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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_155 |
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results: [] |
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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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# scenario-KD-SCR-MSV-EN-EN-D2_data-en-massive_all_1_155 |
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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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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 55 |
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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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### Training results |
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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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| 2.4356 | 1.39 | 500 | nan | 0.0315 | 0.0010 | |
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| 2.4356 | 1.67 | 600 | nan | 0.0315 | 0.0010 | |
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| 2.4356 | 1.94 | 700 | nan | 0.0315 | 0.0010 | |
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| 2.4356 | 2.22 | 800 | nan | 0.0315 | 0.0010 | |
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| 2.4356 | 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 | 14.17 | 5100 | 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 | 15.28 | 5500 | 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 | 17.78 | 6400 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 18.06 | 6500 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 18.33 | 6600 | 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 | 22.78 | 8200 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 23.06 | 8300 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 23.33 | 8400 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 23.61 | 8500 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 23.89 | 8600 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 24.17 | 8700 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 24.44 | 8800 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 24.72 | 8900 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 25.0 | 9000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 25.28 | 9100 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 25.56 | 9200 | 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 | 26.39 | 9500 | 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 | 28.33 | 10200 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 28.61 | 10300 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 28.89 | 10400 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 29.17 | 10500 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 29.44 | 10600 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 29.72 | 10700 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 30.0 | 10800 | nan | 0.0315 | 0.0010 | |
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### Framework versions |
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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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