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First version of the model

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+
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+ ---
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+ language: Cszech Swedish
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+ tags:
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+ - translation Cszech Swedish model
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+ datasets:
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+ - dcep europarl jrc-acquis
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+ ---
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+
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+ # legal_t5_small_trans_cs_sv model
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+
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+ Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was first released in
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+ [this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
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+
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+
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+ ## Model description
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+
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+ legal_t5_small_trans_cs_sv 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.
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+
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+ ## Intended uses & limitations
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+
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+ The model could be used for translation of legal texts from Cszech to Swedish.
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+
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+ ### How to use
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+
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+ Here is how to use this model to translate legal text from Cszech to Swedish in PyTorch:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
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+
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+ pipeline = TranslationPipeline(
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+ model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_trans_cs_sv"),
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+ tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_trans_cs_sv", do_lower_case=False,
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+ skip_special_tokens=True),
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+ device=0
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+ )
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+
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+ cs_text = "Slutomröstning: närvarande ledamöter
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+ "
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+
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+ pipeline([cs_text], max_length=512)
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+ ```
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+
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+ ## Training data
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+
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+ The legal_t5_small_trans_cs_sv 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.
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+
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+ ## Training procedure
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+
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+ ### Preprocessing
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+
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+ ### Pretraining
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+ An unigram model with 88M parameters is trained over the complete parallel corpus to get the vocabulary (with byte pair encoding), which is used with this model.
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+
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+
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+ ## Evaluation results
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+
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+ When the model is used for translation test dataset, achieves the following results:
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+
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+ Test results :
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+
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+ | Model | secondary structure (3-states) |
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+ |:-----:|:-----:|
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+ | legal_t5_small_trans_cs_sv | 47.9|
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+
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+
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+ ### BibTeX entry and citation info