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--- |
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base_model: kravchenko/uk-mt5-base |
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tags: |
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- summarization |
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- generated_from_trainer |
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datasets: |
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- xlsum |
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metrics: |
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- rouge |
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model-index: |
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- name: uk-mt5-base-xlsum-v2 |
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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: xlsum |
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type: xlsum |
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config: ukrainian |
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split: validation |
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args: ukrainian |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 4.4311 |
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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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# uk-mt5-base-xlsum-v2 |
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This model is a fine-tuned version of [kravchenko/uk-mt5-base](https://huggingface.co/kravchenko/uk-mt5-base) on the xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0401 |
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- Rouge1: 4.4311 |
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- Rouge2: 0.8944 |
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- Rougel: 4.4294 |
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- Rougelsum: 4.4527 |
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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: 5.6e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 3.2519 | 1.0 | 2000 | 2.0993 | 4.1141 | 0.5944 | 4.1014 | 4.11 | |
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| 2.5587 | 2.0 | 4000 | 2.0428 | 4.5015 | 0.6167 | 4.4863 | 4.518 | |
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| 2.3299 | 3.0 | 6000 | 2.0175 | 4.4642 | 1.0833 | 4.4528 | 4.5167 | |
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| 2.1543 | 4.0 | 8000 | 2.0183 | 4.3294 | 0.9444 | 4.3408 | 4.3611 | |
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| 2.0276 | 5.0 | 10000 | 2.0039 | 4.6694 | 0.9444 | 4.6264 | 4.6527 | |
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| 1.9119 | 6.0 | 12000 | 2.0139 | 4.9447 | 1.0675 | 4.8908 | 4.9633 | |
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| 1.8305 | 7.0 | 14000 | 2.0134 | 4.9385 | 1.1595 | 4.8774 | 4.9294 | |
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| 1.7669 | 8.0 | 16000 | 2.0253 | 4.2697 | 0.9667 | 4.2524 | 4.3167 | |
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| 1.7141 | 9.0 | 18000 | 2.0354 | 4.4527 | 0.9 | 4.448 | 4.4941 | |
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| 1.681 | 10.0 | 20000 | 2.0401 | 4.4311 | 0.8944 | 4.4294 | 4.4527 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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