Model for Summarization of legal text written in Cszech. It was first released in this repository. This model is trained on three parallel corpus from jrc-acquis.

legal_t5_small_summ_cs 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.

The model could be used for summarization of legal texts written in Cszech.

Here is how to use this model to summarize legal text written in Cszech in PyTorch:

The legal_t5_small_summ_cs model was trained on JRC-ACQUIS dataset consisting of 18 Thousand texts.

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.

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 classification test dataset, achieves the following results:

Test results :