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README.md
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license: apache-2.0
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---
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## Model Details
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### Model Description
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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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[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
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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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### Results
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#### Summary
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## Model Examination [optional]
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[More Information Needed]
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## Environmental Impact
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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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## Technical Specifications [optional]
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### Model Architecture and Objective
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#### Hardware
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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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## Glossary [optional]
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## More Information [optional]
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license: apache-2.0
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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-large")
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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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# [0.99999535 0.7374149 0.9970592 0.00682232]
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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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# {'corpus_id': 2, 'score': 0.99999535},
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# {'corpus_id': 1, 'score': 0.97759527},
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# {'corpus_id': 0, 'score': 0.73741615},
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# ]
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```
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## Benchmarks
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|Model|#Param.(w/o Emb.)|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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|[Ruri-Reranker-Small](https://huggingface.co/cl-nagoya/ruri-reranker-small)|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) (this model)|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-reranker-stage1-large](https://huggingface.co/cl-nagoya/ruri-reranker-stage1-large)
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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).
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