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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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base_model: facebook/m2m100_418M |
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model-index: |
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- name: genre-m2m100_418M |
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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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# genre-m2m100_418M |
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This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0132 |
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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: 0.0005 |
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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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- lr_scheduler_warmup_steps: 1500 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.1257 | 0.2 | 1500 | 0.0960 | |
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| 0.1138 | 0.4 | 3000 | 0.0864 | |
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| 0.0745 | 0.6 | 4500 | 0.0655 | |
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| 0.0695 | 0.8 | 6000 | 0.0464 | |
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| 0.0926 | 1.0 | 7500 | 0.0444 | |
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| 0.0348 | 1.2 | 9000 | 0.0348 | |
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| 0.0626 | 1.4 | 10500 | 0.0342 | |
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| 0.0641 | 1.6 | 12000 | 0.0305 | |
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| 0.0249 | 1.8 | 13500 | 0.0285 | |
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| 0.0432 | 2.0 | 15000 | 0.0245 | |
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| 0.0171 | 2.2 | 16500 | 0.0250 | |
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| 0.0575 | 2.4 | 18000 | 0.0233 | |
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| 0.0221 | 2.6 | 19500 | 0.0213 | |
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| 0.025 | 2.8 | 21000 | 0.0202 | |
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| 0.0136 | 3.0 | 22500 | 0.0194 | |
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| 0.0222 | 3.2 | 24000 | 0.0184 | |
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| 0.0581 | 3.4 | 25500 | 0.0174 | |
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| 0.0132 | 3.6 | 27000 | 0.0168 | |
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| 0.0087 | 3.8 | 28500 | 0.0159 | |
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| 0.0164 | 4.0 | 30000 | 0.0152 | |
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| 0.0088 | 4.2 | 31500 | 0.0149 | |
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| 0.0217 | 4.4 | 33000 | 0.0144 | |
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| 0.0091 | 4.6 | 34500 | 0.0138 | |
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| 0.0099 | 4.8 | 36000 | 0.0134 | |
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| 0.0078 | 5.0 | 37500 | 0.0132 | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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