初始化项目，由ModelHub XC社区提供模型

Model: cross-encoder/quora-distilroberta-base
Source: Original Platform

README.md

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+---
+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