33 lines
1.1 KiB
Markdown
33 lines
1.1 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- sentence-transformers/stsb
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language:
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- en
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base_model:
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- FacebookAI/roberta-base
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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tags:
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- transformers
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---
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# Cross-Encoder for Semantic Textual Similarity
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This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
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## Training Data
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This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
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## Usage and Performance
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Pre-trained models can be used like this:
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```python
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from sentence_transformers import CrossEncoder
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model = CrossEncoder('cross-encoder/stsb-roberta-base')
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scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
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```
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The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
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You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class |