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@@ -9,199 +9,109 @@ base_model: cl-nagoya/ruri-pt-small
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  widget: []
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  pipeline_tag: text-classification
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  license: apache-2.0
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
 
 
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
 
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ### Downstream Use [optional]
 
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
 
 
 
 
 
 
 
 
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
 
 
 
 
 
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- [More Information Needed]
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  ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
 
 
 
 
 
 
 
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- [More Information Needed]
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- ## Model Card Contact
 
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- [More Information Needed]
 
 
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  widget: []
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  pipeline_tag: text-classification
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  license: apache-2.0
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+ datasets:
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+ - cl-nagoya/ruri-dataset-reranker
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  ---
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+ # Ruri-Reranker: Japanese General Reranker
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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("cl-nagoya/ruri-reranker-stage1-small", trust_remote_code=True)
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+ inputs = [
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+ [
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+ "瑠璃色はどんな色?",
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+ "瑠璃色(るりいろ)は、紫みを帯びた濃い青。名は、半貴石の瑠璃(ラピスラズリ、英: lapis lazuli)による。JIS慣用色名では「こい紫みの青」(略号 dp-pB)と定義している[1][2]。",
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+ ],
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+ [
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+ "瑠璃色はどんな色?",
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+ "ワシ、タカ、ハゲワシ、ハヤブサ、コンドル、フクロウが代表的である。これらの猛禽類はリンネ前後の時代(17~18世紀)には鷲類・鷹類・隼類及び梟類に分類された。ちなみにリンネは狩りをする鳥を単一の目(もく)にまとめ、vultur(コンドル、ハゲワシ)、falco(ワシ、タカ、ハヤブサなど)、strix(フクロウ)、lanius(モズ)の4属を含めている。",
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+ ],
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+ [
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+ "ワシやタカのように、鋭いくちばしと爪を持った大型の鳥類を総称して「何類」というでしょう?",
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+ "ワシ、タカ、ハゲワシ、ハヤブサ、コンドル、フクロウが代表的である。これらの猛禽類はリンネ前後の時代(17~18世紀)には鷲類・鷹類・隼類及び梟類に分類された。ちなみにリンネは狩りをする鳥を単一の目(もく)にまとめ、vultur(コンドル、ハゲワシ)、falco(ワシ、タカ、ハヤブサなど)、strix(フクロウ)、lanius(モズ)の4属を含めている。",
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+ ],
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+ [
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+ "ワシやタカのように、鋭いくちばしと爪を持った大型の鳥類を総称して「何類」というでしょう?",
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+ "瑠璃色(るりいろ)は、紫みを帯びた濃い青。名は、半貴石の瑠璃(ラピスラズリ、英: lapis lazuli)による。JIS慣用色名では「こい紫みの青」(略号 dp-pB)と定義している[1][2]。",
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+ ],
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+ ]
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+ scores = model.predict(inputs)
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+ print(scores)
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+ result = model.rank(
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+ query="瑠璃色はどんな色?",
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+ documents=[
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+ "ワシ、タカ、ハゲワシ、ハヤブサ、コンドル、フクロウが代表的である。これらの猛禽類はリンネ前後の時代(17~18世紀)には鷲類・鷹類・隼類及び梟類に分類された。ちなみにリンネは狩りをする鳥を単一の目(もく)にまとめ、vultur(コンドル、ハゲワシ)、falco(ワシ、タカ、ハヤブサなど)、strix(フクロウ)、lanius(モズ)の4属を含めている。",
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+ "瑠璃、または琉璃(るり)は、仏教の七宝の一つ。サンスクリットの vaiḍūrya またはそのプラークリット形の音訳である。金緑石のこととも、ラピスラズリであるともいう[1]。",
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+ "瑠璃色(るりいろ)は、紫みを帯びた濃い青。名は、半貴石の瑠璃(ラピスラズリ、英: lapis lazuli)による。JIS慣用色名では「こい紫みの青」(略号 dp-pB)と定義している[1][2]。",
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+ ],
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+ )
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+ print(result)
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+ ```
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+ ## Benchmarks
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+ |Model|#Param.(w/oEmb.)|JQaRA|JaCWIR|MIRACL|
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+ |:-|:-:|:-:|:-:|:-:|
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+ |[hotchpotch/japanese-reranker-cross-encoder-xsmall-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-xsmall-v1)|107M(11M)|61.4|93.8|90.6|
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+ |[hotchpotch/japanese-reranker-cross-encoder-small-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-small-v1)|118M(21M)|62.5|93.9|92.2|
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+ |[hotchpotch/japanese-reranker-cross-encoder-base-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-base-v1)|111M(86M)|67.1|93.4|93.3|
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+ |[hotchpotch/japanese-reranker-cross-encoder-large-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-large-v1)|337M(303M)|71.0|93.6|91.5|
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+ |[hotchpotch/japanese-bge-reranker-v2-m3-v1](https://huggingface.co/hotchpotch/japanese-bge-reranker-v2-m3-v1)|568M(303M)|69.2|93.7|94.7|
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+ |[BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3)|568M(303M)|67.3|93.4|94.9|
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+ ||||||
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+ |[**Ruri-Reranker-Small**](https://huggingface.co/cl-nagoya/ruri-reranker-small) (this model)|68M(43M)|64.5|92.6|92.3|
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+ |[Ruri-Reranker-Base](https://huggingface.co/cl-nagoya/ruri-reranker-base)|111M(86M)|74.3|93.5|95.6|
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+ |[Ruri-Reranker-Large](https://huggingface.co/cl-nagoya/ruri-reranker-large)|337M(303M)|**77.1**|**94.1**|**96.1**|
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+ ## Model Details
 
 
 
 
 
 
 
 
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [cl-nagoya/ruri-pr-small](https://huggingface.co/cl-nagoya/ruri-pt-small)
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Language:** Japanese
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+ - **License:** Apache 2.0
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  ## Training Details
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+ ### Framework Versions
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+ - Python: 3.10.13
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+ - Sentence Transformers: 3.0.0
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+ - Transformers: 4.41.2
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+ - PyTorch: 2.3.1+cu118
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+ - Accelerate: 0.30.1
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+ - Datasets: 2.19.1
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+ - Tokenizers: 0.19.1
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+ <!-- ## Citation
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+ ### BibTeX
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+ -->
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+ ## License
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+ This model is published under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).