Instructions to use pandma/es_billynator_h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use pandma/es_billynator_h with spaCy:
!pip install https://huggingface.co/pandma/es_billynator_h/resolve/main/es_billynator_h-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("es_billynator_h") # Importing as module. import es_billynator_h nlp = es_billynator_h.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | es_billynator_h |
| Version | 0.0.1 |
| spaCy | >=3.5.3,<3.6.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (29 labels for 1 components)
| Component | Labels |
|---|---|
ner |
BILLING_PERIOD_END, BILLING_PERIOD_START, BILL_OWNER, COMPANY_NAME, CUPS, DIRECTION, DISCOUNT_TOTAL, END_CONTRACT, ENERGY_P1_PRICE, ENERGY_P2_PRICE, ENERGY_P3_PRICE, FISCAL_DIRECTION, IBAN, NIF, POWER_EXCESSES_P1, POWER_EXCESSES_P2, POWER_EXCESSES_P3, POWER_P1_PRICE, POWER_P2_PRICE, POWER_P3_PRICE, POWER_P4_PRICE, POWER_P5_PRICE, POWER_P6_PRICE, REACTIVE_P1, REACTIVE_P2, REACTIVE_P3, TOP_GAS_PRICE, TOP_GAS_TOTAL, TOTAL_IMPORTE |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.88 |
ENTS_P |
99.86 |
ENTS_R |
99.91 |
TRANSFORMER_LOSS |
1347.70 |
NER_LOSS |
26621.94 |
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Evaluation results
- NER Precisionself-reported0.999
- NER Recallself-reported0.999
- NER F Scoreself-reported0.999