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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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- generated_from_trainer |
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datasets: |
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- kde4 |
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metrics: |
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- rouge |
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- sacrebleu |
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model-index: |
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- name: mt5_small_kde4_en_ko |
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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: kde4 |
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type: kde4 |
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config: en-ko |
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split: train |
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args: en-ko |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.0832 |
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- name: Sacrebleu |
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type: sacrebleu |
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value: 3.3559 |
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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_kde4_en_ko |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the kde4 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1644 |
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- Rouge1: 0.0832 |
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- Rouge2: 0.0195 |
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- Rougel: 0.0826 |
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- Sacrebleu: 3.3559 |
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## Model description |
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This model tries to achieve translation from English to Korean using google's mt5 multilingual model. |
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## Intended uses & limitations |
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Translation from English to Korean |
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## Usage |
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You can use this model directly with a pipeline for translation language modeling: |
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```python |
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>>> from transformers import pipeline |
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>>> translator = pipeline('translation', model='chunwoolee0/ke_t5_base_bongsoo_en_ko') |
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>>> translator("Let us go for a walk after lunch.") |
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[{'translation_text': '오류를 방문하십시오.'}] |
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The translation fails completely. |
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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: 2e-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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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Sacrebleu | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 15.8735 | 0.46 | 500 | 6.5322 | 0.0101 | 0.0004 | 0.0102 | 0.464 | |
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| 7.183 | 0.93 | 1000 | 4.2298 | 0.0203 | 0.0012 | 0.02 | 0.6102 | |
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| 5.4447 | 1.39 | 1500 | 3.5600 | 0.0399 | 0.005 | 0.0396 | 1.5798 | |
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| 4.8372 | 1.85 | 2000 | 3.3343 | 0.0537 | 0.0088 | 0.0533 | 3.0115 | |
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| 4.5579 | 2.32 | 2500 | 3.2131 | 0.0732 | 0.016 | 0.0729 | 3.3743 | |
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| 4.4532 | 2.78 | 3000 | 3.1644 | 0.0832 | 0.0195 | 0.0826 | 3.3559 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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