metadata
tags:
- spacy
- token-classification
language:
- en
license: apache-2.0
widget:
- text: >-
But one other thing that we have to re;think is the way that we dy£ our
#c!l.o|th?£+s.
example_title: Word camouflage detection
model-index:
- name: en_roberta_base_leetspeak_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7966001851
- name: NER Recall
type: recall
value: 0.8619559279
- name: NER F Score
type: f_score
value: 0.8279903783
| 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 |