pipeline to extract stonk names, need to adjust for general use as some stonk names are very short. Based on the standard spacy pipeline, but added a pipe and wanted to distribute it easily
| Feature | Description |
|---|---|
| Name | en_stonk_pipeline |
| Version | 0.0.1 |
| spaCy | >=3.4.1,<3.5.0 |
| Default Pipeline | entity_ruler |
| Components | entity_ruler |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | OntoNotes 5 (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) ClearNLP Constituent-to-Dependency Conversion (Emory University) WordNet 3.0 (Princeton University) |
| License | n/a |
| Author | FriendlyUser |
Label Scheme
View label scheme (8 labels for 1 components)
| Component | Labels |
|---|---|
entity_ruler |
COMPANY, COUNTRY, DIVIDENDS, INDEX, MAYBE, STOCK, STOCK_EXCHANGE, THINGS |
Accuracy
| Type | Score |
|---|---|
TOKEN_ACC |
99.93 |
TOKEN_P |
99.57 |
TOKEN_R |
99.58 |
TOKEN_F |
99.57 |
TAG_ACC |
97.26 |
SENTS_P |
91.92 |
SENTS_R |
88.90 |
SENTS_F |
90.39 |
DEP_UAS |
91.66 |
DEP_LAS |
89.78 |
ENTS_P |
85.65 |
ENTS_R |
83.49 |
ENTS_F |
84.56 |
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Evaluation results
- NER Precisionself-reported0.857
- NER Recallself-reported0.835
- NER F Scoreself-reported0.846
- TAG (XPOS) Accuracyself-reported0.973
- Unlabeled Attachment Score (UAS)self-reported0.917
- Labeled Attachment Score (LAS)self-reported0.898
- Sentences F-Scoreself-reported0.904