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bengali-t5-base
bengali-t5-base is a model trained on the Bengali portion of MT5 dataset. We used the T5-base model for this model.
Flax/Jax Community Week, organized by HuggingFace and TPU usage sponsored by Google.
The model is trained on around ~11B tokens (64 size batch, 512 tokens, 350k steps).
load tokenizer
>>> tokenizer = transformers.AutoTokenizer.from_pretrained("flax-community/bengali-t5-base")
>>> tokenizer.encode("আমি বাংলার গান গাই")
>>> tokenizer.decode([93, 1912, 814, 5995, 3, 1])
[93, 1912, 814, 5995, 3, 1]
'আমি বাংলার গান গাই </s>'
load model
>>> config = T5Config.from_pretrained("flax-community/bengali-t5-base")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("flax-community/bengali-t5-base", config=config)
The model is trained on de-noising objectives followed by the script here and here. Currently This model doesn't have any generation capability. If you want this model to have generation capability, please do a finetuning on prefix-LM objective mentioned in the paper.
See the tensorboard log in Training metrics tab.
Please note that we haven't finetuned the model in any downstream task.
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