|
--- |
|
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 |
|
|
