Initial Commit
Browse files- README.md +187 -0
- config.json +159 -0
- eval_results_ml.json +1 -0
- pytorch_model.bin +3 -0
- training_args.bin +3 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: mit
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| 3 |
+
base_model: microsoft/mdeberta-v3-base
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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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| 8 |
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metrics:
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| 9 |
+
- accuracy
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| 10 |
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- f1
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| 11 |
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model-index:
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| 12 |
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- name: scenario-MDBT-TCR_data-en-massive_all_1_1
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| 13 |
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: massive
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type: massive
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config: all_1.1
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split: validation
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args: all_1.1
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.7227317882391521
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- name: F1
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type: f1
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value: 0.6670992426180887
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| 30 |
+
---
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| 31 |
+
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| 32 |
+
<!-- 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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| 34 |
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| 35 |
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# scenario-MDBT-TCR_data-en-massive_all_1_1
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.6914
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- Accuracy: 0.7227
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- F1: 0.6671
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| 42 |
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| 43 |
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## Model description
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| 44 |
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| 45 |
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More information needed
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| 46 |
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## Intended uses & limitations
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| 48 |
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| 49 |
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More information needed
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| 50 |
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| 51 |
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## Training and evaluation data
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| 52 |
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| 53 |
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More information needed
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| 54 |
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## Training procedure
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| 56 |
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| 57 |
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### Training hyperparameters
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| 58 |
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| 59 |
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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| 61 |
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- train_batch_size: 32
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| 62 |
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- eval_batch_size: 64
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| 63 |
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- seed: 66
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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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| 66 |
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- num_epochs: 30
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| 67 |
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| 68 |
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### Training results
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| 69 |
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| 70 |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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| 71 |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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| 72 |
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| No log | 0.28 | 100 | 3.1171 | 0.2852 | 0.0691 |
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| 73 |
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| No log | 0.56 | 200 | 2.3001 | 0.4341 | 0.1961 |
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| 74 |
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| No log | 0.83 | 300 | 1.7494 | 0.5860 | 0.3648 |
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| 75 |
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| No log | 1.11 | 400 | 1.5526 | 0.6387 | 0.4610 |
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| 76 |
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| 1.995 | 1.39 | 500 | 1.5531 | 0.6500 | 0.4780 |
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| 77 |
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| 1.995 | 1.67 | 600 | 1.4151 | 0.6671 | 0.5333 |
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| 1.995 | 1.94 | 700 | 1.2962 | 0.6946 | 0.5785 |
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| 1.995 | 2.22 | 800 | 1.3865 | 0.6875 | 0.5773 |
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| 80 |
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| 1.995 | 2.5 | 900 | 1.2868 | 0.7121 | 0.6082 |
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| 81 |
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| 0.6196 | 2.78 | 1000 | 1.3864 | 0.6981 | 0.6033 |
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| 82 |
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| 0.6196 | 3.06 | 1100 | 1.4551 | 0.6925 | 0.6229 |
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| 83 |
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| 0.6196 | 3.33 | 1200 | 1.4319 | 0.7092 | 0.6216 |
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| 84 |
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| 0.6196 | 3.61 | 1300 | 1.4668 | 0.7035 | 0.6309 |
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| 0.6196 | 3.89 | 1400 | 1.4418 | 0.7056 | 0.6303 |
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| 0.347 | 4.17 | 1500 | 1.4875 | 0.7108 | 0.6562 |
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| 0.347 | 4.44 | 1600 | 1.4943 | 0.7144 | 0.6564 |
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| 0.347 | 4.72 | 1700 | 1.5156 | 0.7122 | 0.6407 |
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| 0.347 | 5.0 | 1800 | 1.5642 | 0.7013 | 0.6506 |
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| 0.347 | 5.28 | 1900 | 1.5904 | 0.7112 | 0.6440 |
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| 0.2195 | 5.56 | 2000 | 1.5237 | 0.7239 | 0.6596 |
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| 0.2195 | 5.83 | 2100 | 1.6728 | 0.7064 | 0.6285 |
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| 93 |
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| 0.2195 | 6.11 | 2200 | 1.6606 | 0.7026 | 0.6457 |
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| 94 |
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| 0.2195 | 6.39 | 2300 | 1.6961 | 0.7117 | 0.6461 |
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| 95 |
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| 0.2195 | 6.67 | 2400 | 1.7144 | 0.7088 | 0.6451 |
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| 0.1729 | 6.94 | 2500 | 1.6841 | 0.7148 | 0.6585 |
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| 0.1729 | 7.22 | 2600 | 1.8309 | 0.7057 | 0.6420 |
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| 0.1729 | 7.5 | 2700 | 1.7698 | 0.7197 | 0.6580 |
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| 0.1729 | 7.78 | 2800 | 1.9600 | 0.7069 | 0.6430 |
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| 0.1729 | 8.06 | 2900 | 2.0215 | 0.6836 | 0.6281 |
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| 0.113 | 8.33 | 3000 | 1.8546 | 0.7191 | 0.6600 |
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| 102 |
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| 0.113 | 8.61 | 3100 | 1.9063 | 0.7190 | 0.6593 |
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| 103 |
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| 0.113 | 8.89 | 3200 | 1.7990 | 0.7263 | 0.6578 |
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| 0.113 | 9.17 | 3300 | 1.8465 | 0.7215 | 0.6613 |
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| 105 |
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| 0.113 | 9.44 | 3400 | 1.9787 | 0.7133 | 0.6522 |
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| 106 |
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| 0.0826 | 9.72 | 3500 | 1.9424 | 0.7168 | 0.6593 |
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| 107 |
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| 0.0826 | 10.0 | 3600 | 2.1079 | 0.6973 | 0.6399 |
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| 108 |
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| 0.0826 | 10.28 | 3700 | 2.0101 | 0.7081 | 0.6510 |
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| 109 |
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| 0.0826 | 10.56 | 3800 | 2.1830 | 0.6990 | 0.6307 |
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| 110 |
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| 0.0826 | 10.83 | 3900 | 2.1300 | 0.7112 | 0.6541 |
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| 111 |
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| 0.066 | 11.11 | 4000 | 2.0432 | 0.7118 | 0.6480 |
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| 112 |
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| 0.066 | 11.39 | 4100 | 2.2643 | 0.7005 | 0.6312 |
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| 113 |
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| 0.066 | 11.67 | 4200 | 2.3124 | 0.7056 | 0.6504 |
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| 114 |
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| 0.066 | 11.94 | 4300 | 2.1704 | 0.7169 | 0.6606 |
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| 115 |
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| 0.066 | 12.22 | 4400 | 2.1669 | 0.7244 | 0.6668 |
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| 116 |
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| 0.0465 | 12.5 | 4500 | 2.0924 | 0.7187 | 0.6566 |
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| 117 |
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| 0.0465 | 12.78 | 4600 | 2.1401 | 0.7192 | 0.6520 |
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| 118 |
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| 0.0465 | 13.06 | 4700 | 2.1376 | 0.7233 | 0.6552 |
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| 119 |
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| 0.0465 | 13.33 | 4800 | 2.1814 | 0.7246 | 0.6625 |
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| 120 |
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| 0.0465 | 13.61 | 4900 | 2.1595 | 0.7232 | 0.6618 |
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| 121 |
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| 0.0321 | 13.89 | 5000 | 2.2037 | 0.7299 | 0.6757 |
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| 122 |
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| 0.0321 | 14.17 | 5100 | 2.2631 | 0.7220 | 0.6736 |
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| 123 |
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| 0.0321 | 14.44 | 5200 | 2.3036 | 0.7178 | 0.6608 |
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| 124 |
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| 0.0321 | 14.72 | 5300 | 2.4098 | 0.7164 | 0.6625 |
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| 125 |
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| 0.0321 | 15.0 | 5400 | 2.3241 | 0.7177 | 0.6615 |
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| 126 |
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| 0.0238 | 15.28 | 5500 | 2.4564 | 0.7105 | 0.6606 |
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| 127 |
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| 0.0238 | 15.56 | 5600 | 2.3782 | 0.7208 | 0.6666 |
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| 128 |
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| 0.0238 | 15.83 | 5700 | 2.3832 | 0.7189 | 0.6591 |
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| 129 |
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| 0.0238 | 16.11 | 5800 | 2.5115 | 0.7075 | 0.6452 |
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| 130 |
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| 0.0238 | 16.39 | 5900 | 2.4870 | 0.7112 | 0.6640 |
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| 131 |
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| 0.0208 | 16.67 | 6000 | 2.5268 | 0.7145 | 0.6636 |
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| 132 |
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| 0.0208 | 16.94 | 6100 | 2.5253 | 0.7134 | 0.6641 |
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| 133 |
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| 0.0208 | 17.22 | 6200 | 2.4308 | 0.7233 | 0.6696 |
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| 134 |
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| 0.0208 | 17.5 | 6300 | 2.4632 | 0.7177 | 0.6668 |
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| 135 |
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| 0.0208 | 17.78 | 6400 | 2.3885 | 0.7253 | 0.6665 |
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| 136 |
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| 0.0169 | 18.06 | 6500 | 2.4380 | 0.7187 | 0.6631 |
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| 137 |
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| 0.0169 | 18.33 | 6600 | 2.4620 | 0.7163 | 0.6681 |
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| 138 |
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| 0.0169 | 18.61 | 6700 | 2.4921 | 0.7195 | 0.6646 |
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| 139 |
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| 0.0169 | 18.89 | 6800 | 2.5746 | 0.7087 | 0.6474 |
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| 140 |
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| 0.0169 | 19.17 | 6900 | 2.5031 | 0.7201 | 0.6645 |
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| 141 |
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| 0.0139 | 19.44 | 7000 | 2.5396 | 0.7183 | 0.6579 |
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| 142 |
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| 0.0139 | 19.72 | 7100 | 2.5645 | 0.7191 | 0.6635 |
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| 143 |
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| 0.0139 | 20.0 | 7200 | 2.5458 | 0.7184 | 0.6614 |
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| 144 |
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| 0.0139 | 20.28 | 7300 | 2.5119 | 0.7210 | 0.6663 |
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| 145 |
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| 0.0139 | 20.56 | 7400 | 2.5254 | 0.7257 | 0.6752 |
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| 146 |
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| 0.0079 | 20.83 | 7500 | 2.5765 | 0.7198 | 0.6709 |
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| 147 |
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| 0.0079 | 21.11 | 7600 | 2.5612 | 0.7203 | 0.6703 |
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| 148 |
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| 0.0079 | 21.39 | 7700 | 2.5182 | 0.7278 | 0.6719 |
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| 149 |
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| 0.0079 | 21.67 | 7800 | 2.5369 | 0.7247 | 0.6711 |
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| 150 |
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| 0.0079 | 21.94 | 7900 | 2.6488 | 0.7208 | 0.6681 |
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| 151 |
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| 0.0045 | 22.22 | 8000 | 2.6237 | 0.7245 | 0.6726 |
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| 152 |
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| 0.0045 | 22.5 | 8100 | 2.5783 | 0.7243 | 0.6722 |
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| 153 |
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| 0.0045 | 22.78 | 8200 | 2.6651 | 0.7209 | 0.6738 |
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| 154 |
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| 0.0045 | 23.06 | 8300 | 2.5498 | 0.7253 | 0.6717 |
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| 155 |
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| 0.0045 | 23.33 | 8400 | 2.6436 | 0.7233 | 0.6687 |
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| 156 |
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| 0.0056 | 23.61 | 8500 | 2.6572 | 0.7245 | 0.6710 |
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| 157 |
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| 0.0056 | 23.89 | 8600 | 2.8399 | 0.7147 | 0.6647 |
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| 158 |
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| 0.0056 | 24.17 | 8700 | 2.7875 | 0.7161 | 0.6682 |
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| 159 |
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| 0.0056 | 24.44 | 8800 | 2.7095 | 0.7195 | 0.6669 |
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| 160 |
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| 0.0056 | 24.72 | 8900 | 2.6328 | 0.7248 | 0.6688 |
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| 161 |
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| 0.0056 | 25.0 | 9000 | 2.6524 | 0.7246 | 0.6693 |
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| 162 |
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| 0.0056 | 25.28 | 9100 | 2.6860 | 0.7219 | 0.6685 |
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| 163 |
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| 0.0056 | 25.56 | 9200 | 2.7291 | 0.7194 | 0.6671 |
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| 164 |
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| 0.0056 | 25.83 | 9300 | 2.7558 | 0.7164 | 0.6625 |
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| 165 |
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| 0.0056 | 26.11 | 9400 | 2.7021 | 0.7185 | 0.6636 |
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| 166 |
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| 0.0023 | 26.39 | 9500 | 2.7087 | 0.7200 | 0.6650 |
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| 167 |
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| 0.0023 | 26.67 | 9600 | 2.7187 | 0.7199 | 0.6688 |
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| 168 |
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| 0.0023 | 26.94 | 9700 | 2.6568 | 0.7241 | 0.6720 |
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| 169 |
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| 0.0023 | 27.22 | 9800 | 2.6873 | 0.7213 | 0.6675 |
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| 170 |
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| 0.0023 | 27.5 | 9900 | 2.7043 | 0.7205 | 0.6667 |
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| 171 |
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| 0.0024 | 27.78 | 10000 | 2.7342 | 0.7178 | 0.6662 |
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| 172 |
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| 0.0024 | 28.06 | 10100 | 2.7089 | 0.7202 | 0.6673 |
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| 173 |
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| 0.0024 | 28.33 | 10200 | 2.7063 | 0.7207 | 0.6674 |
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| 174 |
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| 0.0024 | 28.61 | 10300 | 2.7048 | 0.7208 | 0.6671 |
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| 175 |
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| 0.0024 | 28.89 | 10400 | 2.7010 | 0.7214 | 0.6674 |
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| 176 |
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| 0.0015 | 29.17 | 10500 | 2.6951 | 0.7226 | 0.6670 |
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| 177 |
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| 0.0015 | 29.44 | 10600 | 2.6964 | 0.7223 | 0.6669 |
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| 178 |
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| 0.0015 | 29.72 | 10700 | 2.6925 | 0.7225 | 0.6671 |
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| 179 |
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| 0.0015 | 30.0 | 10800 | 2.6914 | 0.7227 | 0.6671 |
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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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config.json
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|
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eval_results_ml.json
ADDED
|
@@ -0,0 +1 @@
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|
|
|
|
|
|
| 1 |
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ADDED
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