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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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- <!-- 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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- ### 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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- ### Compute Infrastructure
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- #### Hardware
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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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- **APA:**
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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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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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+ language: en
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+ tags:
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+ - fill-mask
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+ kwargs:
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+ timestamp: '2024-05-11T17:51:47'
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+ project_name: ThunBERT_bs32_lr5_emissions_tracker
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+ run_id: 86041803-4df8-4fda-8e76-9ea27a0b1263
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+ duration: 175387.02787947655
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+ emissions: 0.1835748606301428
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+ emissions_rate: 1.046684369133006e-06
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+ cpu_power: 42.5
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+ gpu_power: 0.0
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+ ram_power: 37.5
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+ cpu_energy: 2.070537895744051
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+ gpu_energy: 0
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+ ram_energy: 1.826934782758348
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+ energy_consumed: 3.897472678502429
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+ country_name: Switzerland
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+ country_iso_code: CHE
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+ region: .nan
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+ cloud_provider: .nan
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+ cloud_region: .nan
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+ os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34
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+ python_version: 3.10.4
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+ codecarbon_version: 2.3.4
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+ cpu_count: 4
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+ cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz
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+ gpu_count: .nan
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+ gpu_model: .nan
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+ longitude: .nan
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+ latitude: .nan
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+ ram_total_size: 100
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+ tracking_mode: machine
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+ on_cloud: N
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+ pue: 1.0
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  ---
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+ ## Environmental Impact (CODE CARBON DEFAULT)
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+ | Metric | Value |
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+ |--------------------------|---------------------------------|
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+ | Duration (in seconds) | 175387.02787947655 |
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+ | Emissions (Co2eq in kg) | 0.1835748606301428 |
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+ | CPU power (W) | 42.5 |
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+ | GPU power (W) | [No GPU] |
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+ | RAM power (W) | 37.5 |
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+ | CPU energy (kWh) | 2.070537895744051 |
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+ | GPU energy (kWh) | [No GPU] |
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+ | RAM energy (kWh) | 1.826934782758348 |
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+ | Consumed energy (kWh) | 3.897472678502429 |
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+ | Country name | Switzerland |
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+ | Cloud provider | nan |
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+ | Cloud region | nan |
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+ | CPU count | 4 |
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+ | CPU model | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz |
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+ | GPU count | nan |
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+ | GPU model | nan |
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+ ## Environmental Impact (for one core)
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+ | Metric | Value |
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+ |--------------------------|---------------------------------|
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+ | CPU energy (kWh) | 0.3376200286679923 |
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+ | Emissions (Co2eq in kg) | 0.06869325258612831 |
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+ ## Note
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+ 15 May 2024
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+ ## My Config
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+ | Config | Value |
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+ |--------------------------|-----------------|
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+ | checkpoint | albert-base-v2 |
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+ | model_name | ThunBERT_bs32_lr5 |
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+ | sequence_length | 400 |
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+ | num_epoch | 6 |
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+ | learning_rate | 5e-05 |
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+ | batch_size | 32 |
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+ | weight_decay | 0.0 |
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+ | warm_up_prop | 0.0 |
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+ | drop_out_prob | 0.1 |
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+ | packing_length | 100 |
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+ | train_test_split | 0.2 |
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+ | num_steps | 20393 |
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+ ## Training and Testing steps
 
 
 
 
 
 
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+ Epoch | Train Loss | Test Loss
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+ ---|---|---
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+ | 0.0 | 4.767287 | 3.817442 |