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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cstop_artificial |
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model-index: |
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- name: t5-base-pointer-adv-cstop_artificial |
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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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# t5-base-pointer-adv-cstop_artificial |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the cstop_artificial dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0728 |
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- Exact Match: 0.7925 |
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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: 0.001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 512 |
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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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- training_steps: 3000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Exact Match | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------:| |
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| 1.7423 | 12.5 | 200 | 0.1173 | 0.2397 | |
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| 0.3678 | 25.0 | 400 | 0.0728 | 0.3363 | |
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| 0.3202 | 37.5 | 600 | 0.0879 | 0.3381 | |
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| 0.3452 | 50.0 | 800 | 0.0908 | 0.3363 | |
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| 0.3099 | 62.5 | 1000 | 0.1056 | 0.3435 | |
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| 0.3057 | 75.0 | 1200 | 0.1109 | 0.3470 | |
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| 0.3045 | 87.5 | 1400 | 0.1273 | 0.3453 | |
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| 0.3052 | 100.0 | 1600 | 0.1065 | 0.3417 | |
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| 0.3037 | 112.5 | 1800 | 0.1387 | 0.3381 | |
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| 0.3036 | 125.0 | 2000 | 0.1421 | 0.3453 | |
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| 0.3023 | 137.5 | 2200 | 0.1649 | 0.3399 | |
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| 0.3028 | 150.0 | 2400 | 0.1574 | 0.3399 | |
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| 0.3025 | 162.5 | 2600 | 0.1563 | 0.3399 | |
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| 0.3017 | 175.0 | 2800 | 0.1589 | 0.3399 | |
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| 0.302 | 187.5 | 3000 | 0.1587 | 0.3417 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.0 |
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- Tokenizers 0.13.2 |
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