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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-base-pointer-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-base-pointer-mtop |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mtop dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1131 |
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- Exact Match: 0.7199 |
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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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| 1.7749 | 6.65 | 200 | 0.5892 | 0.0031 | |
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| 0.6021 | 13.33 | 400 | 0.5160 | 0.0139 | |
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| 0.6044 | 19.98 | 600 | 0.4080 | 0.0532 | |
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| 0.3302 | 26.65 | 800 | 0.1865 | 0.3620 | |
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| 0.1483 | 33.33 | 1000 | 0.1267 | 0.5105 | |
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| 0.0768 | 39.98 | 1200 | 0.1131 | 0.5298 | |
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| 0.0525 | 46.65 | 1400 | 0.1219 | 0.5414 | |
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| 0.0801 | 53.33 | 1600 | 0.1186 | 0.5275 | |
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| 0.0331 | 59.98 | 1800 | 0.1306 | 0.5423 | |
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| 0.0254 | 66.65 | 2000 | 0.1396 | 0.5396 | |
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| 0.0168 | 73.33 | 2200 | 0.1560 | 0.5436 | |
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| 0.0129 | 79.98 | 2400 | 0.1659 | 0.5494 | |
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| 0.0105 | 86.65 | 2600 | 0.1699 | 0.5423 | |
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| 0.0088 | 93.33 | 2800 | 0.1742 | 0.5472 | |
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| 0.0077 | 99.98 | 3000 | 0.1775 | 0.5468 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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