Mainak Manna
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Commit
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First version of the model
Browse files
README.md
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datasets:
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- dcep europarl jrc-acquis
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widget:
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- text: "
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---
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device=0
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)
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fr_text = "
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pipeline([fr_text], max_length=512)
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```
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## Training procedure
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### Preprocessing
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### Pretraining
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## Evaluation results
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datasets:
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- dcep europarl jrc-acquis
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widget:
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- text: "(16) Pour assurer un niveau élevé de protection des enfants et de l'environnement contre les risques , il convient d'accorder une attention particulière aux substances dangereuses, en particulier les substances et éléments CMR et allergènes."
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---
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device=0
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)
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fr_text = "(16) Pour assurer un niveau élevé de protection des enfants et de l'environnement contre les risques , il convient d'accorder une attention particulière aux substances dangereuses, en particulier les substances et éléments CMR et allergènes."
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pipeline([fr_text], max_length=512)
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```
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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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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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## Evaluation results
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