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

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README.md

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+---
+license: apache-2.0
+datasets:
+- sentence-transformers/stsb
+language:
+- en
+base_model:
+- distilbert/distilroberta-base
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+tags:
+- transformers
+---
+# Cross-Encoder for Semantic Textual Similarity
+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 [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. 
+
+
+## Usage and Performance
+
+Pre-trained models can be used like this:
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder('cross-encoder/stsb-distilroberta-base')
+scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
+```
+
+The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
+
+You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class

config.json

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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
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+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "type_vocab_size": 1,
+  "vocab_size": 50265
+}

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