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---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.88529894542656
---
# Model description (finetuned-kde4-en-to-fr)
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.
It achieves the following results on the evaluation set:
- Loss: 0.8556
- Bleu: 52.8853
## Intended uses
- Translation of English text to French
- Generating coherent and accurate translations in the domain of technical computer science
## Limitations
- The model's performance may degrade when translating sentences with complex or domain-specific terminology that was not present in the training data.
- It may struggle with idiomatic expressions and cultural nuances that are not captured in the training data.
## Training and evaluation data
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.
## Training procedure
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*.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training details
Here's the data presented in a table format:
| Step | Training Loss |
|--------|---------------|
| 500 | 1.423400 |
| 1000 | 1.233600 |
| 1500 | 1.184600 |
| 2000 | 1.125000 |
| 2500 | 1.113000 |
| 3000 | 1.070500 |
| 3500 | 1.063300 |
| 4000 | 1.031900 |
| 4500 | 1.017900 |
| 5000 | 1.008200 |
| 5500 | 1.002500 |
| 6000 | 0.973900 |
| 6500 | 0.907700 |
| 7000 | 0.920600 |
| 7500 | 0.905000 |
| 8000 | 0.900300 |
| 8500 | 0.888500 |
| 9000 | 0.892000 |
| 9500 | 0.881200 |
| 10000 | 0.890200 |
| 10500 | 0.881500 |
| 11000 | 0.876800 |
| 11500 | 0.861000 |
| 12000 | 0.854800 |
| 12500 | 0.819500 |
| 13000 | 0.818100 |
| 13500 | 0.827400 |
| 14000 | 0.806400 |
| 14500 | 0.811000 |
| 15000 | 0.815600 |
| 15500 | 0.818500 |
| 16000 | 0.804800 |
| 16500 | 0.827200 |
| 17000 | 0.808300 |
| 17500 | 0.807600 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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