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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-base-pointer-adv-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-base-pointer-adv-top_v2 |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the top_v2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0255 |
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- Exact Match: 0.8472 |
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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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| 2.2938 | 0.41 | 200 | 0.5532 | 0.0012 | |
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| 0.671 | 0.82 | 400 | 0.1624 | 0.1610 | |
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| 0.5276 | 1.23 | 600 | 0.0692 | 0.2157 | |
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| 0.4196 | 1.64 | 800 | 0.0491 | 0.2259 | |
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| 0.3593 | 2.05 | 1000 | 0.0400 | 0.2291 | |
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| 0.3471 | 2.46 | 1200 | 0.0335 | 0.2297 | |
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| 0.3416 | 2.87 | 1400 | 0.0307 | 0.2318 | |
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| 0.3351 | 3.29 | 1600 | 0.0307 | 0.2334 | |
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| 0.3316 | 3.7 | 1800 | 0.0297 | 0.2343 | |
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| 0.3312 | 4.11 | 2000 | 0.0282 | 0.2344 | |
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| 0.3271 | 4.52 | 2200 | 0.0262 | 0.2365 | |
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| 0.3241 | 4.93 | 2400 | 0.0263 | 0.2365 | |
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| 0.3227 | 5.34 | 2600 | 0.0259 | 0.2368 | |
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| 0.3201 | 5.75 | 2800 | 0.0257 | 0.2365 | |
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| 0.3227 | 6.16 | 3000 | 0.0255 | 0.2365 | |
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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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