| Feature | Description |
|---|---|
| Name | en_tako_query_filter |
| Version | 0.0.2 |
| spaCy | >=3.7.5,<3.8.0 |
| Default Pipeline | tok2vec, ner, textcat_classify |
| Components | tok2vec, ner, textcat_classify |
| Vectors | 514157 keys, 514157 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (21 labels for 2 components)
| Component | Labels |
|---|---|
ner |
CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, STOCK_TICKER, TIME, WORK_OF_ART |
textcat_classify |
ACCEPT, REJECT |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
0.00 |
ENTS_P |
0.00 |
ENTS_R |
0.00 |
ENTS_PER_TYPE |
0.00 |
CATS_SCORE |
85.07 |
CATS_MICRO_P |
85.31 |
CATS_MICRO_R |
85.31 |
CATS_MICRO_F |
85.31 |
CATS_MACRO_P |
85.35 |
CATS_MACRO_R |
85.31 |
CATS_MACRO_F |
85.31 |
CATS_MACRO_AUC |
91.67 |
TEXTCAT_CLASSIFY_LOSS |
94.04 |
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Evaluation results
- NER Precisionself-reported0.000
- NER Recallself-reported0.000
- NER F Scoreself-reported0.000
!pip install https://huggingface.co/TakoData/en_tako_query_filter/resolve/main/en_tako_query_filter-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_tako_query_filter") # Importing as module. import en_tako_query_filter nlp = en_tako_query_filter.load()