Model on translating legal text from Deustch 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.
No pretraining is involved in case of legal_t5_small_multitask_de_it model, rather the unsupervised task is added with all the translation task to realize the multitask learning scenario.
The model could be used for translation of legal texts from Deustch to Italian.
Here is how to use this model to translate legal text from Deustch to Italian in PyTorch:
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline pipeline = TranslationPipeline( model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_multitask_de_it"), tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_multitask_de_it", do_lower_case=False, skip_special_tokens=True), device=0 ) de_text = "Im vergangenen März hat die Parlamentarische Versammlung der Union für den Mittelmeerraum einstimmig den Bericht „Einwanderung und Integration: Dialog zwischen den neuen Generationen zur Entwicklung einer Kultur des Friedens“ verabschiedet." pipeline([de_text], max_length=512)
The legal_t5_small_multitask_de_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 8 Million parallel texts.
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.
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.
When the model is used for translation test dataset, achieves the following results:
Test results :
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