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deliciouscat
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deberta-v3-base-encoder-decoder-v0.2

Text2Text Generation
Transformers
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encoder-decoder
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Encoder-Decoder model with DeBERTa encoder

pre-trained models

  • Encoder: microsoft/deberta-v3-small

  • Decoder: deliciouscat/deberta-v3-base-decoder-v0.1 (6 transformer layers, 8 attention heads)

-> 297511524(298M) params

Data used

HuggingFaceFW/fineweb -> sampled 124800

Training hparams

  • optimizer: AdamW, lr=2.3e-5, betas=(0.875, 0.997)

  • batch size: 12 (maximal on Colab pro A100 env)

-> training on denoising objective (BART)

How to use 

from transformers import AutoTokenizer, EncoderDecoderModel

model = EncoderDecoderModel.from_pretrained("deliciouscat/deberta-v3-base-encoder-decoder-v0.2")
tokenizer = AutoTokenizer.from_pretrained("deliciouscat/deberta-v3-base-encoder-decoder-v0.2")

Future work!

  • train more scientific data

  • fine-tune on keyword extraction task

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Model size
298M params
Tensor type
F32
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Text2Text Generation
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Dataset used to train deliciouscat/deberta-v3-base-encoder-decoder-v0.2