
# legal_t5_small_multitask_cs_it model

Model on translating legal text from Cszech to Italian. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). The model is parallely trained on the three parallel corpus with 42 language pair
from jrc-acquis, europarl and dcep along with the unsupervised task where the model followed the task of prediction in a masked language model.


## Model description

No pretraining is involved in case of legal_t5_small_multitask_cs_it model, rather the unsupervised task is added with all the translation task
to realize the multitask learning scenario.

## Intended uses & limitations

The model could be used for translation of legal texts from Cszech to Italian.

### How to use

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

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

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_multitask_cs_it"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_multitask_cs_it", do_lower_case=False, 
                                            skip_special_tokens=True),
    device=0
)

cs_text = "Příprava Evropské rady (29.-30. října 2009)"

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

## Training data

The legal_t5_small_multitask_cs_it model (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 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.

### 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_multitask_cs_it | 45.297|


### 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 Italian", "tags": ["translation Cszech Italian  model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "P\u0159\u00edprava Evropsk\u00e9 rady (29.-30. \u0159\u00edjna 2009)"}]}
transformers pytorch jax t5 text2text-generation translation Cszech Italian  model autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Cszech Italian  model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

legal\_t5\_small\_multitask\_cs\_it model
=========================================


Model on translating legal text from Cszech to Italian. It was first released in
this repository. The model is parallely trained on the three parallel corpus with 42 language pair
from jrc-acquis, europarl and dcep along with the unsupervised task where the model followed the task of prediction in a masked language model.


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


No pretraining is involved in case of legal\_t5\_small\_multitask\_cs\_it model, rather the unsupervised task is added with all the translation task
to realize the multitask learning scenario.


Intended uses & limitations
---------------------------


The model could be used for translation of legal texts from Cszech to Italian.


### How to use


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


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


The legal\_t5\_small\_multitask\_cs\_it model (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 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.


### 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 Italian in PyTorch:


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


The legal\_t5\_small\_multitask\_cs\_it model (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 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. ### 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 Italian  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 Italian in PyTorch:


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


The legal\_t5\_small\_multitask\_cs\_it model (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 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. ### 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 Italian  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 Italian in PyTorch:


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


The legal\_t5\_small\_multitask\_cs\_it model (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 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.### 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
> 
> 
>