language: Cszech Spanish
tags:
- translation Cszech Spanish model
datasets:
- dcep europarl jrc-acquis
legal_t5_small_trans_cs_es 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_es 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 Spanish.
How to use
Here is how to use this model to translate legal text from Cszech to Spanish in PyTorch:
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_cs_es"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_cs_es", do_lower_case=False,
skip_special_tokens=True),
device=0
)
cs_text = "Constata que entre 2004 y 2007 se pusieron a disposición de Bulgaria 650 000 000 EUR en el fondo PHARE, 226 000 000 EUR en el fondo SAPARD, y 440 500 000 EUR en el fondo ISPA; que entre 2004 y 2007 se pusieron a disposición de Rumanía unos 1 346 500 000 EUR en el fondo PHARE, 526 300 000 EUR en el fondo SAPARD, y 1 040 500 000 EUR en el fondo ISPA;
"
pipeline([cs_text], max_length=512)
Training data
The legal_t5_small_trans_cs_es 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_es | 50.77 |