File size: 1,272 Bytes
710b35e d7abbfc a0e06f3 d65cf4e a0e06f3 d65cf4e a0e06f3 d65cf4e a0e06f3 d65cf4e a0e06f3 a212615 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
---
license: apache-2.0
language:
- en
pipeline_tag: text-classification
---
# DeTexD-RoBERTa-base delicate text detection
This is a baseline RoBERTa-base model for the delicate text detection task.
* Paper: [DeTexD: A Benchmark Dataset for Delicate Text Detection](TODO)
* [GitHub repository](https://github.com/grammarly/detexd)
## Classification example code
Here's a short usage example with the torch library in a binary classification task:
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="grammarly/detexd-roberta-base")
def predict_binary_score(text: str):
# get multiclass probability scores
scores = classifier(text, top_k=None)
# convert to a single score by summing the probability scores
# for the higher-index classes
return sum(score['score']
for score in scores
if score['label'] in ('LABEL_3', 'LABEL_4', 'LABEL_5'))
def predict_delicate(text: str, threshold=0.72496545):
return predict_binary_score(text) > threshold
print(predict_delicate("Time flies like an arrow. Fruit flies like a banana."))
```
Expected output:
```
False
```
## BibTeX entry and citation info
Please cite [our paper](TODO) if you use this model.
```bibtex
TODO
``` |