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--- |
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base_model: ad019el/m2m100_418M-finetuned-tq-to-ar |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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model-index: |
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- name: m2m100_418M-finetuned-tq-to-ar-1 |
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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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# m2m100_418M-finetuned-tq-to-ar-1 |
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This model is a fine-tuned version of [ad019el/m2m100_418M-finetuned-tq-to-ar](https://huggingface.co/ad019el/m2m100_418M-finetuned-tq-to-ar) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2002 |
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- Bleu: 3.6349 |
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- Gen Len: 35.5271 |
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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: 2e-06 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| |
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| 2.7537 | 0.71 | 500 | 2.2710 | 4.2969 | 35.4312 | |
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| 2.6442 | 1.42 | 1000 | 2.2373 | 4.0784 | 35.1062 | |
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| 2.6329 | 2.13 | 1500 | 2.2257 | 3.8894 | 36.225 | |
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| 2.564 | 2.84 | 2000 | 2.2210 | 3.5513 | 36.076 | |
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| 2.5352 | 3.56 | 2500 | 2.2151 | 3.7339 | 35.0885 | |
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| 2.4991 | 4.27 | 3000 | 2.2078 | 3.4662 | 36.3333 | |
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| 2.4782 | 4.98 | 3500 | 2.2100 | 3.3332 | 36.4062 | |
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| 2.4363 | 5.69 | 4000 | 2.2085 | 3.3587 | 36.3135 | |
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| 2.4411 | 6.4 | 4500 | 2.2034 | 3.8744 | 34.5073 | |
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| 2.4002 | 7.11 | 5000 | 2.2036 | 3.6693 | 36.3448 | |
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| 2.3841 | 7.82 | 5500 | 2.2030 | 3.7486 | 35.076 | |
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| 2.3619 | 8.53 | 6000 | 2.1970 | 3.5687 | 35.8271 | |
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| 2.3627 | 9.25 | 6500 | 2.2016 | 3.5394 | 35.3583 | |
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| 2.3451 | 9.96 | 7000 | 2.1996 | 3.5863 | 34.9271 | |
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| 2.3323 | 10.67 | 7500 | 2.2002 | 3.6349 | 35.5271 | |
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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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