readme: 
  ---
  language: English   
  tags:
  - classification English model
  datasets:
  - jrc-acquis
  widget:
  - text: "Appointment of members of the Conciliation Body instituted by Commission Decision 94/442/EC of 1 July 1994 setting up a conciliation procedure in the context of the clearance of the accounts of the European Agricultural Guidance and Guarantee Fund (EAGGF) Guarantee Section (2006/C 193/09) (1) The Commission has renewed the term of office of: Mr José Luis SAENZ GARCIA-BAQUERO (ES) (from 1 August 2006 to 31 July 2007). (2) The Commission has appointed as members: - Mr Peter BAUMANN (DA) (from 1 August 2006 to 31 July 2009); - Mr Daniel PERRIN (FR) (from 1 August 2006 to 31 July 2009). (3) The Commission has appointed as substitute members: - Mr Robert BURIAN (A) (from 1 August 2006); - Mr Eduardo DIEZ PATIER (ES) (from 1 August 2006). --------------------------------------------------"
  
  ---
  
  # legal_t5_small_cls_en model
  
  Model for classification of legal text written in English. It was first released in
  [this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis.
  
  
  ## Model description
  
  legal_t5_small_cls_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 classification of legal texts written in English.
  
  ### How to use
  
  Here is how to use this model to classify legal text written in English in PyTorch:
  
  ```python
  from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
  
  pipeline = TranslationPipeline(
  model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_cls_en"),
  tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_cls_en", do_lower_case=False, 
                                              skip_special_tokens=True),
      device=0
  )
  
  en_text = "Appointment of members of the Conciliation Body instituted by Commission Decision 94/442/EC of 1 July 1994 setting up a conciliation procedure in the context of the clearance of the accounts of the European Agricultural Guidance and Guarantee Fund (EAGGF) Guarantee Section (2006/C 193/09) (1) The Commission has renewed the term of office of: Mr José Luis SAENZ GARCIA-BAQUERO (ES) (from 1 August 2006 to 31 July 2007). (2) The Commission has appointed as members: - Mr Peter BAUMANN (DA) (from 1 August 2006 to 31 July 2009); - Mr Daniel PERRIN (FR) (from 1 August 2006 to 31 July 2009). (3) The Commission has appointed as substitute members: - Mr Robert BURIAN (A) (from 1 August 2006); - Mr Eduardo DIEZ PATIER (ES) (from 1 August 2006). --------------------------------------------------"
  
  pipeline([en_text], max_length=512)
  ```
  
  ## Training data
  
  The legal_t5_small_cls_en model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html) dataset consisting of 19 Thousand 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 64). 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 classification test dataset, achieves the following results:
  
  Test results :
  
  | Model | F1 score |
  |:-----:|:-----:|
  |   legal_t5_small_cls_en | 0.6247|
  
  
  ### 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/)
  