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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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datasets: |
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- pjura/mahjong_souls_tiles |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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model-index: |
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- name: mahjong_soul_vision |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: pjura/mahjong_souls_tiles |
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type: imagefolder |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9966555183946488 |
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- name: F1 |
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type: f1 |
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value: 0.9966383672069291 |
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- name: Recall |
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type: recall |
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value: 0.9966555183946488 |
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--- |
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# Mahjong Vision Assistant |
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This project uses computer vision and machine learning to provide real-time discard suggestions for the game Mahjong Soul. |
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## Features |
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* **Tile Recognition:** Identifies Mahjong tiles from the Mahjong Soul game window using a fine-tuned Vision Transformer model (`pjura/mahjong_soul_vision`). |
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* **Game State Analysis:** Parses the recognized tiles to understand the current game state (player's hand, melds, discard pools). |
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* **Discard Suggestion:** Employs a neural network (`ImprovedNN`), based on the architecture from the [pjura/mahjong_ai](https://huggingface.co/pjura/mahjong_ai) repository, to predict the optimal discard based on the analyzed game state. |
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* **Live Overlay:** Captures the game window and overlays suggestions directly onto the screen, highlighting the recommended discard tile. |
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## Project Structure |
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* `live_feed.py`: The main script to run the live assistant. It captures the screen, performs tile recognition, predicts discards, and displays the overlay. |
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* `hf_vision_model.ipynb`: Jupyter notebook detailing the training process for the Hugging Face Vision Transformer used for tile recognition. |
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* `tools.py`: Contains utility functions for data processing, model prediction, loss calculation, MLflow interaction, and tile representation translation used by `live_feed.py`. Many cross repo functions. |
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* `model.safetensors`: Saved weights for the discard prediction neural network (`ImprovedNN`). |
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## Setup |
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1. **Environment:** Ensure you have Python installed along with necessary libraries. Key libraries include: |
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* `torch` (with CUDA support if available) |
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* `transformers` |
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* `datasets` |
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* `evaluate` |
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* `opencv-python` (`cv2`) |
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* `Pillow` (`PIL`) |
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* `pygetwindow` |
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* `numpy` |
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* `pyautogui` |
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* `keyboard` |
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* `safetensors` |
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* `mlflow` (Optional, used in `tools.py`, you can use whatever you like to serve the model) |
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* `scipy` |
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* `matplotlib` |
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*(A `requirements.txt` file would be beneficial here, but didn't made one at the time)* |
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2. **Models:** |
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* The tile recognition model (`pjura/mahjong_soul_vision`) will be downloaded automatically by the `transformers` library. |
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* The discard prediction model (`model.safetensors`) should be present in the root directory. |
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## Usage |
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1. Ensure the Mahjong Soul game window is open and titled "MahjongSoul". |
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2. Run the main script: |
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```bash |
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python live_feed.py |
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``` |
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3. The script will capture the game window, analyze the tiles, and highlight the suggested discard tile in the player's hand region. The color of the highlight indicates the model's confidence (Green=High, Red=Low). |
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4. Press 'q' to quit the application. |
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5. **Auto-Click:** When it is your turn (14 tiles in hand/melds) and a suggestion is highlighted, hold the **Spacebar** to automatically move the mouse and click the suggested tile. If Spacebar is not held, only the highlight will be shown. |
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## Notes |
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* The script relies on specific window coordinates and aspect ratios which might need adjustment depending on screen resolution and game layout. |
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* The discard prediction model architecture (`ImprovedNN`) originates from the [pjura/mahjong_ai](https://huggingface.co/pjura/mahjong_ai) repository. The included `model.safetensors` file is an example set of weights for this model, also from that repository, but potentially not the latest version. It was trained on the `pjura/mahjong_board_states` dataset, primarily using the `tenhou_prediction_deepLearning_basic.ipynb` notebook as detailed on the model card. You can add your own logic to load different weights or the latest version from the Hub. |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on a local imagefolder dataset consisting of pictures of Mahjong tiles. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0466 |
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- Accuracy: 0.9967 |
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- F1: 0.9966 |
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- Recall: 0.9967 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 250 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:| |
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| 3.5154 | 1.0 | 17 | 3.5109 | 0.0234 | 0.0154 | 0.0234 | |
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| 3.4741 | 2.0 | 34 | 3.4796 | 0.0769 | 0.0703 | 0.0769 | |
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| 3.3627 | 3.0 | 51 | 3.4305 | 0.1661 | 0.1266 | 0.1661 | |
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| 3.2456 | 4.0 | 68 | 3.3608 | 0.2230 | 0.1652 | 0.2230 | |
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| 3.1598 | 5.0 | 85 | 3.2658 | 0.2676 | 0.1989 | 0.2676 | |
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| 2.9972 | 6.0 | 102 | 3.1531 | 0.3467 | 0.2807 | 0.3467 | |
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| 2.7832 | 7.0 | 119 | 3.0176 | 0.4749 | 0.4135 | 0.4749 | |
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| 2.6689 | 8.0 | 136 | 2.8651 | 0.5507 | 0.4891 | 0.5507 | |
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| 2.3725 | 9.0 | 153 | 2.6983 | 0.6734 | 0.6192 | 0.6734 | |
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| 2.1117 | 10.0 | 170 | 2.5176 | 0.7570 | 0.7124 | 0.7570 | |
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| 1.9014 | 11.0 | 187 | 2.3488 | 0.8105 | 0.7771 | 0.8105 | |
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| 1.6784 | 12.0 | 204 | 2.1735 | 0.8618 | 0.8440 | 0.8618 | |
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| 1.4541 | 13.0 | 221 | 2.0088 | 0.9164 | 0.9092 | 0.9164 | |
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| 1.3576 | 14.0 | 238 | 1.8511 | 0.9487 | 0.9463 | 0.9487 | |
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| 1.2025 | 15.0 | 255 | 1.6971 | 0.9721 | 0.9718 | 0.9721 | |
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| 1.0567 | 16.0 | 272 | 1.5578 | 0.9844 | 0.9842 | 0.9844 | |
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| 0.898 | 17.0 | 289 | 1.4185 | 0.9889 | 0.9887 | 0.9889 | |
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| 0.7663 | 18.0 | 306 | 1.2978 | 0.9900 | 0.9899 | 0.9900 | |
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| 0.7498 | 19.0 | 323 | 1.1911 | 0.9911 | 0.9910 | 0.9911 | |
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| 0.6427 | 20.0 | 340 | 1.0966 | 0.9900 | 0.9899 | 0.9900 | |
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| 0.616 | 21.0 | 357 | 1.0003 | 0.9911 | 0.9910 | 0.9911 | |
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| 0.4962 | 22.0 | 374 | 0.9015 | 0.9900 | 0.9900 | 0.9900 | |
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| 0.4871 | 23.0 | 391 | 0.8413 | 0.9900 | 0.9899 | 0.9900 | |
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| 0.4257 | 24.0 | 408 | 0.7768 | 0.9911 | 0.9910 | 0.9911 | |
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| 0.3961 | 25.0 | 425 | 0.7042 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.3513 | 26.0 | 442 | 0.6645 | 0.9922 | 0.9922 | 0.9922 | |
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| 0.3294 | 27.0 | 459 | 0.6179 | 0.9911 | 0.9911 | 0.9911 | |
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| 0.3021 | 28.0 | 476 | 0.5852 | 0.9900 | 0.9899 | 0.9900 | |
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| 0.2726 | 29.0 | 493 | 0.5444 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.257 | 30.0 | 510 | 0.5177 | 0.9911 | 0.9910 | 0.9911 | |
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| 0.2382 | 31.0 | 527 | 0.4924 | 0.9900 | 0.9899 | 0.9900 | |
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| 0.2222 | 32.0 | 544 | 0.4582 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.2059 | 33.0 | 561 | 0.4408 | 0.9922 | 0.9922 | 0.9922 | |
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| 0.1928 | 34.0 | 578 | 0.4222 | 0.9911 | 0.9910 | 0.9911 | |
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| 0.1864 | 35.0 | 595 | 0.3997 | 0.9922 | 0.9922 | 0.9922 | |
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| 0.176 | 36.0 | 612 | 0.3844 | 0.9922 | 0.9922 | 0.9922 | |
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| 0.1625 | 37.0 | 629 | 0.3693 | 0.9922 | 0.9922 | 0.9922 | |
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| 0.154 | 38.0 | 646 | 0.3539 | 0.9922 | 0.9921 | 0.9922 | |
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| 0.1524 | 39.0 | 663 | 0.3380 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.1415 | 40.0 | 680 | 0.3256 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.1362 | 41.0 | 697 | 0.3147 | 0.9922 | 0.9922 | 0.9922 | |
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| 0.1307 | 42.0 | 714 | 0.3023 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.1263 | 43.0 | 731 | 0.2914 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.1185 | 44.0 | 748 | 0.2811 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.1143 | 45.0 | 765 | 0.2708 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.109 | 46.0 | 782 | 0.2646 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.1023 | 47.0 | 799 | 0.2564 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.1 | 48.0 | 816 | 0.2472 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0969 | 49.0 | 833 | 0.2409 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0931 | 50.0 | 850 | 0.2336 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0926 | 51.0 | 867 | 0.2266 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0874 | 52.0 | 884 | 0.2217 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.0837 | 53.0 | 901 | 0.2134 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0796 | 54.0 | 918 | 0.2099 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.0759 | 55.0 | 935 | 0.2038 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0745 | 56.0 | 952 | 0.1987 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0745 | 57.0 | 969 | 0.1937 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0678 | 58.0 | 986 | 0.1883 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0666 | 59.0 | 1003 | 0.1841 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0642 | 60.0 | 1020 | 0.1805 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0608 | 61.0 | 1037 | 0.1756 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0615 | 62.0 | 1054 | 0.1724 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0582 | 63.0 | 1071 | 0.1689 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0574 | 64.0 | 1088 | 0.1650 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0558 | 65.0 | 1105 | 0.1612 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0551 | 66.0 | 1122 | 0.1581 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.054 | 67.0 | 1139 | 0.1550 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0529 | 68.0 | 1156 | 0.1516 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0508 | 69.0 | 1173 | 0.1491 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0497 | 70.0 | 1190 | 0.1462 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0469 | 71.0 | 1207 | 0.1436 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0478 | 72.0 | 1224 | 0.1417 | 0.9933 | 0.9933 | 0.9933 | |
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| 0.0433 | 73.0 | 1241 | 0.1384 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0406 | 74.0 | 1258 | 0.1359 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0432 | 75.0 | 1275 | 0.1337 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.0425 | 76.0 | 1292 | 0.1315 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0393 | 77.0 | 1309 | 0.1297 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0405 | 78.0 | 1326 | 0.1270 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0403 | 79.0 | 1343 | 0.1250 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.037 | 80.0 | 1360 | 0.1233 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0377 | 81.0 | 1377 | 0.1213 | 0.9944 | 0.9944 | 0.9944 | |
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| 0.0336 | 82.0 | 1394 | 0.1195 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.0366 | 83.0 | 1411 | 0.1174 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.0361 | 84.0 | 1428 | 0.1156 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.0351 | 85.0 | 1445 | 0.1140 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.0333 | 86.0 | 1462 | 0.1126 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.0343 | 87.0 | 1479 | 0.1109 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0316 | 88.0 | 1496 | 0.1096 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.0319 | 89.0 | 1513 | 0.1077 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.0297 | 90.0 | 1530 | 0.1062 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0285 | 91.0 | 1547 | 0.1050 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0288 | 92.0 | 1564 | 0.1037 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0283 | 93.0 | 1581 | 0.1026 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0282 | 94.0 | 1598 | 0.1011 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0281 | 95.0 | 1615 | 0.1001 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0283 | 96.0 | 1632 | 0.0986 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0274 | 97.0 | 1649 | 0.0976 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0261 | 98.0 | 1666 | 0.0965 | 0.9955 | 0.9955 | 0.9955 | |
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| 0.0249 | 99.0 | 1683 | 0.0955 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0252 | 100.0 | 1700 | 0.0941 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0258 | 101.0 | 1717 | 0.0930 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.024 | 102.0 | 1734 | 0.0921 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0244 | 103.0 | 1751 | 0.0910 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0226 | 104.0 | 1768 | 0.0904 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0238 | 105.0 | 1785 | 0.0890 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0233 | 106.0 | 1802 | 0.0881 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0219 | 107.0 | 1819 | 0.0870 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0213 | 108.0 | 1836 | 0.0863 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0221 | 109.0 | 1853 | 0.0855 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0209 | 110.0 | 1870 | 0.0848 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0207 | 111.0 | 1887 | 0.0838 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0203 | 112.0 | 1904 | 0.0828 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0203 | 113.0 | 1921 | 0.0823 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0193 | 114.0 | 1938 | 0.0814 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0199 | 115.0 | 1955 | 0.0806 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0202 | 116.0 | 1972 | 0.0799 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0192 | 117.0 | 1989 | 0.0790 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0193 | 118.0 | 2006 | 0.0784 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0189 | 119.0 | 2023 | 0.0779 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0189 | 120.0 | 2040 | 0.0772 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0176 | 121.0 | 2057 | 0.0765 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.0184 | 122.0 | 2074 | 0.0761 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0169 | 123.0 | 2091 | 0.0754 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0177 | 124.0 | 2108 | 0.0746 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.0173 | 125.0 | 2125 | 0.0739 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.0173 | 126.0 | 2142 | 0.0737 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.016 | 127.0 | 2159 | 0.0729 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.0167 | 128.0 | 2176 | 0.0724 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.0164 | 129.0 | 2193 | 0.0714 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.0158 | 130.0 | 2210 | 0.0711 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.016 | 131.0 | 2227 | 0.0706 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.0159 | 132.0 | 2244 | 0.0701 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.0154 | 133.0 | 2261 | 0.0697 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0149 | 134.0 | 2278 | 0.0694 | 0.9967 | 0.9966 | 0.9967 | |
|
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| 0.0149 | 135.0 | 2295 | 0.0685 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0148 | 136.0 | 2312 | 0.0681 | 0.9967 | 0.9966 | 0.9967 | |
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| 0.0146 | 137.0 | 2329 | 0.0677 | 0.9967 | 0.9966 | 0.9967 | |
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|
| 0.0147 | 138.0 | 2346 | 0.0671 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0147 | 139.0 | 2363 | 0.0667 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0143 | 140.0 | 2380 | 0.0662 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0137 | 141.0 | 2397 | 0.0660 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0138 | 142.0 | 2414 | 0.0656 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0142 | 143.0 | 2431 | 0.0649 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0137 | 144.0 | 2448 | 0.0645 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0137 | 145.0 | 2465 | 0.0641 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0134 | 146.0 | 2482 | 0.0636 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.014 | 147.0 | 2499 | 0.0632 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0132 | 148.0 | 2516 | 0.0632 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0135 | 149.0 | 2533 | 0.0627 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0128 | 150.0 | 2550 | 0.0624 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0123 | 151.0 | 2567 | 0.0619 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0124 | 152.0 | 2584 | 0.0615 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0127 | 153.0 | 2601 | 0.0609 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0127 | 154.0 | 2618 | 0.0607 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0124 | 155.0 | 2635 | 0.0607 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0121 | 156.0 | 2652 | 0.0601 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0118 | 157.0 | 2669 | 0.0599 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0123 | 158.0 | 2686 | 0.0596 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0118 | 159.0 | 2703 | 0.0590 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0116 | 160.0 | 2720 | 0.0589 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0112 | 161.0 | 2737 | 0.0586 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0113 | 162.0 | 2754 | 0.0582 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0116 | 163.0 | 2771 | 0.0579 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.011 | 164.0 | 2788 | 0.0576 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0114 | 165.0 | 2805 | 0.0575 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0109 | 166.0 | 2822 | 0.0572 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0102 | 167.0 | 2839 | 0.0569 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0106 | 168.0 | 2856 | 0.0568 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0103 | 169.0 | 2873 | 0.0564 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0105 | 170.0 | 2890 | 0.0561 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0106 | 171.0 | 2907 | 0.0560 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.01 | 172.0 | 2924 | 0.0556 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0098 | 173.0 | 2941 | 0.0554 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0098 | 174.0 | 2958 | 0.0550 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0107 | 175.0 | 2975 | 0.0549 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0103 | 176.0 | 2992 | 0.0546 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0104 | 177.0 | 3009 | 0.0544 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0096 | 178.0 | 3026 | 0.0542 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0102 | 179.0 | 3043 | 0.0540 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0097 | 180.0 | 3060 | 0.0538 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0096 | 181.0 | 3077 | 0.0535 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0093 | 182.0 | 3094 | 0.0536 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0097 | 183.0 | 3111 | 0.0531 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0093 | 184.0 | 3128 | 0.0529 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0097 | 185.0 | 3145 | 0.0526 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0094 | 186.0 | 3162 | 0.0527 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0095 | 187.0 | 3179 | 0.0524 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0093 | 188.0 | 3196 | 0.0522 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0089 | 189.0 | 3213 | 0.0520 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0091 | 190.0 | 3230 | 0.0520 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0091 | 191.0 | 3247 | 0.0516 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.009 | 192.0 | 3264 | 0.0515 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.009 | 193.0 | 3281 | 0.0514 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0091 | 194.0 | 3298 | 0.0512 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.009 | 195.0 | 3315 | 0.0509 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0087 | 196.0 | 3332 | 0.0510 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.009 | 197.0 | 3349 | 0.0507 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0087 | 198.0 | 3366 | 0.0506 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0084 | 199.0 | 3383 | 0.0505 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.009 | 200.0 | 3400 | 0.0503 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0087 | 201.0 | 3417 | 0.0501 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0088 | 202.0 | 3434 | 0.0500 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0086 | 203.0 | 3451 | 0.0500 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0085 | 204.0 | 3468 | 0.0497 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.009 | 205.0 | 3485 | 0.0496 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0082 | 206.0 | 3502 | 0.0495 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.008 | 207.0 | 3519 | 0.0494 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0082 | 208.0 | 3536 | 0.0493 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0078 | 209.0 | 3553 | 0.0491 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0082 | 210.0 | 3570 | 0.0490 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0082 | 211.0 | 3587 | 0.0489 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0085 | 212.0 | 3604 | 0.0488 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0087 | 213.0 | 3621 | 0.0487 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0079 | 214.0 | 3638 | 0.0485 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0078 | 215.0 | 3655 | 0.0484 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0078 | 216.0 | 3672 | 0.0484 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0082 | 217.0 | 3689 | 0.0483 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0085 | 218.0 | 3706 | 0.0482 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0079 | 219.0 | 3723 | 0.0480 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0079 | 220.0 | 3740 | 0.0480 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0076 | 221.0 | 3757 | 0.0479 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.008 | 222.0 | 3774 | 0.0478 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0078 | 223.0 | 3791 | 0.0477 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0078 | 224.0 | 3808 | 0.0476 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0078 | 225.0 | 3825 | 0.0476 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0077 | 226.0 | 3842 | 0.0475 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0075 | 227.0 | 3859 | 0.0475 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0075 | 228.0 | 3876 | 0.0474 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0076 | 229.0 | 3893 | 0.0473 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0077 | 230.0 | 3910 | 0.0472 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0076 | 231.0 | 3927 | 0.0472 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0074 | 232.0 | 3944 | 0.0471 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0076 | 233.0 | 3961 | 0.0471 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0074 | 234.0 | 3978 | 0.0470 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0077 | 235.0 | 3995 | 0.0470 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0074 | 236.0 | 4012 | 0.0469 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0075 | 237.0 | 4029 | 0.0469 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0072 | 238.0 | 4046 | 0.0469 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0075 | 239.0 | 4063 | 0.0468 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0078 | 240.0 | 4080 | 0.0468 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0075 | 241.0 | 4097 | 0.0468 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0073 | 242.0 | 4114 | 0.0468 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0073 | 243.0 | 4131 | 0.0467 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0068 | 244.0 | 4148 | 0.0467 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0072 | 245.0 | 4165 | 0.0467 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0073 | 246.0 | 4182 | 0.0467 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0077 | 247.0 | 4199 | 0.0467 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0074 | 248.0 | 4216 | 0.0466 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0073 | 249.0 | 4233 | 0.0466 | 0.9967 | 0.9966 | 0.9967 | |
|
|
| 0.0074 | 250.0 | 4250 | 0.0466 | 0.9967 | 0.9966 | 0.9967 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.34.1 |
|
|
- Pytorch 2.1.0+cu121 |
|
|
- Datasets 2.14.6 |
|
|
- Tokenizers 0.14.1 |
