| --- |
| license: apache-2.0 |
| datasets: |
| - sentence-transformers/quora-duplicates |
| language: |
| - en |
| base_model: |
| - distilbert/distilroberta-base |
| pipeline_tag: text-ranking |
| library_name: sentence-transformers |
| tags: |
| - transformers |
| --- |
| # 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. |
|
|
| ## 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 rather low score, as these are not duplicates. |
|
|
| ## Usage and Performance |
|
|
| Pre-trained models can be used like this: |
| ```python |
| from sentence_transformers import CrossEncoder |
| |
| model = CrossEncoder('cross-encoder/quora-distilroberta-base') |
| scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')]) |
| ``` |
|
|
| You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class |
