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--- |
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license: apache-2.0 |
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base_model: google/mt5-small |
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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: mt5-small-xlsum |
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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: train |
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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: 1.1945 |
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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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# mt5-small-xlsum |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8395 |
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- Rouge1: 1.1945 |
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- Rouge2: 0.1467 |
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- Rougel: 1.1902 |
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- Rougelsum: 1.196 |
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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: 8 |
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- eval_batch_size: 8 |
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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: 8 |
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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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| 11.9992 | 1.0 | 125 | 4.0495 | 0.3829 | 0.0 | 0.3905 | 0.3905 | |
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| 5.8176 | 2.0 | 250 | 3.3431 | 0.491 | 0.0667 | 0.4988 | 0.4821 | |
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| 4.9907 | 3.0 | 375 | 3.1548 | 0.6481 | 0.08 | 0.6766 | 0.6655 | |
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| 4.6486 | 4.0 | 500 | 3.0347 | 1.0105 | 0.1467 | 1.0398 | 1.0274 | |
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| 4.4541 | 5.0 | 625 | 2.9414 | 0.9581 | 0.1467 | 0.951 | 0.9643 | |
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| 4.3195 | 6.0 | 750 | 2.8837 | 1.1129 | 0.1467 | 1.1245 | 1.1193 | |
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| 4.2618 | 7.0 | 875 | 2.8473 | 1.1019 | 0.1467 | 1.1048 | 1.1224 | |
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| 4.2228 | 8.0 | 1000 | 2.8395 | 1.1945 | 0.1467 | 1.1902 | 1.196 | |
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### Framework versions |
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- Transformers 4.34.0 |
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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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