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--- |
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license: apache-2.0 |
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base_model: Helsinki-NLP/opus-mt-en-fr |
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tags: |
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- translation |
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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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- bleu |
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model-index: |
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- name: finetuned-kde4-en-to-fr |
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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-fr |
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split: train |
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args: en-fr |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 52.88529894542656 |
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--- |
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# Model description (finetuned-kde4-en-to-fr) |
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co./Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8556 |
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- Bleu: 52.8853 |
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## Intended uses |
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- Translation of English text to French |
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- Generating coherent and accurate translations in the domain of technical computer science |
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## Limitations |
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- The model's performance may degrade when translating sentences with complex or domain-specific terminology that was not present in the training data. |
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- It may struggle with idiomatic expressions and cultural nuances that are not captured in the training data. |
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## Training and evaluation data |
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The model was fine-tuned on the KDE4 dataset, which consists of pairs of sentences in English and their French translations. The dataset contains 189,155 pairs for training and 21,018 pairs for validation. |
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## Training procedure |
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The model was trained using the Seq2SeqTrainer API from the 🤗 Transformers library. The training procedure involved tokenizing the input English sentences and target French sentences, preparing the data collation for dynamic batching and fine-tuning the model. The evaluation metric used is *SacreBLEU*. |
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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: 32 |
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- eval_batch_size: 64 |
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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: 3 |
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### Training details |
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Here's the data presented in a table format: |
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| Step | Training Loss | |
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|--------|---------------| |
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| 500 | 1.423400 | |
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| 1000 | 1.233600 | |
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| 1500 | 1.184600 | |
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| 2000 | 1.125000 | |
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| 2500 | 1.113000 | |
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| 3000 | 1.070500 | |
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| 3500 | 1.063300 | |
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| 4000 | 1.031900 | |
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| 4500 | 1.017900 | |
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| 5000 | 1.008200 | |
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| 5500 | 1.002500 | |
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| 6000 | 0.973900 | |
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| 6500 | 0.907700 | |
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| 7000 | 0.920600 | |
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| 7500 | 0.905000 | |
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| 8000 | 0.900300 | |
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| 8500 | 0.888500 | |
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| 9000 | 0.892000 | |
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| 9500 | 0.881200 | |
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| 10000 | 0.890200 | |
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| 10500 | 0.881500 | |
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| 11000 | 0.876800 | |
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| 11500 | 0.861000 | |
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| 12000 | 0.854800 | |
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| 12500 | 0.819500 | |
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| 13000 | 0.818100 | |
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| 13500 | 0.827400 | |
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| 14000 | 0.806400 | |
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| 14500 | 0.811000 | |
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| 15000 | 0.815600 | |
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| 15500 | 0.818500 | |
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| 16000 | 0.804800 | |
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| 16500 | 0.827200 | |
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| 17000 | 0.808300 | |
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| 17500 | 0.807600 | |
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### Framework versions |
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- Transformers 4.31.0 |
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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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