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---
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license: apache-2.0
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pipeline_tag: text-ranking
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language:
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- en
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library_name: sentence-transformers
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base_model:
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- google/electra-large-discriminator
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tags:
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- transformers
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---
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## Cross-Encoder for Text Ranking
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This model is a port of the [webis/monoelectra-large](https://huggingface.co/webis/monoelectra-large) model from [lightning-ir](https://github.com/webis-de/lightning-ir) to [Sentence Transformers](https://sbert.net/) and [Transformers](https://huggingface.co/docs/transformers).
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The original model was introduced in the paper [A Systematic Investigation of Distilling Large Language Models into Cross-Encoders for Passage Re-ranking](https://arxiv.org/abs/2405.07920). See https://github.com/webis-de/rank-distillm for code used to train the original model.
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The model can be used as a reranker in a 2-stage "retrieve-rerank" pipeline, where it reorders passages returned by a retriever model (e.g. an embedding model or BM25) given some query. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details.
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## Usage with Sentence Transformers
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The usage is easy when you have [SentenceTransformers](https://www.sbert.net/) installed.
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```bash
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pip install sentence-transformers
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```
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Then you can use the pre-trained model like this:
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```python
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from sentence_transformers import CrossEncoder
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model = CrossEncoder("cross-encoder/monoelectra-large", trust_remote_code=True)
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scores = model.predict([
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("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
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("How many people live in Berlin?", "Berlin is well known for its museums."),
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])
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print(scores)
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# [ 6.016401 -3.6922567]
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```
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## Usage with Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/monoelectra-large", trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained("cross-encoder/monoelectra-large")
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features = tokenizer(
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[
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("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
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("How many people live in Berlin?", "Berlin is well known for its museums."),
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],
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padding=True,
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truncation=True,
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return_tensors="pt",
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)
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model.eval()
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with torch.no_grad():
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scores = model(**features).logits.view(-1)
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print(scores)
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# tensor([ 6.0164, -3.6923])
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``` |
