Instructions to use benfm/en_nigeria_tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use benfm/en_nigeria_tagger with spaCy:
!pip install https://huggingface.co/benfm/en_nigeria_tagger/resolve/main/en_nigeria_tagger-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_nigeria_tagger") # Importing as module. import en_nigeria_tagger nlp = en_nigeria_tagger.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_nigeria_tagger |
| Version | 0.0.1 |
| spaCy | >=3.8.11,<3.9.0 |
| Default Pipeline | tok2vec, tagger |
| Components | tok2vec, tagger |
| Vectors | 684830 keys, 342918 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (45 labels for 1 components)
| Component | Labels |
|---|---|
tagger |
#, $, '', (, ), ,, :, CC, CD, DT, EX, FW, IN, JJ, JJR, JJS, LS, MD, NN, NNS, NP, NPS, PDT, POS, PP, PP$, RB, RBR, RBS, RP, SENT, SYM, TO, UH, VB, VBD, VBG, VBN, VBP, VBZ, WDT, WP, WP$, WRB, ```` |
Accuracy
| Type | Score |
|---|---|
TAG_ACC |
97.17 |
POS_ACC |
0.00 |
TAG_MICRO_P |
0.00 |
TAG_MICRO_R |
0.00 |
TAG_MICRO_F |
0.00 |
TOK2VEC_LOSS |
8590.34 |
TAGGER_LOSS |
137405.91 |
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Evaluation results
- TAG (XPOS) Accuracyself-reported0.972
- POS (UPOS) Accuracyself-reported0.000