Instructions to use elisno/is_ner_mim_trf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use elisno/is_ner_mim_trf with spaCy:
!pip install https://huggingface.co/elisno/is_ner_mim_trf/resolve/main/is_ner_mim_trf-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("is_ner_mim_trf") # Importing as module. import is_ner_mim_trf nlp = is_ner_mim_trf.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | is_ner_mim_trf |
| Version | 0.0.1 |
| spaCy | >=3.1.1,<3.2.0 |
| 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 (8 labels for 1 components)
| Component | Labels |
|---|---|
ner |
Date, Location, Miscellaneous, Money, Organization, Percent, Person, Time |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
92.06 |
ENTS_P |
91.93 |
ENTS_R |
92.18 |
TRANSFORMER_LOSS |
248325.98 |
NER_LOSS |
120059.07 |
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Evaluation results
- NER Precisionself-reported0.919
- NER Recallself-reported0.922
- NER F Scoreself-reported0.921