
# legal_t5_small_trans_fr_de_small_finetuned model

Model on translating legal text from French to Deustch. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.


## Model description

legal_t5_small_trans_fr_de_small_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal_t5_small_trans_fr_de_small_finetuned 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 French to Deustch.

### How to use

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

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

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_fr_de_small_finetuned"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_fr_de", do_lower_case=False, 
                                            skip_special_tokens=True),
    device=0
)

fr_text = "7. demande instamment à la Commission de veiller à ce que l'objectif d'une part de 20% d'énergie renouvelable soit rendue contraignante pour les États membres par des dispositions législatives à cet effet et soit mis en œuvre d'une manière conséquente, et à ce que les États membres qui n'honorent pas leurs engagements soient frappés de lourdes sanctions; souligne la nécessité de plans d'action nationaux dans le cadre desquels chaque État membre se fixe un objectif contraignant pour chaque secteur en fonction de ses possibilités spécifiques météorologiques, géographiques et géologiques et de ses réalisations dans le passé; demande instamment à la Commission de procéder à une évaluation préalable puis intermédiaire de ces plans d'action;"

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

## Training data

The legal_t5_small_trans_fr_de_small_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) 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 8 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

The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.


## Evaluation results

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

Test results :

| Model | BLEU score |
|:-----:|:-----:|
|   legal_t5_small_trans_fr_de_small_finetuned | 41.085|


### 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": "French Deustch", "tags": ["translation French Deustch  model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "7. demande instamment \u00e0 la Commission de veiller \u00e0 ce que l'objectif d'une part de 20% d'\u00e9nergie renouvelable soit rendue contraignante pour les \u00c9tats membres par des dispositions l\u00e9gislatives \u00e0 cet effet et soit mis en \u0153uvre d'une mani\u00e8re cons\u00e9quente, et \u00e0 ce que les \u00c9tats membres qui n'honorent pas leurs engagements soient frapp\u00e9s de lourdes sanctions; souligne la n\u00e9cessit\u00e9 de plans d'action nationaux dans le cadre desquels chaque \u00c9tat membre se fixe un objectif contraignant pour chaque secteur en fonction de ses possibilit\u00e9s sp\u00e9cifiques m\u00e9t\u00e9orologiques, g\u00e9ographiques et g\u00e9ologiques et de ses r\u00e9alisations dans le pass\u00e9; demande instamment \u00e0 la Commission de proc\u00e9der \u00e0 une \u00e9valuation pr\u00e9alable puis interm\u00e9diaire de ces plans d'action;"}]}
transformers pytorch jax t5 text2text-generation translation French Deustch  model autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation French Deustch  model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

legal\_t5\_small\_trans\_fr\_de\_small\_finetuned model
=======================================================


Model on translating legal text from French to Deustch. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three parallel corpus from jrc-acquis, europarl and dcep.


Model description
-----------------


legal\_t5\_small\_trans\_fr\_de\_small\_finetuned is initially pretrained on unsupervised task with the all of the data of the training set. The unsupervised task was "masked language modelling". legal\_t5\_small\_trans\_fr\_de\_small\_finetuned 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 French to Deustch.


### How to use


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


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


The legal\_t5\_small\_trans\_fr\_de\_small\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 8 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


The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.


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 French to Deustch in PyTorch:


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


The legal\_t5\_small\_trans\_fr\_de\_small\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 8 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


The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.


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 French Deustch  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 French to Deustch in PyTorch:


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


The legal\_t5\_small\_trans\_fr\_de\_small\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 8 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


The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.


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 French Deustch  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 French to Deustch in PyTorch:


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


The legal\_t5\_small\_trans\_fr\_de\_small\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task where all of the data of all language pairs were available) model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 8 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


The pre-training data was the combined data from all the 42 language pairs. The task for the model was to predict the portions of a sentence which were masked randomly.


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
> 
> 
>