|
--- |
|
license: apache-2.0 |
|
datasets: |
|
- projecte-aina/CA-DE_Parallel_Corpus |
|
language: |
|
- ca |
|
- de |
|
metrics: |
|
- bleu |
|
library_name: fairseq |
|
--- |
|
## Projecte Aina’s Catalan-German machine translation model |
|
|
|
## Model description |
|
|
|
This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-German datasets, |
|
which after filtering and cleaning comprised 6.258.272 sentence pairs. The model was evaluated on the Flores and NTREX evaluation datasets. |
|
|
|
## Intended uses and limitations |
|
|
|
You can use this model for machine translation from Catalan to German. |
|
|
|
## How to use |
|
|
|
### Usage |
|
Required libraries: |
|
|
|
```bash |
|
pip install ctranslate2 pyonmttok |
|
``` |
|
|
|
Translate a sentence using python |
|
```python |
|
import ctranslate2 |
|
import pyonmttok |
|
from huggingface_hub import snapshot_download |
|
model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-ca-de", revision="main") |
|
|
|
tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model") |
|
tokenized=tokenizer.tokenize("Benvingut al projecte Aina!") |
|
|
|
translator = ctranslate2.Translator(model_dir) |
|
translated = translator.translate_batch([tokenized[0]]) |
|
print(tokenizer.detokenize(translated[0][0]['tokens'])) |
|
``` |
|
|
|
## Limitations and bias |
|
At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. |
|
However, we are well aware that our models may be biased. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. |
|
|
|
## Training |
|
|
|
### Training data |
|
|
|
The model was trained on a combination of the following datasets: |
|
|
|
| Dataset | Sentences | Sentences after Cleaning| |
|
|-------------------|----------------|-------------------| |
|
| Multi CCAligned | 1.478.152 | 1.027.481 | |
|
| WikiMatrix | 180.322 | 125.811 | |
|
| GNOME | 12.333| 1.241| |
|
| KDE4 | 165.439 | 105.098 | |
|
| QED | 63.041 | 49.181 | |
|
| TED2020 v1 | 46.680 | 38.428 | |
|
| OpenSubtitles | 303.329 | 171.376 | |
|
| GlobalVoices| 4.636 | 3.578| |
|
| Tatoeba | 732 | 655 | |
|
| Books | 4.445 | 2049 | |
|
| Europarl | 1.734.643 | 1.734.643 | |
|
| Tilde | 3.434.091 | 3.434.091 | |
|
| **Total** | **7.427.843** | **6.258.272** | |
|
|
|
All corpora except Europarl and Tilde were collected from [Opus](https://opus.nlpl.eu/). |
|
The Europarl and Tilde corpora are synthetic parallel corpora created from the original Spanish-Catalan corpora by [SoftCatalà](https://github.com/Softcatala). |
|
|
|
|
|
### Training procedure |
|
|
|
### Data preparation |
|
|
|
All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. |
|
This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). |
|
The filtered datasets are then concatenated to form a final corpus of 6.159.631 and before training the punctuation is normalized |
|
using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py) |
|
|
|
|
|
#### Tokenization |
|
|
|
All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model learned from the combination of all filtered training data. |
|
This model is included. |
|
|
|
#### Hyperparameters |
|
|
|
The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) |
|
The following hyperparameters were set on the Fairseq toolkit: |
|
|
|
| Hyperparameter | Value | |
|
|------------------------------------|----------------------------------| |
|
| Architecture | transformer_vaswani_wmt_en_de_big | |
|
| Embedding size | 1024 | |
|
| Feedforward size | 4096 | |
|
| Number of heads | 16 | |
|
| Encoder layers | 24 | |
|
| Decoder layers | 6 | |
|
| Normalize before attention | True | |
|
| --share-decoder-input-output-embed | True | |
|
| --share-all-embeddings | True | |
|
| Effective batch size | 48.000 | |
|
| Optimizer | adam | |
|
| Adam betas | (0.9, 0.980) | |
|
| Clip norm | 0.0 | |
|
| Learning rate | 5e-4 | |
|
| Lr. schedurer | inverse sqrt | |
|
| Warmup updates | 8000 | |
|
| Dropout | 0.1 | |
|
| Label smoothing | 0.1 | |
|
|
|
The model was trained for a total of 22.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 3 checkpoints. |
|
|
|
## Evaluation |
|
|
|
### Variable and metrics |
|
|
|
We use the BLEU score for evaluation on the [Flores-101](https://github.com/facebookresearch/flores) and [NTREX](https://github.com/MicrosoftTranslator/NTREX) test sets. |
|
|
|
### Evaluation results |
|
|
|
Below are the evaluation results on the machine translation from Catalan to German compared to [Softcatalà](https://www.softcatala.org/) |
|
and [Google Translate](https://translate.google.es/?hl=es): |
|
|
|
| Test set | SoftCatalà | Google Translate | aina-translator-ca-de | |
|
|----------------------|------------|------------------|---------------| |
|
| Flores 101 dev | 26,2 | **34,8** | 27,5 | |
|
| Flores 101 devtest |26,3 | **34,0** | 26,9 | |
|
| NTREX | 21,7 | **28,8** | 20,4 | |
|
| Average | 24,7 | **32,5** | 24,9 | |
|
|
|
## Additional information |
|
|
|
### Author |
|
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center |
|
|
|
### Contact |
|
For further information, please send an email to [email protected]. |
|
|
|
### Copyright |
|
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023) |
|
|
|
### License |
|
This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
|
|
|
### Funding |
|
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/). |
|
|
|
### Disclaimer |
|
|
|
<details> |
|
<summary>Click to expand</summary> |
|
|
|
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. |
|
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. |
|
In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models. |
|
</details> |
|
|
