---
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