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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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- top_v2 |
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model-index: |
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- name: t5-small-pointer-top_v2 |
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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-top_v2 |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the top_v2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0306 |
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- Exact Match: 0.8264 |
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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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| 1.9316 | 0.82 | 200 | 0.4566 | 0.0084 | |
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| 0.3713 | 1.65 | 400 | 0.1473 | 0.1230 | |
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| 0.1747 | 2.47 | 600 | 0.0788 | 0.1984 | |
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| 0.1104 | 3.29 | 800 | 0.0568 | 0.2149 | |
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| 0.0842 | 4.12 | 1000 | 0.0473 | 0.2217 | |
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| 0.0694 | 4.94 | 1200 | 0.0426 | 0.2260 | |
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| 0.0603 | 5.76 | 1400 | 0.0383 | 0.2279 | |
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| 0.0534 | 6.58 | 1600 | 0.0367 | 0.2281 | |
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| 0.0477 | 7.41 | 1800 | 0.0347 | 0.2301 | |
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| 0.0441 | 8.23 | 2000 | 0.0334 | 0.2314 | |
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| 0.0413 | 9.05 | 2200 | 0.0323 | 0.2315 | |
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| 0.0387 | 9.88 | 2400 | 0.0316 | 0.2316 | |
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| 0.0366 | 10.7 | 2600 | 0.0311 | 0.2324 | |
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| 0.0358 | 11.52 | 2800 | 0.0307 | 0.2324 | |
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| 0.0343 | 12.35 | 3000 | 0.0306 | 0.2327 | |
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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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