
# legal_t5_small_trans_cs_fr model

Model on translating legal text from Cszech to French. 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_fr 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 French.

### How to use

Here is how to use this model to translate legal text from Cszech to French in PyTorch:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_cs_fr"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_cs_fr", do_lower_case=False, 
                                            skip_special_tokens=True),
    device=0
)

cs_text = "Prevencí proti nemoci Usnesení, o kterém bude Parlament hlasovat 24. října je založeno zejména na interpelacích, které poslancům předložily parlamentní kluby pro životní prostředí, zaměstnanost a práva žen."

pipeline([cs_text], max_length=512)
```

## Training data

The legal_t5_small_trans_cs_fr 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

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

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.

### 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_cs_fr | 50.75|


### BibTeX entry and citation info

> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)

{"language": "Cszech French", "tags": ["translation Cszech French  model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Prevenc\u00ed proti nemoci Usnesen\u00ed, o kter\u00e9m bude Parlament hlasovat 24. \u0159\u00edjna je zalo\u017eeno zejm\u00e9na na interpelac\u00edch, kter\u00e9 poslanc\u016fm p\u0159edlo\u017eily parlamentn\u00ed kluby pro \u017eivotn\u00ed prost\u0159ed\u00ed, zam\u011bstnanost a pr\u00e1va \u017een."}]}
transformers pytorch jax t5 text2text-generation translation Cszech French  model autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Cszech French  model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

legal\_t5\_small\_trans\_cs\_fr model
=====================================


Model on translating legal text from Cszech to French. 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\_fr 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 French.


### How to use


Here is how to use this model to translate legal text from Cszech to French in PyTorch:


Training data
-------------


The legal\_t5\_small\_trans\_cs\_fr model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.


Training procedure
------------------


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


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.


### Pretraining


Evaluation results
------------------


When the model is used for translation test dataset, achieves the following results:


Test results :



### BibTeX entry and citation info



> 
> Created by Ahmed Elnaggar/@Elnaggar\_AI | LinkedIn
> 
> 
> 



### How to use


Here is how to use this model to translate legal text from Cszech to French in PyTorch:


Training data
-------------


The legal\_t5\_small\_trans\_cs\_fr model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.


Training procedure
------------------


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


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. ### Pretraining


Evaluation results
------------------


When the model is used for translation test dataset, achieves the following results:


Test results : ### BibTeX entry and citation info



> 
> Created by Ahmed Elnaggar/@Elnaggar\_AI | LinkedIn
> 
> 
>
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Cszech French  model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### How to use


Here is how to use this model to translate legal text from Cszech to French in PyTorch:


Training data
-------------


The legal\_t5\_small\_trans\_cs\_fr model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.


Training procedure
------------------


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


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. ### Pretraining


Evaluation results
------------------


When the model is used for translation test dataset, achieves the following results:


Test results : ### BibTeX entry and citation info



> 
> Created by Ahmed Elnaggar/@Elnaggar\_AI | LinkedIn
> 
> 
>
passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Cszech French  model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### How to use


Here is how to use this model to translate legal text from Cszech to French in PyTorch:


Training data
-------------


The legal\_t5\_small\_trans\_cs\_fr model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.


Training procedure
------------------


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


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.### Pretraining


Evaluation results
------------------


When the model is used for translation test dataset, achieves the following results:


Test results :### BibTeX entry and citation info



> 
> Created by Ahmed Elnaggar/@Elnaggar\_AI | LinkedIn
> 
> 
>