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README.md
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- **Language(s) (NLP):** Bahasa Indonesia
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- **Finetuned from model [optional]:** xlm-roberta-large-finetuned-conll03-english
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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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<!-- ### 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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<!-- ## Bias, Risks, and Limitations -->
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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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<!-- ## Training Details -->
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### Training Performance
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| Epoch | Training Loss | Training Accuracy |
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| 2 | 0.002953063364330592 | 0.9979232802183077 |
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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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<!-- ### 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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<!-- #### 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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### Performance
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| Entity Type | Precision | Recall | F1-Score | Support |
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| DATE_TIME | 1.00 | 0.99 | 0.99 | 1474 |
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| EMAIL | 1.00 | 1.00 | 1.00 | 8936 |
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| SSN | 1.00 | 1.00 | 1.00 | 16654 |
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| **Micro Avg** | 1.00 | 1.00 | 1.00 | 59351 |
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| **Macro Avg** | 1.00 | 1.00 | 1.00 | 59351 |
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| **Weighted Avg** | 1.00 | 1.00 | 1.00 | 59351 |
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Validation Loss: 0.0009539824152059737
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Validation Accuracy: 0.999389430961554
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<!-- #### Summary -->
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<!-- ## Model Examination [optional] -->
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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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<!-- ## Citation [optional] -->
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<!-- ## Model Card Contact -->
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<!-- [More Information Needed] --> -->
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- **Language(s) (NLP):** Bahasa Indonesia
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- **Finetuned from model [optional]:** xlm-roberta-large-finetuned-conll03-english
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### Training Performance
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| Epoch | Training Loss | Training Accuracy |
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| 2 | 0.002953063364330592 | 0.9979232802183077 |
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### Performance
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<!-- | Entity Type | Precision | Recall | F1-Score | Support |
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| DATE_TIME | 1.00 | 0.99 | 0.99 | 1474 |
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| EMAIL | 1.00 | 1.00 | 1.00 | 8936 |
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| SSN | 1.00 | 1.00 | 1.00 | 16654 |
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| **Micro Avg** | 1.00 | 1.00 | 1.00 | 59351 |
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| **Macro Avg** | 1.00 | 1.00 | 1.00 | 59351 |
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| **Weighted Avg** | 1.00 | 1.00 | 1.00 | 59351 | -->
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Validation Loss: 0.0009539824152059737
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Validation Accuracy: 0.999389430961554
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