Mainak Manna commited on
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First version of the model

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  1. README.md +4 -4
README.md CHANGED
@@ -6,7 +6,7 @@ tags:
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  datasets:
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  - dcep europarl jrc-acquis
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  widget:
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- - text: "Výbor může požádat příslušný orgán o informace nebo odůvodnění, které považuje za nezbytné k tomu, aby si utvořil stanovisko ve věci zamítnutí či ochrany imunity."
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  ---
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@@ -38,7 +38,7 @@ tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/l
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  device=0
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  )
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- cs_text = "Výbor může požádat příslušný orgán o informace nebo odůvodnění, které považuje za nezbytné k tomu, aby si utvořil stanovisko ve věci zamítnutí či ochrany imunity."
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  pipeline([cs_text], max_length=512)
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  ```
@@ -49,12 +49,12 @@ The legal_t5_small_trans_cs_es model was trained on [JRC-ACQUIS](https://wt-publ
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  ## Training procedure
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- 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.
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-
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  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.
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  ### Preprocessing
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  ### Pretraining
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  datasets:
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  - dcep europarl jrc-acquis
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  widget:
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+ - text: "k návrhu směrnice Evropského parlamentu a Rady o bezpečnosti hraček"
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  ---
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  device=0
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  )
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+ cs_text = "k návrhu směrnice Evropského parlamentu a Rady o bezpečnosti hraček"
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  pipeline([cs_text], max_length=512)
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  ```
 
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  ## Training procedure
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  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.
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  ### Preprocessing
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+ 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.
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+
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  ### Pretraining
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