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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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- mtop |
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model-index: |
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- name: t5-small-pointer-adv-mtop |
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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-small-pointer-adv-mtop |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the mtop dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1341 |
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- Exact Match: 0.5817 |
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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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| 2.1628 | 1.09 | 200 | 0.7205 | 0.0022 | |
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| 1.1208 | 2.17 | 400 | 0.6393 | 0.0013 | |
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| 0.8675 | 3.26 | 600 | 0.5905 | 0.0027 | |
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| 1.8729 | 4.35 | 800 | 0.5726 | 0.0031 | |
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| 3.5417 | 5.43 | 1000 | 0.5371 | 0.0067 | |
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| 0.9087 | 6.52 | 1200 | 0.3512 | 0.1119 | |
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| 1.2224 | 7.61 | 1400 | 0.2739 | 0.1911 | |
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| 0.7597 | 8.69 | 1600 | 0.2151 | 0.3016 | |
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| 0.6981 | 9.78 | 1800 | 0.1736 | 0.3749 | |
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| 0.4779 | 10.87 | 2000 | 0.1548 | 0.4166 | |
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| 0.4397 | 11.96 | 2200 | 0.1377 | 0.4510 | |
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| 0.4101 | 13.04 | 2400 | 0.1480 | 0.4197 | |
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| 0.3323 | 14.13 | 2600 | 0.1396 | 0.4398 | |
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| 0.2565 | 15.22 | 2800 | 0.1351 | 0.4523 | |
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| 0.2108 | 16.3 | 3000 | 0.1341 | 0.4541 | |
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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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