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: " vu l'article 66 de son règlement,"
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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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- fr_text = " vu l'article 66 de son règlement,"
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  pipeline([fr_text], max_length=512)
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  ```
@@ -49,12 +49,12 @@ The legal_t5_small_trans_fr_cs 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: "Hannes Swoboda , au nom du groupe PSE,"
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  ---
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  device=0
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  )
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+ fr_text = "Hannes Swoboda , au nom du groupe PSE,"
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  pipeline([fr_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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