Instructions to use pandma/es_one_invoice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use pandma/es_one_invoice with spaCy:
!pip install https://huggingface.co/pandma/es_one_invoice/resolve/main/es_one_invoice-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("es_one_invoice") # Importing as module. import es_one_invoice nlp = es_one_invoice.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | es_one_invoice |
| Version | 0.0.0 |
| spaCy | >=3.5.2,<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 (19 labels for 1 components)
| Component | Labels |
|---|---|
ner |
BILLING_PERIOD_END, BILLING_PERIOD_START, BILL_OWNER, COMPANY_NAME, CUPS, END_CONTRACT, ENERGY_PRICE_P1, ENERGY_PRICE_P2, ENERGY_PRICE_P6, FISCAL_DIRECTION, IBAN, NIF, POWER_PRICE_P1, POWER_PRICE_P2, POWER_PRICE_P3, POWER_PRICE_P4, POWER_PRICE_P5, POWER_PRICE_P6, TOTAL_IMPORTE |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
100.00 |
ENTS_P |
100.00 |
ENTS_R |
100.00 |
TRANSFORMER_LOSS |
0.00 |
NER_LOSS |
18103.69 |
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Evaluation results
- NER Precisionself-reported1.000
- NER Recallself-reported1.000
- NER F Scoreself-reported1.000