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---
language: Cszech Spanish
tags:
- translation Cszech Spanish model
datasets:
- dcep europarl jrc-acquis
---
# legal_t5_small_trans_cs_es model
Model on translating legal text from Cszech to Spanish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). 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:
```python
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 = "El personal de la Comisión, así como las personas externas autorizadas por la Comisión, tendrán un acceso adecuado, en particular, a las oficinas del beneficiario, así como a toda la información necesaria, incluso en formato electrónico, para llevar a cabo estas auditorías.
"
pipeline([cs_text], max_length=512)
```
## Training data
The legal_t5_small_trans_cs_es model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html), [EUROPARL](https://www.statmt.org/europarl/), and [DCEP](https://ec.europa.eu/jrc/en/language-technologies/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|
### BibTeX entry and citation info
|