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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-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-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: 0.1202 |
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- Exact Match: 0.7445 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 32 |
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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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| 2.1451 | 6.65 | 200 | 0.5966 | 0.0134 | |
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| 0.4695 | 13.33 | 400 | 0.2264 | 0.2998 | |
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| 0.2229 | 19.98 | 600 | 0.1446 | 0.4649 | |
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| 0.1389 | 26.65 | 800 | 0.1227 | 0.5154 | |
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| 0.097 | 33.33 | 1000 | 0.1213 | 0.5221 | |
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| 0.0724 | 39.98 | 1200 | 0.1202 | 0.5365 | |
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| 0.0562 | 46.65 | 1400 | 0.1207 | 0.5436 | |
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| 0.0457 | 53.33 | 1600 | 0.1240 | 0.5441 | |
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| 0.0399 | 59.98 | 1800 | 0.1349 | 0.5441 | |
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| 0.0317 | 66.65 | 2000 | 0.1369 | 0.5477 | |
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| 0.0271 | 73.33 | 2200 | 0.1409 | 0.5490 | |
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| 0.0237 | 79.98 | 2400 | 0.1462 | 0.5539 | |
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| 0.0207 | 86.65 | 2600 | 0.1470 | 0.5517 | |
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| 0.0188 | 93.33 | 2800 | 0.1505 | 0.5508 | |
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| 0.0174 | 99.98 | 3000 | 0.1505 | 0.5512 | |
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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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