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Model: QuantaSparkLabs/Chronos-3B Source: Original Platform
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ModelHub XC
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cross_encoder_model/README.md
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cross_encoder_model/README.md
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---
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tags:
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- sentence-transformers
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- cross-encoder
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- reranker
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base_model: cross-encoder/ms-marco-MiniLM-L12-v2
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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---
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# CrossEncoder based on cross-encoder/ms-marco-MiniLM-L12-v2
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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## Model Details
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### Model Description
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- **Model Type:** Cross Encoder
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- **Base model:** [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) <!-- at revision 7b0235231ca2674cb8ca8f022859a6eba2b1c968 -->
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Output Labels:** 1 label
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- **Supported Modality:** Text
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
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- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
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### Full Model Architecture
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```
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CrossEncoder(
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(0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'BertForSequenceClassification'})
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import CrossEncoder
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# Download from the 🤗 Hub
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model = CrossEncoder("cross_encoder_model_id")
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# Get scores for pairs of inputs
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pairs = [
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['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
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['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
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['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
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]
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scores = model.predict(pairs)
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print(scores)
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# [ 9.6793 -2.1906 1.9515]
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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'How many calories in an egg',
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[
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'There are on average between 55 and 80 calories in an egg depending on its size.',
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'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
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'Most of the calories in an egg come from the yellow yolk in the center.',
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]
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)
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Framework Versions
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- Python: 3.12.13
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- Sentence Transformers: 5.4.1
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- Transformers: 5.0.0
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- PyTorch: 2.10.0+cu128
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- Accelerate: 1.13.0
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- Datasets: 4.0.0
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- Tokenizers: 0.22.2
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## Citation
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### BibTeX
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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<!--
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-->
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<!--
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## Model Card Contact
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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