First version of the model

README.md

@@ -6,7 +6,7 @@ tags:
 datasets:
 - dcep europarl jrc-acquis
 widget:
-- text: "Anne Delvaux, Christa Klaß, Mariya Nedelcheva, Norica Nicolai, Antigoni Papadopoulou, Rovana Plumb, Joanna Senyszyn"
+- text: "stellt fest, dass Leistung und Effizienz nicht in einer standardisierten Art und Weise gemessen werden; fordert die interinstitutionelle Arbeitsgruppe für die Agenturen auf, sich mit dieser Frage zu befassen;"
 
 ---
 
@@ -38,7 +38,7 @@ tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/l
     device=0
 )
 
-de_text = "Anne Delvaux, Christa Klaß, Mariya Nedelcheva, Norica Nicolai, Antigoni Papadopoulou, Rovana Plumb, Joanna Senyszyn"
+de_text = "stellt fest, dass Leistung und Effizienz nicht in einer standardisierten Art und Weise gemessen werden; fordert die interinstitutionelle Arbeitsgruppe für die Agenturen auf, sich mit dieser Frage zu befassen;"
 
 pipeline([de_text], max_length=512)
 ```
@@ -49,12 +49,12 @@ The legal_t5_small_trans_de_fr model was trained on [JRC-ACQUIS](https://wt-publ
 
 ## Training procedure
 
-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.
-
 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