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scenario-KD-SCR-MSV-EN-EN-D2_data-en-massive_all_1_144

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deberta-v2
Generated from Trainer
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Files changed (5) hide show
  1. README.md +171 -0
  2. config.json +159 -0
  3. eval_results_ml.json +1 -0
  4. pytorch_model.bin +3 -0
  5. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - massive
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: scenario-KD-SCR-MSV-EN-EN-D2_data-en-massive_all_1_144
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # scenario-KD-SCR-MSV-EN-EN-D2_data-en-massive_all_1_144
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+
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+ This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1) on the massive dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: nan
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+ - Accuracy: 0.0315
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+ - F1: 0.0010
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 32
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+ - eval_batch_size: 32
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+ - seed: 44
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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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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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+ | No log | 0.28 | 100 | nan | 0.0315 | 0.0010 |
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+ | No log | 0.56 | 200 | nan | 0.0315 | 0.0010 |
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+ | No log | 0.83 | 300 | nan | 0.0315 | 0.0010 |
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+ | No log | 1.11 | 400 | nan | 0.0315 | 0.0010 |
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+ | 2.4588 | 1.39 | 500 | nan | 0.0315 | 0.0010 |
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+ | 2.4588 | 1.67 | 600 | nan | 0.0315 | 0.0010 |
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+ | 2.4588 | 1.94 | 700 | nan | 0.0315 | 0.0010 |
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+ | 2.4588 | 2.22 | 800 | nan | 0.0315 | 0.0010 |
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+ | 2.4588 | 2.5 | 900 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 2.78 | 1000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 3.06 | 1100 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 3.33 | 1200 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 3.61 | 1300 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 3.89 | 1400 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 4.17 | 1500 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 4.44 | 1600 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 4.72 | 1700 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 5.0 | 1800 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 5.28 | 1900 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 5.56 | 2000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 5.83 | 2100 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 6.11 | 2200 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 6.39 | 2300 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 6.67 | 2400 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 6.94 | 2500 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 7.22 | 2600 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 7.5 | 2700 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 7.78 | 2800 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 8.06 | 2900 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 8.33 | 3000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 8.61 | 3100 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 8.89 | 3200 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 9.17 | 3300 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 9.44 | 3400 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 9.72 | 3500 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 10.0 | 3600 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 10.28 | 3700 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 10.56 | 3800 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 10.83 | 3900 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 11.11 | 4000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 11.39 | 4100 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 11.67 | 4200 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 11.94 | 4300 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 12.22 | 4400 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 12.5 | 4500 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 12.78 | 4600 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 13.06 | 4700 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 13.33 | 4800 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 13.61 | 4900 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 13.89 | 5000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 14.44 | 5200 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 14.72 | 5300 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 15.0 | 5400 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 15.56 | 5600 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 15.83 | 5700 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 16.11 | 5800 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 16.39 | 5900 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 16.67 | 6000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 16.94 | 6100 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 17.22 | 6200 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 17.5 | 6300 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 18.61 | 6700 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 18.89 | 6800 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 19.17 | 6900 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 19.44 | 7000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 19.72 | 7100 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 20.0 | 7200 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 20.28 | 7300 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 20.56 | 7400 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 20.83 | 7500 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 21.11 | 7600 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 21.39 | 7700 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 21.67 | 7800 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 21.94 | 7900 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 22.22 | 8000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 22.5 | 8100 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 25.83 | 9300 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 26.11 | 9400 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 26.67 | 9600 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 26.94 | 9700 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 27.22 | 9800 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 27.5 | 9900 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 27.78 | 10000 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 28.06 | 10100 | nan | 0.0315 | 0.0010 |
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+ | 0.0 | 30.0 | 10800 | nan | 0.0315 | 0.0010 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.3
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
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