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--- |
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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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- break_data |
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metrics: |
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- bleu |
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model-index: |
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- name: t5-large-finetuned-break-qdmr-decomposition |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: break_data |
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type: break_data |
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config: QDMR |
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split: validation |
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args: QDMR |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 0.22169382457557757 |
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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-large-finetuned-break-qdmr-decomposition |
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This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the break_data dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1729 |
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- Bleu: 0.2217 |
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- Brevity Penalty: 0.2926 |
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- Length Ratio: 0.4487 |
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- Translation Length: 108954 |
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- Reference Length: 242845 |
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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: 0.0001 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 128 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Brevity Penalty | Length Ratio | Translation Length | Reference Length | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------------:|:------------:|:------------------:|:----------------:| |
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| No log | 1.0 | 346 | 0.2217 | 0.2190 | 0.2973 | 0.4519 | 109738 | 242845 | |
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| 0.3597 | 2.0 | 692 | 0.1898 | 0.2213 | 0.2944 | 0.4499 | 109245 | 242845 | |
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| 0.1943 | 3.0 | 1038 | 0.1780 | 0.2213 | 0.2936 | 0.4494 | 109125 | 242845 | |
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| 0.1943 | 4.0 | 1385 | 0.1722 | 0.2209 | 0.2926 | 0.4486 | 108943 | 242845 | |
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| 0.1588 | 5.0 | 1731 | 0.1708 | 0.2221 | 0.2938 | 0.4495 | 109159 | 242845 | |
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| 0.1395 | 6.0 | 2077 | 0.1699 | 0.2209 | 0.2907 | 0.4473 | 108635 | 242845 | |
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| 0.1395 | 7.0 | 2423 | 0.1699 | 0.2219 | 0.2927 | 0.4487 | 108964 | 242845 | |
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| 0.1245 | 8.0 | 2770 | 0.1717 | 0.2215 | 0.2924 | 0.4485 | 108909 | 242845 | |
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| 0.1152 | 9.0 | 3116 | 0.1724 | 0.2215 | 0.2924 | 0.4485 | 108914 | 242845 | |
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| 0.1152 | 9.99 | 3460 | 0.1729 | 0.2217 | 0.2926 | 0.4487 | 108954 | 242845 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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