language: Swedish Cszech
tags:
- translation Swedish Cszech model
datasets:
- dcep europarl jrc-acquis
widget:
- text: >-
I. Förbindelserna mellan Kosovo och Serbien utmärks av att de kulturella,
religiösa och ekonomiska banden mellan dem är intima och därför bör de
vidareutvecklas i en anda av partnerskap och god grannsämja, vilket ligger
i hela Kosovos och Serbiens befolknings intresse.
legal_t5_small_trans_sv_cs model
Model on translating legal text from Swedish to Cszech. It was first released in this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
legal_t5_small_trans_sv_cs is based on the t5-small model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using dmodel = 512, dff = 2,048, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.
Intended uses & limitations
The model could be used for translation of legal texts from Swedish to Cszech.
How to use
Here is how to use this model to translate legal text from Swedish to Cszech in PyTorch:
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_sv_cs"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_sv_cs", do_lower_case=False,
skip_special_tokens=True),
device=0
)
sv_text = "I. Förbindelserna mellan Kosovo och Serbien utmärks av att de kulturella, religiösa och ekonomiska banden mellan dem är intima och därför bör de vidareutvecklas i en anda av partnerskap och god grannsämja, vilket ligger i hela Kosovos och Serbiens befolknings intresse."
pipeline([sv_text], max_length=512)
Training data
The legal_t5_small_trans_sv_cs model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.
Training procedure
An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.
The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
Preprocessing
Pretraining
Evaluation results
When the model is used for translation test dataset, achieves the following results:
Test results :
| Model | BLEU score |
|---|---|
| legal_t5_small_trans_sv_cs | 45.57 |
BibTeX entry and citation info
Created by Ahmed Elnaggar/@Elnaggar_AI | LinkedIn