Instructions to use TrajanovRisto/en_ner_esg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use TrajanovRisto/en_ner_esg with spaCy:
!pip install https://huggingface.co/TrajanovRisto/en_ner_esg/resolve/main/en_ner_esg-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_ner_esg") # Importing as module. import en_ner_esg nlp = en_ner_esg.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_ner_esg |
| 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 (3 labels for 1 components)
| Component | Labels |
|---|---|
ner |
Environmental, Governance, Social |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
93.71 |
ENTS_P |
95.00 |
ENTS_R |
92.46 |
TRANSFORMER_LOSS |
3213.34 |
NER_LOSS |
2096.88 |
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Evaluation results
- NER Precisionself-reported0.950
- NER Recallself-reported0.925
- NER F Scoreself-reported0.937