metadata
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 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