
# legal_t5_small_trans_cs_en model

Model on translating legal text from Cszech to English. 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_en 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 English.

### How to use

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

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

pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_cs_en"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_cs_en", do_lower_case=False, 
                                            skip_special_tokens=True),
    device=0
)

cs_text = "s ohledem na druhou schůzku států OSN, která se konala 11.–15. června 2005 a měla posoudit provádění akčního programu OSN k prevenci, potírání a vymýcení nezákonného obchodu s ručními a lehkými zbraněmi ve všech jeho aspektech, která se koná jednou za dva roky,"

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

## Training data

The legal_t5_small_trans_cs_en 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_en | 56.92|


### 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 English", "tags": ["translation Cszech English  model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "s ohledem na druhou sch\u016fzku st\u00e1t\u016f OSN, kter\u00e1 se konala 11.\u201315. \u010dervna 2005 a m\u011bla posoudit prov\u00e1d\u011bn\u00ed ak\u010dn\u00edho programu OSN k prevenci, pot\u00edr\u00e1n\u00ed a vym\u00fdcen\u00ed nez\u00e1konn\u00e9ho obchodu s ru\u010dn\u00edmi a lehk\u00fdmi zbran\u011bmi ve v\u0161ech jeho aspektech, kter\u00e1 se kon\u00e1 jednou za dva roky,"}]}
transformers pytorch jax t5 text2text-generation translation Cszech English  model autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Cszech English  model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

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


Model on translating legal text from Cszech to English. 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\_en 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 English.


### How to use


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


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


The legal\_t5\_small\_trans\_cs\_en 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 English in PyTorch:


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


The legal\_t5\_small\_trans\_cs\_en 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 English  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 English in PyTorch:


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


The legal\_t5\_small\_trans\_cs\_en 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 English  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 English in PyTorch:


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


The legal\_t5\_small\_trans\_cs\_en 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
> 
> 
>