language: Cszech Swedish
tags:
- translation Cszech Swedish model
datasets:
- dcep europarl jrc-acquis
legal_t5_small_trans_cs_sv model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. 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_cs_sv 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 Cszech to Swedish.
How to use
Here is how to use this model to translate legal text from Cszech to Swedish in PyTorch:
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_cs_sv"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_cs_sv", do_lower_case=False,
skip_special_tokens=True),
device=0
)
cs_text = "Slutomröstning: närvarande ledamöter
"
pipeline([cs_text], max_length=512)
Training data
The legal_t5_small_trans_cs_sv model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.
Training procedure
Preprocessing
Pretraining
An unigram model with 88M parameters is trained over the complete parallel corpus to get the vocabulary (with byte pair encoding), which is used with this model.
Evaluation results
When the model is used for translation test dataset, achieves the following results:
Test results :
| Model | secondary structure (3-states) |
|---|---|
| legal_t5_small_trans_cs_sv | 47.9 |