|
--- |
|
datasets: |
|
- quora |
|
language: en |
|
license: mit |
|
pipeline_tag: text-classification |
|
tags: |
|
- roberta |
|
- text-classification |
|
--- |
|
# Cross-Encoder for Quora Duplicate Questions Detection |
|
|
|
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. |
|
|
|
This model uses [roberta-large](https://huggingface.co/roberta-large). |
|
|
|
## Training Data |
|
|
|
This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset. |
|
|
|
The model will predict a score between 0 and 1: How likely the two given questions are duplicates. |
|
|
|
Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates. |
|
|
|
## Usage and Performance |
|
|
|
The trained model can be used like this: |
|
|
|
```python |
|
from sentence_transformers import CrossEncoder |
|
|
|
model = CrossEncoder('model_name') |
|
scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')]) |
|
|
|
print(scores) |
|
``` |
|
|
