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spacy
/
en_core_web_trf

Token Classification
spaCy
English
Eval Results
Model card Files
adrianeboyd commited on
Commit
11764b8
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1 Parent(s): 607d57e

Update spaCy pipeline

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Files changed (11) hide show
  1. LICENSES_SOURCES +2 -22
  2. README.md +16 -16
  3. accuracy.json +191 -191
  4. attribute_ruler/patterns +0 -0
  5. en_core_web_trf-any-py3-none-any.whl +2 -2
  6. meta.json +195 -195
  7. ner/model +1 -1
  8. parser/model +1 -1
  9. tagger/model +1 -1
  10. transformer/model +2 -2
  11. vocab/strings.json +2 -2
LICENSES_SOURCES CHANGED
@@ -68,30 +68,10 @@ Princeton University and LICENSEE agrees to preserve same.```
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  * Author: Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov
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  * URL: https://github.com/pytorch/fairseq/tree/master/examples/roberta
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- * License: MIT
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  ```
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- MIT License
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-
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- Copyright (c) Facebook, Inc. and its affiliates.
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-
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- Permission is hereby granted, free of charge, to any person obtaining a copy
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- of this software and associated documentation files (the "Software"), to deal
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- in the Software without restriction, including without limitation the rights
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- to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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- copies of the Software, and to permit persons to whom the Software is
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- furnished to do so, subject to the following conditions:
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-
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- The above copyright notice and this permission notice shall be included in all
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- copies or substantial portions of the Software.
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-
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- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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- OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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- SOFTWARE.```
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  * Author: Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov
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  * URL: https://github.com/pytorch/fairseq/tree/master/examples/roberta
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+ * License:
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  ```
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+ ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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README.md CHANGED
@@ -14,41 +14,41 @@ 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: 0.9017005601
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  - name: NER Recall
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  type: recall
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- value: 0.8948818109
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  - name: NER F Score
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  type: f_score
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- value: 0.8982782456
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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: 0.9781415701
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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: 0.9519734881
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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: 0.9386831877
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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: 0.9015817834
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  ---
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  ### Details: https://spacy.io/models/en#en_core_web_trf
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@@ -57,7 +57,7 @@ English transformer pipeline (roberta-base). Components: transformer, tagger, pa
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  | Feature | Description |
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  | --- | --- |
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  | **Name** | `en_core_web_trf` |
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- | **Version** | `3.4.0` |
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  | **spaCy** | `>=3.4.0,<3.5.0` |
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  | **Default Pipeline** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
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  | **Components** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
@@ -89,11 +89,11 @@ English transformer pipeline (roberta-base). Components: transformer, tagger, pa
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  | `TOKEN_R` | 99.58 |
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  | `TOKEN_F` | 99.57 |
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  | `TAG_ACC` | 97.81 |
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- | `SENTS_P` | 95.30 |
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- | `SENTS_R` | 85.54 |
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- | `SENTS_F` | 90.16 |
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- | `DEP_UAS` | 95.20 |
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- | `DEP_LAS` | 93.87 |
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- | `ENTS_P` | 90.17 |
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- | `ENTS_R` | 89.49 |
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- | `ENTS_F` | 89.83 |
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  metrics:
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  - name: NER Precision
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  type: precision
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+ value: 0.8953563447
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  - name: NER Recall
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  type: recall
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+ value: 0.9028445513
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  - name: NER F Score
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  type: f_score
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+ value: 0.8990848565
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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: 0.9780884117
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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: 0.9532621063
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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: 0.9398782759
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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: 0.9121217361
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  ---
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  ### Details: https://spacy.io/models/en#en_core_web_trf
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  | Feature | Description |
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  | --- | --- |
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  | **Name** | `en_core_web_trf` |
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+ | **Version** | `3.4.1` |
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  | **spaCy** | `>=3.4.0,<3.5.0` |
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  | **Default Pipeline** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
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  | **Components** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
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  | `TOKEN_R` | 99.58 |
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  | `TOKEN_F` | 99.57 |
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  | `TAG_ACC` | 97.81 |
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+ | `SENTS_P` | 95.86 |
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+ | `SENTS_R` | 86.99 |
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+ | `SENTS_F` | 91.21 |
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+ | `DEP_UAS` | 95.33 |
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+ | `DEP_LAS` | 93.99 |
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+ | `ENTS_P` | 89.54 |
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+ | `ENTS_R` | 90.28 |
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+ | `ENTS_F` | 89.91 |
accuracy.json CHANGED
@@ -3,152 +3,152 @@
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  "token_p": 0.9956819193,
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  "token_r": 0.9957659295,
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  "token_f": 0.9957239226,
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- "sents_f": 0.9015817834,
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@@ -156,74 +156,74 @@
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@@ -231,100 +231,100 @@
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  "f": 0.7692307692
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  }
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