_core_news_md` |
81
- | **Version** | `3.4.0` |
82
- | **spaCy** | `>=3.4.0,<3.5.0` |
83
  | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
84
  | **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
85
  | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
@@ -105,22 +105,22 @@ French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, s
105
 
106
  | Type | Score |
107
  | --- | --- |
108
- | `TOKEN_ACC` | 99.90 |
109
  | `TOKEN_P` | 98.44 |
110
  | `TOKEN_R` | 98.96 |
111
  | `TOKEN_F` | 98.70 |
112
- | `POS_ACC` | 97.20 |
113
- | `MORPH_ACC` | 96.09 |
114
- | `MORPH_MICRO_P` | 98.55 |
115
- | `MORPH_MICRO_R` | 97.49 |
116
- | `MORPH_MICRO_F` | 98.02 |
117
- | `SENTS_P` | 90.21 |
118
- | `SENTS_R` | 91.75 |
119
- | `SENTS_F` | 90.97 |
120
- | `DEP_UAS` | 89.99 |
121
- | `DEP_LAS` | 85.97 |
122
- | `TAG_ACC` | 94.33 |
123
- | `LEMMA_ACC` | 90.76 |
124
- | `ENTS_P` | 83.36 |
125
- | `ENTS_R` | 83.31 |
126
- | `ENTS_F` | 83.33 |
14
  metrics:
15
  - name: NER Precision
16
  type: precision
17
+ value: 0.8317031703
18
  - name: NER Recall
19
  type: recall
20
+ value: 0.8322525119
21
  - name: NER F Score
22
  type: f_score
23
+ value: 0.8319777504
24
  - task:
25
  name: TAG
26
  type: token-classification
27
  metrics:
28
  - name: TAG (XPOS) Accuracy
29
  type: accuracy
30
+ value: 0.9450741962
31
  - task:
32
  name: POS
33
  type: token-classification
34
  metrics:
35
  - name: POS (UPOS) Accuracy
36
  type: accuracy
37
+ value: 0.9737221764
38
  - task:
39
  name: MORPH
40
  type: token-classification
41
  metrics:
42
  - name: Morph (UFeats) Accuracy
43
  type: accuracy
44
+ value: 0.9648987166
45
  - task:
46
  name: LEMMA
47
  type: token-classification
48
  metrics:
49
  - name: Lemma Accuracy
50
  type: accuracy
51
+ value: 0.9134812988
52
  - task:
53
  name: UNLABELED_DEPENDENCIES
54
  type: token-classification
55
  metrics:
56
  - name: Unlabeled Attachment Score (UAS)
57
  type: f_score
58
+ value: 0.8947398877
59
  - task:
60
  name: LABELED_DEPENDENCIES
61
  type: token-classification
62
  metrics:
63
  - name: Labeled Attachment Score (LAS)
64
  type: f_score
65
+ value: 0.8562998959
66
  - task:
67
  name: SENTS
68
  type: token-classification
69
  metrics:
70
  - name: Sentences F-Score
71
  type: f_score
72
+ value: 0.8851269649
73
  ---
74
  ### Details: https://spacy.io/models/fr#fr_core_news_md
75
 
78
  | Feature | Description |
79
  | --- | --- |
80
  | **Name** | `fr_core_news_md` |
81
+ | **Version** | `3.5.0` |
82
+ | **spaCy** | `>=3.5.0,<3.6.0` |
83
  | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
84
  | **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
85
  | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
105
 
106
  | Type | Score |
107
  | --- | --- |
108
+ | `TOKEN_ACC` | 99.80 |
109
  | `TOKEN_P` | 98.44 |
110
  | `TOKEN_R` | 98.96 |
111
  | `TOKEN_F` | 98.70 |
112
+ | `POS_ACC` | 97.37 |
113
+ | `MORPH_ACC` | 96.49 |
114
+ | `MORPH_MICRO_P` | 98.68 |
115
+ | `MORPH_MICRO_R` | 97.98 |
116
+ | `MORPH_MICRO_F` | 98.33 |
117
+ | `SENTS_P` | 88.19 |
118
+ | `SENTS_R` | 89.46 |
119
+ | `SENTS_F` | 88.51 |
120
+ | `DEP_UAS` | 89.47 |
121
+ | `DEP_LAS` | 85.63 |
122
+ | `TAG_ACC` | 94.51 |
123
+ | `LEMMA_ACC` | 91.35 |
124
+ | `ENTS_P` | 83.17 |
125
+ | `ENTS_R` | 83.23 |
126
+ | `ENTS_F` | 83.20 |
accuracy.json CHANGED
@@ -1,68 +1,68 @@
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  {
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- "token_acc": 0.9989751998,
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  "token_p": 0.9844389844,
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  "token_r": 0.9896058454,
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  "token_f": 0.9870156531,
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- "pos_acc": 0.9720146369,
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- "morph_acc": 0.9609157471,
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- "morph_micro_p": 0.9854661904,
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- "morph_micro_r": 0.9748994761,
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- "morph_micro_f": 0.980154355,
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  "morph_per_feat": {
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  "Definite": {
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- "p": 0.989010989,
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  "Gender": {
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- "p": 0.9808884298,
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- "r": 0.9431616341,
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  "Tense": {
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- "p": 1.0,
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  "r": 1.0,
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  },
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  "Voice": {
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  "Poss": {
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  "p": 1.0,
@@ -70,181 +70,181 @@
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  "f": 1.0
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  },
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  "Polarity": {
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- "p": 1.0,
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  "r": 0.9882352941,
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- "f": 0.9940828402
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  }
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  },
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- "sents_p": 0.9021479714,
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- "sents_r": 0.9174757282,
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- "dep_uas": 0.8998842593,
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  "dep_las_per_type": {
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  "expl:comp": {
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  "ccomp": {
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  "parataxis": {
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  "p": 0.0,
@@ -267,9 +267,9 @@
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  "f": 0.0
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  },
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  "vocative": {
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  "r": 0.625,
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  "dislocated": {
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@@ -277,9 +277,9 @@
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  "orphan": {
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  "p": 0.0,
@@ -297,32 +297,32 @@
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  "f": 0.0
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  }
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  },
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- "tag_acc": 0.9432561975,
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- "lemma_acc": 0.9076243321,
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- "ents_p": 0.8336070499,
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  "ents_per_type": {
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  "MISC": {
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- "speed": 4200.9996712213
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  }
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  "token_p": 0.9844389844,
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  "token_r": 0.9896058454,
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  "token_f": 0.9870156531,
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+ "pos_acc": 0.9737221764,
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+ "morph_micro_p": 0.9868073879,
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  "morph_per_feat": {
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  "Definite": {
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  "Number": {
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  "PronType": {
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  "Gender": {
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  "Mood": {
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  "Person": {
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+ "p": 0.9897959184,
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  "Tense": {
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  "VerbForm": {
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  "Reflex": {
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  "Voice": {
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  },
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  "Poss": {
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  "p": 1.0,
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  "Polarity": {
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+ "p": 0.9882352941,
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  "r": 0.9882352941,
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+ "f": 0.9882352941
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  }
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  },
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+ "sents_p": 0.8819277108,
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+ "sents_f": 0.8851269649,
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+ "dep_uas": 0.8947398877,
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+ "dep_las": 0.8562998959,
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  "dep_las_per_type": {
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  "det": {
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+ "f": 0.9790153349
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