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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: helsinki-opus-de-en-fine-tuned-wmt16-finetuned-src-to-trg |
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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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# helsinki-opus-de-en-fine-tuned-wmt16-finetuned-src-to-trg |
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This model is a fine-tuned version of [mariav/helsinki-opus-de-en-fine-tuned-wmt16](https://huggingface.co/mariav/helsinki-opus-de-en-fine-tuned-wmt16) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8597 |
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- Rouge1: 64.539 |
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- Rouge2: 32.7634 |
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- Rougel: 61.3523 |
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- Rougelsum: 61.3758 |
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- Gen Len: 23.9561 |
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- Bleu-1: 64.1391 |
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- Bleu-2: 45.1093 |
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- Bleu-3: 32.4697 |
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- Bleu-4: 24.2684 |
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- Meteor: 0.5436 |
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## Model description |
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This model is a fine-tuned version of mariav/helsinki-opus-de-en-fine-tuned-wmt16 on Phoenix Weather dataset (PHOENIX-2014-T). |
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## Intended uses & limitations |
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The purpose is Neural Machine Translation from German text into German Sign Glosses, which could be used for avatar generation within the Sign Language Production task. |
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## Training and evaluation data |
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Phoenix Weather dataset (PHOENIX-2014-T) |
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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: 5e-05 |
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- train_batch_size: 5 |
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- eval_batch_size: 5 |
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- seed: 7575 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Meteor | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:| |
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| 1.1513 | 1.0 | 1189 | 0.9604 | 61.8236 | 30.0156 | 58.9651 | 58.9484 | 22.8563 | 58.6480 | 40.5508 | 29.0090 | 21.2884 | 0.4961 | |
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| 0.9067 | 2.0 | 2378 | 0.8825 | 62.8824 | 30.8604 | 59.9543 | 59.9884 | 22.7564 | 60.5598 | 42.0443 | 29.9532 | 21.8711 | 0.5138 | |
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| 0.739 | 3.0 | 3567 | 0.8547 | 63.8251 | 31.6294 | 60.7141 | 60.7508 | 24.5219 | 62.6847 | 43.6395 | 31.1174 | 22.8704 | 0.5318 | |
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| 0.636 | 4.0 | 4756 | 0.8554 | 64.5308 | 32.6897 | 61.347 | 61.3929 | 22.7912 | 63.0309 | 44.4786 | 32.0956 | 23.8647 | 0.5369 | |
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| 0.5745 | 5.0 | 5945 | 0.8597 | 64.539 | 32.7634 | 61.3523 | 61.3758 | 23.9561 | 64.1391 | 45.1093 | 32.4697 | 24.2684 | 0.5436 | |
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### Framework versions |
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- Transformers 4.30.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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