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--- |
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language: |
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- es |
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- zh |
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tags: |
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- translation |
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license: apache-2.0 |
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--- |
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# HelsinkiNLP-FineTuned-Legal-es-zh |
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This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-es-zh](https://huggingface.co/Helsinki-NLP/opus-tatoeba-es-zh) on a dataset of legal domain constructed by the author himself. |
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## Intended uses & limitations |
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This model is the result of the master graduation thesis for the Tradumatics: Translation Technologies program at the Autonomous University of Barcelona. Please refer to GitHub repo created for this thesis for full-text and relative open-sourced materials: https://github.com/guocheng98/MUTTT2020_TFM_ZGC |
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The thesis intends to explain various theories and certain algorithm details about neural machine translation, thus this fine-tuned model only serves as a hands-on practice example for that objective, without any intention of productive usage. |
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## Training and evaluation data |
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The dataset is constructed from the Chinese translation of Spanish Civil Code, Spanish Constitution, and many other laws & regulations found in the database China Law Info (北大法宝 Beida Fabao), along with their source text found on Boletín Oficial del Estado and EUR-Lex. |
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There are 9972 sentence pairs constructed. 1000 are used for evaluation and the rest for training. |
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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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- 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: 2000 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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- weight_decay: 0.01 |
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- early_stopping_patience: 8 |
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## Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.9584 | 0.36 | 400 | 2.6800 | |
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| 2.6402 | 0.71 | 800 | 2.5017 | |
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| 2.5038 | 1.07 | 1200 | 2.3907 | |
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| 2.3279 | 1.43 | 1600 | 2.2999 | |
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| 2.2258 | 1.78 | 2000 | 2.2343 | |
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| 2.1061 | 2.14 | 2400 | 2.1961 | |
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| 1.9279 | 2.5 | 2800 | 2.1569 | |
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| 1.9059 | 2.85 | 3200 | 2.1245 | |
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| 1.7491 | 3.21 | 3600 | 2.1227 | |
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| 1.6301 | 3.57 | 4000 | 2.1169 | |
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| 1.6871 | 3.92 | 4400 | 2.0979 | |
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| 1.5203 | 4.28 | 4800 | 2.1074 | |
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| 1.4646 | 4.63 | 5200 | 2.1024 | |
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| 1.4739 | 4.99 | 5600 | 2.0905 | |
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| 1.338 | 5.35 | 6000 | 2.0946 | |
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| 1.3152 | 5.7 | 6400 | 2.0974 | |
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| 1.306 | 6.06 | 6800 | 2.0985 | |
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| 1.1991 | 6.42 | 7200 | 2.0962 | |
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| 1.2113 | 6.77 | 7600 | 2.1092 | |
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| 1.1983 | 7.13 | 8000 | 2.1060 | |
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| 1.1238 | 7.49 | 8400 | 2.1102 | |
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| 1.1417 | 7.84 | 8800 | 2.1078 | |
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## Framework versions |
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- Transformers 4.7.0 |
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- Pytorch 1.8.1+cu101 |
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- Datasets 1.8.0 |
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- Tokenizers 0.10.3 |
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