Instructions to use Huertas97/en_roberta_base_leetspeak_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Huertas97/en_roberta_base_leetspeak_ner with spaCy:
!pip install https://huggingface.co/Huertas97/en_roberta_base_leetspeak_ner/resolve/main/en_roberta_base_leetspeak_ner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_roberta_base_leetspeak_ner") # Importing as module. import en_roberta_base_leetspeak_ner nlp = en_roberta_base_leetspeak_ner.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_roberta_base_leetspeak_ner |
| Version | 0.0.0 |
| spaCy | >=3.2.1,<3.3.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | roberta-base pre-trained model on English language using a masked language modeling (MLM) objective by Yinhan Liu et al. LeetSpeak-NER app where this model is in production for countering information disorders |
| License | Apache 2.0 |
| Author | Álvaro Huertas García at AI+DA |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
INV_CAMO, LEETSPEAK, MIX, PUNCT_CAMO |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
82.80 |
ENTS_P |
79.66 |
ENTS_R |
86.20 |
TRANSFORMER_LOSS |
177808.42 |
NER_LOSS |
608427.31 |
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Evaluation results
- NER Precisionself-reported0.797
- NER Recallself-reported0.862
- NER F Scoreself-reported0.828